Excellent Formability Research Articles

Performance Prediction Data Mining System for Disabled Students Using Machine Learning

This study aims to explore the educational achievements of individuals aged 21 to 38, specifically examining the differences between those with disabilities and those without. The research delves into the realm of Online Learning Platforms, which are recognized for offering extensive online courses that cater to both educational institutions and individual learners. Additionally, the study investigates Collaboration and Communication Platforms, which are designed to enhance interaction and cooperation among students and educators through various tools like discussion forums, chats, and shared workspaces. Adaptive Learning Platforms: Employing advanced algorithms and data analytics, this study used a dataset covering the UK from July 2013 to June 2020 to examine the highest skill levels of these two different groups. The data set, originally in Excel format, was carefully organized and structured for analytical purposes. The approach included the use of Python libraries such as NumPy for numerical calculations, and Matplotlib for visualization and proposed integration in a cloud-based system. The study's methodology is underpinned by sophisticated data analysis techniques, utilizing Python libraries such as NumPy, renowned for its efficiency in handling complex numerical calculations, and Matplotlib, which offers powerful visualization tools that are instrumental in elucidating the trends and patterns within the data. It is not only robust but also versatile, accommodating the integration of additional Python libraries such as Pandas for data manipulation and SciPy for more advanced scientific computations, thereby enhancing the depth and breadth of the analysis. Furthermore, the proposed integration of this analytical setup into a cloud-based system underscores the study's forward-thinking approach, aiming to leverage the scalability, accessibility, and collaborative potential of cloud computing. This integration promises to streamline the data analysis process, facilitating real-time data processing and enabling a dynamic exploration of the dataset. The study's methodology is underpinned by sophisticated data analysis techniques, utilizing Python libraries such as NumPy, renowned for its efficiency in handling complex numerical calculations, and Matplotlib, which offers powerful visualization tools that are instrumental in elucidating the trends and patterns within the data. This analytical framework is not only robust but also versatile, accommodating the integration of additional Python libraries such as Pandas for data manipulation and SciPy for more advanced scientific computations, thereby enhancing the depth and breadth of the analysis.

Energy Intelligent Control and Energy Saving System for Computer Room Based on Artificial Intelligence

In order to understand the energy intelligent control energy-saving system in computer rooms, the author proposes a research on an artificial intelligence based energy intelligent control energy-saving system in computer rooms. The author first analyzed the energy monitoring and analysis system of artificial intelligence in computer rooms. Based on the existing energy consumption data resources of computer rooms, through monitoring and scientific analysis of various aspects of energy consumption data in computer rooms, it provides decision-making support for managers to better formulate energy-saving measures. The system functions are divided into four sub modules: energy consumption environment monitoring, energy consumption behavior analysis, energy-saving renovation suggestions, and energy-saving effect evaluation; We designed an energy consumption anomaly detection method using the GESD algorithm and implemented a B/S architecture using the SSH framework. Finally, in terms of data display, we utilized two technologies: Extjs and BIRT reports, and used POI to export energy consumption data files in Excel format. Practical applications have shown that the system provides assistance for managers to develop energy-saving and emission reduction measures.

Deep belief networks in understanding and predicting changes in students’ political attitudes

Abstract Leveraging data collection software, this study gathered and preprocessed text data reflecting students’ political attitudes across five dimensions: political cognition, ruling party cognition, positive political emotion, negative political emotion, and political behavioral tendency. The processed data were subsequently stored in Excel format. These dimensions of change in students’ political attitudes served as inputs for a deep belief network (DBN) model, with the changes in attitudes being the outputs. The model’s parameters were meticulously tuned to optimize its predictive capabilities, aimed at effectively constructing a prediction model for shifts in students’ political attitudes using the deep belief network. To refine the model’s parameters and minimize errors during inverse fine-tuning, the chimpanzee algorithm (ChOA) was employed. This novel approach, termed ChOA-DBN, facilitated the successful construction of the predictive model for students’ political attitude changes. The model’s efficacy was assessed using the collected data on students’ political attitudes. Notably, the DBN model optimized via the chimpanzee algorithm demonstrated robust predictive performance, particularly when processing sample points exhibiting significant changes in students’ political attitudes (ranging from 20 to 45). The model’s predictions predominantly fell within a ±15% error margin. This research not only showcases the accuracy of the ChOA-DBN model in predicting alterations in students’ political attitudes but also underscores its potential contribution to fostering the development of accurate political perspectives among students.

