Search

One of the world's deadliest diseases is lung cancer. Based on a few features, machine learning techniques can help in the diagnosis of lung cancer. The performance of several classifiers: support vector machine (SVM), logistic regression (LR), Naïve Bayes (NB), random forest (RF), and K-nearest neighbor (KNN), was evaluated by the authors using the dataset available on Kaggle to create a systematic approach for the diagnosis of lung cancer disease based on readily observable signs and historical medical data without the requirement of CT scan images. The authors have proposed a novel approach for classification called Pearson correlation weighted KNN (PCWKNN), which is a modified version of KNN and uses Pearson correlation coefficient values to determine weights in a weighted KNN. The performance of the classifiers was evaluated using the hold-out validation method. SVM, LR, and RF were 96.77% accurate. NB obtained 95.16% accuracy. KNN achieved 91.93% accuracy. PCWKNN outperformed the employed classifiers and obtained an accuracy of 98.39%. Addressing the imperative for improved model generalization, the researchers utilized PCWKNN on an alternative, more extensive lung cancer dataset and subsequently broadened its application to diverse diseases, including the brain stroke dataset. The encouraging outcomes underscore PCWKNN's resilience and adaptability, suggesting its viability for real-world implementation. [ABSTRACT FROM AUTHOR]

A group of CoCrFeNiTiNbx high entropy alloys (HEAs) coatings were produced by laser cladding. The effect of Nb content on the microstructure and wear resistance of the HEAs coatings was investigated. The results indicated that adding Nb promoted the phase transition from BCC to FCC and the formation of Fe2Nb Laves phase, The diffraction peaks of FCC and BCC phases were firstly shifted to smaller angle as Nb content was increased, and then shifted to larger angle. Adding Nb promoted brittle fracture of more coarse dendrites to formed fine dendrites and equiaxed crystals homogenizing the microstructure in the HEAs coatings, as well as the formation of dense dislocations and dislocation interaction. The microhardness of the HEAs coatings was firstly increased and then decreased as Nb content was increased, and the change of the mass loss and friction coefficient was opposite trend. Compared with CoCrFeNiTiNb0.0 HEAs coatings, the microhardness of the CoCrFeNiTiNb1.0 HEAs coatings was improved by 25.00%, the mass loss was reduced by 28.27%, and and friction coefficient was the lowest. The wear mechanism of the HEAs coatings was transformed from the adhesive wear and oxidative wear accompanied by the abrasive wear to the abrasive wear accompanied by the adhesive wear and oxidative wear as Nb content was gradually increased. [ABSTRACT FROM AUTHOR]

To investigate the influence mechanism of Nb solute and precipitates on pearlite transformation, the effects of Nb content and heat‐treatment process on pearlite transformation and microstructure in high‐carbon steel wire rods are analyzed in this article. During the austenite deformation stage, the increase in solute Nb and strain‐induced precipitates (SIPs) suppresses austenite recrystallization, while the decrease in prior austenite grain size promotes the reduction of refined pearlite colonies size. During the continuous cooling transformation process, an increase in Nb content and cooling rate increases the supercooling of pearlite transformation and refines the interlamellar spacing (ILS) of pearlite. During the isothermal transformation process, the increase in isothermal temperature increases the incubation period of pearlite transformation and slows down the rate of pearlite transformation. With the increase in Nb content, the incubation period of pearlite transformation lengthens. But uneven distribution of C in austenite is caused by the SIP, which promotes interface migration, and shorts the pearlite transformation time. In high‐carbon steel wire rods, the refinement of pearlite colonies and pearlite ILS is achieved by increasing the Nb content, raising the cooling rate, and lowering the isothermal temperature, thereby increasing the content of precipitates and improving their mechanical properties. [ABSTRACT FROM AUTHOR]

The degenerative neurological condition known as Parkinson's disease is distinguished by tremors, stiffness, and difficulties with walking, balance, and motor coordination. Parkinson's disease is a progressive condition. Identifying whether this guy suffers from Parkinson's disease is the purpose of this puzzle. Early detection and effective treatment of Parkinson's disease can significantly improve the quality of life for those affected. This highlights the significance of early disease detection. The objective of this initiative is to facilitate the identification of Parkinson's disease through the use of hand-drawn graphics. Three unique Machine Learning algorithms are used in this article. The adaptability of the model has been demonstrated through the usage of a variety of designs, including but not limited to spirals, waves, cubes, and triangles. Experiment results showed that datasets 89.7% correct, and proposed XGBOOST algorithm is 98.75% accurate. [ABSTRACT FROM AUTHOR]

