Search Results (15)

The effect of a heterogeneous structure obtained via cold rotary swaging (CRS) and post-deformation annealing (PDA) on the dynamic mechanical properties of a non-equiatomic 49.5Fe-30Mn-10Co-10Cr-0.5C (at.%) medium-entropy alloy at room and cryogenic temperatures was studied. CRS to a reduction of 92% and subsequent PDA at 500–600 °C developed a heterogeneous structure consisting of a twinned γ-matrix and dislocation-free γ-grains in the rod core and an ultrafine-grained microstructure of γ-phase at the rod edge. Therefore, the maximum stress (σ_m) value increased. Charpy V-notch impact toughness (KCV) decreased after CRS to a reduction of 18% and stabilized after further straining. However, the contribution of the crack initiation energy consumption (KCV_i) increased, while the crack propagation energy consumption (KCV_P) decreased. PDA resulted in increases in KCV_i and KCV_P. A ductile-to-brittle transition occurred from −90 °C to −190 °C. Cryogenic Charpy impact testing of the heterostructured material revealed inflections on impact load–deflection curves. The phenomenon contributed to an increase in KCV_P, providing a longer crack propagation path. The heterostructured material possessed an excellent σ_m-KCV combination in the temperature range between −90 °C and +20 °C. Full article

This research addresses the challenges of early detection of Acute Lymphoblastic Leukemia (ALL), a life-threatening blood cancer particularly prevalent in children. Manual diagnosis of ALL is often error-prone, time-consuming, and reliant on expert interpretation, leading to delays in treatment. This study proposes an automated binary classification model based on the EfficientNetV2-S architecture to overcome these limitations, enhanced with 5-fold cross-validation (5KCV) for robust performance. A novel aspect of this research lies in leveraging the symmetry concepts of symmetric and asymmetric patterns within the microscopic imagery of white blood cells. Symmetry plays a critical role in distinguishing typical cellular structures (symmetric) from the abnormal morphological patterns (asymmetric) characteristic of ALL. By integrating insights from generative modeling techniques, the study explores how asymmetric distortions in cellular structures can serve as key markers for disease classification. The EfficientNetV2-S model was trained and validated using the normalized C-NMC_Leukemia dataset, achieving exceptional metrics: 97.34% accuracy, recall, precision, specificity, and F1-score. Comparative analysis showed the model outperforms recent classifiers, making it highly effective for distinguishing abnormal white blood cells. This approach accelerates diagnosis, reduces costs, and improves patient outcomes, offering a transformative tool for early ALL detection and treatment planning. Full article

King cobra (Ophiophagus hannah) venom comprises a diverse array of proteins and peptides. However, the roles and properties of these individual components are still not fully understood. Among these, Cysteine-rich secretory proteins (CRiSPs) are recognized but not fully characterized. This study investigates the biological effects of Opharin, the CRiSP from king cobra venom (KCV). The effects of Opharin on cytokine production, specifically on IL-1β, IL-6, IL-8, TNF-α, and IL-10 release, were evaluated over 24 h in monocyte-derived macrophage (MDM) cells. Notably, the levels of these inflammatory cytokines were significantly increased over 24 h, with values higher than those observed in cells treated with crude KCV at most time points. Additionally, the in vivo Miles assay in mice revealed that Opharin increased vascular permeability by 26% compared to the negative control group. These findings highlight the Opharin’s role in severe inflammatory and vascular responses observed in king cobra envenomation. Still, further research is essential to elucidate the pharmacological and toxicological effects of venom components, ultimately enhancing the clinical management of envenomation. Full article

As the world’s energy demand continues to expand, shale oil has a substantial influence on the global energy reserves. The third submember of the Mbr 3 of the Shahejie Fm, characterized by complicated mudrock lithofacies, is one of the significant shale oil enrichment intervals of the Bohai Bay Basin. The classification and identification of lithofacies are key to shale oil exploration and development. However, the efficiency and reliability of lithofacies identification results can be compromised by qualitative classification resulting from an incomplete workflow. To address this issue, a comprehensive technical workflow for mudrock lithofacies classification and logging prediction was designed based on machine learning. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were conducted to realize the automatic classification of lithofacies, which can classify according to the internal relationship of the data without the disturbance of human factors and provide an accurate lithofacies result in a much shorter time. The PCA and HCA results showed that the third submember can be split into five lithofacies: massive argillaceous limestone lithofacies (MAL), laminated calcareous claystone lithofacies (LCC), intermittent lamellar argillaceous limestone lithofacies (ILAL), continuous lamellar argillaceous limestone lithofacies (CLAL), and laminated mixed shale lithofacies (LMS). Then, random forest (RF) was performed to establish the identification model for each of the lithofacies and the obtained model is optimized by grid search (GS) and K-fold cross validation (KCV), which could then be used to predict the lithofacies of the non-coring section, and the three validation methods showed that the accuracy of the GS–KCV–RF model were all above 93%. It is possible to further enhance the performance of the models by resampling, incorporating domain knowledge, and utilizing the mechanism of attention. Our method solves the problems of the subjective and time-consuming manual interpretation of lithofacies classification and the insufficient generalization ability of machine-learning methods in the previous works on lithofacies prediction research, and the accuracy of the model for mudrocks lithofacies prediction is also greatly improved. The lithofacies machine-learning workflow introduced in this study has the potential to be applied in the Bohai Bay Basin and comparable reservoirs to enhance exploration efficiency and reduce economic costs. Full article

