Metallurgical and Materials Engineering (E-Journal)
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HR Practices as a Spark for Innovation: A Route to Better Organizational Outcomes
Get PDFThe objectives of this research project is to evaluate the efficacy of particular human resource practices implemented through banking organizations in Pakistan. Different organizations' policies, techniques, and human resource practices (HRPs) have multiple effect on productivity and overall growth. The purpose of this study is to measure how certain HR practices (RS, TD) affect an organization's performance while measuring the mediating impact of innovative work practices in the banking industry in developing nations such as Pakistan. In the present era, organizations are characterized through continuous change in all aspects. The survey included the distribution of close-ended questionnaires to personnel of private banks in Sindh, in order to gather the necessary data for the research. The researcher has implemented quantitative methodology through PLS-SEM 4 statistical software. Additionally, 400 questionnaires were circulated; 390 were fully collected and taken into consideration for the data analysis procedure, while 10 were rejected as incomplete or blindly filled. Consequently, it is imperative for firms to accumulate knowledge capital and retain it in order to maintain a sustainable competitive advantage. Only the full utilization its human resources base can make the company inventive, productive, and responsive to the always changing needs of its customers
Impact of AI-Driven Learning Management Systems on Institutional Efficiency and Student Engagement
Get PDFAI-based Learning Management Systems (LMS) have transformed the educational industry by streamlining operational activities and boosting student engagement. The study investigates how the use of AI-based LMS can change certain educational parameters by looking at how they help to automate administrative processes, personalize learning, and enforce data-driven decision-making in academic settings. The research employs a mixed-methods approach, gathering quantitative data from faculty and students using structured surveys and qualitative insights through interviews with institutional administrators. AI for Learning Management: Streamlining Operations and Improving Outcomes AI-Powered LMS: Operational Efficiency The data-driven nature of AI for learning management systems allows organizations to optimize resource allocation and course delivery. In addition to that, it also leads to better student engagement and academic performance, through AI-based adaptive learning tools, smart feedback systems as well as predictive analytics. While the digital era has led to novel online learning frameworks, the emergence of AI-based LMS serves as a modernized educational approach, one that accommodates educational scalability through improved institutional productivity alongside enhanced student learning experiences, as we conclude from the analysis above. Yet future directions of research may include the investigating beyond the mere implications and the role of new AI technologies that may continue and push the education even further
Main Directions of Physical Geography Research in Azerbaijan
Get PDFSpecialists who master the specialty of physical geography have the ability to effectively use nature. Physical geography is one of the earliest branches of geography and is a section where research has been conducted for a long time. Physical geography is a main branch of geography and is connected to its other branches. Therefore, the study of this field is of great importance
In Vitro Evaluation of the Corrosion Resistance and Biocompatibility of Different Dental Implant Metals
Get PDFObjective: To evaluate the corrosion resistance and biocompatibility of different dental implant metals in vitro.
METHODS: Six dental implant metals (titanium, titanium alloy, stainless steel, cobalt-chromium alloy, nickel-titanium alloy, and zirconium) were evaluated for corrosion resistance using electrochemical impedance spectroscopy and potentiodynamic polarization tests. Biocompatibility was assessed using cell culture tests with human osteoblast-like cells.
RESULTS: Titanium and titanium alloy showed the highest corrosion resistance (10.2 ± 0.5 Ω/cm² and 9.5 ± 0.4 Ω/cm², respectively) and biocompatibility (95.2 ± 2.1% and 92.1 ± 2.5% cell viability, respectively). Zirconium also showed high corrosion resistance (8.1 ± 0.4 Ω/cm²) and biocompatibility (90.5 ± 2.8% cell viability). Stainless steel, cobalt-chromium alloy, and nickel-titanium alloy had lower corrosion resistance and biocompatibility.
