Search Results (150)

The increasing complexity of modern control systems highlights the need for reliable and robust fault detection, isolation, and identification (FDII) methods, particularly in safety-critical and industrial applications. The study focuses on the FDII of multiplicative faults in a DC motor and its electronic amplifier. To simulate such scenarios, a complete laboratory platform was developed for real-time FDII, using relay-based switching and custom LabVIEW software 2009. This platform enables real-time experimentation and represents an important component of the study. Two estimation-based fault detection (FD) algorithms were implemented: the Sliding Window Algorithm (SWA) for discrete-time models and a modified Sliding Integral Algorithm (SIA) for continuous-time models. The modification introduced to the SIA limits the data length used in least squares estimation, thereby reducing the impact of transient effects on parameter accuracy. Both algorithms achieved high model output-to-measured signal agreement, up to 98.6% under nominal conditions and above 95% during almost all fault scenarios. Moreover, the proposed fault isolation and identification methods, including a decision algorithm and an indirect estimation approach, successfully isolated and identified faults in key components such as amplifier resistors (R₁, R₉, R₁₂), capacitor (C₈), and motor parameters, including armature resistance (R_a), inertia (J), and friction coefficient (B). The decision algorithm, based on continuous-time model coefficients, demonstrated reliable fault isolation and identification, while the reduced Jacobian-based approach in the discrete model enhanced fault magnitude estimation, with deviations typically below 10%. Additionally, the platform supports remote experimentation, offering a valuable resource for advancing model-based FDII research and engineering education. Full article

Background: Many patients harbor minimal residual disease (MRD)—small clusters of residual tumor cells that survive therapy and evade conventional detection but drive recurrence. Although advances in molecular and computational methods have improved circulating tumor DNA (ctDNA)-based MRD detection, these approaches face challenges: ctDNA shedding fluctuates widely across tumor types, disease stages, and histological features. Additionally, low levels of driver mutations originating from healthy tissues can create background noise, complicating the accurate identification of bona fide tumor-specific signals. These limitations underscore the need for refined technologies to further enhance MRD detection beyond DNA sequences in solid malignancies. Methods: Profiling circulating cell-free mRNA (cfmRNA), which is hyperactive in tumor and non-tumor microenvironments, could address these limitations to inform postoperative surveillance and treatment strategies. This study reported the development of OncoMRD BREAST, a customized, gene signature-informed cfmRNA assay for residual disease monitoring in breast cancer. OncoMRD BREAST introduces several advanced technologies that distinguish it from the existing ctDNA-MRD tests. It builds on the patient-derived gene signature for capturing tumor activities while introducing significant upgrades to its liquid biopsy transcriptomic profiling, digital scoring systems, and tracking capabilities. Results: The OncoMRD BREAST test processes inputs from multiple cutting-edge biomarkers—tumor and non-tumor microenvironment—to provide enhanced awareness of tumor activities in real time. By fusing data from these diverse intra- and inter-cellular networks, OncoMRD BREAST significantly improves the sensitivity and reliability of MRD detection and prognosis analysis, even under challenging and complex conditions. In a proof-of-concept real-world pilot trial, OncoMRD BREAST’s rapid quantification of potential tumor activity helped reduce the risk of incorrect treatment strategies, while advanced predictive analytics contributed to the overall benefits and improved outcomes of patients. Conclusions: By tailoring the assay to individual tumor profiles, we aimed to enhance early identification of residual disease and optimize therapeutic decision-making. OncoMRD BREAST is the world’s first and only gene signature-powered test for monitoring residual disease in solid tumors. Full article

Face recognition systems often falter when deployed in uncontrolled settings, grappling with low light, unexpected occlusions, motion blur, and the degradation of sensor signals. Most contemporary algorithms chase raw accuracy yet overlook the pragmatic need for uncertainty estimation and multispectral reasoning rolled into a single framework. This study introduces HUE-Net—a Human-centric, Uncertainty-aware, Event-fused Network—designed specifically to thrive under severe environmental stress. HUE-Net marries the visible RGB band with near-infrared (NIR) imagery and high-temporal-event data through an early-fusion pipeline, proven more responsive than serial approaches. A custom hybrid backbone that couples convolutional networks with transformers keeps the model nimble enough for edge devices. Central to the architecture is the perturbed multi-branch variational module, which distills probabilistic identity embeddings while delivering calibrated confidence scores. Complementing this, an Adaptive Spectral Attention mechanism dynamically reweights each stream to amplify the most reliable facial features in real time. Unlike previous efforts that compartmentalize uncertainty handling, spectral blending, or computational thrift, HUE-Net unites all three in a lightweight package. Benchmarks on the IJB-C and N-SpectralFace datasets illustrate that the system not only secures state-of-the-art accuracy but also exhibits unmatched spectral robustness and reliable probability calibration. The results indicate that HUE-Net is well-positioned for forensic missions and humanitarian scenarios where trustworthy identification cannot be deferred. Full article

