bluuebunny/crossref_metadata_2025 huggingface dataset:
A Causal Inference Methodology to Support Research on Osteopenia for Breast Cancer Patients
Authors: Kiriakidou Niki , Ballas Aristotelis , Hernando Cristina Meliá , Miralles Anna , Stamati Teta , Anagnostopoulos Dimosthenis , Diou Christos
Published: October 2024

Breast cancer is the most common cancer in the world. With a 5-year survival rate of over 90% for patients at the early disease stages, the management of side-effects of breast cancer treatment has become a pressing issue. Observational, real-world data such as electronic health records, insurance claims, or data from wearable devices have the potential to support research on the quality of life (QoL) of breast cancer patients (BCPs), but care must be taken to avoid errors introduced due to data quality and bias. This paper proposes a causal inference methodology for using observational data to support research on the QoL of BCPs, focusing on the osteopenia of patients undergoing treatment with aromatase inhibitors (AIs). We propose a machine learning-based pipeline to estimate the average and conditional average treatment effects (ATE and CATE). For evaluation, we develop a Structural Causal Model for the osteopenia of BCPs and rely on synthetically generated data to study the effectiveness of the proposed methodology under various data challenges. A set of studies were designed to estimate the effect of high-intensity exercise on bone mineral density loss using synthetic datasets of BCPs under AI treatment. Four observational study scenarios were evaluated, corresponding to synthetically generated data of 1000 BCPs with (a) no bias, (b) sampling bias, (c) hidden confounder bias, and (d) bias due to unobserved mediator. In all cases, evaluations were performed under both complete and missing data scenarios. In particular, machine learning-based models based on tree ensembles and neural networks achieved a lower estimation error by 23.8–51.3% and 32.4–89.3% for ATE and CATE, respectively, compared to direct estimation using sample averages. The proposed approach shows improved effectiveness in treatment effect estimation in the presence of missing values and sampling bias, compared to a “traditional” statistical analysis workflow. This suggests that the application of causal effect estimation methods for the study of BCPs’ quality of life using real-world data is promising and worth pursuing further.
Generalized Nested Logit-Based Stochastic User Equilibrium Considering Static Wayfinding Instructions
Authors: Wei Yutong , Zhou Ronggui , Yang Jie , Chen Yiting , Li Wenhan
Published: October 2024

Despite the availability of electronic navigators and automated vehicles, static wayfinding instructions remain widely used due to their resistance to signal disturbances, as well as their economic and environmental advantages over electronic signs. To investigate the impact of static wayfinding on the network cost and flow distribution, this paper presents an efficient method for updating the incident matrix and extends the stochastic user equilibrium (SUE) framework to incorporate static wayfinding instructions by using the generalized nested logit (GNL) choice model to represent user behavior. The SUE principle relaxes the assumption that users possess perfect knowledge of traffic conditions and always choose the optimal link to minimize their costs. The GNL model improves generalization performance of the cross-nested logit (CNL) model while solving the overlap problem of the multinomial logit (MNL) model. The disaggregate simplicial decomposition (DSD) algorithm is applied to solve proposed user equilibrium by iteratively finding decent directions through an auxiliary solution and determining step size using different methods. The results indicate that the self-regulated averaging (SRA) method can solve the proposed model efficiently. Additionally, increasing travel time cost on guided links and even outer links can be potential influences caused by static wayfinding instructions. The study results can assist decision-makers in quantitatively assessing the value of placing static wayfinding instructions at certain locations and choosing effective layout information.
Applications of Probabilistic Forecasting in Demand Response
Authors: Ruiz-Abellón María Carmen , Fernández-Jiménez Luis Alfredo , Guillamón Antonio , Gabaldón Antonio
Published: October 2024

Studies on probabilistic demand forecasting remain relatively limited compared with point forecasting, despite it being especially valuable for operational and planning purposes. This paper demonstrates different applications of probabilistic forecasting in demand response, together with Physical-Based Load Models. The first application shows how to determine the percentage of uncertainty potentially covered by demand response throughout a day, whereas the second application deals with obtaining the number of consumers that would be needed in demand response actions for a desired percentage of coverage. Finally, in the third application, a specific hourly strategy is proposed for demand response policies. These applications facilitate the aggregator’s energy purchasing in the market and the planning of subsequent consumption adjustments through demand response to minimize deviations from the purchased energy. The approach is illustrated using hourly demand data from a small Spanish city, and two machine learning methods were used to produce a set of probabilistic forecasts and compare results: Linear Quantile Regression and a Quantile Regression Forest. Main results show a significant reduction in deviations from the purchased energy after applying the proposed strategy, especially in the case of the forecasting method being less accurate (40.95% and 33.62% of reduction, respectively).
A Self-Supervised One-Shot Learning Approach for Seismic Noise Reduction
Authors: Pereira Pinheiro Catarina de Nazaré , Sardinha Roosevelt de Lima , Barros Pablo Machado , Bulcão André , Costa Bruno Vieira , Evsukoff Alexandre Gonçalves
Published: October 2024