CONCLUSION: This study suggests that titanium and titanium alloy are suitable materials for dental implants due to their high corrosion resistance and biocompatibility. Zirconium may also be a suitable alternative
Predictive Maintenance and Monitoring of Industrial Compressors Using Machine Learning: A Proactive Approach
Get PDFIn the era of Industry 4.0, predictive maintenance has become a cornerstone for ensuring operational efficiency, minimizing downtime, and extending the lifespan of industrial equipment. This paper presents a comprehensive approach to predictive maintenance and real-time monitoring of industrial air compressors using machine learning techniques integrated with Internet of Things (IoT) infrastructure. The proposed framework leverages a multi-sensor setup to continuously collect critical parameters such as temperature, pressure, and flow rate from compressor units. These data streams are transmitted to a cloud-based Structured Query Language (SQL) database, enabling centralized and scalable storage for real-time analytics. A Linear Regression algorithm was trained on historical sensor data to detect performance anomalies and forecast potential failures. The optimized model was then deployed for real-time inference. When monitored parameters exceeded pre-set thresholds, the system autonomously triggered alerts through email notifications, allowing timely intervention and preventive action. The machine learning model demonstrated high reliability, achieving a prediction accuracy of 98% as measured by the Mean Squared Error (MSE) metric. The integration of IoT and machine learning facilitates proactive maintenance strategies, reducing the risk of unexpected equipment failure and enabling continuous condition monitoring without manual intervention. The findings underscore the potential of intelligent maintenance systems to drive significant improvements in asset management, cost efficiency, and operational safety across industrial settings
Detection and Defense Mechanisms for Covert Timing communications
Get PDFThis study presents a literature overview on methods for discovering and removing covert channels. Data transmission methods known as covert channels take advantage of system resources already in place but weren't intended for this purpose, such as firewalls, to transport data undetected. By setting up a seemingly secure channel of communication between two parties, sensitive information could be leaked from the more secure party to the less secure party. Using a shared network, two parties can easily communicate and exchange information to send confidential data without being detected. As a result, discovering covert communications is difficult. Network protocols place restrictions on Covert Storage Channels (CSC), preventing it from deviating from a set of guidelines. On the other hand, CTCs have stochastic behavior, which makes detection more challenging. Analysis of the state of art systems is done in this paper & it is found that the most likely option is an active warden which is a specialized network security system that is designed to filter out a range of irregularities seen in network data
Enhancing Flight Delay Prediction Using Residual Neural Networks (ResNets)
Get PDFThe most concern in airline sectors is flight delays because they have a big impact on airlines, passengers, and airports. This study used Residual Neural Network (ResNets), XGBoost, and LightGBM. The aim is to enhance flight delay prediction using ResNets. The performance of ResNets, which is a deep learning model, has been compared to the performances of XGBoost and LightGBM models, which are machine learning models. The dataset used is the domestic flights of United States from January 2019 to August 2023. The confusion matrix is used to make the comparisons between the selected models by summarizing prediction results, which are F1-score, accuracy, sensitivity, and precision. In addition, the validation and models' information, such as file size and prediction time, are used to assess the models' performance. The ResNets models have the best results, followed by LightGBM. The XGBoost has the worst results compared to other models
A Modified Deep Convolutional Network for Detection of Covid19 from Chest X-Rays Based on Concatenation of Image Preprocessing Techniques and RESnCOV
Get PDFThe fast-spreading coronavirus disease called COVID-19 has impacted millions of people worldwide. It becomes difficult for medical experts to rapidly detect the illness and stop its spread because of its rapid growth and rising numbers. One of the newer areas of study where this issue can be more carefully addressed is medical image analysis. In this study, we implemented an image processing system utilizing deep learning and neural networks to previse the 2019-nCoV using chest roentgen ray images. In order to recognize COVID-19 positive and healthy patients using chest roentgen ray images, this paper suggests employing convolutional neural networks, deep learning, and machine learning. We proposed a neural network composed of various features taken from two convolutional neural networks, ResNet50 and ResNet152V2, in order to successfully manage the intricate structural complexity of an image. We tested our network on 7940 images to see how well it performs in real-world situations. The proposed network detects normal and COVID-19 cases with an average accuracy of 95% and can be used as an aid in the radiology department
Performance Analysis of Face Forgery Recognition and Classification Using Advanced Deep Learning Methods
Get PDFThe adoption of web technology has come to be accompanied by a number of worrying security issues, one of which is deep fakes that are now counted among the top visual deceits in the field. The need for identifying such manipulations which is on the rise is the need for stronger methods that can be used to identify such manipulations. This article deals with the usage of fully connected neural networks (FCNN), convolutional neural networks (CNN), and deep convolutional neural networks (DCNN) to determine if a presented facial image is original or fake. In this case, the methods apply the use of the improvements in the feature extracting techniques to catch even the smallest differences in modified materials. Tests conducted on kaggle benchmark datasets depend on that that the methods are the best for it as a solution for safeguarding reliable and efficient forgery detection. The outcome is suggestive of that integration of deep learning methods like CNN, FCNN, and DCNN automated systems has the potential for advancing the struggle against manipulation in media field. As compared with the other models, the CNN is excelling in my testing and it is far better than the rest. More precisely, the CNN is the most perfect while FCNN had its drawbacks in the precision and specificity
Strongly Isolate Perfect Domination in Graphs
Get PDFA dominating set of a graph is said to be an isolate dominating set (IDS) if < > has at least one isolated vertex. The ID number of is represented by . An ID-set is considered as strongly isolate dominating set (SIDS) if there exists a ∈ such that , where and . A dominating set is called a perfect dominating set if every vertex in V (G) − has exactly one neighbour in . By using the above concept and the definition of SID, we define a new concept called ”Strongly Isolate Perfect Domination”(SIPD). An isolate dominating set is said to be strongly isolate perfect dominating set if there exists such that and is a perfect dominating set of . This paper involves some basic features of SIPDS and compare SIPDS with dominating set, ID-set and efficient dominating set (EDS). At the end, includes SIPD number of path, cycle, complete bi- partite graph, complete b- partite graph and some group of graphs