bluuebunny/crossref_metadata_2025 huggingface dataset:
Life Cycle Assessment (LCA) of Bioplastics Production from Lignocellulosic Waste (Study Case: PLA and PHB)
Authors: Senila Lacrimioara , Kovacs Eniko , Resz Maria-Alexandra , Senila Marin , Becze Anca , Roman Cecilia
Published: November 2024

Life cycle assessment of a technology is the key to technological development in the context of sustainable development. Orchard waste has been identified as a potential source of bioplastics. The objective of this study was to conduct a life cycle assessment of two specific bioplastic materials, namely, L-polylactic acid (PLA) and poly(3-hydroxybutyrate) (PHB). Bioplastics, such as PLA acid and PHB, can be used as alternatives to conventional plastics due to their biodegradability and non-toxicity, both of which have the potential to replace conventional petroleum-based plastics. Polylactic acid was synthesized from orchard waste in a series of stages, including biomass processing, pretreatment for carbohydrate extraction, simultaneous saccharification and fermentation (SSF), and microwave polymerization. PHB, another biodegradable polymer, is produced by microorganisms through the fermentation of sugars obtained from the same biomass. Applied LCAs show that for PLA production, the stages having the greatest environmental impact are biomass processing, pretreatment, and the SSF process, and for PHB production, very energy-intensive stages significantly contributing to the environmental impacts are biomass processing and pretreatment stages. For both PLA and PHB, the initial stages of biomass processing and pretreatment are the most energy-intensive and significant contributors to CO2 emissions.
Research on the Chloramphenicol Removal Performance of Co-Doped Porous Carbon Materials Derived from Co-Zn Bimetallic ZIFs
Authors: Chen Ke-Hong , Gu Ao-Tian , Zhang Liang , Tang Hao , Zhang Zhi-Ming , Zhou Yi-Ming , Cheng Yu-Qi , Yang Yi
Published: November 2024

Chloramphenicol antibiotics (CAPs) are broad-spectrum antibiotics, and excessive consumption has led to increasingly dangerous residues in the environment. The accumulation of these highly toxic and difficult-to-biodegrade CAPs and their long-term exposure in ecological environments can pose insidious and long-term hazards to human health and aquatic organisms. In this study, co-carbon composite nanocatalysts (CoxZn10−x-NC) with many carbon nanotubes on the surface were prepared via the one-step pyrolysis of bimetallic CoxZn10−x-ZIF with different Co/Zn ratios and used for the degradation of trace amounts of CAPs in a water column. The microstructure and chemical composition of the prepared catalysts were fully characterized using SEM, TEM, and XPS. The CAP degradation experiments demonstrated that Co6Zn4-NC in CoxZn10−x-NC possessed the highest catalytic activity level, removing 100% of the CAPs in 60 min. The CAPs had a corresponding reaction rate constant of 0.22 min−1, and Co6Zn4-NC was able to completely mineralize 44.57% of them. Doping moderate amounts of Zn can effectively improve the carbon nanotube structure on the catalyst surface and promote the generation of monoatomic Co, thus improving catalytic activity. The results of the free-radical burst experiments and electron paramagnetic resonance (EPR) showed that the free-radical pathway mainly dominated within the Co6Zn4-NC+PMS system, in which SO4•− was the main ROS for CAP degradation.
Experience Using a New High-Density Polyethylene-Based Porous Orbital Implant: Explorative 1-Year Performance and Safety Results
Authors: Garrido-Hermosilla Antonio Manuel , Martínez-Alberquilla Irene , Díaz-Ruiz María Concepción , Monge-Carmona Raquel , Méndez-Muros Mariola , Sánchez-Margalet Víctor , Gutiérrez-Sánchez Estanislao , Relimpio-López María Isabel , Rodríguez-de-la-Rúa-Franch Enrique
Published: November 2024

OBJECTIVES: To describe our experience with a new high-density porous polyethylene orbital implant post-enucleation and evisceration and comment on their performance and safety. METHODS: Patients with an indication of enucleation or evisceration with orbital implantation were included in a prospective study. All patients were implanted with OCULFIT orbital implants (AJL Ophthalmic S.A.) and followed up over 12 months. Anatomical and functional parameters, motility, and aesthetic appearance were evaluated. Patient satisfaction on a scale of 0 (very bad) to 5 (excellent), complications and success rates were reported. RESULTS: Overall, 16 enucleated and 17 eviscerated eyes were analyzed. Orbital implant motility was good for 93.8% and 100% of enucleated and eviscerated patients, respectively. No shortened fornixes were found after external prosthesis placement, and palpebral fissure, orbital volume, and lower eyelid laxity were symmetric with the contralateral eye for the vast majority. The aesthetic appearance was good for 87.5% and 100% of enucleated and eviscerated patients at the last visit with no differences between groups. Excellent satisfaction was reported for 100% and 94.1% of enucleated and eviscerated patients. Anatomical and functional success rates were 78.8% and 81.8%, respectively. No exposure, infection or complications related to surgery were reported during the follow-up. CONCLUSIONS: After 1 year of follow-up, OCULFIT orbital implants provide excellent patient satisfaction and performance in terms of motility and symmetry with the contralateral eye after external prosthesis placement with no complications related to the surgical procedure. No differences between enucleated and eviscerated eyes were found, and aesthetic results were remarkable for both groups. Conclusions should be interpreted with caution due to the small sample size.
Accurate Method for Solar Power Generation Estimation for Different PV (Photovoltaic Panels) Technologies
Authors: Meflah Aissa , Chekired Fathia , Drir Nadia , Canale Laurent
Published: November 2024