Neural networks have been used in various computer vision applications, including noise removal. However, removing seismic noise via deep learning approaches faces a specific issue: the scarcity of labeled data. To address this difficulty, this work introduces an adaptation of the Noise2Self algorithm featuring a one-shot learning approach tailored for the seismic context. Essentially, the method leverages a single noisy image for training, utilizing a context-centered masking system and convolutional neural network (CNN) architectures, thus eliminating the dependence on previously labeled data. In tests with Gaussian noise, the method was competitive with established approaches such as Noise2Noise. Under real noise conditions, it demonstrated effective noise suppression removal for a smaller architecture. Therefore, our proposed method is a robust alternative for noise removal that is especially valuable in scenarios lacking sufficient data and labels. With a new approach to processing seismic images, particularly in terms of denoising, our method contributes to the ongoing evolution and enhancement of techniques in this field.
Mathematical Foundations and Implementation of CONIKS Key Transparency
Authors: Mollakuqe Elissa , Dag Hasan , Dimitrova Vesna
Published: October 2024

This research paper explores the CONIKS key management system’s security and efficiency, a system designed to ensure transparency and privacy in cryptographic operations. We conducted a comprehensive analysis of the underlying mathematical principles, focusing on cryptographic hash functions and digital signature schemes, and their implementation in the CONIKS model. Through the use of Merkle trees, we verified the integrity of the system, while zero-knowledge proofs were utilized to ensure the confidentiality of key bindings. We conducted experimental evaluations to measure the performance of cryptographic operations like key generation, signing, and verification with varying key sizes and compared the results against theoretical expectations. Our findings demonstrate that the system performs as predicted by cryptographic theory, with only minor deviations in computational time complexities. The analysis also reveals significant trade-offs between security and efficiency, particularly when larger key sizes are used. These results confirm that the CONIKS system offers a robust framework for secure and efficient key management, highlighting its potential for real-world applications in secure communication systems.
Full Road Transport Sector Transition Towards 100% Autonomous Renewable Energy Supply in Isolated Systems: Tenerife Island Test Case
Authors: Santana-Méndez Itziar , García-Afonso Óscar , González-Díaz Benjamín
Published: October 2024

The transition towards sustainable energy systems is a key challenge faced by society. Among the different sectors, road transport becomes one of the most difficult due to the large energy consumption and infrastructure requirements. In this context, although zero-tailpipe-emission vehicle adoption is seen as a promising route, the energy provision through renewable sources is still uncertain, especially with hydrogen. This paper explores a 100% renewable energy supply scenario for both power-generation and road transport sectors in the isolated system of Tenerife. With this aim, the island’s energy system has been modelled in the software EnergyPLAN. Taking as reference the current renewable technology roadmap in the island, the impact of a full deployment of zero-tailpipe-emission vehicles on the energy system has been evaluated, providing the power and energy storage capacity requirements. The obtained results indicate the need for 6 GW of renewable power (nearly 20 times the current figures) and 12 GWh of a yet non-existent storage capacity. This deployment must be accompanied with approximately 1 GW of dispatchable sources and 1.3 GW of electrolysis capacity to carry out a complete decarbonisation of the transport sector in the island. Finally, a series of recommendations to policy makers are suggested to support the definition of future roadmaps.
Prediction of Fading for Painted Cultural Relics Using the Optimized Gray Wolf Optimization-Long Short-Term Memory Model
Authors: Liu Zhen , Zhao An-Ran , Liu Si-Lu
Published: October 2024

Cultural heritage digitization is of great significance for the protection, restoration, and rejuvenation of cultural relics. In particular, the investigation of fading mechanisms is essential for virtual restoration to accurately recreate the original appearance of artifacts and facilitate humanistic and historical research. For the purpose of investigating the color fading mechanism of pigments, we propose a color fading time series model using a combined long short-term memory recurrent neural network modified by the gray wolf optimization algorithm (GWOAD-LSTM). First, the general gray wolf algorithm was scaled up to two dimensions and combined with an LSTM model for optimal parameter search. Second, six pigments commonly used in painted artifacts were subjected to simulated aging experiments. Third, by applying the experimental data to different predictors, the results of the Back Propagation Neural Network (BPNN), Long Short-Term Memory (LSTM), Long Short-Term Memory on Gray Wolf Optimizer (GWO-LSTM), and GWOAD-LSTM models were compared. The results showed that our proposed GWOAD-LSTM model outperformed other models in terms of accuracy and generalization ability, especially in predicting hLC color attributes. Our study aimed to provide a new application tool for the restoration and rejuvenation of painted artifacts.
CFD Analysis of the Thermal-Hydraulic Performance of Traditional and Alternative Oils for Transformer Cooling
Authors: Salerno Elisabetta , Leonforte Adriano , Grespan Mattia , Angeli Diego , Corticelli Mauro A.
Published: October 2024