Brook Trout (Salvelinus fontinalis) populations have experienced marked declines throughout their native range and are presently threatened due to isolation in small habitat fragments, land use changes, and climate change. The existence of numerous, spatially distinct populations poses substantial challenges for monitoring population status (e.g., abundance, recruitment, or occupancy). Genetic monitoring with estimates of effective number of breeders (Nb) provides a potentially powerful metric to complement existing population monitoring, assessment, and prioritization. We estimated Nb for 71 Brook Trout habitat units in mid‐Atlantic region of the United States and obtained a mean Nb of 73.2 (range 6.90–493). Our modeling approach tested whether Nb estimates were sensitive to differences in habitat size, presence of non‐native salmonids, base flow index, temperature, acidic precipitation, and indices of anthropogenic disturbance. We found significant support for three of our hypotheses including the positive influences of available habitat and base flow index and negative effect of temperature. Our results are consistent with presently observed and predicted future impacts of climate change on populations of this cold‐water fish. Importantly, these findings support the use of Nb in population assessments as an index of relative population status. [ABSTRACT FROM AUTHOR]

Copyright of Diamond & Abrasives Engineering is the property of Zhengzhou Research Institute for Abrasives & Grinding and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives: The aim was to prepare a variety of non-stoichiometric (Ti, Nb)Cx PCD tool binder composites using TiC and transition metal Nb by mechanical alloying (MA) technology. The effects of different sintering temperatures and Nb contents on the phase compositions, microstructures, and mechanical properties of the composites were investigated to provide a scientific basis for optimizing the properties of PCD tool binders. The specific tasks included preparing (Ti, Nb)Cx composites with varying ratios, analyzing their solid-solution behavior at different temperatures, and evaluating their hardness and fracture toughness. Methods: High purity TiC and Nb powders were selected as raw materials for the experiment, and the MA technology was used to achieve uniform mixing of the two materials. In order to investigate the effect of sintering temperature on the properties of composite materials, various sintering temperatures ranging from 1300 to 1700 ℃ were set. The sintered samples were subjected to phase analysis using an X-ray diffractometer, and the data were analyzed using Jade software. Subsequently, the fracture morphology of the sintered body was observed using scanning electron microscopy (SEM), and the hardness and fracture toughness of the composite materials were measured using a Vickers hardness tester. Results: Within the sintering temperature range of 1300 to 1700 ℃, the solid-solution degree of TiC and Nb gradually increases with the increase in temperature. At higher temperatures, the diffusion between TiC and Nb accelerates, forming a more stable solid-solution, and the phase composition tends to stabilize. At the same sintering temperature, the hardness of the (Ti, Nb)Cx composite increases gradually with the increase in Nb content, indicating that the introduction of Nb enhances the overall hardness of the composite. Especially when the sintering temperature is 1600 ℃, the (Ti, Nb)C0.50.5 composite exhibits the best mechanical properties with a hardness of 23.0 GPa and fracture toughness of 7.20 MPa·m1/2. The results show that under these temperature and ratio conditions, the composite achieves the best solid-solution state, has fewer internal defects, moderate grain size, and optimal mechanical properties. Conclusions: The sintering temperature and Nb content have significant impacts on the phase composition and mechanical properties of (Ti,Nb)Cx composite materials. Controlling these two parameters can optimize the hardness and toughness of the composite materials, thereby enhancing their application potential in PCD cutting tools. The higher sintering temperature is conducive to the full solid-solution of TiC and Nb, forming a more stable crystalline phase structure and improving the mechanical properties of the material. Future research could explore the influences of introducing other transition group metals on the properties of composite materials in order to develop higher-performance PCD tool binders. Others: Although the main objective of this study is to optimize the performance of (Ti,Nb)Cx PCD tool binders, the mechanical alloying techniques and analytical methods used in this research have the potential for broader applications. The mechanical alloying technology is not only suitable for the development of PCD tool materials but also for the preparation of other high-performance composite materials. At the same time, the combination of X-ray diffraction analysis and scanning electron microscopy provides valuable data support for the field of materials science, which helps deepen the understanding of the microstructure and phase composition of materials, thereby promoting research progress in the field.

