bluuebunny/crossref_metadata_2025 huggingface dataset:
Arm Swing Movements during Walking as an Early Predictor of Multiple Sclerosis Progression
Authors: Leblebici Gökçe , Ramari Cintia , Güngör Feray , Ovacık Uğur , Soysal Aysun , Tarakcı Ela , Feys Peter , Meyns Pieter
Published: July 2024

Persons with Multiple Sclerosis (pwMS) are at a high risk of falling, with abnormal gait pattern. The upper limbs play an important role in postural control and gait stability. The presence of arm swing changes during walking in pwMS, especially in the early period, may be an indicator of balance problems. The current study aimed to assess arm swing during walking in early MS. A total of 18 pwMS were evaluated in two time points. The first time was after their first (stable) diagnosis (pre-evaluation) and the second time was 3 months after the pre-evaluation. In addition, 10 healthy controls were evaluated once. Arm swing analysis during walking, using video recording, was applied to both groups. Additionally, the MS group performed the Two-Minute Walk Test, Timed Up and Go, and Timed 25-Foot Walk Test. The pwMS showed similar joint angles at both the first and second evaluations. Only the elbow ROM value on the least affected side was lower in pwMS than healthy controls at the second evaluation (p = 0.027). The early MS patients showed altered arm swing pattern. As walking speed and mobility scores did not change over time, the decrease in elbow amplitude over a 3-month period indicates that the arm swing may present a pattern resulting from MS-specific disorders rather than being a compensatory mechanism in walking. From the earliest stages of the disease, variations in arm swing movements during walking may be considered as a disease progression-predictor for MS.
Enhancing Manufacturing Excellence with Digital-Twin-Enabled Operational Monitoring and Intelligent Scheduling
Authors: Yang Jingzhe , Zheng Yili , Wu Jian , Wang Yuejia , He Jinyang , Tang Lingxiao
Published: July 2024

This research examines the potential of digital twin (DT) technology for reformation within China’s traditional solid-wood-panel processing industry, which currently suffers from production inefficiencies and the slow adoption of digital technology. The research centers around developing a digital twin system, elucidating improvements in manufacturing efficiency, waste management, process simulation, and real-time monitoring. These capabilities facilitate immediate problem solving and offer transparency in the process. The digital twin system is comprised of physical, transport, virtual, and application layers, employing a MySQL database and using the Open Platform Communications Unified Architecture (OPC UA) protocol for communication. The application of this system has led to heightened production efficiency and better material use in the solid-wood-panel manufacturing line. Integrating the dynamic selection adaptive genetic algorithm (DSAGA) into the virtual layer drives the system’s efficiency forward. This evolved approach has allowed for an enhancement of 8.93% in the scheduling efficiency of DSAGA compared to traditional genetic algorithms (GAs), thereby contributing to increased system productivity. Real-time mapping and an advanced simulation interface have strengthened the system’s monitoring aspect. These additions enrich data visualization, leading to better comprehension and a holistic process view. This research has ignited improvements in solid-wood-panel production, illustrating the tangible benefits and representing progress in incorporating digital technology into traditional industries. This research sets a path for transforming these industries into smart manufacturing by effectively bridging the gap between physical production and digital monitoring. Furthermore, the adjustability of this approach extends beyond solid-wood-panel production, indicating the capability to expedite movement towards intelligent production in various other manufacturing sectors.
Simulation of Different Control Strategies of a Three-Phase Induction Motor Coupled to a Real Decanter Centrifuge for Olive Oil Extraction Focusing on Energy Saving
Authors: Mammadov Orkhan , Altieri Giuseppe , Genovese Francesco , Laveglia Sabina , Matera Attilio , Rashvand Mahdi , Scarano Luciano , Di Renzo Giovanni Carlo
Published: July 2024

This study presents the analysis and modeling of the optimal connection between an asynchronous three-phase electrical induction motor and a horizontal centrifuge used in olive oil extraction. The simulation, carried out using the well-established Simscape Electrical by Matlab R2024a, offers the advantage of reducing the purchasing cost of the electronic drivers and electric motors to be tested. Different connection cases among the electronic motor driver, the electric motor, and the horizontal centrifuge were examined. The findings indicate that the oversizing of the electric motor is required to increase the machine efficiency and to reduce the overall specific energy consumption; however, the asymptotic behavior of the specific energy consumption, being related to the motor power, places a limit on the motor oversizing. Indeed, raising the motor power reduces the specific energy consumption; however, this effect can be unimportant if considering the higher cost involved in purchasing both the electrical motor and the electronic motor driver. The results show that an overall percentage saving of specific energy consumption of 3.1% and 4.0% can be achieved with a slight (7.5 kW) and a medium (11 kW) oversizing of the electric motor, respectively.
Multimodal Affective Communication Analysis: Fusing Speech Emotion and Text Sentiment Using Machine Learning
Authors: Resende Faria Diego , Weinberg Abraham Itzhak , Ayrosa Pedro Paulo
Published: July 2024

