The paper focuses on the relationship between businesses and digital transformation, and how digital transformation has changed manufacturing in several ways. Aspects like Cloud Computing, vertical and horizontal integration, data communication, and the internet have contributed to sustainable manufacturing by decentralizing supply chains. In addition, digital transformation inventions such as predictive analysis and big data analytics have helped optimize sustainable manufacturing by reducing overproduction or underproduction through predicting customer demands. It integrates digital technology to enhance business operations, consumer engagement, supply chains, and coordination, the manufacturing process, energy conservation, efficiency, and environmental conservation and culture to satisfy business needs. Businesses’ failure to embrace digital transformation in this era contributes to their demise. This research paper will analyze and contrast several businesses and the extent of digital transformation’s influence on them during COVID-19. A two-stage study is conducted, the first stage assesses a chosen exemplary business success over three years. The second stage investigates the reasons for success, or otherwise, and the connection to digitalization in the business. Our outcomes suggest that digital transformation strongly influences firms’ effectiveness and survival from a technology-centric and business model standpoint. Some essential generic recommendations are suggested based on the results obtained.

Polyvinylidene Fluoride (PVDF) piezoelectric electrospun nanofibers have been intensively used for sensing and actuation applications in the last decade. However, in most cases, random PVDF piezoelectric nanofiber mats have moderate piezoelectric response compared to aligned PVDF nanofibers. In this work, we demonstrate the effect of alignment conducted by a collector setup composed of two-metal bars with gab inside where the aligned fiber can be formed. That is what we called static aligned nanofibers, which is distinct from the dynamic traditional technique using a high speed rotating drum. The two-bar system shows a superior alignment degree for the PVDF nanofibers. Also, the effect of added carbon nanotubes (CNTs) of different concentrations to PVDF nanofibers is studied to observe the enhancement of piezoelectric response of PVDF nanofibers. Improvement of β-phase content of aligned (PVDF) nanofibers, as compared to randomly orientated fibers, is achieved. Significant change in the piezoelectricity of PVDF fiber is produced with added CNTs with saturation response in the case of 0.3 wt % doping of CNTs, and piezoelectric sensitivity of 73.8 mV/g with applied masses down to 100 g.

This paper introduces a new usage of piezoelectric poly (vinylidene fluoride) (PVDF) electrospun nanofiber (NF) membrane as a sensing unit for acoustic signals. In this work, an NF mat has been used as a transducer to convert acoustic signals into electric voltage outcomes. The detected voltage has been analyzed as a function of both frequency and amplitude of the excitation acoustic signal. Additionally, the detected AC signal can be retraced as a function of both frequency and amplitude with some wave distortion at relatively higher amplitudes and within a certain acoustic spectrum region. Meanwhile, the NFs have been characterized through piezoelectric responses, beta sheet calculations and surface morphology. This work is promising as a low-cost and innovative solution to harvest acoustic signals coming from wide resources of sound and noise.

Infographics are visual representations of data that utilize various graphic elements, including pie charts, bar graphs, line graphs, and histograms. Educators and designers can maximize the potential of infographics as powerful educational tools by carefully addressing challenges and capitalizing on emerging technologies. However, current education systems showcase the need for development guidelines and the best practices targeted at designing and developing infographics while exploring the major economic and social impacts of infographics on education. This study examines the concept and role of infographics in education, methodologies, trends, and obstacles. It evaluates potential economic implications and gives insights to design and development experts. The study is based on a scoping literature review methodology, uncovering the conceptual background and the role of infographics. The study emphasizes the unique functions of infographics in data visualization for educational purposes and investigates the current trends and practices in infographics creation. The key challenges associated with the use of infographics are also discussed. Furthermore, the study attempts to identify the cutting-edge frameworks for infographic creation and development while evaluating their economic implications for the role of global education. Finally, the potential recommendations for creating successful infographics while focusing on professional design and development are also covered. The guided literature review will be vital for understanding and using infographics in education.

This study investigates the classification of individuals as healthy or at risk of Parkinson’s disease using machine learning (ML) models, focusing on the impact of dataset size and preprocessing techniques on model performance. Four datasets are created from an original dataset: $$DS_0$$ (normal dataset), $$DS_1$$ ( $$DS_0$$ subjected to Canny edge detection and Hessian filtering), $$DS_2$$ (augmented $$DS_0$$ ), and $$DS_3$$ (augmented $$DS_1$$ ). We evaluate a range of ML models-Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), XGBoost (XBG), Naive Bayes (NB), Support Vector Machine (SVM), and AdaBoost (AdB)-on these datasets, analyzing prediction accuracy, model size, and prediction latency. The results show that while larger datasets lead to increased model memory footprints and prediction latencies, the Canny edge detection preprocessing supplemented by Hessian filtering (used in $$DS_1$$ and $$DS_3$$ ) degrades the performance of most models. In our experiment, we observe that Random Forest (RF) maintains a stable memory footprint of 61 KB across all datasets, while models like KNN and SVM show significant increases in memory usage, from 5.7-7 KB on $$DS_0$$ to 102-220 KB on $$DS_2$$ , and similar increases in prediction time. Logistic Regression, Decision Tree, and Naive Bayes show stable memory footprints and fast prediction times across all datasets. XGBoost’s prediction time increases from 180-200 ms on $$DS_0$$ to 700-3000 ms on $$DS_2$$ . Statistical analysis using the Mann-Whitney U test with 100 prediction accuracy observations per model (98 degrees of freedom) reveals significant differences in performance between models trained on $$DS_0$$ and $$DS_2$$ (p-values < 1e-34 for most models), while the effect sizes measured by estimating Cliff’s delta values (approaching $$-1$$ ) indicate large shifts in performance, especially for SVM and XGBoost. These findings highlight the importance of selecting lightweight models like LR and DT for deployment in resource-constrained healthcare applications, as models like KNN, SVM, and XGBoost show significant increases in resource demands with larger datasets, particularly when Canny preprocessing is applied.

Object detection Through-the-Walls enables localization and identification of hidden objects behind the walls. While numerous studies have exploited Channel State Information of Multiple Input Multiple Output (MIMO) WiFi and radar devices in association with Artificial Intelligence based algorithms (AI) to detect and localize objects behind walls, this study proposes a novel non-invasive Through-the-Walls human motion direction prediction system based on a Single-Input-Single-Output (SISO) communication channel model and Shallow Neural Network (SNN). The motion direction prediction accuracy of SNN is highlighted against the other types of Machine Learning (ML) models. The comparative analysis of models in this study shows that unique human movement patterns, superimposed on received pilot radio signal, can be classified precisely by SNN, with an accuracy of approximately 89.13% compared to the other ML based models. The results of this study would guide scholars, active in developing human motion recognition systems, intrusion detection systems, or Well-being and healthcare systems, and in processes that innovate and improve processing techniques for monitoring and control.