Ion-pair extraction enables the recovery of anionic metal ions, such as pertechnetate and chlorinated rhodium ions, by protonated extractants with tertiary amino N atom in their structures. We expand this technique for other anionic species dissolved in different mineral acids. Extractions of Zr, Hf, Nb, and Ta, metal anions present in sulfuric and hydrofluoric acids (H2SO4 and HF), are examined. Zr and Hf in H2SO4, and Zr, Hf, Nb, and Ta in HF can be extracted by ion-pair extraction using NTAamide (nitrilotriacetamide), methylimino-N, N'-dioctylacetamide (MIDOA), and trioctylamine (TOA). Basic information about their extraction behavior was obtained through this study. [ABSTRACT FROM AUTHOR]

This study investigated the effects of partially replacing expensive Mo with cheaper Nb on the microstructure and properties of high-strength low-alloy (HSLA) steel during reverse austenisation. The mechanical properties of the steel in the hot-rolled state were lower with a partial replacement of Mo by Nb. However, after pre-tempering and reheating and quenching, the strength increased greatly while the ductility and toughness did not decrease much. Thus, the negative effects of replacing Mo with Nb were mostly alleviated, and a good balance between strength, ductility and toughness was achieved. After heat treatment, the mass percentage of precipitates increased substantially, which helped to pin grain boundaries during austenisation. The percent of high-angle grain boundaries greatly increased while the average effective grain size decreased, which improved grain refinement. The results showed that combining a partial replacement of Mo by Nb with heat treatment allows the microstructure and mechanical properties of HSLA steel to be effectively controlled while improving the balance between cost and performance. These findings provide valuable insights into the preparation and design of steels with similar microstructures. [ABSTRACT FROM AUTHOR]

Copyright of Iron Steel Vanadium Titanium is the property of Iron Steel Vanadium Titanium Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The effect of niobium ion implantation on the surface and scratch properties of a WC-5wt.%Ni cold sprayed coating was investigated with accelerating energies of 60 and 170 keV and fluences of 5 × 1016 and 8 × 1016 ions cm−2, respectively. High-resolution Rutherford backscattering spectroscopy spectra revealed that Ni-Nb and WC-Nb amorphous layers had likely formed. Using ion implantation parameters of 60 keV with a fluence of 8 × 1016 ions cm-2 (coating 608-NB), higher Nb peak intensities were found. Oxide formation in the form of NbO, NbO2, and Nb2O5 was detected with high-resolution x-ray photoelectron spectroscopy spectra analyses on the 608-NB coating, in conjunction with the intermetallic, thereby having the best coating properties: higher hardness, low roughness, and scratch properties compared to the other ion-doped samples. The failure mechanisms of all the coatings were found to be similar, showing angular cracks, cone cracking, and primary delamination. [ABSTRACT FROM AUTHOR]

Sodium-ion batteries (SIBs) have emerged as a promising alternative to lithium-ion batteries (LIBs) due to the abundance and low cost of sodium resources. Cathode material plays a crucial role in the performance of sodium ion batteries determining the capacity, cycling stability, and rate capability. Na3V2(PO4)3 (NVP) is a promising cathode material due to its stable three-dimensional NASICON structure, but its discharge capacity is low and its decay is serious with the increase of cycle period. We focused on modifying NVP cathode material by coating carbon and doping Nb5+ ions for synergistic electrochemical properties of carbon-coated NVP@C as a cathode material. X-ray diffraction analysis was performed to confirm the phase purity and crystal structure of the Nb5+ doped NVP material, which exhibited characteristic diffraction peaks that matched well with the NASICON structure. Nb5+-doped NVP@C@Nbx materials were prepared using the sol–gel method and characterized by X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), Raman and Brunauer -Emmett-Teller (BET) analysis. First-principles calculations were performed based on density functional theory. VASP and PAW methods were chosen for these calculations. GGA in the PBE framework served as the exchange-correlation functional. The results showed the NVP unit cell consisted of six NVP structural motifs, each containing octahedral VO6 and tetrahedral PO4 groups to form a polyanionomer [V2(PO4)3] along with the c-axis direction by PO4 groups, which had Na1(6b) and Na2(18e) sites. And PDOS revealed that after Nb doping, the d orbitals of the Nb atoms also contributed electrons that were concentrated near the Fermi surface. Additionally, the decrease in the effective mass after Nb doping indicated that the electrons could move more freely through the material, implying an enhancement of the electron mobility. The electrochemical properties of the Nb5+ doped NVP@C@Nb cathode material were evaluated through cyclic voltammetry (CV), galvanostatic charge-discharge tests, electrochemical impedance spectroscopy (EIS), and X-ray photoelectric spectroscopy (XPS). The results showed that NVP@C@Nb0.15 achieved an initial discharge capacity as high as 114.27 mAhg−1, with a discharge capacity of 106.38 mAhg−1 maintained after 500 cycles at 0.5C, and the retention rate of the NVP@C@Nb0.15 composite reached an impressive 90.22%. NVP@C@Nb0.15 exhibited low resistance and high capacity, enabling it to create more vacancies and modulate crystal structure, ultimately enhancing the electrochemical properties of NVP. The outstanding performance can be attributed to the Nb5+-doped carbon layer, which not only improves electronic conductivity but also shortens the diffusion length of Na+ ions and electrons, as well as reduces volume changes in electrode materials. These preliminary results suggested that the as-obtained NVP@C@Nb0.15 composite was a promising novel cathode electrode material for efficient sodium energy storage. [ABSTRACT FROM AUTHOR]