Operational modal analysis (OMA) is widely used for its simplicity and reliance on ambient noise. While commercial OMA software exists, they often limit user control. Some researchers develop their own tools, but independent software tools remain scarce. The number of such independent software is limited, and the development of new ones with enhanced features, better performance, and varied user interfaces would be beneficial to spread the informed use of dynamic identification techniques, leading to more reliable and valuable results for structural engineering applications. This work introduces the new DYMOS software for OMA from ambient vibration test recordings. DYMOS includes various state-of-art algorithms and tools for vibration-based modal identification and for optimal sensor placement (OSP), allowing for customization of analysis parameters and procedures with the aim of reducing the gap between the needs of professional practice and research. Additionally, a new graphical tool is introduced for visualizing results in both buildings and bridges. By using CAD drawings as input, it streamlines model construction, making the process faster, more intuitive, and efficient. The article aims to describe DYMOS and to demonstrate its potential for OMA and OSP in civil engineering through the application on two real case studies dynamically tested. Full article

In the context of the Internet, the personalization and diversification of customer demands present a significant challenge for research on the identification, combination, and utilization of personalized demand feature elements. A key difficulty lies in achieving real-time perception, processing, and recognition of customer needs to dynamically identify and understand personalized customer intent. To address the limitations, we propose a Personalized customization-based Neural Network (PerNN), designed to enhance the performance and accuracy of recommendation systems in large-scale and complex information environments. The PerNN model introduces a Personalized Features Layer (PF), which effectively integrates multi-dimensional information—including historical interaction data, social network relationships, and users’ temporal behavior patterns—to generate fine-grained, personalized user feature representations. This approach significantly improves the model’s ability to predict user preferences. Extensive experiments conducted on public datasets demonstrate that the PerNN model consistently outperforms existing methods, particularly regarding the accuracy and response speed of personalized recommendations. The results validate the effectiveness and superiority of the proposed model in managing complex and recommendation tasks, offering a novel and efficient solution for personalized customization scenarios. Full article

Background/Objectives: Focal therapy (FT) and technology are closely connected. Advanced imaging allows for precise identification of the index lesion, enabling the targeted use of various thermal and non-thermal energy sources through different approaches, with specific techniques tailored to lesion location and operator expertise. This personalized approach enhances both safety and effectiveness, facilitating customized treatment planning. Methods: The International Consultation on Urological Diseases formed a committee to review the current literature on FT for prostate cancer (PCa), focusing specifically on the technique. Following in-depth discussions, the committee chose a “by lesion” approach rather than the traditional “by energy” approach to structure the review. A comprehensive PubMed search was conducted to gather relevant articles on the various energy modalities and procedural approaches used in FT for PCa. Results: Lesions in the apex, anterior, and posterior regions of the prostate can be accessed through several FT approaches, each associated with specific energy modalities and techniques. The transrectal approach utilizes high-intensity focused ultrasound (HIFU) and focal laser ablation (FLA), while the transperineal approach is compatible with energy sources such as cryotherapy, irreversible electroporation (IRE), brachytherapy, and FLA. The transurethral approach supports methods such as transurethral ultrasound ablation (TULSA). Each approach offers distinct advantages based on lesion location, treatment area, and energy modality. The choice of technique evaluated the safety and efficacy of each energy source and approach based on specific treatment areas within the prostate, highlighting the need for robust research across lesion locations and modalities, rather than focusing solely on each modality for a specific region. Conclusions: FT is rapidly advancing with new energy sources, technological improvements, and increasing operator expertise. To further optimize FT, research should prioritize evaluating the safety and effectiveness of different energy sources for various lesion locations, focusing on the treatment area rather than the energy modality itself. Full article