Affective communication, encompassing verbal and non-verbal cues, is crucial for understanding human interactions. This study introduces a novel framework for enhancing emotional understanding by fusing speech emotion recognition (SER) and sentiment analysis (SA). We leverage diverse features and both classical and deep learning models, including Gaussian naive Bayes (GNB), support vector machines (SVMs), random forests (RFs), multilayer perceptron (MLP), and a 1D convolutional neural network (1D-CNN), to accurately discern and categorize emotions in speech. We further extract text sentiment from speech-to-text conversion, analyzing it using pre-trained models like bidirectional encoder representations from transformers (BERT), generative pre-trained transformer 2 (GPT-2), and logistic regression (LR). To improve individual model performance for both SER and SA, we employ an extended dynamic Bayesian mixture model (DBMM) ensemble classifier. Our most significant contribution is the development of a novel two-layered DBMM (2L-DBMM) for multimodal fusion. This model effectively integrates speech emotion and text sentiment, enabling the classification of more nuanced, second-level emotional states. Evaluating our framework on the EmoUERJ (Portuguese) and ESD (English) datasets, the extended DBMM achieves accuracy rates of 96% and 98% for SER, 85% and 95% for SA, and 96% and 98% for combined emotion classification using the 2L-DBMM, respectively. Our findings demonstrate the superior performance of the extended DBMM for individual modalities compared to individual classifiers and the 2L-DBMM for merging different modalities, highlighting the value of ensemble methods and multimodal fusion in affective communication analysis. The results underscore the potential of our approach in enhancing emotional understanding with broad applications in fields like mental health assessment, human–robot interaction, and cross-cultural communication.
Band-Pass and Band-Stop Filter Frequency Selective Surface with Harmonic Suppression
Authors: Hwang Dong , Jeong Taeyong , Kim Jun , Hwang Keum
Published: July 2024

This study investigated a frequency-selective surface (FSS) that is used to suppress harmonics affecting the performance and accuracy of radar systems. One side of the FSS features a metal grid structure, and when converted into an equivalent circuit model, it exhibits the characteristics of a band-pass filter with an L and C parallel structure. The other side of the FSS features a metallic loop structure and when represented as an equivalent circuit model, it exhibits the characteristics of a band-stop filter with an L and C series structure. The reflection coefficient (S11) and transmission coefficient (S21) of the FSS designed based on theory are compared using a CST studio suite and Keysight’s Advanced Design System. In addition, the transmission coefficients are verified through actual measurements, wherein the measured transmission coefficient is −0.1 dB at 3.0 GHz and approximately −50 dB at the harmonic frequency of 6.0 GHz. The designed FSS is attached to an actual radar system, and the 2D radiation pattern and maximum gain are measured during steering in boresight, azimuth (30∘) and elevation (30∘) directions. At 3.0 GHz, the maximum gain in boresight is 17.25 dB without the FSS and 17.12 dB with the FSS. At 6.0 GHz, the maximum gain is 12.79 dB without the FSS and 2.69 dB with the FSS. At 3.0 GHz, the maximum gain during azimuth steering is 16.13 dB without the FSS and 16.68 dB with the FSS, and the maximum gain during elevation steering is 15.74 dB without the FSS and 15.90 dB with the FSS.
Bioactives of the Freshwater Aquatic Plants, Nelumbo nucifera and Lemna minor, for Functional Foods, Cosmetics and Pharmaceutical Applications, with Antioxidant, Anti-Inflammatory and Antithrombotic Health Promoting Properties
Authors: Seferli Marina , Kotanidou Christina , Lefkaki Melina , Adamantidi Theodora , Panoutsopoulou Ellie , Finos Marios Argyrios , Krey Grigorios , Kamidis Nikolaos , Stamatis Nikolaos , Anastasiadou Chryssa , Tsoupras Alexandros
Published: July 2024