The search for alternative, more environmentally friendly coolants to replace mineral oils in power transformers represents a relevant challenge for the power industry. In this frame, a CFD analysis is carried out by means of an open-source, conjugate heat transfer Finite Volume solver to assess the heat transfer performance of different coolants in a disc-type winding of an oil-immersed power transformer, whose geometry is taken from a reference case in the literature. Four different oils are considered: two mineral oils widely established among power transformer manufacturers, a synthetic ester and a natural ester. The latter two represent environmentally friendly alternatives to mineral oils, of great interest for the specific sector. The influence of temperature-dependent thermophysical properties on the overall flow and on the location and value of the hot spots in the copper conductors is discussed, highlighting the strength and weaknesses of the four oils as transformer coolants. In particular, the natural ester exhibits the best heat transfer performance, whereas high hot-spot temperatures and strong fluctuations are found for both mineral oils and the synthetic ester.
Research on the Uplift Characteristics of Pantographs of High-Speed Trains Under Crosswind Conditions
Authors: Zhao Meng , Duan Yaning , Lan Xingbo , Jia Zile
Published: October 2024

Through the geometric relationships and force analysis of the main components of pantographs on high-speed trains, the coefficients of aerodynamic forces and lift transmission between the pantograph and main components under crosswind conditions were derived. Based on the aerodynamic forces acting on the pantograph at different crosswind speeds, wind angles, and operating speeds, the aerodynamic lift of the pantograph and main components was ultimately determined. The results indicate that the aerodynamic lift of the pantograph is mainly distributed on the bow structure, with the aerodynamic lift of the upper frame all being negative values, while the absolute value of the aerodynamic lift of the lower arm rod is the smallest. The operating speed of the pantograph and the wind angle of the crosswind have a significant impact on the aerodynamic lift of the main components, while the impact of the crosswind speed is relatively small. At the same operating speed of the pantograph, the lower the corresponding crosswind speed, the smaller the aerodynamic lift of the pantograph. The aerodynamic lift of the pantograph tends to decrease gradually with the increase in crosswind speed, and the impact of crosswind speed decreases gradually with the increase in the pantograph operating speed. A comprehensive relationship formula between the aerodynamic lift of the pantograph and the operating speed, crosswind speed, and wind angle is obtained, and the empirical formula for the contact force of the bow net and train operating speed is modified. The research results are of great significance and value for the study and application of lift forces on pantographs under crosswind conditions.
Application of Forced Oscillation Technique in Assessing Pulmonary Fibrosis in Hermansky–Pudlak Syndrome
Authors: De Jesús-Rojas Wilfredo , Reyes-Peña Luis , Muñiz-Hernandez José , Mena-Ventura Rolando , Camareno-Soto Gabriel , Rosario-Ortiz Gabriel , Ramos-Benitez Marcos J. , Egozcue-Dionisi Monica , Rivera-Jimenez Enid , Román-Carlo Rosa
Published: October 2024

Hermansky–Pudlak syndrome (HPS) is a rare autosomal recessive disorder characterized by defects in lysosome-related organelles. Given the high mortality rate associated with HPS pulmonary fibrosis (PF) and the significant risks tied to lung transplantation, it is essential to explore new tools for the early surveillance of PF to monitor its progression before clinical symptoms become apparent. This study evaluates the forced oscillation technique (FOT) for assessing PF in five adult patients with HPS, all homozygous for the HPS-1 (c.1472_1487dup p.His497Glnfs*90) founder mutation. Using the Resmon™ Pro V3 device, the FOT measured resistance (Rrs) and reactance (Xrs) at 5, 11, and 19 Hertz (Hz). High-resolution computed tomography (HRCT) scans of the chest were reviewed for radiographic findings. The cohort (n = 5) had a median age of 43 years. All patients exhibited HPS clinical features, including oculocutaneous albinism and respiratory symptoms such as dry cough and dyspnea. Radiographic analysis revealed PF in four patients (80%), with traction bronchiectasis, reticular patterns, honeycombing, and ground-glass opacities. The FOT detected progressive changes in pulmonary resistance and reactance correlating with fibrosis severity. These findings suggest that the FOT is a valuable non-invasive tool for monitoring PF in patients with HPS-1, potentially improving early diagnosis and management.
Low-Complexity SAOR and Conjugate Gradient Accelerated SAOR Based Signal Detectors for Massive MIMO Systems
Authors: Khoso Imran A. , Ali Mazhar , Irshad Muhammad Nauman , Chaudhary Sushank , Vanichchanunt Pisit , Wuttisittikulkij Lunchakorn
Published: October 2024

A major challenge for massive multiple-input multiple-output (MIMO) technology is designing an efficient signal detector. The conventional linear minimum mean square error (MMSE) detector is capable of achieving good performance in large antenna systems but requires computing the matrix inverse, which has very high complexity. To address this problem, several iterative signal detection methods have recently been introduced. Existing iterative detectors perform poorly, especially as the system dimensions increase. This paper proposes two detection schemes aimed at reducing computational complexity in massive MIMO systems. The first method leverages the symmetric accelerated over-relaxation (SAOR) technique, which enhances convergence speed by judiciously selecting the relaxation and acceleration parameters. The SAOR technique offers a significant advantage over conventional accelerated over-relaxation methods due to its symmetric iteration. This symmetry enables the use of the conjugate gradient (CG) acceleration approach. Based on this foundation, we propose a novel accelerated SAOR method named CGA-SAOR, where CG acceleration is applied to further enhance the convergence rate. This combined approach significantly enhances performance compared to the SAOR method. In addition, a detailed analysis of the complexity and numerical results is provided to demonstrate the effectiveness of the proposed algorithms. The results illustrate that our algorithms achieve near-MMSE detection performance while reducing computations by an order of magnitude and significantly outperform recently introduced iterative detectors.
Assessing the Impact of Climate Change on Methane Emissions from Rice Production Systems in Southern India
Authors: Kovilpillai Boomiraj , Jothi Gayathri Jawahar , Antille Diogenes L. , Chidambaram Prabu P. , Karunaratne Senani , Bhatia Arti , Shanmugam Mohan Kumar , Rose Musie , Kandasamy Senthilraja , Selvaraj Selvakumar , Mainuddin Mohammed , Chandrasekeran Guruanand , Ramasamy Sangeetha Piriya , Vellingiri Geethalakshmi
Published: October 2024

