>把苦中作乐作为一种生活方式，这就是让力不从心者摆脱困境、积极向上的途径有人曾问过我这样的问题：“看你工作时间超长或是遇到难题时也很疲惫，但总能很快到恢复精力充沛的状态，但很多人面对困难的工作或是长时间工作后总感到很累，无法恢复元气，工作也没了激情。如何让自己激情永驻、活力长存呢?”
In the era of Industry 4.0, advanced assistance technologies have permeated manufacturing processes, yet the absence of systematic implementation methods remains a challenge. This research presents a multidimensional framework developed through inductive reasoning, encompassing technological capabilities and instructional considerations. Its primary objective is to guide the development of context-specific assistance solutions, shifting the focus from automated solutions to thoughtful technology selection and implementation. The framework's application has the potential to elevate stakeholder design processes through interactive visualizations and experiential anchoring. Notably, the adoption of cognitive assistance systems within manufacturing is on the rise, driven by the maturation of virtual and augmented reality solutions. However, a notable gap exists in systematic approaches for selecting and designing these technologies. This paper addresses this gap by proposing a comprehensive framework grounded in an inductive approach. This research seeks to enhance awareness among decision-makers in companies regarding essential selection and decision criteria. Furthermore, it offers guidance for a systematic and participatory implementation process. The research results underscore the importance of adopting such a framework in the industry 4.0 landscape, shedding light on the need for strategic technology integration. This study suggests that the proposed framework can serve as a valuable tool to facilitate informed decision-making and drive the successful implementation of assistance technologies in manufacturing processes.
The use of technology in learning is used as a means of learning. Learning media is anything that is used in the process of learning activities and can stimulate students' thoughts, feelings, interests and attention when learning, so that the process of interaction, communication and education between teachers and students can take place in accordance with the learning objectives. This research aims to produce a product in the form of a web-based interactive multimedia learning application, which is of good quality and highly suitable for use as a learning medium for students in English subjects. The research location was conducted at UPT SMP Negeri. 35 Medan. This multimedia learning product has gone through a strict review and revision process, based on suggestions from teachers and supervisors. This product was tested in a class with 31 students. The results show that learning management at UPT SMP Negeri 35 Medan using this interactive application has achieved learning management well, this can be seen from the acquisition section to the retrieval system of existing information/learning. Thus, this product can be used in the learning process to provide new, more meaningful experiences to students. In addition to this multimedia product, the teaching materials can be studied anywhere and at any time, which can increase students' learning motivation.
The efficacy of machine learning models in speaker recognition tasks is critical for advancements in security systems, biometric authentication, and personalized user interfaces. This study provides a comparative analysis of three prominent machine learning models: Naive Bayes, Logistic Regression, and Gradient Boosting, using the LibriSpeech test-clean dataset—a corpus of read English speech from audiobooks designed for training and evaluating speech recognition systems. Mel-Frequency Cepstral Coefficients (MFCCs) were extracted as features from the audio samples to represent the power spectrum of the speakers’ voices. The models were evaluated based on precision, recall, F1-score, and accuracy to determine their performance in correctly identifying speakers. Results indicate that Logistic Regression outperformed the other models, achieving nearly perfect scores across all metrics, suggesting its superior capability for linear classification in high-dimensional spaces. Naive Bayes also demonstrated high efficiency and robustness, despite the inherent assumption of feature independence, while Gradient Boosting showed slightly lower performance, potentially due to model complexity and overfitting. The study underscores the potential of simpler machine learning models to achieve high accuracy in speaker recognition tasks, particularly where computational resources are limited. However, limitations such as the controlled nature of the dataset and the focus on a single feature type were noted, with recommendations for future research to include more diverse environmental conditions and feature sets.
This study delves into the application of machine learning (ML) and deep learning (DL) techniques for the analysis of seismic data, aiming to identify and categorize patterns and anomalies within seismic events. Using a robust dataset, we applied three distinct clustering approaches: K-Means, DBSCAN, and an Autoencoder-based method, each offering unique perspectives on the data. K-Means clustering provided a fundamental partitioning of the data into five predefined clusters, facilitating the identification of broad seismic patterns. DBSCAN, a density-based clustering algorithm, offered insights into the spatial distribution and density of seismic events, adeptly pinpointing anomalies and outliers that signify unusual seismic activity. The Autoencoder, leveraging deep learning, excelled in capturing complex and non-linear relationships within the data, revealing subtle patterns not immediately apparent through traditional methods. The effectiveness of these clustering techniques was quantitatively evaluated using the Silhouette Score and the Davies-Bouldin Score, alongside visual assessments through PCA and t-SNE for dimensionality reduction. The results indicated that while K-Means provided clear partitioning, DBSCAN excelled in outlier detection, and the Autoencoder offered a balanced approach with its nuanced analysis capabilities. Our comprehensive analysis underscores the significance of employing a multi-methodological approach in seismic data analysis, as each method contributes uniquely to the understanding of seismic events. The insights gained from this study are valuable for enhancing predictive models and improving disaster risk management strategies in seismology. Future research directions include the integration of additional seismic features, validation against larger datasets, and the development of hybrid models to further refine the predictive accuracy of seismic event analysis.