Customer opinions and reviews play a vital role in marketing expansion. Big companies worldwide assign a lot of their efforts to analyzing customers' feedback to keep track of their needs. Natural Language Processing (NLP) is widely used to analyze such review texts. Arabic customer analysis and classification also began to gain researchers' attention due to the wide range of Arabic language speakers. Working with Arabic Language is a very challenging task because of the orthographic nature of Arabic. Also, customers often write their reviews dialectically, diverting from standard Arabic. This study presents a method to classify Arabic customer reviews using four classifiers (K-nearest Neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (RL), and Naïve Bayes (NB)). The classification phase uses three stemming techniques (Snowball, Khoja, and Tashaphyne). The HARD dataset is adopted to perform the experiments. The results stated that the stemming methods could enhance classification performance despite the complexity of Arabic scripts and dialects, where the best accuracy of the results was 91% when using SVM and LR with Snowball Stemmer. This work sheds light on utilizing and investigating more machine learning (ML) techniques and evaluating the results. [ABSTRACT FROM AUTHOR]

High-nickel ternary materials are currently the most promising lithium battery cathode materials due to their development and application potential. Nevertheless, these materials encounter challenges like cation mixing, lattice oxygen loss, interfacial reactions, and microcracks. These issues are exacerbated at high voltages, compromising their cyclic stability and safety. In this study, we successfully prepared Nb5+-doped high-nickel ternary cathode materials via a high-temperature solid-phase method. We investigated the impact of Nb5+ doping on the microstructure and electrochemical properties of LiNi0.88Co0.05Mn0.07O2 ternary cathode materials by varying the amount of Nb2O5 added. The experimental results suggest that Nb5+ doping does not alter the crystal structure but modifies the particle morphology, yielding radially distributed, elongated, rod-like structures. This morphology effectively mitigates the anisotropic volume changes during cycling, thereby bolstering the material's cyclic stability. The material exhibits a discharge capacity of 224.4 mAh g−1 at 0.1C and 200.3 mAh g−1 at 1C, within a voltage range of 2.7 V–4.5 V. Following 100 cycles at 1C, the capacity retention rate maintains a high level of 92.9%, highlighting the material's remarkable capacity retention and cyclic stability under high-voltage conditions. The enhancement of cyclic stability is primarily due to the synergistic effects caused by Nb5+ doping. Nb5+ modifies the particle morphology, thereby mitigating the formation of microcracks. The formation of high-energy Nb-O bonds prevents oxygen precipitation at high voltages, minimizes the irreversibility of the H2–H3 phase transition, and thereby enhances the stability of the composite material at high voltages. [ABSTRACT FROM AUTHOR]

Copyright of Bányászati és Kohászati Lapok - Kohászat is the property of Hungarian Mining & Metallurgical Society and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this work, we integrated narrow band (NB) and Ultra-Wide Band (UWB) four-port MIMO antennas designed and implemented in IoT applications. The designed antenna consists of two quarter-wavelength asymmetrical NB radiators and a reverse P-shaped UWB radiator. The two NBs are generated by two quarter-wavelength non-identical radiators, and the UWB is generated by a semi-circle-shaped monopole radiator. Further, the NB radiators and UWB radiators are connected in a 3-mm-wide feed line. The antenna is designed to operate at 1.8 GHz (3G), 2.4 GHz (WLAN), and 3.1–12 GHz (UWB) for IoT applications. In this designed four-port MIMO antenna, two proposed NB and UWB radiators are mounted on the front side of the FR4 substrate and the other two on the rear side of the FR4 substrate. The proposed MIMO antenna has a dimension of $ 65 \times 65 \times 1.6\,{\rm mm}^3 $ 65 × 65 × 1.6 mm 3 . The antenna performance and MIMO performance are analyzed. The diversity metrics such as ECC, DG, TARC, MEG, and CCL are computed, and their values are 0.26, 9.87 dB, −22 dB, −6 dB, and 0.27 bits/Hz/s, respectively, within the bounds. The real-time implementation of the designed antenna is demonstrated at 2.4 GHz for smart home applications. [ABSTRACT FROM AUTHOR]