In 2023, solar photovoltaic energy alone accounted for 75% of the global increase in renewable capacity. Moreover, this natural energy resource is the one that requires the least investment, which makes it accessible to developing countries. Increasing return on investment in these regions requires a particular evaluation of environmental parameters influencing PV systems performance. Higher temperatures decrease PV module efficiency and, as a result, their power output. Additionally, fluctuations in solar irradiance directly impact the energy generated by these systems. Consequently, it is essential for investors to improve accurate predictive models that assess the power generation capacity of photovoltaic systems under local environmental conditions. Therefore, accurate estimation of maximum power generation is then crucial for optimizing photovoltaic (PV) system performances and selecting suitable PV modules for specific climates. In this context, this study presents an experimental comparison of three maximum power prediction methods for four PV module types (amorphous silicon, monocrystalline silicon, micromorphous silicon, and polycrystalline silicon) under real outdoor conditions. Experimental data gathered over the course of a year are analyzed and processed for the four PV technologies. Three different methods taking into account environmental parameters are presented and analyzed. The first estimation method utilizes irradiance as the primary input parameter, while two additional methods incorporate ambient temperature and PV module temperature for enhanced accuracy. The performance of each method is evaluated using standard statistical metrics, including the root mean square error (RMSE) and coefficient of determination (R2). The results demonstrate the effectiveness of all three methods, with RMSE values ranging from 1.6 W to 3.8 W and R2 values consistently above 0.95. The most appropriate method for estimating PV power output is determined by the specific type of photovoltaic module and the availability of meteorological parameters. This study provides valuable insights for selecting an appropriate maximum power prediction method and choosing the most suitable PV module for a given climate.
The Cost of Borrowing as a Limiting Factor of Non-Life Insurance Development: The Italian Case
Authors: Millo Giovanni
Published: November 2024

We address the effect of local financial conditions on the demand for non-life insurance. We consider the spread between the interest rates faced by the insured on the local credit market and the return rates earned by the insurer on national or international financial markets, sketching how it influences the present value of an insurance policy; we then use the local invariance of the insurer’s returns to identify the effect on demand. Drawing on a panel of Italian provinces with ample variability in insurance density as well as borrowing conditions, we show that the demand for non-life insurance decreases with the borrowing rate. We separate between different non-life insurance lines, finding a stronger effect for the lines prevailing in advanced economic systems. Credit conditions turn out to be an important factor of non-life insurance development, and they help to explain the underdevelopment of insurance markets in Southern Italy.
Non-Destructive Detection of Pesticide-Treated Baby Leaf Lettuce During Production and Post-Harvest Storage Using Visible and Near-Infrared Spectroscopy
Authors: Kasampalis Dimitrios S. , Tsouvaltzis Pavlos I. , Siomos Anastasios S.
Published: November 2024

The market demand for baby leaf lettuce is constantly increasing, while safety has become one of the most important traits in determining consumer preference driven by human health hazards concerns. In this study, the performance of visible and near-infrared (vis/NIR) spectroscopy was tested in discriminating pesticide-free against pesticide-treated lettuce plants. Two commercial fungicides (mancozeb and fosetyl-al) and two insecticides (deltamethrin and imidacloprid) were applied as spray solutions at the recommended rates on baby leaf lettuce plants. Untreated-control plants were sprayed with water. Reflectance data in the wavelength range 400–2500 nm were captured on leaf samples until harvest on the 10th day upon pesticide application, as well as after 4 and 8 days during post-harvest storage at 5 °C. In addition, biochemical components in leaf tissue were also determined during storage, such as antioxidant enzymes’ activities (peroxidase [POD], catalase [CAT], and ascorbate peroxidase [APX]), along with malondialdehyde [MDA] and hydrogen peroxide [H2O2] content. Partial least square discriminant analysis (PLSDA) combined with feature-selection techniques was implemented, in order to classify baby lettuce tissue into pesticide-free or pesticide-treated ones. The genetic algorithm (GA) and the variable importance in projection (VIP) scores identified eleven distinct regions and nine specific wavelengths that exhibited the most significant effect in the detection models, with most of them in the near-infrared region of the electromagnetic spectrum. According to the results, the classification accuracy of discriminating pesticide-treated against non-treated lettuce leaves ranged from 94% to 99% in both pre-harvest and post-harvest periods. Although there were no significant differences in enzyme activities or H2O2, the MDA content in pesticide-treated tissue was greater than in untreated ones, implying that the chemical spray application probably induced a stress response in the plant that was disclosed with the reflected energy. In conclusion, vis/NIR spectroscopy appears as a promising, reliable, rapid, and non-destructive tool in distinguishing pesticide-free from pesticide-treated lettuce products.
Adaptive Beamforming, Cell-Free Resource Allocation and NOMA in Large-Scale Wireless Networks
Authors: Gkonis Panagiotis , Lavdas Spyros , Vardoulias George , Trakadas Panagiotis , Sarakis Lambros , Papadopoulos Konstantinos
Published: November 2024