High-voltage direct-current (HVdc) transmission lines are gaining more attention as an integral part of modern power system networks. Monitoring the dc current is important for metering and the development of dynamic line rating control schemes. However, this has been a challenging task, and there is a need for wireless sensing methods with high accuracy and a dynamic range. Conventional methods require direct contact with the high-voltage conductors and utilize bulky and complex equipment. In this paper, an ultra-high-frequency (UHF) radio frequency identification (RFID)-based sensor is introduced for the monitoring of the dc current of an HVdc transmission line. The sensor is composed of a passive RFID tag with a custom-designed antenna, integrated with a Hall effect magnetic field device and an RF power harvesting unit. The dc current is measured by monitoring the dc magnetic field around the conductor using the Hall effect device. The internal memory of the RFID tag is encoded with the magnetic field data. The entire RFID sensor can be wirelessly powered and interrogated using a conventional RFID reader. The advantage of this approach is that the sensor does not require batteries and does not need additional maintenance during its lifetime. This is an important feature in a high-voltage environment where any maintenance requires either an outage or special equipment. In this paper, the detailed design of the RFID sensor is presented, including the antenna design and measurements for both the RFID tag and the RF harvesting section, the microcontroller interfacing design and testing, the magnetic field sensor calibration, and the RF power harvesting section. The UHF RFID-based magnetic field sensor was fabricated and tested using a laboratory experimental setup. In the experiment, a 40 mm-diameter-aluminum conductor, typically used in 500 kV HVdc transmission lines carrying a dc current of up to 1200 A, was used to conduct dc current tests for the fabricated sensor. The sensor was placed near the conductor such that the Hall effect device was close to the surface of the conductor, and readings were acquired by the RFID reader. The sensitivity of the entire RFID sensor was 30 mV/mT, with linear behavior over a magnetic flux density range from 0 mT to $4.5$ mT. Full article

Cultural factors shape students’ expectations and perceptions within diverse educational settings. The perceived quality of a Higher Education Institution (HEI) is crucial to its success, with student satisfaction determined mainly by their overall experiences. The concept of Student eXperience (SX) can be analyzed through the lens of Customer eXperience (CX) from a Human–Computer Interaction (HCI) perspective, positioning students as the “customers” of the institution. SX encompasses academic and physical interactions and students’ emotional, social, and psychological responses toward an institution’s systems, products, and services. By accounting for factors such as emotions, socioeconomic status, disabilities, and, importantly, cultural background, SX provides a comprehensive measure of student experiences. Building upon our previous SX model and Hofstede’s national culture model, we have developed a Student eXperience evaluation methodology that serves as a diagnostic tool to assess both student satisfaction and how effectively HEIs serve a diverse student population. This methodology ensures that all students, regardless of their background, are considered in the evaluation process, facilitating the early identification of institutional strengths and weaknesses. Incorporating cultural aspects into the assessment delivers more precise results. Furthermore, our approach supports HEIs in promoting equity, diversity, and inclusion by addressing the needs of minority students and students with disabilities, as well as reducing gender disparities. These objectives align with UNESCO’s Sustainable Development Goals, contributing to fostering an equitable learning environment. By adopting such inclusive evaluation practices, HEIs can enhance the perceived quality of education and their responsiveness to the needs of an increasingly multicultural student body. Full article

SARS-CoV-2 high transmission and genomic mutations result in the emergence of new variants that impact COVID-19 vaccine efficacy and virus transmission by evading the host immune system. Wastewater-based epidemiology is an effective approach to monitor SARS-CoV-2 variants circulation in the population but is a challenge due to the presence of reaction inhibitors and the low concentrations of SARS-CoV-2 in this environment. Here, we aim to improve SARS-CoV-2 variant detection in wastewater by employing nested PCR followed by next-generation sequencing (NGS) of small amplicons of the S gene. Eight SARS-CoV-2 wastewater samples from Alegria Wastewater Treatment Plant, in Rio de Janeiro, Brazil, were collected monthly from February to September 2021. Samples were submitted to virus concentration, RNA extraction and nested PCR followed by NGS. The small amplicons were used to prepare libraries for sequencing without the need to perform any fragmentation step. We identified and calculated the frequencies of 29 mutations matching the Alpha, Beta, Gamma, Delta, Omicron, and P.2 variants. Omicron matching-mutations were detected before the lineage was classified as a variant of concern. SARS-CoV-2 wastewater sequences clustered with SARS-CoV-2 variants detected in clinical samples that circulated in 2021 in Rio de Janeiro. We show that sequencing of selected small amplicons of SARS-CoV-2 S gene allows the identification of SARS-CoV-2 variants matching mutations and their frequencies’ calculation. This approach may be expanded using customizing primers for additional genomic regions, in order to differentiate current variants. Approaches that allow us to learn how variants emerge and how they relate to clinical outcomes are crucial for our understanding of the dynamics of virus variants circulation, providing valuable data for public health management. Full article