Sentiment Analysis (SA) is a crucial task in understanding public opinions and perceptions towards emerging technologies. In this study, we focus on SA for a self-driving car dataset as it provides valuable insights into public perceptions and opinions towards a transformative technology. The dataset consists of textual reviews associated with sentiment labels, providing insights into how people perceive self-driving car technology. Our objective is to analyze the sentiments expressed in these reviews using Deep Learning (DL) models, namely, Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), and Bidirectional GRU (BiGRU). We compared our results with an existing technique in the field of self-driving car sentiment classification, that implemented various Machine Learning (ML) and DL models, including Support Vector Machines (SVM), Naïve Bayes (NB), Logistic Regression (LR), Random Forest (RF), CNN, and LSTM. In our study, we expanded upon this research by evaluating the performance of ANN, BiLSTM, GRU, and BiGRU. Results reveal that BiLSTM, GRU, and BiGRU exhibit superior performance in sentiment classification within the self-driving car dataset. These findings offer valuable insights into public sentiment towards self-driving cars, contributing significantly to the advancement of SA techniques in the domain of autonomous vehicles. Additionally, the results are statistically tested and are statistically significant. [ABSTRACT FROM AUTHOR]

This article presents an in-depth investigation of wearable microwave antenna sensors (MASs) used for vital sign detection (VSD) and lung water level (LWL) monitoring. The study looked at two different types of MASs, narrowband (NB) and ultra-wideband (UWB), to decide which one was better. Unlike recent wearable respiratory sensors, these antennas are simple in design, low-profile, and affordable. The narrowband sensor employs an offset-feed microstrip transmission line, which has a bandwidth of 240 MHz at −10 dB reflection coefficient for the textile substrate. The UWB microwave sensor uses a CPW-fed line to excite an unbalanced U-shaped radiator, offering an extended simulated operating bandwidth from 1.5 to 10 GHz with impedance matching ≤−10 dB. Both types of microwave sensors are designed on a flexible RO 3003 substrate and textile conductive fabric attached to a cotton substrate. The specific absorption rate (SAR) of the sensors is measured at different resonant frequencies on 1 g and 10 g of tissue, according to the IEEE C95.3 standard, and both sensors meet the standard limit of 1.6 W/kg and 2 W/kg, respectively. A simple peak-detection algorithm is used to demonstrate high accuracy in the detection of respiration, heartbeat, and lung water content. Based on the experimental results on a child and an adult volunteer, it can be concluded that UWB MASs offer superior performance when compared to NB sensors. [ABSTRACT FROM AUTHOR]

Effective software defect prediction is a crucial aspect of software quality assurance, enabling the identification of defective modules before the testing phase. This study aims to propose a comprehensive five-stage framework for software defect prediction, addressing the current challenges in the field. The first stage involves selecting a cleaned version of NASA's defect datasets, including CM1, JM1, MC2, MW1, PC1, PC3, and PC4, ensuring the data's integrity. In the second stage, a feature selection technique based on the genetic algorithm is applied to identify the optimal subset of features. In the third stage, three heterogeneous binary classifiers, namely random forest, support vector machine, and naïve Bayes, are implemented as base classifiers. Through iterative tuning, the classifiers are optimized to achieve the highest level of accuracy individually. In the fourth stage, an ensemble machine-learning technique known as voting is applied as a master classifier, leveraging the collective decision-making power of the base classifiers. The final stage evaluates the performance of the proposed framework using five widely recognized performance evaluation measures: precision, recall, accuracy, Fmeasure, and area under the curve. Experimental results demonstrate that the proposed framework outperforms state-of-the-art ensemble and base classifiers employed in software defect prediction and achieves a maximum accuracy of 95.1%, showing its effectiveness in accurately identifying software defects. The framework also evaluates its efficiency by calculating execution times. Notably, it exhibits enhanced efficiency, significantly reducing the execution times during the training and testing phases by an average of 51.52% and 52.31%, respectively. This reduction contributes to a more computationally economical solution for accurate software defect prediction. [ABSTRACT FROM AUTHOR]