The goal of the study presented in this work is to evaluate the performance of a proposed adaptive beamforming approach when combined with non-orthogonal multiple access (NOMA) in cell-free massive multiple input multiple output (CF m-MIMO) orientations. In this context, cooperative beamforming is employed taking into consideration the geographically adjacent access points (APs) of a virtual cell, aiming to minimize co-channel interference (CCI) among mobile stations (MSs) participating in NOMA transmission. Performance is evaluated statistically via extensive Monte Carlo (MC) simulations in a two-tier wireless orientation. As the results indicate, for high data rate services, various key performance indicators (KPIs) can be improved compared to orthogonal multiple access, such as the minimum number of users in the topology as well as the available PRBs for downlink transmission. Although in NOMA transmission more directional beamforming configurations are required to compensate for the increased CCI levels, the increase in the number of hardware elements is reduced compared to the corresponding gain in the considered KPIs.
MCCA-VNet: A Vit-Based Deep Learning Approach for Micro-Expression Recognition Based on Facial Coding
Authors: Zhang Dehao , Zhang Tao , Sun Haijiang , Tang Yanhui , Liu Qiaoyuan
Published: November 2024

In terms of facial expressions, micro-expressions are more realistic than macro-expressions and provide more valuable information, which can be widely used in psychological counseling and clinical diagnosis. In the past few years, deep learning methods based on optical flow and Transformer have achieved excellent results in this field, but most of the current algorithms are mainly concentrated on establishing a serialized token through the self-attention model, and they do not take into account the spatial relationship between facial landmarks. For the locality and changes in the micro-facial conditions themselves, we propose the deep learning model MCCA-VNET on the basis of Transformer. We effectively extract the changing features as the input of the model, fusing channel attention and spatial attention into Vision Transformer to capture correlations between features in different dimensions, which enhances the accuracy of the identification of micro-expressions. In order to verify the effectiveness of the algorithm mentioned, we conduct experimental testing in the SAMM, CAS (ME) II, and SMIC datasets and compared the results with other former best algorithms. Our algorithms can improve the UF1 score and UAR score to, respectively, 0.8676 and 0.8622 for the composite dataset, and they are better than other algorithms on multiple indicators, achieving the best comprehensive performance.
A Fusion Localization System for Security Robots Based on Millimeter Wave Radar and Inertial Sensors
Authors: Zheng Rui , Sun Geng , Li Fang Dong
Published: November 2024

In smoggy and dusty environments, vision- and laser-based localization methods are not able to be used effectively for controlling the movement of a robot. Autonomous operation of a security robot can be achieved in such environments by using millimeter wave (MMW) radar for the localization system. In this study, an approximate center method under a sparse point cloud is proposed, and a security robot localization system based on millimeter wave radar is constructed. To improve the localization accuracy of the robot, inertial localization of the robot is integrated with MMW radar. Based on the concept of inertial localization, the state equation for the motion principle of the robot is deduced. According to principle of MMW localization, the measurement equation is derived, and a kinematics model of the robot is constructed. Further, by applying the Kalman filtering algorithm, a fusion localization system of the robot based on MMWs and inertial localization is proposed. The experimental results show that with iterations of the filtering algorithm, the gain matrix converges gradually, and the error of the fusion localization system decreases, leading to the stable operation of the robot. Compared to the localization system with only MMW radar, the average localization error is approximately reduced from 11 cm to 8 cm, indicating that the fusion localization system has better localization accuracy.
Position-Constrained Calibration Compensation for Hand–Eye Calibration in Industrial Robots
Authors: Lin Jinsong , Feng Yuxing , Ren Wenze , Feng Jiahui , Zheng Jun
Published: November 2024

The hand–eye calibration of laser profilers and industrial robots is a critical component of the laser vision system in welding applications. To improve calibration accuracy and efficiency, this study proposes a position-constrained calibration compensation algorithm aimed at optimizing the hand–eye transformation matrix. Initially, the laser profiler is mounted on the robot and used to scan a standard sphere from various poses to obtain the theoretical center coordinates of the sphere, which are then utilized to compute the hand–eye transformation matrix. Subsequently, the positional data of the standard sphere’s surface are collected at different poses using the welding gun tip mounted on the robot, allowing for the fitting of the sphere’s center coordinates as calibration values. Finally, by minimizing the error between the theoretical and calibrated sphere center coordinates, the optimal hand–eye transformation matrix is derived. Experimental results demonstrate that, following error compensation, the average distance error in hand–eye calibration decreased from 4.5731 mm to 0.7069 mm, indicating that the proposed calibration method is both reliable and effective.
Time Efficiency and Ergonomic Assessment of a Robotic Wheelchair Transfer System
Authors: Satpute Shantanu A. , Uribe Kaylee J. , Olaore Oluwatofunmi O. , Iizuka Minori , McCumber Gandara Ian C. , Schoy William J. , Kulkarni Rutuja A. , Cooper Rosemarie , Koontz Alicia M. , Flaugh Owen , Cooper Rory A.
Published: November 2024