Mechanical impacts on loose biological objects caused by technological equipment can result in both external and internal damage, compromising the quality, storage life, and reproductive capacity of biological materials. This study addresses the need for a reliable methodology to assess such damage. The research aims to develop a systematic approach for identifying damage parameters in biological objects. The methodology involves applying artificial loading to biological samples, determining destructive forces, conducting tomography, processing images, and evaluating damage extent. Experiments were performed using a standard material testing machine and a custom-built impact test bench with varying parameters such as static and dynamic characteristics, object orientation, and load magnitude. The microstructure of the sample, in the form of 2D cross-sections and 3D images, was obtained using X-ray computed tomography. Image processing, with the Monte Carlo method, allowed for the calculation of microdamage coefficients. The key result of this study is the identification of a relationship between the microdamage coefficient of corn seeds and external load parameters. These findings are critical for understanding the effects of mechanical impact on biological materials. Future research should focus on expanding the study to other biological objects and enhancing measurement techniques for more precise damage assessment. Full article

This study proposes a design method for the evaluation and redesign of product–service systems (PSSs) from the perspective of social sustainability, one that applies Max-Neef’s framework of fundamental human needs. The proposed method systematically connects PSS functions and requirements—identified through service blueprints and value graphs—to “satisfiers” and “barriers” extracted via needs-based workshops. This connection enables the identification of functions that either contribute to or hinder the fulfillment of fundamental human needs and guide the generation of redesign proposals aimed at sufficiency-oriented outcomes. A case study involving a smart-cart system in Osaka, Japan, was conducted to demonstrate the applicability of the method. Through an online workshop, satisfiers and barriers related to both physical and online shopping experiences were identified. The analysis revealed that existing functions such as promotional information and automated checkout processes negatively impacted needs such as understanding and affection due to information overload and reduced human interaction. In response, redesign concepts were developed, including filtering options for information, product background storytelling, and optional slower checkout lanes with human assistants. The redesigned functions contribute to the fulfillment of fundamental human needs, indicating that the proposed method can enhance social sustainability in PSS design. This study offers a novel framework that extends beyond traditional customer requirement-based approaches by explicitly incorporating human needs into function-level redesign. Full article

As a key global food reserve, rice disease detection technology plays an important role in promoting food production, protecting ecological balance and supporting sustainable agricultural development. However, existing rice disease identification techniques face many challenges, such as low training efficiency, insufficient model accuracy, incompatibility with mobile devices, and the need for a large number of training datasets. This study aims to develop a rice disease detection model that is highly accurate, resource efficient, and suitable for mobile deployment to address the limitations of existing technologies. We propose the Transfer Layer iRMB-YOLOv8 (TLI-YOLO) model, which modifies some components of the YOLOv8 network structure based on transfer learning. The innovation of this method is mainly reflected in four key components. First, transfer learning is used to import the pretrained model weights into the TLI-YOLO model, which significantly reduces the dataset requirements and accelerates model convergence. Secondly, it innovatively integrates a new small object detection layer into the feature fusion layer, which enhances the detection ability by combining shallow and deep feature maps so as to learn small object features more effectively. Third, this study is the first to introduce the iRMB attention mechanism, which effectively integrates Inverted Residual Blocks and Transformers, and introduces deep separable convolution to maintain the spatial integrity of features, thus improving the efficiency of computational resources on mobile platforms. Finally, this study adopted the WIoUv3 loss function and added a dynamic non-monotonic aggregation mechanism to the standard IoU calculation to more accurately evaluate and penalize the difference between the predicted and actual bounding boxes, thus improving the robustness and generalization ability of the model. The final test shows that the TLI-YOLO model achieved 93.1% precision, 88% recall, 95% mAP, and a 90.48% F1 score on the custom dataset, with only 12.60 GFLOPS of computation. Compared with YOLOv8n, the precision improved by 7.8%, the recall rate improved by 7.2%, and mAP@.5 improved by 7.6%. In addition, the model demonstrated real-time detection capability on an Android device and achieved efficiency of 30 FPS, which meets the needs of on-site diagnosis. This approach provides important support for rice disease monitoring. Full article