The impact of climate change on methane (CH4) emissions from rice production systems in the Coimbatore region (Tamil Nadu, India) was studied by leveraging field experiments across two main treatments and four sub-treatments in a split-plot design. Utilizing the closed-chamber method for gas collection and gas chromatography analysis, this study identified significant differences in CH4 emissions between conventional cultivation methods and the system of rice intensification (henceforth SRI). Over two growing seasons, conventional cultivation methods reported higher CH4 emissions (range: from 36.9 to 59.3 kg CH4 ha−1 season−1) compared with SRI (range: from 2.2 to 12.8 kg CH4 ha−1 season−1). Experimental data were subsequently used to guide parametrization and validation of the DeNitrification–DeComposition (DNDC) model. The validation of the model showed good agreement between the measured and modeled data, as denoted by the statistical tests performed, which included CRM (0.09), D-index (0.99), RMSE (7.16), EF (0.96), and R2 (0.92). The validated model was then used to develop future CH4 emissions projections under various shared socio-economic pathways (henceforth SSPs) for the mid- (2021–2050) and late (2051–2080) century. The analysis revealed a potential increase in CH4 emissions for the simulated scenarios, which was dependent on specific soil and irrigation management practices. Conventional cultivation produced the highest CH4 emissions, but it was shown that they could be reduced if the current practice was replaced by minimal flooding or through irrigation with alternating wetting and drying cycles. Emissions were predicted to rise until SSP 370, with a marginal increase in SSP 585 thereafter. The findings of this work underscored an urgency to develop climate-smart location-specific mitigation strategies focused on simultaneously improving current water and nutrient management practices. The use of methanotrophs to reduce CH4 production from rice systems should be considered in future work. This research also highlighted the critical interaction that exists between agricultural practices and climate change, and emphasized the need to implement adaptive crop management strategies that can sustain productivity and mitigate the environmental impacts of rice-based systems in southern India.
Dropsonde Data Impact on Rain Forecasts in Taiwan Under Southwesterly Flow Conditions with Observing System Simulation Experiments
Authors: Chien Fang-Ching , Chiu Yen-Chao
Published: October 2024

This paper conducts an observing system simulation experiment (OSSE) to assess the impact of assimilating traditional sounding and surface data, along with dropsonde observations over the northern South China Sea (SCS) on heavy rain forecasts in Taiwan. Utilizing the hybrid ensemble transform Kalman filter (ETKF) and the three-dimensional variational (3DVAR) data assimilation (DA) system, this study focuses on an extreme precipitation event near Taiwan on 22 May 2020. The event was mainly influenced by strong southwesterly flow associated with an eastward-moving southwest vortex (SWV) from South China to the north of Taiwan. A nature run (NR) serves as the basis, generating virtual observations for radiosonde, surface, and dropsonde data. Three experiments—NODA (no DA), CTL (traditional observation DA), and T5D24 (additional dropsonde DA)—are configured for comparative analyses. The NODA experiment shows premature and weaker precipitation events across all regions compared with NR. The CTL experiment improved upon NODA’s forecasting capabilities, albeit with delayed onset but prolonged precipitation duration, particularly noticeable in southern Taiwan. The inclusion of dropsonde DA in the T5D24 experiment further enhanced precipitation forecasting, aligning more closely with NR, particularly in southern Taiwan. Investigations of DA impact reveal that assimilating traditional observations significantly enhances the SWV structure and wind fields, as well as the location of frontal systems, with improvements persisting for 40 to 65 h. However, low-level moisture field enhancements are moderate, leading to insufficient precipitation forecasts in southern Taiwan. Additional dropsonde DA over the northern SCS further refines low-level moisture and wind fields over the northern SCS, as well as the occurrence of frontal systems, extending positive impacts beyond 35 h and thus improving the rain forecast.
Optimization of Retired Lithium-Ion Battery Pack Reorganization and Recycling Using 3D Assessment Technology
Authors: Chen Wan , Su Jiaoyue , Shen Lei , Gu Xinfa , Xie Junjie , Sun Na , Huang Hui , Ji Jie
Published: October 2024