Design and Build an Application for Monitoring the Spread of Covid-19 Based on GIS for the Cileungsi District

An infectious respiratory disease called Covid-19 is brought on by the SARS-CoV-2 virus. It is thought that the rare Covid-19 Omicron variant is a global epidemic since it is disseminating so swiftly and broadly. It is essential that the community receives timely and accurate information on Covid -19. Because handwritten reports in Excel format are still used for Covid-19 patient data collection, it is difficult to reliably and effectively gather data. Comprehensive and in-depth Covid-19 data will be provided for the Cileungsi sub-district in the Bogor Regency as part of this project. The prototype approach was chosen for this study because it provides a method for quickly and gradually developing apps and allows user-direct evaluation. Since Leaflet can be trusted to display data in the form of a spatial map and its attributes, it will be utilized to build the application. The use of the MySQL database will significantly aid in the storage of map data in the form of spatial and other general data. The software will be simpler to use for the general public if data is displayed in a user-friendly, interactive, real-time GIS map format.

Moderating Effect of Company Size in Corporate Governance Mechanism and Corporate Social Responsibility Disclosure of Listed Manufacturing Companies in Nigeria

This study sought to determine the moderating effect of company size in the effect of corporate governance mechanism on corporate social responsibility disclosure of listed manufacturing companies in Nigeria. The study adopted a post-positivist research philosophy and expost factor research design was used. Secondary data was obtained from financial statements and accounts of listed manufacturing companies from 2010 to 2022 using disclosure check listed adopted from previous studies. The collected data was converted into excel format for easy arrangement into panels. The estimation of the relationship between the variables of the study was achieved using E-view version 10 statistical software. As a panel data, ordinary least square was used to identify the regression model with the highest explanatory power. Findings of the study revealed that company size positively and significantly moderate the effect of corporate governance mechanism on corporate social responsibility disclosure of listed manufacturing companies in Nigeria. The study recommended that corporate affairs commission and the manufacturers association of Nigeria should put up adequate regulatory framework and guidelines to ensure more disclosure of corporate social responsibility activities of the manufacturing sector is achieved Nigeria.

Who found it easy to avail nutritional support through direct benefit transfer under national TB elimination programme in Eastern India: an early evidence from Jharkhand

Background: National TB Programme of India launched Nikshay Poshan Yojana (NPY) through Direct Benefit Transfer (DBT) in April 2018, to provide financial support for additional nutritional requirement during TB treatment. This study aims to understand the enabling attributes in availing Nikshay Poshan Yojana (NPY) through Direct Benefit Transfer (DBT) among notified TB patients from the Indian state of Jharkhand for the year 2019. Methods: This is a retrospective cross sectional descriptive study using secondary data obtained from Nikshay web portal for the Indian state of Jharkhand in the year 2019. State level data on TB Notification and NPY DBT scheme is downloaded from the Nikshay portal in excel format and analysed using Excel 2016 version. Odds Ratio and P value are calculated and analysed to prove any statistically significant associations. Results: The study found higher odds (1.08, 95% CI: 1.03, 1.14; P value 0.004) of having patients who belong to 15 to 65 years, female sex (odds ratio 1.05, 95% CI: 1.01, 1.09; P value 0.018), seeking care in public sector (odds ratio 4.05, 95% CI: 3.83, 4.27; P value <0.00001) and with drug sensitive TB (DS TB) (odds ratio 1.39, 95% CI: 1.1, 1.59; P value <0.00001), among those who received at least one instalment of DBT. Conclusions: TB patients belonging to productive age group, female sex, seeking care in public sector and with DS TB have higher odds of receiving NPY among the study population.