This paper reports the microstructure and corrosion behavior of a Nb-containing stainless steel surfacing layer prepared by tungsten inert gas (TIG) powder surfacing. The focus was to achieve a clear understanding of the relationships between the Nb-addition and cavitation erosion behavior. In order to eliminate the microstructure inhomogeneity of as-welded samples, solution heat treatment was carried out. It is considered to set the heat treatment temperature at 1150 °C and 1250 °C to minimize the precipitation phase. Results showed that due to the Nb addition the as-welded 22Cr9Ni3MoNb surfacing layer had higher hardness, a lower pitting potential, and better cavitation erosion resistance. Because of the existence of a great quantity of precipitates, heat treatment at 1250 °C had an adverse effect on the mechanical properties and cavitation erosion resistance of surfacing layer samples. [ABSTRACT FROM AUTHOR]

The detection of bearing defects while the machinery is in use is essential for predicting the incipient failure and thereby providing an opportunity to take remedial measures for preventing the costly downtime and ensuring the safe and efficient operation of rotating machinery. With the increasing availability of vibration sensor data and the development of machine learning techniques, the ML methods have become a popular approach for automated fault diagnosis in bearings. In this paper, an attempt has been made to detect the faults in the outer race of bearing using different ML algorithms. An experimental setup has been designed and fabricated to conduct experiments on healthy and faulty bearings and the vibration signals were captured. The captured vibration signals were directly employed as images for training the ML algorithms without the need for conducting the spectral analysis. Six machine learning algorithms, namely, Linear Regression (LR), Decision Tree (DTR), KNN Regression (KNNR), Random Forest Regression (RFR), Convolution Neural Network (CNN), Naive Bayes (NB) were separately applied to classify the location of defects within the outer race of the ball bearing. The accuracy table are used to find the best suitable algorithm for the predictions. The methodology includes data preprocessing techniques, network architectures, training strategies, and evaluation metrics. It has been established that the use of ML technique is very effective in detecting the bearing defects and CNN is able to achieve 100% accuracy.

The aim of this study is to use sentiment analysis to compare the efficiency of old and new fintech technologies by collecting data from various sources and analyzing it using the SVM and NB algorithms. The study seeks to identify opinions or feelings from text in order to provide a clear picture of public opinion and the direction of the debate regarding old and new fintech technologies. The results of the study show that the SVM algorithm has an average accuracy of 87.32% and the NB algorithm has an average accuracy of 81.56% in testing the sample data in a comparison of old and new fintech technology on the internet. The study tested data in a comparison of two specific arguments, namely the debate about which technology is more efficient in old and new fintech on the internet. Despite many unresolved arguments, the study successfully proved that new fintech is more preferred than old fintech, with 71% positive sentiment directed towards new fintech. However, the dataset also found that 62% negative sentiment is directed towards new fintech, indicating that although new fintech is more preferred, there are still some issues that need to be addressed. One reason for negative sentiment towards new fintech may be the continued concerns about security and privacy of user data. Furthermore, other factors that may cause negative sentiment towards new fintech include a lack of understanding about how the technology works.

This study investigated the effects of partially replacing expensive Mo with cheaper Nb on the microstructure and properties of high-strength low-alloy (HSLA) steel during reverse austenisation. The mechanical properties of the steel in the hot-rolled state were lower with a partial replacement of Mo by Nb. However, after pre-tempering and reheating and quenching, the strength increased greatly while the ductility and toughness did not decrease much. Thus, the negative effects of replacing Mo with Nb were mostly alleviated, and a good balance between strength, ductility and toughness was achieved. After heat treatment, the mass percentage of precipitates increased substantially, which helped to pin grain boundaries during austenisation. The percent of high-angle grain boundaries greatly increased while the average effective grain size decreased, which improved grain refinement. The results showed that combining a partial replacement of Mo by Nb with heat treatment allows the microstructure and mechanical properties of HSLA steel to be effectively controlled while improving the balance between cost and performance. These findings provide valuable insights into the preparation and design of steels with similar microstructures.

This experiment investigates the effect of Nb on the oxidation resistance of high-chromium cast iron by adding different contents of Nb to high-chromium cast iron. The experimental results show that the oxidation resistance of high-chromium cast iron with the addition of 1 wt% Nb is increased by 43% compared with high-chromium cast iron without the addition of Nb, which may be related to the formation of a large area of weak oxidation area on the surface of high-chromium cast iron. When more than 2.5 wt% Nb is added to high-chromium cast iron, the oxidation resistance of high-chromium cast iron deteriorates significantly due to a large amount of Fe oxide formed around the Nb2O5. However, as the subsequent Nb content continues to increase to 5 wt%, the oxidation resistance of high-chromium cast iron is improved compared to 2.5 wt% Nb because the dispersion of NbC in the matrix decreases.