Despite significant progress, certain inflammation related to chronic disorders, including cardiovascular diseases (CVD) and cancer, still present high mortality rates. Thus, further study is needed to address such pathologies more appropriately. Apart from classic therapeutics, natural bioactives with less toxic side effects have gained attention, including those with potential pharmaceutical properties derived from several plants. Within this article, the potential utilization of freshwater aquatic plants as sources of bioactives with antithrombotic, anti-inflammatory and antioxidant properties is outlined. Emphasis is given to a well-established aquatic plant with known but not fully clarified and overviewed bio-functional and pharmaceutical properties, the Chinese lotus (Nelumbo nucifera), as well as to a so far neglected aquatic plant, Lemna minor, which has not yet been thoroughly reviewed for such applications. The latent usually grows naturally in large numbers at the surface of stored water basins of retrieved water from the last stages of wastewater treatment facilities. The continuous growth of this aquatic plant in such conditions further suggests that it can be a sustainable source of natural bioactives, if appropriately valorized, with an economic benefit and in a friendly environmental approach. The abundant content of both freshwater aquatic plants in bioactive components with potent antioxidant, anti-inflammatory and antithrombotic activities is thoroughly outlined, while their applications as functional ingredients in several functional products (functional foods, cosmetics and pharmaceuticals) are also discussed. The outlined outcomes urge further study of both aquatic plants and especially of Lemna spp. to fully elucidate their potential as alternative sustainable sources of bioactive ingredients for functional foods, supplements, nutraceuticals, nutricosmetics, cosmeceuticals, cosmetics and pharmaceutical products with health-promoting properties against inflammation and thrombosis related manifestations and their associated chronic disorders.
Challenges in the Guidance, Navigation and Control of Autonomous and Transport Vehicles
Authors: Horri Nadjim , Holderbaum William , Giulietti Fabrizio
Published: July 2024

In recent years, autonomous and transportation vehicles have been evolving towards more electric or hybrid electric systems, with new challenges arising in their guidance, navigation and control (GNC) systems [...]
The Effect of Additional Silane Pre-Treatment on the Microtensile Bond Strength of Resin-Based Composite Post-and-Core Build-Up Material
Authors: Wu Chia-Ying , Nakamura Keigo , Miyashita-Kobayashi Aya , Haruyama Akiko , Yokoi Yukiko , Kuroiwa Akihiro , Yoshinari Nobuo , Kameyama Atsushi
Published: July 2024

The aim of this study was to evaluate the effect of additional silane treatment on the immediate and aged microtensile bond strength (µTBS) between resin-based composite (RBC) post-and-core build-up material and an RBC CAD/CAM block. Twelve sample blocks (12 mm × 14 mm × 9 mm) were prepared using RBC post-and-core build-up material and were divided into six groups: Kerr Silane Primer (Sil) + OptiBond eXTRa Universal adhesive (EXA); OptiBond eXTRa Universal Primer (EXP) + EXA; Sil + OptiBond Universal (OBU); OBU; Sil + OptiBond Solo Plus (OSP); and OSP. Each treated sample was luted to a CAD/CAM block using an NX3 Nexus Third-Generation instrument. After storage in 37 °C water for 1 week, microspecimens were fabricated, and μTBS was tested immediately or after further immersion in water at 37 °C for 6 months. The failure mode of each specimen was determined using stereomicroscopy and scanning electron microscopy. For the immediate groups, no significant difference in µTBS was found between specimens with or without silane treatment for each adhesive (p > 0.05). For the aged groups, silane treatment significantly decreased µTBS for OptiBond eXTRa Universal (p < 0.05). Our findings indicate that additional silane treatment prior to the application of adhesive did not enhance µTBS.
XAI-Based Clinical Decision Support Systems: A Systematic Review
Authors: Kim Se Young , Kim Dae Ho , Kim Min Ji , Ko Hyo Jin , Jeong Ok Ran
Published: July 2024

With increasing electronic medical data and the development of artificial intelligence, clinical decision support systems (CDSSs) assist clinicians in diagnosis and prescription. Traditional knowledge-based CDSSs follow an accumulated medical knowledgebase and a predefined rule system, which clarifies the decision-making process; however, maintenance cost issues exist in the medical data quality control and standardization processes. Non-knowledge-based CDSSs utilize vast amounts of data and algorithms to effectively make decisions; however, the deep learning black-box problem causes unreliable results. EXplainable Artificial Intelligence (XAI)-based CDSSs provide valid rationales and explainable results. These systems ensure trustworthiness and transparency by showing the recommendation and prediction result process using explainable techniques. However, existing systems have limitations, such as the scope of data utilization and the lack of explanatory power of AI models. This study proposes a new XAI-based CDSS framework to address these issues; introduces resources, datasets, and models that can be utilized; and provides a foundation model to support decision-making in various disease domains. Finally, we propose future directions for CDSS technology and highlight societal issues that need to be addressed to emphasize the potential of CDSSs in the future.
Environmental Analysis of the Impact of Changing Shrink Film in the Mass Bottle Packaging Process
Authors: Walichnowska Patrycja , Mazurkiewicz Adam , Martínez Valle José Miguel , Polishchuk Oleh
Published: July 2024