Impact of Nikshay Poshan Yojana on tuberculosis outcomes: preliminary evidence from eastern India

Background: National TB programme of India implemented Nikshay Poshan Yojana (NPY) through direct benefit transfer (DBT) in April 2018. This is to address the dual evils of under nutrition and unfavourable outcomes among tuberculosis patients in the country. This study aimed to understand the impact of Nikshay Poshan Yojana (NPY) through direct benefit transfer (DBT) on the outcomes of notified TB patients from the Indian state of Jharkhand for the year 2019. Methods: This was a retrospective cross sectional descriptive study using secondary data obtained from Nikshay web portal for the Indian state of Jharkhand for the year 2019. State level data on TB notification and NPY DBT scheme was downloaded in excel format and analysed using Excel 2016 version. Results: The study found higher odds (odds ratio 3.14, 95% CI: 2.97, 3.33; p value <0.00001) of having successful outcomes, among those who received at least one instalment of NPY. The risk of lost to follow up (LTFU) was 2.44 times (95% CI: 2.2-2.8, p value: 3.9×10-43) higher among those who did not receive any DBT payments as compared to those who received at least one DBT instalment. Conclusions: The ‘individual and health system enablers’ have a crucial role to play in successful TB treatment outcomes than NPY. However, LTFU rate reduces significantly with timely payment of NPY. This would facilitate improved treatment adherence and thus a favourable outcome.

Growth temperature dependence of MnSb synthesis on GaAs (111) B using molecular beam epitaxy

In this study, we employed MBE to synthesize four MnSb samples on GaAs (111) B substrate at growth temperatures 300 °C, 400 °C, 500 °C, and 600 °C for GT-300, GT-400, GT-500, and GT-600 samples respectively. Surface morphology and elemental composition were characterized using scanning electron microscopy and energy-dispersive X-ray spectroscopy for four prepared samples. X-ray diffraction was performed to assess the crystal formation and surface quality of all samples. Epitaxial growth confirmation was performed using electron backscatter diffraction. Magnetic properties were assessed via superconducting quantum interference device measurements. Based on these comprehensive characterizations, the GT-500 sample, grown at a temperature of 500 °C (pyrometer 410 °C), demonstrated excellent surface morphology, crystal formation, surface quality, and magnetic properties. This sample holds outstanding potential for future applications, particularly in fabricating spintronics devices as a high-quality ferromagnetic source/drain, powering remote sensors, and thermoelectric devices.

Social media users' online behavior with regard to the circulation of hate speech

Online hate speech is ripping Ethiopian society apart and threatening the values of democracy, human dignity, and peaceful coexistence. The current study argues that understanding people's responses to hateful posts helps combat hate speech online. Therefore, this study aims to comprehend the roles social media users play in responding to online hate speech. To this end, 14 ethnic-based hate speech posts each with more than 1,000 comments were collected from the public space of four purposefully selected YouTube news channels and four Facebook accounts, which are considered as hot spots for the circulation of hate speech during data collection period. Then, 100 random comments were collected from each hate speech post using “exportcomment.com” which automatically extract comments from social media posts in excel format. After extracting a total of 1,400 random comments, 460 of them were removed because they were found irrelevant and unclear to be coded and analyzed. Then, inductive coding was employed to identify, refine, and name codes and themes that describe the main roles played by social media users in reacting to the hate speeches. The findings showed five major roles social media users play in responding to hatful contents: trolling, pace-making, peace-making, informing, and guarding. The paper discusses the findings and provides recommendations deemed necessary to counter online hate speeches.

Online information-seeking behavior of Iranian web users on Google about Henoch–Schönlein purpura (HSP): an infodemiology study

Backgrounds: Previous studies have indicated that users’ health information-seeking behavior can serve as a reflection of current health issues within a community. This study aimed to investigate the online information-seeking behavior of Iranian web users on Google about Henoch–Schönlein purpura (HSP).MethodsGoogle Trends (GTr) was utilized to collect big data from the internet searches conducted by Iranian web users. A focus group discussion was employed to identify users’ selected keywords when searching for HSP. Additionally, keywords related to the disease’s symptoms were selected based on recent clinical studies. All keywords were queried in GTr from January 1, 2012 to October 30, 2022. The outputs were saved in an Excel format and analyzed using SPSS.ResultsThe highest and lowest search rates of HSP were recorded in winter and summer, respectively. There was a significant positive correlation between HSP search rates and the terms “joint pain” (P = 0.007), “vomiting” (P = 0.032), “hands and feet swelling” (P = 0.041) and “seizure” (P < 0.001).ConclusionThe findings were in accordance with clinical facts about HSP, such as its seasonal pattern and accompanying symptoms. It appears that the information-seeking behavior of Iranian users regarding HSP can provide valuable insights into the outbreak of this disease in Iran.