Background: Caregivers experience high rates of occupational injuries, especially during wheelchair transfers, which often result in back pain and musculoskeletal disorders due to the physical demands of lifting and repositioning. While mechanical floor lifts, the current standard, reduce back strain, they are time-consuming and require handling techniques that subject caregivers to prolonged and repeated non-neutral trunk postures, increasing the risk of long-term back injuries. Aims: The aim was to assess the time efficiency and ergonomics of the powered personal transfer system (PPTS), a robotic transfer device designed for bed-to/from-wheelchair transfers. Methods: We evaluated transfers with the PPTS and mechanical lift with eight able-bodied participants who assisted with transfers between a bed and a wheelchair. Inertial measurement units (IMUs) were placed on participants to track their motion and assess trunk joint angles during transfers. Results: The PPTS significantly reduced the transfer time (144.31 s vs. 525.82 s, p < 0.001) and required significantly less range of motion for trunk flexion (p < 0.001), lateral bending (p = 0.008), and axial rotation (p = 0.001), all of which have been associated with back injuries. Additionally, the PPTS significantly reduced the time caregivers spent in non-neutral trunk postures, potentially lowering injury risks. Conclusions: These findings suggest that the PPTS improves transfer efficiency and caregiver safety, offering a promising alternative to the current standard of care for wheelchair-to/from-bed transfers.
Non-Destructive Monitoring of External Quality of Date Palm Fruit (Phoenix dactylifera L.) During Frozen Storage Using Digital Camera and Flatbed Scanner
Authors: Noutfia Younes , Ropelewska Ewa , Jóźwiak Zbigniew , Rutkowski Krzysztof
Published: November 2024

The emergence of new technologies focusing on “computer vision” has contributed significantly to the assessment of fruit quality. In this study, an innovative approach based on image analysis was used to assess the external quality of fresh and frozen ‘Mejhoul’ and ‘Boufeggous’ date palm cultivars stored for 6 months at −10 °C and −18 °C. Their quality was evaluated, in a non-destructive manner, based on texture features extracted from images acquired using a digital camera and flatbed scanner. The whole process of image processing was carried out using MATLAB R2024a and Q-MAZDA 23.10 software. Then, extracted features were used as inputs for pre-established algorithms–groups within WEKA 3.9 software to classify frozen date fruit samples after 0, 2, 4, and 6 months of storage. Among 599 features, only 5 to 36 attributes were selected as powerful predictors to build desired classification models based on the “Functions-Logistic” classifier. The general architecture exhibited clear differences in classification accuracy depending mainly on the frozen storage period and imaging device. Accordingly, confusion matrices showed high classification accuracy (CA), which could reach 0.84 at M0 for both cultivars at the two frozen storage temperatures. This CA indicated a remarkable decrease at M2 and M4 before re-increasing by M6, confirming slight changes in external quality before the end of storage. Moreover, the developed models on the basis of flatbed scanner use allowed us to obtain a high correctness rate that could attain 97.7% in comparison to the digital camera, which did not exceed 85.5%. In perspectives, physicochemical attributes can be added to developed models to establish correlation with image features and predict the behavior of date fruit under storage.
Adaptive Switching Redundant-Mode Multi-Core System for Photovoltaic Power Generation
Authors: Liu Liang , Zhang Xige , Zhou Jiahui , Niu Kai , Guo Zixuan , Zhao Yawen , Zhang Meng
Published: November 2024

As maximum power point tracking (MPPT) algorithms have developed towards multi-task intelligent computing, processors in photovoltaic power generation control systems must be capable of achieving a higher performance. However, the challenges posed by the complex environment of photovoltaic fields with regard to processor reliability cannot be overlooked. To address these issues, we proposed a novel approach. Our approach uses error rate and performance as switching metrics and performs joint statistics to achieve efficient adaptive switching. Based on this, our work designed a redundancy-mode switchable three-core processor system to balance performance and reliability. Additionally, by analyzing the relationship between performance and reliability, we proposed optimization methods to improve reliability while ensuring a high performance was maintained. Finally, we designed an error injection method and verified the system’s reliability by analyzing the error rate probability model in different scenarios. The results of the analysis show that compared with the traditional MPPT controller, the redundancy mode switchable multi-core processor system proposed in this paper exhibits a reliability approximately 5.58 times that of a non-fault-tolerant system. Furthermore, leveraging the feature of module switching, the system’s performance has been enhanced by 26% compared to a highly reliable triple modular redundancy systems, significantly improving the system’s reliability while ensuring a good performance is maintained.
First Stability Characterization for a CZT Detection System in an e+e− Collider Environment
Authors: Abbene Leonardo , Artibani Francesco , Bettelli Manuele , Buttacavoli Antonino , Principato Fabio , Zappettini Andrea , Bazzi Massimiliano , Borghi Giacomo , Bragadireanu Mario , Cargnelli Michael , Carminati Marco , Clozza Alberto , Clozza Francesco , De Paolis Luca , Del Grande Raffaele , Dulski Kamil , Fabbietti Laura , Fiorini Carlo , Guaraldo Carlo , Iliescu Mihail , Iwasaki Masahiko , Khreptak Aleksander , Manti Simone , Marton Johann , Moskal Pawel , Napolitano Fabrizio , Niedźwiecki Szymon , Ohnishi Hiroaki , Piscicchia Kristian , Sada Yuta , Sgaramella Francesco , Sirghi Diana Laura , Sirghi Florin , Skurzok Magdalena , Silarski Michal , Spallone Antonio , Toho Kairo , Toscano Lorenzo , Tüchler Marlene , Doce Oton Vasquez , Zmeskal Johann , Curceanu Catalina , Scordo Alessandro
Published: November 2024