In this work, the technique of equal-channel angular pressing (ECAP) that enables producing bulk billets was used to form a UFG structure in Ti-6Al-4V alloy. A subsequent warm upsetting simulates die forging and the production of a part. We studied the evolution of the UFG alloy’s crystallographic texture in the process of deformation during the production of a semi-product and/or a part, as well as its effect on the ductile–brittle transition region in the temperature range from −196 °C to 500 °C and the material’s fracture mechanisms. To test Charpy impact strength, standard samples of square cross-section with a V-shape notch were used (KCV). It was found that the impact toughness anisotropy is caused by textural effects and has a pronounced character at temperatures in the ductile–brittle transition range. Up to 100 °C the KCV values are close in the specimens processed by ECAP and ECAP+upsetting (along and perpendicularly to the upsetting axis—along the Z-axis and along the Y-axis, respectively), while a large difference is observed at test temperatures of 200 °C and higher. At a temperature of 500 °C, the impact toughness of the UFG Ti-6Al-4V alloy after ECAP reaches a level of that after ECAP+upsetting in the fracture direction along the Z-axis (1.60 and 1.77 MJ/m², respectively). Additionally, an additional ECAP upsetting after ECAP decreases the ductile–brittle transition temperature of the UFG Ti-6Al-4V alloy, which increases the temperature margin of the toughness of the structural material and reduces the risk of the catastrophic failure of a product. The fractographic analysis of the fracture surface of the specimens after Charpy tests in a wide temperature range revealed the features of crack propagation depending on the type of the alloy’s microstructure and texture in the fracture direction. Full article

In this article, we introduce explainable methods to understand how Human Activity Recognition (HAR) mobile systems perform based on the chosen validation strategies. Our results introduce a new way to discover potential bias problems that overestimate the prediction accuracy of an algorithm because of the inappropriate choice of validation methodology. We show how the SHAP (Shapley additive explanations) framework, used in literature to explain the predictions of any machine learning model, presents itself as a tool that can provide graphical insights into how human activity recognition models achieve their results. Now it is possible to analyze which features are important to a HAR system in each validation methodology in a simplified way. We not only demonstrate that the validation procedure k-folds cross-validation (k-CV), used in most works to evaluate the expected error in a HAR system, can overestimate by about 13% the prediction accuracy in three public datasets but also choose a different feature set when compared with the universal model. Combining explainable methods with machine learning algorithms has the potential to help new researchers look inside the decisions of the machine learning algorithms, avoiding most times the overestimation of prediction accuracy, understanding relations between features, and finding bias before deploying the system in real-world scenarios. Full article

Fine-grained steels belonging to the HSLA group (High-Strength Low-Alloy steels) of steels are becoming increasingly popular and are used in both statically and dynamically stressed structures. Due to the method of their production, and thus also the method use to obtain the required mechanical properties, it is really necessary to limit the heat input values for these steels during welding. When applying temperature cycles, HSLA steels in highly heated heat-affected zones (HAZ) reveal intensive grain coarsening and also softening behaviour. This subsequently results in changes in both mechanical and brittle-fracture properties, and the fatigue life of welded joints. While grain coarsening and structure softening have a major effect on the change of strength properties and KCV (Charpy V-notch impact toughness) values of statically stressed welded joints, the effect of these changes on the fatigue life of cyclically stressed welded joints has not yet been quantified. The paper is therefore conceived so as to make it possible to assess and determine the percentage impact of individual aspects of the welding process on changes in their fatigue life. To be more specific, the partial effects of angular deformation, changes that occur in the HAZ of weld, and the notch effect due to weld geometry are assessed. Full article