The aim of this study was to assess the environmental impact of using recycled polyethylene film for shrink-wrapping bottles. For this aim, film properties were tested and the harmfulness of the packaging process was simulated for film made from virgin and recycled material. For the recycled film, the results showed an increase of 14.7% in impact resistance, a change from −21.6 to +94.3% in tear resistance, and a decrease of up to 45.4% in tensile strength in dependence on the test direction. Using differential scanning calorimetry (DSC), the changes in the properties of the two types of film with temperature changes were evaluated. DSC analysis showed that recycled film has a 1.94 °C lower glass transition temperature and a 1.85 °C lower melting point in comparison to polyethylene film. This can reduce the temperature of the packaging process and lead to energy savings. A study conducted with SimaPro 9.3 software showed that a change in films made of virgin raw material to recycled films reduces the negative impact on the environment from 68.5 to 11.5%. The change also reduces resource consumption by about 80 percent. The results of conducted tests and simulations showed that using recycled film for bottle packaging allows reducing the negative environmental impact of examined process, especially in terms of resource consumption and energy savings.
Iron Removal from Low-Grade Pyrophyllite Ore by Microwave Irradiation and Dry Magnetic Separation
Authors: Ahmed Hussin A. M. , Khairy Nesren , Ali Maaz A.
Published: July 2024

Owing to its unique properties, pyrophyllite is an economical alternative to many minerals in different applications. The presence of iron-bearing minerals in Saudi pyrophyllite hampers its industrial uses. The aim of this study was to examine the removal of iron from Saudi low-grade pyrophyllite ore using two approaches. The first approach involves dry high-intensity magnetic separation, whereas the second approach involves microwave pretreatment of the ore before dry magnetic separation. For the first approach, the studied operating parameters were roll speed; feed rate, field intensity, and feed particle size. For the alternative approach, microwave treatment followed by dry magnetic separation, the microwave irradiation time and the magnetic field intensity were studied. The results show that the combined microwave treatment and dry separation method could provide high-purity pyrophyllite for filler industries. Microwave irradiation for 30 min was optimal to change impurity phases (i.e., pyrite, hematite) into ferromagnetic phases in microwave-treated pyrophyllite samples. At a magnetic field intensity of 2000 Gauss, the 30 min microwave-irradiated pyrophyllite sample achieved an iron recovery of 11.2% in non-magnetic fractions, with a removal efficiency of 89% with an alumina recovery of 91.31%.
Correction of Aero-Optical Effect with Blow–Suction Control for Hypersonic Vehicles
Authors: Li Yubo , Deng Shuanghou , Xue Caijun , Xiao Tianhang
Published: July 2024

High-speed turbulence induces significant aero-optical effects that severely disrupt the functionality of imaging systems of hypersonic vehicles. In this study, the aero-optical correction of various jet cooling modes is investigated using a Terminal High Altitude Area Defense (THAAD)-like seeker model and the imaging impact of high-speed flow field and flow control on the optical window is analyzed by the Delayed Detached Eddy Simulation (DDES) method. The findings reveal that a jet mode parallel to the window exhibits better cooling effectiveness compared to a perpendicular jet mode along the body axis; however, it introduces additional wavefront distortion, leading to degraded imaging quality. Although micro-vortex generators (MVGs) can reduce density fluctuations near the window from a refractive index perspective, they do not effectively mitigate wavefront distortion or improve window cooling efficiency. Finally, incorporating suction control, a comprehensive flow control solution, significantly improves the flow field structure near the window, resulting in a more uniform temperature distribution and reduced wavefront distortion. Applying this flow control method results in a 14.7% reduction in wavefront distortion at 3 Ma and an approximately 20% maximum value reduction at 5 Ma. This study proposes a novel and comprehensive flow control method to effectively mitigate the aero-optical effect in hypersonic flows, providing a new avenue for subsequent researchers in this field.
Interleaved Modified SEPIC Converters with Soft Switching and High Power Factor for LED Lighting Appliance
Authors: Cheng Hung-Liang , Cheng Chun-An , Chang Chien-Hsuan , Chang En-Chih , Hwang Lain-Chyr , Hung Yi-Chan
Published: July 2024