The SIDDHARTA-2 collaboration has developed a novel X-ray detection system based on cadmium-zinc-telluride (CZT, CdZnTe), marking the first application of this technology at the DAΦNE electron-positron collider at INFN-LNF. This work aims to demonstrate the stability of the detectors’ performance in terms of linearity and resolution over short and long periods, thereby establishing their suitability for precise spectroscopic measurements within a collider environment. A reference calibration spectrum is presented in association with findings from assessments of linearity and resolution stability. Additionally, this study introduces a validated model of the response function of the detector. The relative deviations from the nominal values for the source transitions, obtained by fitting the entire spectrum with a background function and the previously introduced response function, are reported. Finally, a comparison of the calibration performance with and without beams circulating in the collider’s rings is presented. These promising results pave the way for applying CZT detectors in kaonic atom studies and, more generally, in particle and nuclear physics spectroscopy.
Analysis of Resistance in Magnetic Flux Leakage (MFL) Detectors for Natural Gas Pipelines
Authors: Zhang Zenggang , Chen Xiangjun , Tai Chuanmin , Tian Guansan , Han Guozhao
Published: November 2024

This study systematically explores the sources and influencing factors of resistance encountered by magnetic flux leakage (MFL) detectors in natural gas pipelines through a theoretical analysis, experimental investigation, and numerical simulation. The research methodology involves the development of a fluid–structure interaction model using ABAQUS 2023 finite element software, complemented by the design and implementation of a pull-testing platform for MFL detectors. This platform simulates detector operation under various interference conditions and quantifies the resulting frictional resistance. The findings reveal that the primary source of frictional resistance is the contact interaction between the MFL detector and the pipeline wall. Key factors influencing the magnitude of this resistance include the detector’s mass, the structural design and materials of the sealing cups and support plates, as well as the surface roughness of the pipeline. Both experimental results and numerical simulations demonstrate a pronounced increase in frictional resistance with heightened interference levels. The theoretical model exhibits strong agreement with experimental data, though deviations are observed under conditions of severe interference. This study provides a detailed understanding of frictional resistance patterns under diverse structural and operational scenarios, offering both theoretical guidance and practical recommendations for the design of low-resistance MFL detectors.
Ultrasound Versus Elastography in the Diagnosis of Hepatic Steatosis: Evaluation of Traditional Machine Learning Versus Deep Learning
Authors: Marques Rodrigo , Santos Jaime , André Alexandra , Silva José
Published: November 2024

The prevalence of fatty liver disease is on the rise, posing a significant global health concern. If left untreated, it can progress into more serious liver diseases. Therefore, accurately diagnosing the condition at an early stage is essential for more effective intervention and management. This study uses images acquired via ultrasound and elastography to classify liver steatosis using classical machine learning classifiers, including random forest and support vector machine, as well as deep learning architectures, such as ResNet50V2 and DenseNet-201. The neural network demonstrated the most optimal performance, achieving an F1 score of 99.5% on the ultrasound dataset, 99.2% on the elastography dataset, and 98.9% on the mixed dataset. The results from the deep learning approach are comparable to those of machine learning, despite objectively not achieving the highest results. This research offers valuable insights into the domain of medical image classification and advocates the integration of advanced machine learning and deep learning technologies in diagnosing steatosis.
CASSAD: Chroma-Augmented Semi-Supervised Anomaly Detection for Conveyor Belt Idlers
Authors: Alharbi Fahad , Luo Suhuai , Alsaedi Abdullah , Zhao Sipei , Yang Guang
Published: November 2024

Idlers are essential to conveyor systems, as well as supporting and guiding belts to ensure production efficiency. Proper idler maintenance prevents failures, reduces downtime, cuts costs, and improves reliability. Most studies on idler fault detection rely on supervised methods, which depend on large labelled datasets for training. However, acquiring such labelled data is often challenging in industrial environments due to the rarity of faults and the labour-intensive nature of the labelling process. To address this, we propose the chroma-augmented semi-supervised anomaly detection (CASSAD) method, designed to perform effectively with limited labelled data. At the core of CASSAD is the one-class SVM (OC-SVM), a model specifically developed for anomaly detection in cases where labelled anomalies are scarce. We also compare CASSAD’s performance with other common models like the local outlier factor (LOF) and isolation forest (iForest), evaluating each with the area under the curve (AUC) to assess their ability to distinguish between normal and anomalous data. CASSAD introduces chroma features, such as chroma energy normalised statistics (CENS), the constant-Q transform (CQT), and the chroma short-time Fourier transform (STFT), enhanced through filtering to capture rich harmonic information from idler sounds. To reduce feature complexity, we utilize the mean and standard deviation (std) across chroma features. The dataset is further augmented using additive white Gaussian noise (AWGN). Testing on an industrial dataset of idler sounds, CASSAD achieved an AUC of 96% and an accuracy of 91%, surpassing a baseline autoencoder and other traditional models. These results demonstrate the model’s robustness in detecting anomalies with minimal dependence on labelled data, offering a practical solution for industries with limited labelled datasets.
Electrical Bioimpedance-Based Monitoring of Intracochlear Tissue Changes After Cochlear Implantation
Authors: Sijgers Leanne , Geys Marlies , Geissler Gunnar , Boyle Patrick , Huber Alexander , Pfiffner Flurin
Published: November 2024