Potassium ion (K⁺) channels have been observed in diverse viruses that infect eukaryotic marine and freshwater algae. However, experimental evidence for functional K⁺ channels among these alga-infecting viruses has thus far been restricted to members of the family Phycodnaviridae, which are large, double-stranded DNA viruses within the phylum Nucleocytoviricota. Recent sequencing projects revealed that alga-infecting members of Mimiviridae, another family within this phylum, may also contain genes encoding K⁺ channels. Here we examine the structural features and the functional properties of putative K⁺ channels from four cultivated members of Mimiviridae. While all four proteins contain variations of the conserved selectivity filter sequence of K⁺ channels, structural prediction algorithms suggest that only two of them have the required number and position of two transmembrane domains that are present in all K⁺ channels. After in vitro translation and reconstitution of the four proteins in planar lipid bilayers, we confirmed that one of them, a 79 amino acid protein from the virus Tetraselmis virus 1 (TetV-1), forms a functional ion channel with a distinct selectivity for K⁺ over Na⁺ and a sensitivity to Ba²⁺. Thus, virus-encoded K⁺ channels are not limited to Phycodnaviridae but also occur in the members of Mimiviridae. The large sequence diversity among the viral K⁺ channels implies multiple events of lateral gene transfer. Full article

Chloroviruses are large, plaque-forming, dsDNA viruses that infect chlorella-like green algae that live in a symbiotic relationship with protists. Chloroviruses have genomes from 290 to 370 kb, and they encode as many as 400 proteins. One interesting feature of chloroviruses is that they encode a potassium ion (K⁺) channel protein named Kcv. The Kcv protein encoded by SAG chlorovirus ATCV-1 is one of the smallest known functional K⁺ channel proteins consisting of 82 amino acids. The Kcv_ATCV-1 protein has similarities to the family of two transmembrane domain K⁺ channel proteins; it consists of two transmembrane α-helixes with a pore region in the middle, making it an ideal model for studying K⁺ channels. To assess their genetic diversity, kcv genes were sequenced from 103 geographically distinct SAG chlorovirus isolates. Of the 103 kcv genes, there were 42 unique DNA sequences that translated into 26 new Kcv channels. The new predicted Kcv proteins differed from Kcv_ATCV-1 by 1 to 55 amino acids. The most conserved region of the Kcv protein was the filter, the turret and the pore helix were fairly well conserved, and the outer and the inner transmembrane domains of the protein were the most variable. Two of the new predicted channels were shown to be functional K⁺ channels. Full article

Kinetics and analytical formulas for radical-mediated thiol–ene photopolymerization were developed in this paper. The conversion efficacy of thiol–ene systems was studied for various propagation to chain transfer kinetic rate-ratio (R_K), and thiol–ene concentration molar-ratio (R_C). Numerical data were analyzed using analytical formulas and compared with the experimental data. We demonstrated that our model for a thiol–acrylate system with homopolymerization effects, and for a thiol–norbornene system with viscosity effects, fit much better with the measured data than a previous model excluding these effects. The general features for the roles of R_K and R_C on the conversion efficacy of thiol (C_T) and ene (C_V) are: (i) for R_K = 1, C_V and C_T have the same temporal profiles, but have a reversed dependence on R_C; (ii) for R_K >> 1, C_T are almost independent of R_C; (iii) for R_K << 1, C_V and C_T have the same profiles and both are decreasing functions of the homopolymerization effects defined by k_CV; (iv) viscosity does not affect the efficacy in the case of R_K >> 1, but reduces the efficacy of C_V for other values of R_K. For a fixed light dose, higher light intensity has a higher transient efficacy but a lower steady-state conversion, resulting from a bimolecular termination. In contrast, in type II unimolecular termination, the conversion is mainly governed by the light dose rather than its intensity. For optically thick polymers, the light intensity increases with time due to photoinitiator depletion, and thus the assumption of constant photoinitiator concentration (as in most previous models) suffers an error of 5% to 20% (underestimated) of the crosslink depth and the efficacy. Scaling law for the overall reaction order, defined by [A]^m[B]ⁿ and governed by the types of ene and the rate ratio is discussed herein. The dual ratio (R_K and R_C) for various binary functional groups (thiol–vinyl, thiol–acrylate, and thiol–norbornene) may be tailored to minimize side effects for maximal monomer conversion or tunable degree of crosslinking. Full article