A Dual Anion Chemistry‐Based Superionic Glass Enabling Long‐Cycling All‐Solid‐State Sodium‐Ion Batteries

AbstractGlassy Na‐ion solid‐state electrolytes (GNSSEs) are an important group of amorphous SSEs. However, the insufficient ionic conductivity of state‐of‐the‐art GNSSEs at room temperature lessens their promise in the development of all‐solid‐state Na‐ion batteries (ASSNIBs) with high energy density and improved safety. Here we report the discovery of a new sodium superionic glass, 0.5Na2O2‐TaCl5 (NTOC), based on dual‐anion sublattice of oxychlorides. The unique local structures with abundant bridging and non‐bridging oxygen atoms contributes to a highly disordered Na‐ion distribution as well as low Na+ migration barrier within NTOC, enabling an ultrahigh ionic conductivity of 4.62 mS cm−1 at 25 °C (more than 20 times higher than those of previously reported GNSSEs). Moreover, the excellent formability of glassy NTOC electrolyte and its high electrochemical oxidative stability ensure a favourable electrolyte‐electrode interface, contributing to superior cycling stability of ASSNIBs for over 500 cycles at room temperature. The discovery of glassy NTOC electrolyte would reignite research enthusiasm in superionic glassy SSEs based on multi‐anion chemistry.

A Dual Anion Chemistry-Based Superionic Glass Enabling Long-Cycling All-Solid-State Sodium-Ion Batteries.

Glassy Na-ion solid-state electrolytes (GNSSEs) are an important group of amorphous SSEs. However, the insufficient ionic conductivity of state-of-the-art GNSSEs at room temperature lessens their promise in the development of all-solid-state Na-ion batteries (ASSNIBs) with high energy density and improved safety. Here we report the discovery of a new sodium superionic glass, 0.5Na2 O2 -TaCl5 (NTOC), based on dual-anion sublattice of oxychlorides. The unique local structures with abundant bridging and non-bridging oxygen atoms contributes to a highly disordered Na-ion distribution as well as low Na+ migration barrier within NTOC, enabling an ultrahigh ionic conductivity of 4.62 mS cm-1 at 25 °C (more than 20 times higher than those of previously reported GNSSEs). Moreover, the excellent formability of glassy NTOC electrolyte and its high electrochemical oxidative stability ensure a favourable electrolyte-electrode interface, contributing to superior cycling stability of ASSNIBs for over 500 cycles at room temperature. The discovery of glassy NTOC electrolyte would reignite research enthusiasm in superionic glassy SSEs based on multi-anion chemistry.

A donor-acceptor-type photoactive material based cathode molecularly imprinted photoelectrochemical sensor for luteolin

Developing new optoelectronic materials is the eternal pursuit and key strategy for building high-performance photoelectrochemical sensors. In this work, a novel organic polymer film containing D-A unit was firstly in-situ electro-synthesized on ZnS/rGO surface by using 9,9′-(9,9′-spirobi[fluorene]−2,7-diyl)-bis-9 H-carbazole (SFC) and carbazole as functional monomers. The micromorphology, element composition, structure, photoelectric efficiency and recognition ability of the key component and/or the sensing composite were investigated by SEM, XPS, XRD and transient photocurrent spectra, and the possible mechanism of charge carrier separation and transmission was proposed. Owing to the good light absorption ability, excellent film formation and imprinting ability, effective electrons-holes separation path, and satisfactory application stability, the prepared photoelectrochemical sensor exhibited good sensing performance for luteolin. Under the optimized conditions, the photocurrent intensity of the sensor showed a good linear relationship with the logarithmic concentration of luteolin from 0.001 μmol L−1 to 2 μmol L−1, with a detection limit of 0.35 nmol L−1 (S/N = 3). When applied in actual samples including carrot, peanut shells and chrysanthemum tea, highly consistent results with classical HPLC method were obtained. This work provided a new photoelectrochemical approach for the highly selective and sensitive detection of small molecules besides luteolin.