This study introduces a sophisticated methodology that integrates 3D assessment technology for the reorganization and recycling of retired lithium-ion battery packs, aiming to mitigate environmental challenges and enhance sustainability in the electric vehicle sector. By deploying a kernel extreme learning machine (KELM), variational mode decomposition (VMD), and an advanced sparrow search algorithm (SSA), the research achieves a marked increase in the precision of battery classification and performance forecasting. Implementing a three-dimensional dynamic evaluation model, the study optimizes battery pack grouping strategies, culminating in superior secondary utilization rates, extended operational lifespans, and minimized ecological footprints. The research demonstrates that balanced weight distribution strategies, which maximize energy density to 61.37571 Wh/L and cycle counts up to 947 cycles, are pivotal for the efficient reorganization of battery packs, substantiating the economic feasibility and environmental sustainability of recycling initiatives. Future endeavors will extend this research to investigate the influence of diverse battery materials and morphologies on reorganization efficacy, with the aim of broadening the application horizons to include real-world scenarios, thereby refining battery performance and lifespan predictions and propelling forward the frontiers of recycling technology and policy development.
Explainable Aspect-Based Sentiment Analysis Using Transformer Models
Authors: Perikos Isidoros , Diamantopoulos Athanasios
Published: October 2024

An aspect-based sentiment analysis (ABSA) aims to perform a fine-grained analysis of text to identify sentiments and opinions associated with specific aspects. Recently, transformers and large language models have demonstrated exceptional performance in detecting aspects and determining their associated sentiments within text. However, understanding the decision-making processes of transformers remains a significant challenge, as they often operate as black-box models, making it difficult to interpret how they arrive at specific predictions. In this article, we examine the performance of various transformers on ABSA and we employ explainability techniques to illustrate their inner decision-making processes. Firstly, we fine-tune several pre-trained transformers, including BERT, RoBERTa, DistilBERT, and XLNet, on an extensive set of data composed of MAMS, SemEval, and Naver datasets. These datasets consist of over 16,100 complex sentences, each containing a couple of aspects and corresponding polarities. The models were fine-tuned using optimal hyperparameters and RoBERTa achieved the highest performance, reporting 89.16% accuracy on MAMS and SemEval and 97.62% on Naver. We implemented five explainability techniques, LIME, SHAP, attention weight visualization, integrated gradients, and Grad-CAM, to illustrate how transformers make predictions and highlight influential words. These techniques can reveal how models use specific words and contextual information to make sentiment predictions, which can improve performance, address biases, and enhance model efficiency and robustness. These also point out directions for further focus on the analysis of models’ bias in combination with explainability methods, ensuring that explainability highlights potential biases in predictions.
Tetracalcium Phosphate/Monetite/Calcium Sulfate Hemihydrate Biocement for Alveolar Bone Augmentation After Tooth Extraction in Pig Mandible
Authors: Vdoviaková Katarína , Krešáková Lenka , Humeník Filip , Danko Ján , Čurgali Kristína , Jenča Andrej , Jenča Andrej , Petrášová Adriána , Jenčová Janka , Vrzgula Marko , Giretová Mária , Štulajterová Radoslava , Medvecký Ľubomír
Published: October 2024

A tetracalcium phosphate/monetite/calcium sulfate hemihydrate powder cement mixture (CAS) in the form of a paste was used as a socket preservative to prevent alveolar ridge resorption after the extraction of the second premolar tooth in the mandible of a pig model. During the post-surgery period, the animals were monitored daily. No signs of inflammation, infection, or other complications were observed in the defect site for six months. Six months after surgery, the mandible defects in pigs were evaluated using macroscopic, histological, and radiological techniques. Treatment of the extraction sites with CAS biocement resulted in the uniform filling of the defects with alveolar bone tissue, characterized by a relatively smooth and homogeneous gum surface with no visible biocement residues. The formation of new bone tissue with osteoblasts, osteocytes, and mineralized matrices was confirmed. The results showed the similar morphology, thickness, and complete integration of the newly formed bone with the surrounding tissue. CAS biocement could be an effective material to prevent alveolar bone reduction as well as soft tissue loss and could support dental implant placement with long-term functionality.
Critical Design Considerations for Longer-Term Wear and Comfort of On-Body Medical Devices
Authors: Stuart Shavini , de Kok Margreet , O’Searcoid Ben , Morrisroe Hannah , Serban Irina Bianca , Jagers Ferry , Dulos Remon , Houben Steven , van de Peppel Linda , van den Brand Jeroen
Published: October 2024

The commercialization of a growing number of wearable devices has been enabled within recent years due to the availability of miniaturized sensor modalities, the development of new materials, and the scalability of flexible electronics. With the increase in resource shortages within healthcare, there is a demand to translate wearable devices from the commercial consumer stand-point to the medical field. Clinical-grade signal quality, wearability, and comfort all need to be tailored to a wearable design. Wear and comfort for user compliance and durability for longer-term use are commonly overlooked. In this study, the relationship of on-body location and material layer composition is investigated. Five non-woven medical tapes noted for longer wear time are tested over a 7-day timeframe. The impact of material properties, such as elasticity, isotropy, and hysteresis, as well as the moisture vapor transmission rate (MVTR) and adhesive thickness, are evaluated in relation to skin properties on the lower torso of 30, high-activity-level volunteers. User perception was quantified via Likert-scale questionnaires and images were obtained for the material–skin interaction. The results indicate that critical characteristics, such as MVTR and elasticity, noted for positive skin interaction in commercial products, may not translate to improved user perception and durability over time. Future work will assess new design options to manipulate material properties for improved wear and comfort.
Systematic Meta-Analysis of Computer-Aided Detection of Breast Cancer Using Hyperspectral Imaging
Authors: Leung Joseph-Hang , Karmakar Riya , Mukundan Arvind , Thongsit Pacharasak , Chen Meei-Maan , Chang Wen-Yen , Wang Hsiang-Chen
Published: October 2024

