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The purpose of this paper is to develop a familiarity analysis of resistive-capacitive (RC) network active circuit operating with unfamiliar low-pass (LP) type negative group delay (NGD) behavior. The design method of NGD circuit is validated by simulation with commercial tool and experimental measurement.

The present research work methodology is structured in three main parts. The familiarity theory of RC-network LP-NGD circuit is developed. The LP-NGD circuit parameters are expressed in function of the targeted time-advance. Then, the feasibility study is based on the theory, simulation and measurement result comparisons.

The RC-network based LP-NGD proof of concept is validated with −1 and −0.5 ms targeted time-advances after design, simulation, test and characterized. The LP-NGD circuit unity gain prototype presents NGD cut-off frequencies of about 269 and 569 Hz for the targeted time-advances, −1 and −0.5 ms, respectively. Bi-exponential and arbitrary waveform signals were tested to verify the targeted time-advance.

The performance of the unfamiliar LP-NGD topology developed in the present study is limited by the parasitic elements of constituting lumped components.

The NGD circuit enables to naturally reduce the undesired delay effect from the electronic and communication systems. The NGD circuit can be exploited to reduce the delay induced by electronic devices and system.

As social impacts of the NGD circuit application, the NGD function is one of prominent solutions to improve the technology performances of future electronic device in term of communication aspect and the transportation system.

The originality of the paper concerns the theoretical approach of the RC-network parameters in function of the targeted time-advance and the input signal bandwidth. In addition, the experimental results are also particularly original.

This study aims to propose a method for monitoring bearing health in the time–frequency domain, termed the Lock-in spectrum, to track the evolution of bearing faults over time and frequency.

The Lock-in spectrum uses vibration signals captured by vibration sensors and uses a lock-in process to analyze specified frequency bands. It calculates the distribution of signal amplitudes around fault characteristic frequencies over short time intervals.

Experimental results demonstrate that the Lock-in spectrum effectively captures the degradation process of bearings from fault inception to complete failure. It provides time-varying information on fault frequencies and amplitudes, enabling early detection of fault growth, even in the initial stages when fault signals are weak. Compared to the benchmark short-time Fourier transform method, the Lock-in spectrum exhibits superior expressive ability, allowing for higher-resolution, long-term monitoring of bearing condition.

The proposed Lock-in spectrum offers a novel approach to bearing health monitoring by capturing the dynamic evolution of fault frequencies over time. It surpasses traditional methods by providing enhanced frequency resolution and early fault detection capabilities.

Torque is one of the main parameters reflecting the operation status and detection of a mechanical rotation system. The use of quartz pillar to design torque sensors has advantage over the use of quartz disk, but research into the torsional effect of quartz pillar is rare. This paper aims to investigate a novel type of torque sensor based on piezoelectric torsional effect.

Based on the theory of anisotropic elasticity and the Maxwell electromagnetism, the torsion stress and distribution of surface charge of a rectangular quartz pillar are calculated. Using finite element analysis, the polarized electric field of the piezoelectric pillar is solved. According to the theoretical calculation of torsional effect of piezoelectric quartz pillar, detection electrodes are mounted on the surface of the quartz pillar and a new type of torque sensor is designed.

The calibration experimental results show that the bound charges are proportional to the torque applied, and the torque sensor has fully reached the dynamometer standard.

This paper shows that the torsional effect of the developed piezoelectric quartz pillar can be used to create a new type of piezoelectric torque sensor.

Ciliated microelectromechanical system (MEMS) vector hydrophones pick up sound signals through Wheatstone bridge in cross beam-ciliated microstructures to achieve information transmission. This paper aims to overcome the complexity and variability of the marine environment and achieve accurate location of targets. In this paper, a new method for ocean noise denoising based on improved complete ensemble empirical mode decomposition with adaptive noise combined with wavelet threshold processing method (CEEMDAN-WT) is proposed.

Based on the CEEMDAN-WT method, the signal is decomposed into different intrinsic mode functions (IMFs), and relevant parameters are selected to obtain IMF denoised signals through WT method for the noisy mode components with low sample entropy. The final pure signal is obtained by reconstructing the unprocessed mode components and the denoising component, effectively separating the signal from the wave interference.

