Automatic Detection of Laryngeal Pathology on Sustained Vowels Using Short-Term Cepstral Parameters: Analysis of Performance and Theoretical Justification

The majority of speech signal analysis procedures for automatic detection of laryngeal pathologies mainly rely on parameters extracted from time domain processing. Moreover, calculation of these parameters often requires prior pitch period estimation; therefore, their validity heavily depends on the robustness of pitch detection. Within this paper, an alternative approach based on cepstral- domain processing is presented which has the advantage of not requiring pitch estimation, thus providing a gain in both simplicity and robustness. While the proposed scheme is similar to solutions based on Mel-frequency cepstral parameters, already present in literature, it has an easier physical interpretation while achieving similar performance standards

A blockchain enabled framework for notarizing manufacturing data - an application case in the footwear industry

LAUREA MAGISTRALENel dinamico panorama della moderna manifattura, l'integrazione della tecnologia blockchain si configura come una soluzione promettente per le critiche sfide legate all'integrità e trasparenza dei dati. Sebbene ricerche precedenti abbiano illuminato le ampie applicazioni della blockchain in settori diversi, persiste una significativa lacuna nella letteratura riguardante l'integrazione dei sistemi blockchain con i Sistemi di Esecuzione della Produzione (MES) per la precisa notarizzazione dei dati critici all'interno dei processi produttivi. Questo lavoro affronta questa lacuna adoperando una metodologia sistematica, che comprende revisione della letteratura, analisi di casi studio e modellazione teorica e validazione, per esplorare l'implementazione pratica della tecnologia blockchain nella produzione. Il framework proposto offre approfondimenti sulla precisa notarizzazione dei dati critici di produzione utilizzando le caratteristiche di sicurezza, trasparenza e immutabilità della blockchain. Il modello è stato applicato al caso di studio di Tacchificio Villa Cortese, una piccola media impresa lombarda che produce componenti per tacchi per l'industria calzaturiera. Al centro del framework proposto si trova un nuovo enfasi sulla notarizzazione della veridicità dei dati condivisi, sfruttando le capacità della blockchain Polygon per superare le metodologie di notarizzazione convenzionali. Introducendo meccanismi per la notarizzazione dei dati sulla blockchain Polygon, il framework favorisce la fiducia e trasparenza tra gli stakeholder. Colmando il divario tra la tecnologia blockchain e la gestione dei dati di produzione, questa tesi contribuisce ad avanzare la conoscenza e la pratica nel settore. Il framework proposto ha un potenziale significativo per rivoluzionare l'integrità e la trasparenza dei dati nei processi produttivi, aprendo la strada a una maggiore efficienza, responsabilità e affidabilità nell'era digitale della produzione.In the dynamic landscape of modern manufacturing, the integration of blockchain technology stands as a promising solution to the critical challenges surrounding data integrity and transparency. While previous research has illuminated the broad applications of blockchain across diverse sectors, a significant literature gap persists in the specific domain of integrating blockchain systems with Manufacturing Execution Systems (MES) for precise notarization of critical data within manufacturing processes. This thesis addresses this gap by employing a systematic methodology, including literature review, case study analysis, and theoretical modelling and validation, to explore the practical implementation of blockchain technology in manufacturing. The framework proposed offers insights into precise notarization of critical manufacturing data using blockchain's attributes of security, transparency, and immutability. The model has been applied on Tacchificio Villa Cortese, a Lombardy-based small medium enterprise producing heel components for the footwear manufacturing industry. At the core of the proposed framework lies a novel emphasis on notarizing the truthfulness of shared data, leveraging the capabilities of Polygon blockchain to surpass conventional notarization methodologies. By introducing mechanisms for data notarization on the Polygon blockchain, the framework fosters trust and transparency among stakeholders. By bridging the gap between blockchain technology and manufacturing data management, this thesis contributes to advancing knowledge and practice in the field. The proposed framework holds significant potential to revolutionize data integrity and transparency in manufacturing processes, paving the way for enhanced efficiency, accountability, and trustworthiness in the digital age of manufacturing

Falsely low IgG4 in routine analysis - How not to miss IgG4 disease

Background IgG4 disease can have apparently “normal” levels of IgG4 due to antigen excess conditions. IgG4 measurement therefore appears falsely low. UK NEQAS data and other reports have suggested this problem occurred despite pre-existing antigen excess detection steps. Methods We examined the prevalence and characteristics of prozoning in our laboratory and patient cohorts, to determine the clinical relevance of the problem. Results We establish that the prevalence of raised IgG4 in routine IgG4 analysis is low (<1%) using one of the 2 routine methods in use in the UK. We show that subsequent assay modification appears to have reduced the likelihood of misleading readings. However, the original version of the assay prozoned to low levels (below 0.64g/L) in 41% of high IgG4 samples in our patients. This may explain the previous reports of low sensitivity of raised IgG4 for IgG4RD, and predictive values should be re-evaluated in this disease using modified prozone-resistant protocols. Conclusions All laboratories providing IgG4 measurements should verify that their assays are fit for the clinical quality requirement of detection raised IgG4 levels and must verify the upper limit of their reference ranges and freedom from prozoning