The most commonly occurring cancer in the world is breast cancer with more than 500,000 cases across the world. The detection mechanism for breast cancer is endoscopist-dependent and necessitates a skilled pathologist. However, in recent years many computer-aided diagnoses (CADs) have been used to diagnose and classify breast cancer using traditional RGB images that analyze the images only in three-color channels. Nevertheless, hyperspectral imaging (HSI) is a pioneering non-destructive testing (NDT) image-processing technique that can overcome the disadvantages of traditional image processing which analyzes the images in a wide-spectrum band. Eight studies were selected for systematic diagnostic test accuracy (DTA) analysis based on the results of the Quadas-2 tool. Each of these studies’ techniques is categorized according to the ethnicity of the data, the methodology employed, the wavelength that was used, the type of cancer diagnosed, and the year of publication. A Deeks’ funnel chart, forest charts, and accuracy plots were created. The results were statistically insignificant, and there was no heterogeneity among these studies. The methods and wavelength bands that were used with HSI technology to detect breast cancer provided high sensitivity, specificity, and accuracy. The meta-analysis of eight studies on breast cancer diagnosis using HSI methods reported average sensitivity, specificity, and accuracy of 78%, 89%, and 87%, respectively. The highest sensitivity and accuracy were achieved with SVM (95%), while CNN methods were the most commonly used but had lower sensitivity (65.43%). Statistical analyses, including meta-regression and Deeks’ funnel plots, showed no heterogeneity among the studies and highlighted the evolving performance of HSI techniques, especially after 2019.
Evaluating AI Methods for Pulse Oximetry: Performance, Clinical Accuracy, and Comprehensive Bias Analysis
Authors: Cabanas Ana María , Sáez Nicolás , Collao-Caiconte Patricio O. , Martín-Escudero Pilar , Pagán Josué , Jiménez-Herranz Elena , Ayala José L.
Published: October 2024

Blood oxygen saturation (SpO2) is vital for patient monitoring, particularly in clinical settings. Traditional SpO2 estimation methods have limitations, which can be addressed by analyzing photoplethysmography (PPG) signals with artificial intelligence (AI) techniques. This systematic review, following PRISMA guidelines, analyzed 183 unique references from WOS, PubMed, and Scopus, with 26 studies meeting the inclusion criteria. The review examined AI models, key features, oximeters used, datasets, tested saturation intervals, and performance metrics while also assessing bias through the QUADAS-2 criteria. Linear regression models and deep neural networks (DNNs) emerged as the leading AI methodologies, utilizing features such as statistical metrics, signal-to-noise ratios, and intricate waveform morphology to enhance accuracy. Gaussian Process models, in particular, exhibited superior performance, achieving Mean Absolute Error (MAE) values as low as 0.57% and Root Mean Square Error (RMSE) as low as 0.69%. The bias analysis highlighted the need for better patient selection, reliable reference standards, and comprehensive SpO2 intervals to improve model generalizability. A persistent challenge is the reliance on non-invasive methods over the more accurate arterial blood gas analysis and the limited datasets representing diverse physiological conditions. Future research must focus on improving reference standards, test protocols, and addressing ethical considerations in clinical trials. Integrating AI with traditional physiological models can further enhance SpO2 estimation accuracy and robustness, offering significant advancements in patient care.
CT-Derived Features as Predictors of Clot Burden and Resolution
Authors: Auster Quentin , Almetwali Omar , Yu Tong , Kelder Alyssa , Nouraie Seyed Mehdi , Mustafaev Tamerlan , Rivera-Lebron Belinda , Risbano Michael G. , Pu Jiantao
Published: October 2024