AUSTRAL WRIGHT ALLOY B25 (UNS C17200)

Abstract Austral Wright Alloy B25 (UNS C17200) is a wrought beryllium copper alloy. It provides the highest strength of any copper alloy, with electrical conductivity considerably greater than that of other high-strength copper alloys. Since it is heat treated after forming, it provides excellent formability and ductility. This alloy features good stress relaxation resistance and high fatigue strength. This datasheet provides information on composition, physical properties, hardness, elasticity, and tensile properties. It also includes information on corrosion resistance as well as forming, heat treating, and joining. Filing Code: Cu-943. Producer or source: Austral Wright Metals.

SAE 1010, CEN C10, ISO C10

Abstract 1010, C10E, and C10R are wrought low-carbon (carbon = 0.07–0.13%) non-alloy steels that are used in the as rolled, annealed, normalized, or cold worked condition. These steels are characterized by low strength, high ductility, excellent cold formability, and excellent weldability. Within the carbon range of these steel grades, strength and hardness will increase with increase in carbon content and with cold work. Such increases in strength are at the sacrifice of ductility or the ability to withstand cold deformation. 1010, C10E, and C10R are widely used for lightly stressed parts where cold formability is the primary requisite. They are less expensive than 1008 and can be used when the requirements for cold forming are less exacting. 1010, C10E, and C10R may also be used in the case hardened, nitrided, or nitrocarburized condition. This datasheet provides information on composition, physical properties, microstructure, hardness, and tensile properties. It also includes information on forming and heat treating. Filing Code: CS-265. Producer or source: Heat Treater's Guide.

A new database of the analysis of the physiological needs in amateur female basketball during official matches

ssThe PHYAFB database is a valuable resource for studying the physiological demands of female amateur basketball players during high-stress official games. It contains heart rate data from ten players aged 18 to 26, collected during ten crucial relegation phase matches, with 348,232 HR samples in CSV and Excel formats for easy access and analysis. The database includes Python source code for initial examination. The primary aim of the PHYAFB database is to provide a useful reference for other teams facing similar situations. Furthermore, the database represents a unique and valuable resource for sports scientists, coaches, and trainers seeking to comprehend the physiological demands of female basketball players during official competitions. Through the analysis of heart rate data, coaches and trainers can identify the intensity and duration of physical activity during games, enabling the development of more effective training programs. Additionally, the database can be used to compare the physiological demands placed on male and female basketball players or to investigate the impact of different game strategies on player performance.

Green synthesis of Kenaf-based activated carbons with excellent rate capability and cycle-life via hydrothermal-co-activation process for high-performance capacitive energy storage

The invention of high-performance electrode materials through a simple and green approach is always important for energy storage applications. Herein, we have prepared highly porous activated carbons (ACs) from biomass kenaf (KF) by hydrothermal and steam activation methods. A number of optimization experiments, such as hydrothermal pre-treatment (100, 150 and 200 °C), steam activation temperature (600, 700, 800, and 900 °C) and time of steam flow (15, 30, 60 and 90 min) were performed to control the specific surface area, micro- and/or meso-pores, total pore volume for an excellent electrochemical performance. The as-obtained porous ACs exhibited a high specific surface area (∼1733 m2/g) with high total pore volume (∼0.916 cm3/g) and high mesopore volume (∼28.4%), which are beneficial for the excellent double layer formation over the surface of activated electrode and also provide transport channel for an effective diffusion of electrolyte ions. The as-derived ACs were used as electrode materials for the fabrication of symmetric supercapacitor device in 6 M KOH. The fabricated device exhibited a high energy density of 4.86 Wh kg−1 at power density of 2500 W kg−1 with outstanding stability (∼96.3%) over 10,000 charge-discharge cycles.