A novel ac/dc LED driver with power factor correction and soft-switching functions is proposed. The circuit topology mainly consists of two modified single-ended primary inductance converters (SEPIC) with interleaved operation. The first half stage of SEPIC operates like a boost converter and the second half stage operates like a buck–boost converter. Each boost converter is designed to operate in discontinuous current mode (DCM) to function as a power factor corrector (PFC). The two buck–boost converters that share a commonly coupled inductor are designed to operate at near boundary conduction mode (BCM). Without using any active clamping circuit, auxiliary switch or snubber circuit, the active switches can achieve zero-voltage switching on, and all diodes achieve zero-current switching off. First, operation modes in steady state are analyzed, and the mathematical equations for design component parameters are derived. Finally, a prototype circuit of 180 W rated power was built and tested. Experimental results show satisfactory performance of the proposed circuit.
Research on Pupil Center Localization Detection Algorithm with Improved YOLOv8
Authors: Xue Kejuan , Wang Jinsong , Wang Hao
Published: July 2024

Addressing issues such as low localization accuracy, poor robustness, and long average localization time in pupil center localization algorithms, an improved YOLOv8 network-based pupil center localization algorithm is proposed. This algorithm incorporates a dual attention mechanism into the YOLOv8n backbone network, which simultaneously attends to global contextual information of input data while reducing dependence on specific regions. This improves the problem of difficult pupil localization detection due to occlusions such as eyelashes and eyelids, enhancing the model’s robustness. Additionally, atrous convolutions are introduced in the encoding section, which reduce the network model while improving the model’s detection speed. The use of the Focaler-IoU loss function, by focusing on different regression samples, can improve the performance of detectors in various detection tasks. The performance of the improved Yolov8n algorithm was 0.99971, 1, 0.99611, and 0.96495 in precision, recall, MAP50, and mAP50-95, respectively. Moreover, the improved YOLOv8n algorithm reduced the model parameters by 7.18% and the computational complexity by 10.06%, while enhancing the environmental anti-interference ability and robustness, and shortening the localization time, improving real-time detection.
Effect of Genotype × Environment Interactions on the Yield and Stability of Sugarcane Varieties in Ecuador: GGE Biplot Analysis by Location and Year
Authors: Torres-Ordoñez Luis Henry , Valenzuela-Cobos Juan Diego , Guevara-Viejó Fabricio , Galindo-Villardón Purificación , Vicente-Galindo Purificación
Published: July 2024

Yield and stability are desirable characteristics that crops need to have high agronomic value; sugarcane stands out globally due to its diverse range of products and by-products. However, genotype-environment (G × E) interactions can affect the overall performance of a crop. The objective of this study is to identify genotypes with the highest yield and stability, as well as to understand their independent and interactive effects. A collection of 10 sugarcane varieties was evaluated, including Colombian, Dominican, Ecuadorian lines, and a group of clones planted across five different locations from 2018 to 2020. A two-way ANOVA along with the GGE biplot technique were used to analyze yield and stability. The ANOVA model shows highly significant effects in all cases (p < 0.001) except for the genotype by year and sector interaction (G × Y × S); however, the decomposition by sectors reveals a significant triple interaction in sector 04 (p < 0.05). The GGE biplot model accounted for up to 74.77% of the total variance explained in its PC1 and PC2 components. It also highlighted the group of clones as having the highest yield and environmental instability, and the Ecuadorian varieties EC-07 and EC-08 as having the best yield-stability relationship. We conclude that the combined results of the ANOVA and GGE biplot models provide a more synergistic and effective evaluation of sugarcane varieties, offering theoretical and practical bases for decision-making in the selection of specific varieties.
A Verifiable Multi-Secret Sharing Scheme for Hierarchical Access Structure
Authors: Alam Irfan , Alali Amal S. , Ali Shakir , Asri Muhammad S. M.
Published: July 2024