Objectives: To evaluate the prognostic utility of CT-imaging-derived biomarkers in distinguishing acute pulmonary embolism (PE) resolution and its progression to chronic PE, as well as their association with clot burden. Materials and Methods: We utilized a cohort of 45 patients (19 male (42.2%)) and 96 corresponding CT scans with exertional dyspnea following an acute PE. These patients were referred for invasive cardiopulmonary exercise testing (CPET) at the University of Pittsburgh Medical Center from 2018 to 2022, for whom we have ground truth classification of chronic PE, as well as CT-derived features related to body composition, cardiopulmonary vasculature, and PE clot burden using artificial intelligence (AI) algorithms. We applied Lasso regularization to select parameters, followed by (1) Ordinary Least Squares (OLS) regressions to analyze the relationship between clot burden and the selected parameters and (2) logistic regressions to differentiate between chronic and resolved patients. Results: Several body composition and cardiopulmonary factors showed statistically significant association with clot burden. A multivariate model based on cardiopulmonary features demonstrated superior performance in predicting PE resolution (AUC: 0.83, 95% CI: 0.71–0.95), indicating significant associations between airway ratio (negative correlation), aorta diameter, and heart volume (positive correlation) with PE resolution. Other multivariate models integrating demographic features showed comparable performance, while models solely based on body composition and baseline clot burden demonstrated inferior performance. Conclusions: Our analysis suggests that cardiopulmonary and demographic features hold prognostic value for predicting PE resolution, whereas body composition and baseline clot burden do not. Clinical Relevance: Our identified prognostic factors may facilitate the follow-up procedures for patients diagnosed with acute PE.
Advances in Targeted Microbeam Irradiation Methods for Live Caenorhabditis elegans
Authors: Suzuki Michiyo
Published: October 2024

Charged-particle microbeam irradiation devices, which can convert heavy-ion or proton beams into microbeams and irradiate individual animal cells and tissues, have been developed and used for bioirradiation in Japan, the United States, China, and France. Microbeam irradiation technology has been used to analyze the effects of irradiation on mammalian cancer cells, especially bystander effects. In 2006, individual-level microbeam irradiation of the nematode Caenorhabditis elegans was first realized using JAEA-Takasaki’s (now QST-TIAQS’s) TIARA collimated microbeam irradiation device. As of 2023, microbeam irradiation of C. elegans has been achieved at five sites worldwide (one in Japan, one in the United States, one in China, and two in France). This paper summarizes the global progress in the field of microbeam biology using C. elegans, while focusing on issues unique to microbeam irradiation of live C. elegans, such as the method of immobilizing C. elegans for microbeam experiments.
Psoriasis: The Versatility of Mesenchymal Stem Cell and Exosome Therapies
Authors: Dairov Aidar , Sekenova Aliya , Alimbek Symbat , Nurkina Assiya , Shakhatbayev Miras , Kumasheva Venera , Kuanysh Sandugash , Adish Zhansaya , Issabekova Assel , Ogay Vyacheslav
Published: October 2024

Mesenchymal stem cells (MSCs) are multilineage differentiating stromal cells with extensive immunomodulatory and anti-inflammatory properties. MSC-based therapy is widely used in the treatment of various pathologies, including bone and cartilage diseases, cardiac ischemia, diabetes, and neurological disorders. Along with MSCs, it is promising to study the therapeutic properties of exosomes derived from MSCs (MSC-Exo). A number of studies report that the therapeutic properties of MSC-Exo are superior to those of MSCs. In particular, MSC-Exo are used for tissue regeneration in various diseases, such as healing of skin wounds, cancer, coronary heart disease, lung injury, liver fibrosis, and neurological, autoimmune, and inflammatory diseases. In this regard, it is not surprising that the scientific community is interested in studying the therapeutic properties of MSCs and MSC-Exo in the treatment of psoriasis. This review summarizes the recent advancements from preclinical and clinical studies of MSCs and MSC-Exo in the treatment of psoriasis, and it also discusses their mechanisms of therapeutic action involved in the treatment of this disease.
Repair of Beam End Joints Using Steel Rods and Wood Prosthesis in Heritage Buildings: Implantation in the Structure of the Zabala Palace in Ordizia (Basque Country, Spain)
Authors: Benito-Ayúcar Josu , González-Serna Pablo , Luengas-Carreño Daniel , Uribe-Rus Ekaitz
Published: October 2024

This paper presents the methodology developed in the repair of three oak beam ends in a protected heritage building: the Zabala Palace in Ordizia (Basque Country, Spain). It describes the structural assessment, design, calculation and execution process, as well as the experimental tests carried out in the laboratory to verify and validate the structural capacity of the repair method. The intervention consisted of cutting and removing the beam ends degraded by fungi and replacing them with wooden prostheses. These elements were connected to the beams by means of threaded steel rods and epoxy resin. Calculations based on standards and the literature were verified by laboratory tests where aspects such as the fluidity, filling and pull-out resistance of four commercial epoxy resins were tested. Once the epoxy resin was selected, three samples of the reinforcement design were also flexure tested. The results of the different tests show capacities much higher than those resulting from the application of the calculation procedures in the current bibliography and standards. The implemented solution allowed the conservation of most of the original patrimonial timber, following the criteria of minimum intervention.
Influence of the Rustic Bamboo Envelope Construction Technique on the Thermal Performance of Vernacular Housing in the Ecuadorian Coastal Region: The Case of El Carmen-Manabí
Authors: Carpio Rodrigo , Valarezo Fernanda , Aguirre-Maldonado Eduardo , Balcázar-Arciniega Cristian
Published: October 2024