Rotaxane Ligands for Incorporation into Metal-Organic Framework Materials

This dissertation focuses on studies of mechanically interlocked molecules (MIMs) specifically [2]rotaxanes in three principle areas: (1) creating [2]rotaxane linkers to incorporate into metal-organic frameworks (MOFs); (2) studying the rate of shuttling motion in solution and finally (3) investigating the shuttling motion inside the MOF. Chapter 1 provides a brief introduction to MIMs, rotaxanes, MOFs and all previous studies on dynamic motions of MIMs in MOFs. Chapter 2 describes a [2]rotaxane linker with donor atoms attached to both the axle and the wheel. The linker contains four carboxylate groups attached to a rigid, H-shaped axle and two carboxylate units appended to a crown ether wheel. In the resulting Zn-based MOF, three independent 3-periodic frameworks (threefold interpenetration) are interconnected only by virtue of the threading of their individual components in the rotaxane linker. In Chapter 3, a [2]rotaxane linker was synthesized which combines an H-shaped axle containing four 3-carboxyphenyl groups and a macrocyclic wheel with two 4-pyridyl groups. The synthesized Zn and Cu MOFs showed two independent lattices threaded together by interlocking of the linker. In Chapter 4, a series of [2]rotaxane molecular shuttles was synthesized with varying track lengths between recognition sites. The rates of shuttling of the macrocycle along the rigid track were measured by variable temperature 1H NMR spectroscopy for the neutral compounds and EXSY experiments for the dicationic species. It was determined that the length of the axle does not affect the shuttling rate. In Chapter 5, molecular shuttling inside Zr-based MOFs under acid-base conditions was studied. 13C SSNMR studies on the first MOF, UWDM-6 (University of Windsor Dynamic Material) consisting of two linkers 2′,3′,5′,6′-tetramethylterphenyl-4,4″ dicarboxylic acid (H4TTTP) and [2]rotaxane demonstrated no shuttling because of steric hindrance of methyl groups of H4TTTP linker. This steric hindrance limitation was eliminated for UWDM-7 by changing the linear ligand to terphenyl dicarboxylate (TPDC). In Chapter 6, a bistable [2]rotaxane molecular shuttle inside a Zr-based MOFs was studied. The synthesized MOF, UWDM-8 consisted of [2]rotaxane with two non-equivalent recognition sites and linear linker H4TTTP. Switching was driven by the addition of acid or lithium ions and monitored by 15N SSNMR spectroscopy

Correction of Retinal Nerve Fiber Layer Thickness Measurement on Spectral-Domain Optical Coherence Tomographic Images Using U-net Architecture

Purpose: In this study, an algorithm based on deep learning was presented to reduce the retinal nerve fiber layer (RNFL) segmentation errors in spectral domain optical coherence tomography (SD-OCT) scans using ophthalmologists’ manual segmentation as a reference standard. Methods: In this study, we developed an image segmentation network based on deep learning to automatically identify the RNFL thickness from B-scans obtained with SD-OCT. The scans were collected from Farabi Eye Hospital (500 B-scans were used for training, while 50 were used for testing). To remove the speckle noise from the images, preprocessing was applied before training, and postprocessing was performed to fill any discontinuities that might exist. Afterward, output masks were analyzed for their average thickness. Finally, the calculation of mean absolute error between predicted and ground truth RNFL thickness was performed. Results: Based on the testing database, SD-OCT segmentation had an average dice similarity coefficient of 0.91, and thickness estimation had a mean absolute error of 2.23 ± 2.1 μm. As compared to conventional OCT software algorithms, deep learning predictions were better correlated with the best available estimate during the test period (r2 = 0.99 vs r2 = 0.88, respectively; P &lt; 0.001). Conclusion: Our experimental results demonstrate effective and precise segmentation of the RNFL layer with the coefficient of 0.91 and reliable thickness prediction with MAE 2.23 ± 2.1 μm in SD-OCT B-scans. Performance is comparable with human annotation of the RNFL layer and other algorithms according to the correlation coefficient of 0.99 and 0.88, respectively, while artifacts and errors are evident

The Influence of E-Wom on the Users’ Purchase Decision of M-Learning Apps

This study examines the influence of electronic word-of-mouth (e-WOM) on information adoption and purchase intentions in mobile learning (M-learning) applications. By extending the Information Adoption Model (IAM) to an educational technology context, it contributes theoretically to understanding how digital peer communication shapes technology uptake. A survey of 345 Iranian young adults reveals that e-WOM significantly affects adoption decisions, with perceived usefulness emerging as a central driver of purchase intentions. The findings underscore the dual role of positive and negative user reviews in shaping app reputation and credibility. Beyond its contextual focus on Iran, where app usage is restricted, the study advances research on e-WOM dynamics in digital learning ecosystems of developing countries. The results provide practical insights for marketers, developers, and educational institutions seeking to leverage user-generated content to enhance trust, strengthen credibility, and foster wider acceptance of M-learning solutions