An efficient framework for obtaining the initial cluster centers

Clustering is an important tool for data mining since it can determine key patterns without any prior supervisory information. The initial selection of cluster centers plays a key role in the ultimate effect of clustering. More often researchers adopt the random approach for this purpose in an urge to get the centers in no time for speeding up their model. However, by doing this they sacrifice the true essence of subgroup formation and in numerous occasions ends up in achieving malicious clustering. Due to this reason we were inclined towards suggesting a qualitative approach for obtaining the initial cluster centers and also focused on attaining the well-separated clusters. Our initial contributions were an alteration to the classical K-Means algorithm in an attempt to obtain the near-optimal cluster centers. Few fresh approaches were earlier suggested by us namely, far efficient K-means (FEKM), modified center K-means (MCKM) and modified FEKM using Quickhull (MFQ) which resulted in producing the factual centers leading to excellent clusters formation. K-means, which randomly selects the centers, seem to meet its convergence slightly earlier than these methods, which is the latter’s only weakness. An incessant study was continued in this regard to minimize the computational efficiency of our methods and we came up with farthest leap center selection (FLCS). All these methods were thoroughly analyzed by considering the clustering effectiveness, correctness, homogeneity, completeness, complexity and their actual execution time of convergence. For this reason performance indices like Dunn’s Index, Davies–Bouldin’s Index, and silhouette coefficient were used, for correctness Rand measure was used, for homogeneity and completeness V-measure was used. Experimental results on versatile real world datasets, taken from UCI repository, suggested that both FEKM and FLCS obtain well-separated centers while the later converges earlier.

Spray pyrolysis-based synthesis of pure molybdenum powder with nanoscale primary particles and its sintering characteristics

Molybdenum (Mo) is a refractory metal with exceptional high-temperature stability, resistance to creep, and low coefficient of thermal expansion, it thus has great potential for various high-temperature applications. Nevertheless, conventional methods of synthesizing Mo powder, such as mechanical milling and wet chemical processing, have encountered limitations in producing sintered bodies with high relative density and a fine grain structure while maintaining a low sintering temperature. Therefore, it is crucial to develop an efficient powder synthesis method that enables precise and uniform control over the powder's size and shape while minimizing contamination. Such an approach would contribute to achieving excellent formability and sinterability for the resulting Mo sintered body. In this study, we successfully demonstrated the ultrasonic spray pyrolysis (USP) technique in combination with hydrogen reduction for synthesizing pure Mo powder. The resulting Mo powder exhibited micro-sized secondary particles composed of nanoscale primary particles, leading to improved formability and sinterability, essential for fabricating high-quality sintered bodies. In order to comprehensively understand the properties of the as-prepared molybdenum oxide and hydrogen-reduced Mo powder, we employed various methods to analyze its physicochemical characteristics systematically. Subsequently, the fine Mo particles were subjected to a pressureless sintering process after a conventional uniaxial powder molding. Due to its excellent formability and sinterability, we achieved a Mo sintered body with a grain size of approximately 3 μm while maintaining a relatively high sintering density of 98.3% at a comparatively low sintering temperature of 1400 °C. The resulting microstructure contributed to a relatively high hardness of around 234 Hv. These results indicate the successful synthesis of pure Mo powder with desirable properties and highlight its potential in various applications, particularly those requiring high-quality sintered bodies with enhanced hardness.

Research on multi-stage deep drawing with electro-permanent magnet technology

Nowadays, the application of highly efficient forming techniques has been paid great attention to multi-stage sheet metal forming. In this work, the deep drawing of a circular sheet with stainless steel 304 into a cylindrical contour is performed via electro-permanent magnetic (EPM) multi-stage forming (EPMIF). Applying Lorenz force generated by directional moving electric charges, EPMIF is a hybrid forming process that combines a quasi-static stamping technique and EPM actuators to form a workpiece. During the process of EPMIF, the optimal feed increment value is designed theoretically and confirmed experimentally, which is unequal spacing and gradually decreasing for better forming performance. Excellent formability of the sheet can be observed without significant rapture and wrinkling defects, in detail, the relative forming height of the metal slab could reach 0.65. Furthermore, under different feed increments, the evolution law of metal sheet deformation is investigated by common evaluation indices including forming height, strain distribution, and thinning ratio. Meanwhile, the microscopic structure of the workpiece is compared with the simulated profile. The result shows that the deep drawing with EPMIF is conducive to improving the forming performance of metal sheets and avoiding the fracture induced by uneven deformation at the same time.