Background: This study examined electrical bioimpedance as a biomarker for intracochlear tissue changes after cochlear implant surgery, comparing monopolar, three-point, and four-point impedance measurements over time and evaluating different measurement systems and approaches. Methods: Impedance measurements were obtained from 21 participants during surgery and at four postoperative stages. Monopolar impedances were recorded using the Bionic Ear Data Collection System (BEDCS) and the Active Insertion Monitoring (AIM) system. Three- and four-point impedances were recorded directly using BEDCS, and indirect three-point impedances were additionally derived from Electrical Field Imaging matrices recorded using BEDCS or AIM. Results: There was an 11% relative error between monopolar measurements from BEDCS and AIM and a 25% discrepancy between direct and indirect three-point measurements. Despite this, direct and indirect measurements from both systems were useful for tracking postoperative impedance shifts. Three- and four-point measurements showed a strong relationship both during and after surgery. Our results suggest that three- and four-point measurements are more specific than monopolar impedances in capturing localized tissue changes. Conclusions: Three- and four-point impedance measurements are potential markers of intracochlear tissue changes over time. While direct three-point impedance measurements offer higher accuracy, indirect measurements provide a feasible alternative for monitoring intracochlear changes in clinical settings lacking the option of direct measurements.
A Dynamic Load Modulation Power Amplifier with Ferroelectric-Based Tunable Matching Network
Authors: Turalchuk Pavel , Filipiuk Irina , Iskakov Bayazet
Published: November 2024

Power amplifiers are crucial components that significantly influence the linearity and energy efficiency of next-generation communication system radio units. A key challenge in designing power amplifiers is managing high peak-to-average power ratio (PAPR) in order to achieve both high linearity and energy efficiency during back-off conditions. This paper presents simulation and measurement results for a dynamic load modulation power amplifier based on a ferroelectric tunable matching network to operate at 2.5 GHz. Experimental studies on a power amplifier with the tunable output matching network confirm its performance at 8 dB back-off while varying the control voltage applied to the ferroelectric element. Additionally, a bias modulator to adjust the transistor’s load in relation to input power was designed. Measurement studies of the dynamic load modulation power amplifier have demonstrated an efficiency of at least 50% at 8 dB back-off and more than 60% at peak power at 2.5 GHz. Furthermore, it was found that the modulator output voltage adjustment function on input power of the bias modulator affects the linearity of the output power. Different bias responses were studied and, as a result, the optimal output voltage response was found. The proposed load modulation power amplifier is promising for operation with high PAPR digital signals.
Performance Analysis of Cardioid and Omnidirectional Microphones in Spherical Sector Arrays for Coherent Source Localization
Authors: Nnonyelu Chibuzo Joseph , Jiang Meng , Adamopoulou Marianthi , Lundgren Jan
Published: November 2024

Traditional spherical sector microphone arrays using omnidirectional microphones face limitations in modal strength and spatial resolution, especially within spherical sector configurations. This study aims to enhance array performance by developing a spherical sector array employing first-order cardioid microphones. A model based on spherical sector harmonic (SSH) functions is introduced to extend the benefits of spherical harmonics to sector arrays. Modal strength analysis demonstrates that cardioid microphones in open spherical sectors enhance nonzero-order strengths and eliminate the nulls associated with spherical Bessel functions. We find that the spatial resolution of spherical cap arrays depends on the array’s maximum order and the limiting polar angle, but is independent of the microphone gain pattern. We assess direction-of-arrival (DOA) estimation performance for coherent wideband sources using the array manifold interpolation method, and compare cardioid and omnidirectional arrays through simulations in both open and rigid hemispherical configurations. The results indicate that cardioid arrays outperform omnidirectional ones on DOA estimation tasks, with performance improving alongside increased microphone directivity in the open hemispherical configuration. Specifically, hypercardioid microphones yielded the best results in the open configuration, while subcardioid microphones (without nulls) were optimal in rigid configurations. These findings demonstrate that spherical sector arrays of first-order cardioid microphones offer improved modal strength and DOA estimation capabilities over traditional omnidirectional arrays, providing significantly enhancing performance in spherical sector array processing.
Structural Damage Early Warning Method of Quayside Container Crane Based on Fuzzy Entropy Ratio Variation Deviation
Authors: Liu Jiahui , Zhao Jian , Zhao Dong , Qin Xianrong
Published: November 2024