The article presents the influence of structure formation on the properties of 321 metastable austenitic stainless steel in the process of cold radial forging (CRF). The steel under study after austenitization was subjected to CRF at room temperature with degrees of true strain (e) 0.26, 0.56, 1.00, 1.71 and 2.14. It has been shown that structure formation of the studied steel during CRF consists of three stages: formation of the lamellar structure of austenite, formation of the trapezoidal structure, and formation of the equiaxial grain structure. The kinetics of the strain-induced α′-martensitic transformation is related to the stages of structure evolution. Hardness, ultimate tensile strength and yield strength uniformly increase in all stages of structure formation with a significant decrease of elongation to fracture during the first stage of structure formation while the value of elongation to fracture remains constant in the subsequent stages of deformation. Impact strength of fatigue cracked specimens (KCT) decreases sharply at the first stage of structure formation and smoothly increases at the second and third stages. However, the impact strength of V-notch specimens (KCV) continuously decreases when deformation degree increases in the overall investigated deformation range. Full article

To support high-level wind energy utilization, wind power prediction has become a more and more attractive topic. To improve prediction accuracy and flexibility, joint point-interval prediction of wind power via a stepwise procedure is studied in this paper. Firstly, time-information-granularity (TIG) is defined for ultra-short-term wind speed prediction. Hidden features of wind speed in TIGs are extracted via principal component analysis (PCA) and classified via adaptive affinity propagation (ADAP) clustering. Then, Gaussian process regression (GPR) with joint point-interval estimation ability is adopted for stepwise prediction of the wind power, including wind speed prediction and wind turbine power curve (WTPC) modeling. Considering the sequential uncertainties of stepwise prediction, theoretical support for an uncertainty enlargement effect is deduced. Uncertainties’ transmission from single-step or receding multi-step wind speed prediction to wind power prediction is explained in detail. After that, normalized indexes for point-interval estimation performance are presented for GPR parameters’ optimization via a hybrid particle swarm optimization-differential evolution (PSO-DE) algorithm. K-fold cross validation (K-CV) is used to test the model stability. Moreover, due to the timeliness of data-driven GPR models, an evolutionary prediction mechanism via sliding time window is proposed to guarantee the required accuracy. Finally, measured data from a wind farm in northern China are acquired for validation. From the simulation results, several conclusions can be drawn: the multi-model structure has insignificant advantages for wind speed prediction via GPR; joint point-interval prediction of wind power is realizable and very reasonable; uncertainty enlargement exists for stepwise prediction of wind power while it is more significant after receding multi-step prediction of wind speed; a reasonable quantification mechanism for uncertainty is revealed and validated. Full article

Decoupling research on flexible tactile sensors play a very important role in the intelligent robot skin and tactile-sensing fields. In this paper, an efficient machine learning method based on the improved back-propagation (BP) algorithm is proposed to decouple the mapping relationship between the resistances of force-sensitive conductive pillars and three-dimensional forces for the 6 × 6 novel flexible tactile sensor array. Tactile-sensing principles and numerical experiments are analyzed. The tactile sensor array model accomplishes the decomposition of the force components by its delicate structure, and avoids direct interference among the electrodes of the sensor array. The force components loaded on the tactile sensor are decoupled with a very high precision from the resistance signal by the improved BP algorithm. The decoupling results show that the k-cross validation (k-CV) algorithm is a highly effective method to improve the decoupling precision of force components for the novel tactile sensor. The large dataset with the k-CV method obtains a better decoupling accuracy of the force components than the small dataset. All of the decoupling results are fairly good, and they indicate that the improved BP model with a strong non-linear approaching ability has an efficient and valid performance in decoupling force components for the tactile sensor. Full article

Signals recorded at the cell membrane are meaningful indicators of the physiological vs. pathological state of a cell and will become useful diagnostic elements in nanomedicine. In this project we present a coherent strategy for the design and fabrication of a bio-nano-sensor that monitors changes in intracellular cell calcium concentration and allows an easy read out by converting the calcium signal into an electrical current in the range of microampere that can be easily measured by conventional cell electrophysiology apparatus. Full article

To tackle robust object tracking for video sensor-based applications, an online discriminative algorithm based on incremental discriminative structured dictionary learning (IDSDL-VT) is presented. In our framework, a discriminative dictionary combining both positive, negative and trivial patches is designed to sparsely represent the overlapped target patches. Then, a local update (LU) strategy is proposed for sparse coefficient learning. To formulate the training and classification process, a multiple linear classifier group based on a K-combined voting (KCV) function is proposed. As the dictionary evolves, the models are also trained to timely adapt the target appearance variation. Qualitative and quantitative evaluations on challenging image sequences compared with state-of-the-art algorithms demonstrate that the proposed tracking algorithm achieves a more favorable performance. We also illustrate its relay application in visual sensor networks. Full article