Sharing confidential information is a critical concern in today’s world. Secret sharing schemes facilitate the sharing of secrets in a way that ensures only authorized participants (shareholders) can access the secret using their allocated shares. Hierarchical secret sharing schemes (HSSSs) build upon Shamir’s scheme by organizing participants into different levels based on priority. Within HSSS, participants at each level can reconstruct the secret if a specified number, denoted as the threshold value (t), or more of them are present. Each level has a predetermined threshold value. If the number of participants falls below the threshold at any level, higher-level participants must be involved in reconstructing the secret at lower levels. Our paper proposes schemes that implement hierarchical access structures and enable the sharing of multiple secrets. Additionally, our proposed scheme includes share verification. We have analyzed potential attacks and demonstrated the scheme’s resistance against them. Through security analysis and comparison with existing schemes, we highlight the novelty and superiority of our proposed approach, contributing to advancements in secure information-sharing practices.
Transfer Learning for Logistic Regression with Differential Privacy
Authors: Hou Yiming , Song Yunquan
Published: July 2024

Transfer learning, as a machine learning approach enhancing model generalization across different domains, has extensive applications in various fields. However, the risk of privacy leakage remains a crucial consideration during the transfer learning process. Differential privacy, with its rigorous mathematical foundation, has been proven to offer consistent and robust privacy protection. This study delves into the logistic regression transfer learning problem supported by differential privacy. In cases where transferable sources are known, we propose a two-step transfer learning algorithm. For scenarios with unknown transferable sources, a non-algorithmic, cross-validation-based transferable source detection method is introduced, to mitigate adverse effects from non-informative sources. The effectiveness of the proposed algorithm is validated through simulations and experiments with real-world data.
Influence of Vinylene Carbonate and Fluoroethylene Carbonate on Open Circuit and Floating SoC Calendar Aging of Lithium-Ion Batteries
Authors: Geuder Karsten , Klick Sebastian , Finster Philipp , Graff Karl Martin , Winter Martin , Nowak Sascha , Seifert Hans Jürgen , Ziebert Carlos
Published: July 2024

The purpose of this study was to investigate the calendar aging of lithium-ion batteries by using both open circuit and floating current measurements. Existing degradation studies usually focus on commercial cells. The initial electrolyte composition and formation protocol for these cells is often unknown. This study investigates the role of electrolyte additives, specifically, vinylene carbonate (VC) and fluoroethylene carbonate (FEC), in the aging process of lithium-ion batteries. The results showed that self-discharge plays a significant role in determining the severity of aging for cells without additives. Interestingly, the aging was less severe for the cells without additives as they deviated more from their original storage state of charge. It was also observed that the addition of VC and FEC had an effect on the formation and stability of the solid electrolyte interphase (SEI) layer on the surface of the carbonaceous anode. By gaining a better understanding of the aging processes and the effects of different electrolyte additives, we can improve the safety and durability of lithium-ion batteries, which is critical for their widespread adoption in various applications.
Time-Series Feature Extraction by Return Map Analysis and Its Application to Bearing-Fault Detection
Authors: Ponomareva Veronika , Druzhina Olga , Logunov Oleg , Rudnitskaya Anna , Bobrova Yulia , Andreev Valery , Karimov Timur
Published: July 2024

Developing new features for time-series characterization is a current challenge in data science and machine learning. In this paper, we propose a new metric based on a simple and efficient algorithm, namely, the return map. The return map analysis is well established in the field of non-linear dynamics, in particular, for fitting the parameters of a chaotic system from a waveform, or to attack a chaotic communication channel. We show that our metrics work well for both non-linear dynamics and time-series feature extraction problems in the field of machine learning. In an experiment aiming to classify vibration signals of normal and damaged bearings, we compare our method with two other methods that reported to have excellent accuracy, based on entropy and statistical feature distribution, respectively. We show that our method achieves higher accuracy with almost the lowest time costs, which was confirmed in experiments with two different datasets containing three main classes of bearings: normal, with inner race faults, and with outer race faults, having different damage origins and recorded in various conditions. In particular, for the dataset supplied by Case Western Reserve University, our method reached an accuracy of 100% at signals of 5000 sample points length, with a total time of 0.4 s required for feature estimation, while the entropy-based method reached an accuracy of 95% with a time of 100 s, and a statistical feature distribution method reached an accuracy of 93% with a total time of 1.9 s. Results show that the developed method is better suited to real-time bearing condition monitoring applications than most of the methods reported to date.
A Meta-Survey on Intelligent Energy-Efficient Buildings
Authors: Islam Md Babul , Guerrieri Antonio , Gravina Raffaele , Fortino Giancarlo
Published: July 2024