Rebar is an extremely important building material. The cooling rate and the presence of niobium (Nb) element are key factors influencing the overall performance of rebars. In this work, the high-strength rebar’s microstructure, precipitated phase, and mechanical properties were characterized using scanning electron microscopy, transmission electron microscopy, HVS-1000 hardness tester, and MTS810 universal tensile testing machine. The results showed that a shift in cooling rate from 0.3 to 3°C·s−1 resulted in noticeable changes in the microstructures of rebars, particularly between Nb-free and Nb-containing variants. In the case of Nb-containing rebars, there was an increase of 8.26% in the proportion of pearlite, along with a decrease of 10.63 μm in the average grain size of ferrite. Furthermore, the lamellar spacing of pearlite experienced a decrease of 0.0495 μm, the proportion of low-angle grain boundaries saw an increment of 4.13%, and the size of the precipitated phase (Nb, Ti, V) C reduced by 18.9 nm. These changes collectively led to a significant increase in hardness (98.56 HV), yield strength (179.02 MPa), and ultimate strength (199.43 MPa). The resultant fracture morphology manifested as a dimple pattern.

The microstructure evolution and variation of impact toughness in the heat-affected zone (HAZ) of X80 pipeline steel with different Nb content under different peak temperatures in the secondary thermal cycle were studied through welding thermal simulation, the Charpy impact test, EBSD analysis, SEM observation, and TEM observation in this study. The results indicate that when the peak temperatures of the second pass were lower than Ac1, both X80 pipeline steels had high impact toughness. For secondary peak temperatures in the range of Ac1 to Ac3, both X80 pipeline steels had the worst impact toughness, mainly due to the formation of massive blocky M-A constituents in chain form on grain boundaries. When the secondary peak temperatures were higher than Ac3, both X80 pipeline steels had excellent impact toughness. Smaller grain size and higher proportions of HAGBs can effectively improve the impact toughness. Meanwhile, high Nb X80 pipeline steel had higher impact absorption energy and smaller dispersion. Adding an appropriate amount of Nb to X80 pipeline steel can ensure the impact toughness of SCCGHAZ and SCGHAZ in welded joints. [ABSTRACT FROM AUTHOR]

Steaming‐assisted conversion route, a new strategy, is first adapted for the synthesis of highly crystallized anatase TiO2 2D inverse opal (IO) monolayer films, and then to Nb‐doped TiO2 and W‐doped TiO2 2D IO monolayer films. Pure water, ammonia, or HCl solutions are used as a source of steaming vapor to convert dry films of amorphous TiO2 IO, NbCl5/TiO2, and WCl6/TiO2 composite IOs into anatase TiO2, Nb‐doped TiO2, and W‐doped TiO2 IO films. This new strategy renders possible the doping of metal ions within the framework of the anatase TiO2 IO films under low temperature and liquid‐free conditions. Further, the ordered array structure of the IO films is also effectively retained. The low steaming conversion temperature allows high dopant rates of homogeneously distributed heteroatoms, resulting in Nb doping as high as ≈34%. The thus prepared TiO2, Nb‐doped TiO2, and W‐doped TiO2 anatase IO films are successfully used as active electrodes in the fabrication of electrochromic devices. [ABSTRACT FROM AUTHOR]

Microarray technology presents a challenge due to the large dimensionality of the data, which can be difficult to interpret. To address this challenge, the article proposes a feature extraction‐based cancer classification technique coupled with artificial bee colony optimization (ABC) algorithm. The ABC‐support vector machine (SVM) method is used to classify the lung cancer datasets and compared them with existing techniques in terms of precision, recall, F‐measure, and accuracy. The proposed ABC‐SVM has the advantage of dealing with complex nonlinear data, providing good flexibility. Simulation analysis was conducted with 30% of the data reserved for testing the proposed method. The results indicate that the proposed attribute classification technique, which uses fewer genes, performs better than other modalities. The classifiers, such as naïve Bayes, multi‐class SVM, and linear discriminant analysis, were also compared and the proposed method outperformed these classifiers and state‐of‐the‐art techniques. Overall, this study demonstrates the potential of using intelligent algorithms and feature extraction techniques to improve the accuracy of cancer diagnosis using microarray gene expression data. [ABSTRACT FROM AUTHOR]