This paper addresses the issue of distinguishing commercially played songs from non-music audio in radio broadcasts, where automatic song identification systems are commonly employed for reporting purposes. Service call costs increase because these systems need to remain continuously active, even when music is not being broadcast. Our solution serves as a preliminary filter to determine whether an audio segment constitutes “music” and thus warrants a subsequent service call to an identifier. We collected 139 h of non-consecutive 5 s audio samples from various radio broadcasts, labeling segments from talk shows or advertisements as “non-music”. We implemented multiple data augmentation strategies, including FM-like pre-processing, trained a custom Convolutional Neural Network, and then built a live inference platform capable of continuously monitoring web radio streams. This platform was validated using 1360 newly collected audio samples, evaluating performance on both 5 s chunks and 15 s buffers. The system demonstrated consistently high performance on previously unseen stations, achieving an average accuracy of 96% and a maximum of 98.23%. The intensive pre-processing contributed to these performances with the benefit of making the system inherently suitable for FM radio. This solution has been incorporated into a commercial product currently utilized by Italian clients for royalty calculation and reporting purposes. Full article

This study explores the marketing challenges perceived by aspiring entrepreneurs, focusing on undergraduate business students’ perspectives. Drawing on an empirical qualitative approach, this research utilizes data from semi-structured interviews with 31 students at a midwestern U.S. university to examine key barriers that hinder business development and growth. The research identifies five key themes: budget constraints, customer identification and engagement, branding and market differentiation, digital marketing barriers, and the role of education. The findings reveal that financial limitations and resource constraints hinder students’ ability to implement effective marketing strategies, while limited practical experience complicates customer engagement and market research efforts. Additionally, participants emphasized challenges in establishing distinct brand identities and adapting to rapidly evolving digital marketing tools. These insights align with existing entrepreneurial marketing theories, reinforcing the role of adaptability and resourcefulness in early-stage business ventures. Education emerged as both a strength and an area for improvement, with students valuing theoretical foundations but identifying gaps in hands-on, experiential learning opportunities. This study contributes to entrepreneurial marketing literature by highlighting the interplay between resource constraints, creativity, and adaptability in understanding marketing challenges. It further underscores the importance of integrating digital marketing competencies and alternative financing strategies, such as crowdfunding and fintech solutions, into entrepreneurship education. It also emphasizes the need for educational reforms that integrate practical applications, mentorship, and digital marketing training to prepare students for real-world entrepreneurial endeavors. By addressing these gaps, the findings offer actionable insights for educators, policymakers, and entrepreneurial support systems to better equip aspiring entrepreneurs for sustainable success. Full article

In an increasingly digitized environment, the deterioration of cell phone cables has led to a significant environmental impact due to the lack of adequate protection and care. This often results in cell phone charging cables being in poor condition. Cable damage can include situations such as dirt accumulation or incorrect bending, leading to breakage. As a result, the objective was determined to design a prototype of a cell phone holder with internal biodegradable cable winding. Ulrich and Eppinger served as the methodological basis for the design, following phases including customer needs identification, setting objective values, product concept generation, concept selection, concept testing, and final specification filtering. A survey of 100 individuals provided valuable data for validating certain metrics. Additionally, two focus groups with 15 users were conducted, two experts were interviewed, and a 72 h usage test was carried out, all supported by the agile Scrum methodology and the Scamper technique, allowing for feedback and validation of the initial concept. The final prototype was modeled in 3D using the Lumion 11 program and physically constructed, ensuring functionality and adaptability of the cell phone and charger. In conclusion, a cell phone holder with a cable winder was designed, facilitating easy transport and prolonging the lifespan of any charger cable. Full article