The rise of the Internet of Things (IoT) has enabled the development of smart cities, intelligent buildings, and advanced industrial ecosystems. When the IoT is matched with machine learning (ML), the advantages of the resulting enhanced environments can span, for example, from energy optimization to security improvement and comfort enhancement. Together, IoT and ML technologies are widely used in smart buildings, in particular, to reduce energy consumption and create Intelligent Energy-Efficient Buildings (IEEBs). In IEEBs, ML models are typically used to analyze and predict various factors such as temperature, humidity, light, occupancy, and human behavior with the aim of optimizing building systems. In the literature, many review papers have been presented so far in the field of IEEBs. Such papers mostly focus on specific subfields of ML or on a limited number of papers. This paper presents a systematic meta-survey, i.e., a review of review articles, that compares the state of the art in the field of IEEBs using the Prisma approach. In more detail, our meta-survey aims to give a broader view, with respect to the already published surveys, of the state-of-the-art in the IEEB field, investigating the use of supervised, unsupervised, semi-supervised, and self-supervised models in a variety of IEEB-based scenarios. Moreover, our paper aims to compare the already published surveys by answering five important research questions about IEEB definitions, architectures, methods/models used, datasets and real implementations utilized, and main challenges/research directions defined. This meta-survey provides insights that are useful both for newcomers to the field and for researchers who want to learn more about the methodologies and technologies used for IEEBs’ design and implementation.
Peritoneal Infusion of Oxygen Microbubbles Alters the Metabolomic Profile of the Lung and Spleen in Acute Hypoxic Exposure
Authors: Lisk Christina , Fan Alex , Cendali Francesca I. , Kakiuchi Kenta , Swindle Delaney , Pak David I. , Tolson Robert , Grier Abby , Buesing Keely , Zaeske Seth , D’Alessandro Angelo , Borden Mark A. , Irwin David C.
Published: July 2024

Administration of oxygen microbubbles (OMBs) has been shown to increase oxygen and decrease carbon dioxide in systemic circulation, as well as reduce lung inflammation and promote survival in preclinical models of hypoxia caused by lung injury. However, their impact on microenvironmental oxygenation remains unexplored. Herein, we investigated the effects of intraperitoneal administration of OMBs in anesthetized rats exposed to hypoxic ventilation (FiO2 = 0.14). Blood oxygenation and hemodynamics were evaluated over a 2 h time frame, and then organ and tissue samples were collected for hypoxic and metabolic analyses. Data showed that OMBs improved blood SaO2 (~14%) and alleviated tissue hypoxia within the microenvironment of the kidney and intestine at 2 h of hypoxia. Metabolomic analysis revealed OMBs induced metabolic differences in the cecum, liver, kidney, heart, red blood cells and plasma. Within the spleen and lung, principal component analysis showed a metabolic phenotype more comparable to the normoxic group than the hypoxic group. In the spleen, this shift was characterized by reduced levels of fatty acids and 2-hydroxygluterate, alongside increased expression of antioxidant enzymes such as glutathione and hypoxanthine. Interestingly, there was also a shuttle effect within the metabolism of the spleen from the tricarboxylic acid cycle to the glycolysis and pentose phosphate pathways. In the lung, metabolomic analysis revealed upregulation of phosphatidylethanolamine and phosphatidylcholine synthesis, indicating a potential indirect mechanism through which OMB administration may improve lung surfactant secretion and prevent alveolar collapse. In addition, cell-protective purine salvage was increased within the lung. In summary, oxygenation with intraperitoneal OMBs improves systemic blood and local tissue oxygenation, thereby shifting metabolomic profiles of the lung and spleen toward a healthier normoxic state.
An Innovative Thermal Imaging Prototype for Precise Breast Cancer Detection: Integrating Compression Techniques and Classification Methods
Authors: Ahmed Khaled S. , Sherif Fayroz F. , Abdallah Mohamed S. , Cho Young-Im , ElMetwally Shereen M.
Published: July 2024