Real-time monitoring and early warning of structures are essential for assessing structural health and ensuring safety maintenance. To improve the timeliness of early warnings for structural abnormal states in quayside container cranes (QCCs) with incomplete damage data, a structural abnormal state early warning method based on fuzzy entropy ratio variation deviation (FERVD) is proposed. First, monitoring data are subjected to dual-tree complex wavelet transform (DTCWT). The adaptive frequency bands obtained from the decomposition, combined with fuzzy entropy (FE), are used to extract response signal features and construct the FERVD warning indicator. Based on this indicator, dynamic thresholds for early warning are established to differentiate between structural health states and various damage conditions. Secondly, a finite element model of structure for QCCs is developed. By simulating damage at various locations and severities through the stiffness reduction of different elements, a comprehensive structural simulation monitoring dataset is generated. The efficacy of the proposed early warning method is validated through numerical experiments and engineering case studies. The numerical results demonstrate that the proposed method effectively distinguishes between different damage conditions and provides timely warnings for various damage states. Furthermore, engineering case analysis shows that when the structure is in a healthy state, the FERVD values at different monitoring points fluctuate within the threshold range, indicating the applicability of the proposed method in the structural health monitoring (SHM) of QCCs.
A Novel Technique for Monitoring Carbonate and Scale Precipitation Using a Batch-Process-Based Hetero-Core Fiber Optic Sensor
Authors: Satake Sakurako , Hosoki Ai , Kuramitz Hideki , Ueda Akira
Published: November 2024

Techniques for monitoring calcium carbonate and silica deposits (scale) in geothermal power plants and hot spring facilities using fiber optic sensors have already been reported. These sensors continuously measure changes in light transmittance with a detector and, when applied to field tests, require the installation of a power supply and sensor monitoring equipment. However, on some sites, a power supply may not be available, or a specialist skilled in handling scale sensors is required. To overcome this problem, we have developed a method for evaluating scale formation that is based on a batch process that can be used by anyone. In brief, this method involves depositing scale on a section of the optical fiber sensor and then fusing this section to the optical fiber and measuring it. Using this sensor, a technician in the field can simply place the sensor in the desired location, collect the samples at any given time, and send them to the laboratory to measure their transmittance. This simple and easy method was achieved by using a hetero-core type of fiber optic. This evaluation method can measure with the same sensitivity as conventional real-time methods, while its transmittance response for the sensor corresponds to the saturation index (SI) changes in the scale components in the solution due to increases in temperature and concentration. In the field of carbon dioxide capture and storage (CCS), this evaluation method can be used to quantitatively measure the formation of carbonate minerals, and it can also be used as an indicator for determining the conditions for CO2 mineral fixation, as well as in experiments using batch-type autoclaves in laboratory testing. It is also expected to be used in geothermal power plants as a method for evaluating scale formation, such as that of amorphous silica, and to protect against agents that hinder stable operation.
Evaluation of Electrical Characteristics of Weft-Knitted Strain Sensors for Joint Motion Monitoring: Focus on Plating Stitch Structure
Authors: Oh You-Kyung , Kim Youn-Hee
Published: November 2024

We developed a sensor optimized for joint motion monitoring by exploring the effects of the stitch pattern, yarn thickness, and NP number on the performance of knitted strain sensors. We conducted stretching experiments with basic weft-knit patterns to select the optimal stitch pattern and analyze its sensitivity and reproducibility. The plain stitch with a conductive yarn located on the reverse side exhibited the highest gauge factor value (143.68) and achieved excellent performance, with a stable change in resistance even after repeated sensing. For an in-depth analysis, we developed six sensors using the aforementioned pattern with different combinations of yarn thickness (1-ply, 2-ply) and NP numbers (12, 13, 14). Based on bending experiments, the GF across all sensors was 60.2–1092, indicating noticeable differences in sensitivity. However, no significant differences were observed in reproducibility, reliability, and responsiveness, confirming that all the sensors are capable of joint motion monitoring. Therefore, the plain-patterned plating stitch structure with conductive yarn on the reverse side is optimal for joint motion monitoring, and the yarn thickness and NP numbers can be adjusted to suit different purposes. This study provides basic data for developing knitted strain sensors and offers insights into how knitting methods impact sensor performance.
Multi-Pilot Channel Estimation for Orthogonal Time–Frequency Space Systems Based on Constant-Amplitude Zero-Autocorrelation Sequences
Authors: Ju Renjie , Guo Yangyanhao , Hou Xiaojuan , He Jian , Li Ting , Lan Zhiqiang , Chou Xiujian
Published: November 2024

Future communication systems must support high-speed mobile scenarios, while the mainstream Orthogonal Frequency Division Multiplexing (OFDM) technology faces severe inter-carrier interference in such environments. Therefore, the adoption of Orthogonal Time–Frequency Space (OTFS) modulation in 6G systems is an effective solution. The widely used single-pilot channel estimation in OTFS systems is susceptible to path loss and inaccurate fading coefficient estimation, leading to reduced estimation accuracy, signal distortion, and degraded overall system communication quality. To address this problem, this paper proposes a Constant-Amplitude Zero-Autocorrelation (CAZAC) sequence-based multi-pilot OTFS channel estimation scheme. The proposed method inserts multiple low-power pilots in the delayed Doppler domain (DD) and employs joint signal processing at the receiver to effectively suppress noise, thereby significantly improving the accuracy and reliability of channel estimation. Additionally, this paper analyzes the impact of CAZAC sequence length on estimation performance and provides reasonable parameter selection recommendations. In summary, this work proposes an innovative solution to the channel estimation challenge in OTFS systems, laying a solid theoretical foundation for the realization of future high-speed mobile communication technologies such as 6G, with important academic value and application prospects.
Expanding on Methodologies for Analyzing Truck Speeds in States with Differential Speed Limits
Authors: Brinster Gregory L. , Desai Jairaj , Sakhare Rahul Suryakant , Mathew Jijo K. , Bullock Darcy M.
Published: November 2024