Bamboo, particularly Guadua Angustifolia cane, offers significant thermal benefits for construction in warm, humid climates and has been a key material in Ecuador for over 9500 years due to its low cost, versatility, and low thermal conductivity. In the coastal region, including the settlement of El Carmen, traditional architectural techniques have evolved to incorporate local materials like Guadua cane. A common method involves using crushed cane as wall cladding, where longitudinally split bamboo is applied, creating openings in the structure. These openings, often viewed as a construction defect, influence passive cooling systems. This study investigated the impact of these irregularities on indoor thermal comfort by documenting the construction systems and climatic conditions of the area. Computational models were reconstructed and tested through CFD simulations to assess thermal behavior. The findings highlight the thermal implications of the vernacular construction system, revealing how wall openings affect passive cooling strategies and overall indoor comfort in the housing typologies of El Carmen.
Quality Evaluation of Sizeable Surveying-Industry-Produced Terrestrial Laser Scanning Point Clouds That Facilitate Building Information Modeling—A Case Study of Seven Point Clouds
Authors: Varbla Sander , Puust Raido , Ellmann Artu
Published: October 2024

Terrestrial laser scanning can provide high-quality, detailed point clouds, with state-of-the-art research reporting the potential for sub-centimeter accuracy. However, state-of-the-art research may not represent real-world practices reliably. This study aims to deliver a different perspective through collaboration with the surveying industry, where time constraints and productivity requirements limit the effort which can go to ensuring point cloud quality. Seven sizeable buildings’ point clouds (490 to 1392 scanning stations) are evaluated qualitatively and quantitatively. Quantitative evaluations based on independent total station control surveys indicate that sub-centimeter accuracy is achievable for smaller point cloud portions (e.g., a single building story) but caution against such optimism for sizable point clouds of large, multi-story buildings. The control surveys reveal common registration errors around the 5 cm range, resulting from complex surface geometries, as in stairways. Potentially hidden from visual inspection, such systematic errors can cause misalignments between point cloud portions in the compound point cloud structure, which could be detrimental to further applications of the point clouds. The study also evaluates point cloud georeferencing, affirming the resection method’s capability of providing high consistency and an accuracy of a few centimeters. Following the study’s findings, practical recommendations for terrestrial laser scanning surveys and data processing are formulated.
Integration of Proactive Building Fire Risk Management in the Building Construction Sector: A Conceptual Framework to Understand the Existing Condition
Authors: Barua Uttama , Han Hoon , Mojtahedi Mohammad , Ansary Mehedi Ahmed
Published: October 2024

In recent decades, the frequency and severity of building fires have increased with rapid urbanization, particularly in high-rise buildings and high-building-density areas. Although proactive building fire risk management (PBFRM) can address this issue, significant research gaps exist. This research aims to propose a conceptual framework for understanding the existing condition of integrating proactive building fire risk management in the building construction sector, and the effect on improving building fire safety. It has been performed through a comprehensive review of the relevant literature. This framework identifies five components by answering the following questions: what? (building fire safety), of what? (buildings), to what? (integrate PBFRM in the building construction sector), for what? (ensuring resilience, sustainability and smartness), and how? (through building construction sector governance). According to the proposed conceptual framework, building fire safety can be achieved by integrating the PBFRM measures in different building attributes throughout the building life cycle. At the same time, resilience, sustainability and smartness must be ensured. These can be achieved through governance in the building construction sector. The proposed conceptual framework will contribute to policy and practice by facilitating policymakers and stakeholders in assessing the existing conditions and the efficacy of building fire safety measures, and thereby in devising strategies to further reinforce them.
Differences in Physical Activity Levels, Screen Time, and Body Mass Index and Their Associations with Oral Health in Schoolchildren in Mallorca
Authors: Coll Irene , Vallejos Daniela , López-Safont Nora
Published: October 2024

Background: The time that adolescents spend using screens is associated with an elevated body mass index (BMI) and decreased physical activity, with gender being an important determinant. There is evidence that an elevated BMI can affect oral health. Aim: To analyze gender differences in physical activity levels and screen time, as well as screen use and BMI, and their associations with DMFT. Methods: Physical activity levels, screen time, and their relationship with the oral health status and BMI were studied in 468 schoolchildren aged 12 to 15 years. To analyze the differences in the numerical data, an analysis of the mean by the Student t-test or a one-way analysis of variance followed by the Bonferroni post hoc analysis was used. Moreover, to analyze the differences in the categorical data, the chi-square test was used. Results: Boys were more active (59.9%) than girls (40.1%) (p < 0.001) in terms of the means of transportation that they used to travel to school. It was observed that boys spent more mean hours playing outdoors than girls (boys: 1.38 ± 0.04 vs. girls: 1.24 ± 0.04; p = 0.040). The mean number of hours engaged in sports activities outside school was higher for boys than girls (boys: 2.22 ± 0.06 vs. girls: 1.77 ± 0.73; p ≤ 0.001). The mean number of hours spent using electronic devices during the weekend was higher in boys than girls (boys: 2.89 ± 0.08 vs. girls 2.44 ± 0.09; p ≤ 0.001). Children with a DMF > 0 had a higher mean BMI than those with a DMFT = 0 (DMFT > 0; 21.95 ±4.80 vs. DMFT = 0; 20.77 ± 3.67; p = 0.003). Conclusions: An increased number of hours spent in front of a computer correlates with a higher BMI. Sex seems to be a determining factor when it comes to engaging in active activities. Caries is more frequent in children with a higher BMI.