The three methods of empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD) and CEEMDAN are compared and analyzed by simulation. The simulation results show that the CEEMDAN method has higher signal-to-noise ratio and smaller reconstruction error than EMD and EEMD. The feasibility and practicability of the combined denoising method are verified by indoor and outdoor experiments, and the underwater acoustic experiment data after processing are combined beams. The problem of blurry left and right sides is solved, and the high precision orientation of the target is realized.

This algorithm provides a theoretical basis for MEMS hydrophones to achieve accurate target positioning in the ocean, and can be applied to the hardware design of sonobuoys, which is widely used in various underwater acoustic work.

This article introduces SigBERT, a novel approach that fine-tunes bidirectional encoder representations from transformers (BERT) for the purpose of distinguishing between intact and impaired structures by analyzing vibration signals. Structural health monitoring (SHM) systems are crucial for identifying and locating damage in civil engineering structures. The proposed method aims to improve upon existing methods in terms of cost-effectiveness, accuracy and operational reliability.

SigBERT employs a fine-tuning process on the BERT model, leveraging its capabilities to effectively analyze time-series data from vibration signals to detect structural damage. This study compares SigBERT's performance with baseline models to demonstrate its superior accuracy and efficiency.

The experimental results, obtained through the Qatar University grandstand simulator, show that SigBERT outperforms existing models in terms of damage detection accuracy. The method is capable of handling environmental fluctuations and offers high reliability for non-destructive monitoring of structural health. The study mentions the quantifiable results of the study, such as achieving a 99% accuracy rate and an F-1 score of 0.99, to underline the effectiveness of the proposed model.

SigBERT presents a significant advancement in SHM by integrating deep learning with a robust transformer model. The method offers improved performance in both computational efficiency and diagnostic accuracy, making it suitable for real-world operational environments.

The Internet has changed consumer decision-making and influenced business behaviour. User-generated product information is abundant and readily available. This paper argues that user-generated content can be efficiently utilised for business intelligence using data science and develops an approach to demonstrate the methods and benefits of the different techniques.

Using Python Selenium, Beautiful Soup and various text mining approaches in R to access, retrieve and analyse user-generated content, we argue that (1) companies can extract information about the product attributes that matter most to consumers and (2) user-generated reviews enable the use of text mining results in combination with other demographic and statistical information (e.g. ratings) as an efficient input for competitive analysis.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The study makes several contributions to the marketing and management literature, mainly by illustrating the methodological advantages of text mining and accompanying statistical analysis, the different types of distilled information and their use in decision-making.

Arthroscopic osteochondroplasty is a minimally invasive procedure that has been used to treat femoroacetabular impingement syndrome, leading to significant improvements in patients’ clinical outcomes and quality of life. However, some studies suggest that inadequate bone resection can substantially alter hip biomechanics. These modifications may generate different contact profiles and higher contact forces, increasing the risk of developing premature joint degeneration. To improve control over bone resection and biomechanical outcomes during arthroscopic osteochondroplasty surgery, this study aims to present a novel system for measuring femoroacetabular contact forces.

Following a structured design process for the development of medical devices, the steps required for its production using additive manufacturing with material extrusion and easily accessible sensors are described. The system comprises two main devices, one for measuring femoroacetabular contact forces and the other for quantifying the force applied by the assistant surgeon during lower limb manipulation. The hip device was designed for use within an arthroscopic environment, eliminating the need for additional portals.

To evaluate its performance, the system was first tested in a laboratory setup and later under in-service conditions. The 3D printing parameters were tuned to ensure the watertighness of the device and sustain the intraoperative fluid pressures. The final prototype allowed for the controlled measurement of the hip contact forces in real-time.

Using additive manufacturing and readily available sensors, the present work presents the first device to quantify joint contact forces during arthroscopic surgeries, serving as an additional tool to support the surgeon’s decision-making process regarding bone resection.

This study aims to explore the relationship between personal innovativeness, perceived symbolic value and green purchase intentions within the context of sustainable consumption. Specifically, it investigates how personal innovativeness influences both green purchase intentions and perceived symbolic value and examines whether perceived symbolic value mediates the relationship between personal innovativeness and green purchase intentions.