The Federal Motor Carrier Safety Administration (FMCSA) and National Highway Traffic Safety Administration (NHTSA) reported that in 2020, 7.3% of large truck driver fatalities had speed as a contributing factor. Several states have implemented truck differential speed limits (DSLs) with the objective of improving safety. This study compares truck speeds in 16 states, 8 of which have implemented DSLs (Arkansas, California, Idaho, Indiana, Michigan, Montana, Oregon, and Washington) and 8 of which have not (Illinois, Kentucky, Minnesota, Oklahoma, South Dakota, Tennessee, Wisconsin, and Wyoming). The DSLs ranged from 55 MPH in California (CA) to 70 MPH in Montana (MT). Over 240,000 speed samples from connected trucks were analyzed during a one-week period from 15–22 April 2024. The 50th percentile truck speeds ranged from 60 MPH in Oregon to 69 MPH in Wyoming. The 85th percentile truck speeds ranged from 65 MPH in Washington, Oregon, and California to 74 MPH in Wyoming. The 85th percentile speeds across all segments were greater than the posted truck speed limit in 90% of segments with DSLs, but only 12.5% of segments without DSLs. The average interquartile range (IQR) of truck speeds for the eight states with DSLs was 19% smaller than the average IQR of the eight states without DSLs. The methodologies and visuals presented by this study are easily scalable to any route and location provided the availability of a representative connected truck dataset.
Distribution and In Vitro Bioaccessibility of Potentially Toxic Metals in Soils at Select Urban Parks at Eastern Canadian Cities
Authors: Dodd Matt , Durojaiye Abdurrazzaq , Dupuis Julia
Published: November 2024

This study investigated the human health risks associated with exposure to potentially toxic metals, including arsenic, barium, cadmium, chromium, cobalt, copper, lead, nickel, and zinc, at select parks in Eastern Canadian cities. Except for arsenic in Halifax, the mean metal concentrations in the cities, including Saint John, Fredericton, Ottawa, Toronto, London, Windsor, Woodstock, Kitchener, Guelph, Chatham, and Montreal, were below the Canadian Council of Ministers of Environment soil quality guideline for parkland use. Metal distribution reflected either the regional natural-occurring concentrations or anthropogenic sources such as industrial activities, historical land use, and heavy traffic corridors. In vitro bioaccessibility values were variable and in the order chromium < nickel < cobalt < arsenic < zinc < copper < lead < cadmium. The risk associated with incidental soil ingestion for children, incorporating bioaccessibility, indicated unacceptable levels of non-carcinogenic effects for 6 out of the 101 samples analyzed. For adults, unacceptable non-carcinogenic effects were noted for only one sample. Lead was the leading contributor to the non-carcinogenic risk. Carcinogenic risk for arsenic was limited to two samples. The overall risks associated with exposure to metals in soils in most of the parks studied were deemed low except for arsenic and lead at a few parks.
Evolution of Key Factors Influencing Performance Across Phases in Junior Short Sprints
Authors: Oku Kyosuke , Kai Yoshihiro , Koda Hitoshi , Gonno Megumi , Tanaka Maki , Matsui Tomoyuki , Watanabe Yuya , Morihara Toru , Kida Noriyuki
Published: November 2024

Sprint performance plays a crucial role in various sports. Short sprints vary depending on the size of the court/playing field and on competitive characteristics, but are common in many sports. Although the relationship between age and muscle strength has been explored in short sprints, there is limited understanding of how various physical factors interact, particularly concerning differences in the acceleration phase. This study examined the relationship between sprint times at 0–2.5 m, 2.5–5 m, and 5–10 m intervals and various factors (body composition, flexibility, muscle strength, physical fitness) in junior athletes (13 boys; 13 girls; average age 11.37 ± 1.30 years; 7 in badminton, 8 in fencing, 5 in rowing, and 6 in climbing). The sprint time was measured using four timing lights at 0 m (start point), 2.5 m, 5 m, and 10 m (finish point). The results indicated that sprint times increased with age, and is correlated with muscle strength and flexibility. A partial correlation analysis showed that faster times in the 0–2.5 m interval were associated with higher hip flexibility (right: r = −0.42, p = 0.035; left: r = −0.60, p = 0.001); in the 2.5–5 m interval, faster times were associated with higher core flexibility (right: r = −0.34, p = 0.091; left: r = −0.40, p = 0.046); and in the 5–10 m interval, a relationship with standing long jump performance was confirmed (r = −0.56, p = 0.003). Furthermore, a lower fat-free body weight translated to higher performance (0–2.5 m: r = 0.40, p = 0.047; 2.5 m: r = 0.37, p = 0.071; 5–10 m: r = 0.55, p = 0.004). In the acceleration phase of 10 m, flexibility immediately after the start and the subsequent horizontal propulsive force are important factors that are strongly related to performance change in each interval. These results emphasize that even over a short distance such as 10 m, the factors influencing performance can change significantly. This highlights the importance of overall flexibility, propulsive power and body fat regulation in junior short sprinters, as well as the need for daily training carefully tailored to the specific sprint distances required in each sport.