Hate speech detection research is a recent sizzling topic in natural language processing (NLP). Unburdened uses of social media platforms make people over-opinionative, which crosses the limit of leaving comments and posts toxic. A toxic outlook increases violence towards the neighbour, state, country, and continent. Several laws have been introduced in different countries to end the emergency problem. Now, all the media platforms have started working on restricting hate posts or comments. Hate speech detection is generally a text classification problem if considered a supervised observation. To tackle text in terms of computation perspective is challenging because of its semantic and complex grammatical nature. Resource-rich languages leverage their richness, whereas resource scarce language suffers significantly from a lack of dataset. This paper makes a multifaceted contribution encompassing resource generation, experimentation with Machine Learning (ML), Deep Learning (DL) and state-of-the-art transformer-based models, and a comprehensive evaluation of model performance, including thorough error analysis. In the realm of resource generation, it adds to the North-East Indian Hate Speech tagged dataset (NEIHS version 1), which encompasses two languages: Assamese and Bodo. [ABSTRACT FROM AUTHOR]

A multiple gas tungsten arc welding was performed on the fabrication of the Fe–Cr–Ni-based weld metal. Microstructures and hot corrosion behaviors of the non-equilibrium weld metals with different Nb contents in 75 wt%Na2SO4–25 wt%NaCl molten salt at 700 °C have been investigated. A bilayer oxide film composed of an inner Cr2O3 layer and an outer Fe2O3 layer formed on the surface of the weld metals. The mass gain rate and the thickness of the surface oxide film were enlarged with Nb addition, which could be attributed to the rapid inward diffusion of O through the cracks resulting from the Nb-oxide. Oxidation and sulfidation occur in the matrix, resulting in a large internal oxidation zone (IOZ) in the weld metal. Nb seemly promoted the sulfidation reaction due to a short-circuit diffusion of S through the interface of the Nb(C, N) and matrix and made the internal corrosion products to be distributed along the interdendritic zone of the weld metal. An apparent Cr-dealloyed layer appears in the IOZ of the weld metal. Dealloying was the detrimental form of hot corrosion of Fe–Cr–Ni-based weld metal exposed to molten salts containing chloride salts. Alloying with 0.8 wt% of Nb further accelerated the dealloying and resulted in a profound attack. [ABSTRACT FROM AUTHOR]

This research examines how Nb affects the stress corrosion cracking (SCC) behavior of various heat-affected zone (HAZ) microstructures of E690 steel in simulated saltwater under cathodic protection. The results showed that the Nb is in the form of solid solution in coarse-grain HAZ (CGHAZ). In fine-grain HAZ and intercritical HAZ (ICHAZ), the nanosized NbC can precipitate. The anodic dissolution (AD) mechanism governs the SCC behavior of HAZ at open-circuit potential (OCP), and the addition of Nb lowers the SCC susceptibility. Nb in CGHAZ solubilized in the matrix can decrease the corrosion rate by reducing the corrosion current density. In FGHAZ, intercritical HAZ (ICHAZ), the formation of NbC precipitates can restrain the growth of MA islands by optimizing the microstructures. Under cathodic protection, the SCC behavior of steels is controlled by HE. Adding Nb in different microstructures acts as different roles to reduce the SCC susceptibility. In CGHAZ, Nb exists as atoms in solid solution, which can avoid recrystallization and refine the microstructures. In FGHAZ and ICHAZ, nanosized NbC precipitates play a significant role in avoiding the aggregation of hydrogen. [ABSTRACT FROM AUTHOR]

The detection of bearing defects while the machinery is in use is essential for predicting the incipient failure and thereby providing an opportunity to take remedial measures for preventing the costly downtime and ensuring the safe and efficient operation of rotating machinery. With the increasing availability of vibration sensor data and the development of machine learning techniques, the ML methods have become a popular approach for automated fault diagnosis in bearings. In this paper, an attempt has been made to detect the faults in the outer race of bearing using different ML algorithms. An experimental setup has been designed and fabricated to conduct experiments on healthy and faulty bearings and the vibration signals were captured. The captured vibration signals were directly employed as images for training the ML algorithms without the need for conducting the spectral analysis. Six machine learning algorithms, namely, Linear Regression (LR), Decision Tree (DTR), KNN Regression (KNNR), Random Forest Regression (RFR), Convolution Neural Network (CNN), Naive Bayes (NB) were separately applied to classify the location of defects within the outer race of the ball bearing. The accuracy table are used to find the best suitable algorithm for the predictions. The methodology includes data preprocessing techniques, network architectures, training strategies, and evaluation metrics. It has been established that the use of ML technique is very effective in detecting the bearing defects and CNN is able to achieve 100% accuracy. [ABSTRACT FROM AUTHOR]