Using a survey-based quantitative approach, data were collected from a sample of Indonesian consumers using online panels via SurveyMonkey®. The study used Rogers’ diffusion theory to conceptualize personal innovativeness and drew on theories of symbolic consumption to frame perceived symbolic value. Partial least square structural equation modeling was used to test the proposed hypotheses and explore the relationships among the constructs.

The results indicate that personal innovativeness positively affects green purchase intentions and enhances the perceived symbolic value of green products. Moreover, perceived symbolic value influences green purchase intentions and partially mediates the relationship between personal innovativeness and green purchase intentions. These findings underscore the critical role of symbolic value in the adoption of green products and highlight personal innovativeness as a key driver in sustainable consumer behavior.

This research contributes to the sustainable consumption literature by integrating personal innovativeness with symbolic value considerations in green purchasing decisions, offering a novel insight into how individual traits and product symbolism interact to influence consumer behavior in the context of environmental sustainability.

The purpose of this study is to model the global dynamic hysteresis properties with an improved Jiles–Atherton (J-A) model through a unified set of parameters.

First, the waveform scaling parameters β, λ_k and λ_c are used to improve the calculation accuracy of hysteresis loops at low magnetic flux density. Second, the Riemann–Liouville (R-L) type fractional derivatives technique is applied to modified static inverse J-A model to compute the dynamic magnetic field considering the skin effect in wideband frequency magnetization conditions.

The proposed model is identified and verified by modeling the hysteresis loops whose maximum magnetic flux densities vary from 0.3 to 1.4 T up to 800 Hz using B30P105 electrical steel. Compared with the conventional J-A model, the global simulation ability of the proposed dynamic model is much improved.

Accurate modeling of the hysteresis properties of electrical steels is essential for analyzing the loss behavior of electrical equipment in finite element analysis (FEA). Nevertheless, the existing inverse Jiles–Atherton (J-A) model can only guarantee the simulation accuracy with higher magnetic flux densities, which cannot guarantee the analysis requirements of considering both low magnetic flux density and high magnetic flux density in FEA. This paper modifies the dynamic J-A model by introducing waveform scaling parameters and the R-L fractional derivative to improve the hysteresis loops’ simulation accuracy from low to high magnetic flux densities with the same set of parameters in a wide frequency range.

After completion of the case study, students will be able to understand the format of for-profit social enterprise working for menstrual hygiene sustainability and its contribution toward U.N. Sustainable Development Goals, to appreciate the company’s alignment with the triple bottom line framework, to analyze the blue ocean mechanism that the company has developed to create an impact and to critique the strategies the Asan Cups company could adopt to increase its market share and growth.

Asan Cups was a for-profit social enterprise founded by Ira Guha in 2021. The company crafted reusable menstrual cups from liquid silicone, sporting a patented design in India, the UK, Europe and the USA. Successfully retailing its products in India, the UK and Europe, Asan Cups operated on a bootstrap model with a compact team of four, led by its visionary founder. From the get-go, the company embraced a compelling 1-for-1 donation initiative. For every cup sold, Asan Cups generously donated another to women who could not afford it. Collaborating with nongovernmental organizations, schools, educational institutions and social workers, the company spearheaded campaigns to heighten menstrual hygiene awareness. This proactive approach aimed to boost the acceptance of menstrual cups among rural women and championed the cause of environmental sustainability. The company did not just stop at providing an eco-friendly alternative. Asan Cups fervently educated the masses on the detrimental environmental impact of traditional disposable period products like sanitary pads and tampons. Fast-forwarding to 2023, Asan Cups had garnered approximately 30,000 users, with the adoption rate steadily climbing. The company strategically used an education-intensive model to foster awareness about period products in collaboration with partners nationwide. However, being a for-profit entity, the founder, Guha, was at a crossroads. Balancing the need for profitability, there was mounting pressure to explore additional revenue streams and expand operations and market reach. The dilemma loomed large: opt for a quicker marketing strategy or stay true to the company’s foundational education-centered approach. This case study delves into the dynamic strategies, impactful operations and growth scenarios Asan Cups navigated since its inception. It examines the pivotal choices faced by the founder and explores potential strategies for sustained growth.

This case study can be used at both undergraduate and master’s levels. The case study will be handy for strategic management and business strategy courses and can also be used for social entrepreneurship, marketing and entrepreneurship courses.

Teaching notes are available for educators only.

CSS 11: Strategy.