Breast cancer detection at an early stage is crucial for improving patient survival rates. This work introduces an innovative thermal imaging prototype that incorporates compression techniques inspired by mammography equipment. The prototype offers a radiation-free and precise cancer diagnosis. By integrating compression and illumination methods, thermal picture quality has increased, and the accuracy of classification has improved. Essential components of the suggested thermography device include an equipment body, plates, motors, pressure sensors, light sources, and a thermal camera. We created a 3D model of the gadget using the SolidWorks software 2020 package. Furthermore, the classification research employed both cancer and normal images from the experimental results to validate the efficacy of the suggested system. We employed preprocessing and segmentation methods on the obtained dataset. We successfully categorized the thermal pictures using various classifiers and examined their performance. The logistic regression model showed excellent performance, achieving an accuracy of 0.976, F1 score of 0.977, precision of 1.000, and recall of 0.995. This indicates a high level of accuracy in correctly classifying thermal abnormalities associated with breast cancer. The proposed prototype serves as a highly effective tool for conducting initial investigations into breast cancer detection, offering potential advancements in early-stage diagnosis, and improving patient survival rates.
An Automated Diagnosis Method for Lung Cancer Target Detection and Subtype Classification-Based CT Scans
Authors: Wang Lingfei , Zhang Chenghao , Zhang Yu , Li Jin
Published: July 2024

When dealing with small targets in lung cancer detection, the YOLO V8 algorithm may encounter false positives and misses. To address this issue, this study proposes an enhanced YOLO V8 detection model. The model integrates a large separable kernel attention mechanism into the C2f module to expand the information retrieval range, strengthens the extraction of lung cancer features in the Backbone section, and achieves effective interaction between multi-scale features in the Neck section, thereby enhancing feature representation and robustness. Additionally, depth-wise convolution and Coordinate Attention mechanisms are embedded in the Fast Spatial Pyramid Pooling module to reduce feature loss and improve detection accuracy. This study introduces a Minimum Point Distance-based IOU loss to enhance correlation between predicted and ground truth bounding boxes, improving adaptability and accuracy in small target detection. Experimental validation demonstrates that the improved network outperforms other mainstream detection networks in terms of average precision values and surpasses other classification networks in terms of accuracy. These findings validate the outstanding performance of the enhanced model in the localization and recognition aspects of lung cancer auxiliary diagnosis.
Structural Magnetic Resonance Imaging-Based Surface Morphometry Analysis of Pediatric Down Syndrome
Authors: Levman Jacob , McCann Bernadette , Baumer Nicole , Lam Melanie Y. , Shiohama Tadashi , Cogger Liam , MacDonald Allissa , Takahashi Emi
Published: July 2024

Down syndrome (DS) is a genetic disorder characterized by intellectual disability whose etiology includes an additional partial or full copy of chromosome 21. Brain surface morphometry analyses can potentially assist in providing a better understanding of structural brain differences, and may help characterize DS-specific neurodevelopment. We performed a retrospective surface morphometry study of 73 magnetic resonance imaging (MRI) examinations of DS patients (aged 1 day to 22 years) and compared them to a large cohort of 993 brain MRI examinations of neurotypical participants, aged 1 day to 32 years. Surface curvature measurements, absolute surface area measurements, and surface areas as a percentage of total brain surface area (%TBSA) were extracted from each brain region in each examination. Results demonstrate broad reductions in surface area and abnormalities of surface curvature measurements across the brain in DS. After adjusting our regional surface area measurements as %TBSA, abnormally increased presentation in DS relative to neurotypical controls was observed in the left precentral, bilateral entorhinal, left parahippocampal, and bilateral perirhinal cortices, as well as Brodmann’s area 44 (left), and the right temporal pole. Findings suggest the presence of developmental abnormalities of regional %TBSA in DS that can be characterized from clinical MRI examinations.
Increasing Endoglin Deletion in Endothelial Cells Exacerbates the Severity of Brain Arteriovenous Malformation in Mouse
Authors: Shabani Zahra , Do Prado Leandro Barbosa , Zhang Rui , Zhu Wan , Shaligram Sonali S. , Yadav Alka , Wang Calvin , Su Hua
Published: July 2024

Endoglin (ENG) mutation causes type 1 hereditary hemorrhagic telangiectasia (HHT1). HHT1 patients have arteriovenous malformations (AVMs) in multiple organs, including the brain. In mice, Eng deletion induced by R26RCreER or SM22αCre leads to AVM development in the brain and other organs. We hypothesized that an increase in Eng- negative ECs will enhance AVM severity. To increase EC Eng deletion, we used a codon-improved cre (icre), which is more potent in recombination of the floxed alleles than the wild-type (WT) cre. R26RCreER;Engf/f mice that have a Rosa promoter driving and tamoxifen (TM)-inducible WT cre expression globally, and PdgfbiCreER;Engf/f mice that have a Pdgfb promoter driving and TM-inducible icre expression in ECs were treated with three intra-peritoneal injections of TM (2.5 mg/25 g of body weight) to delete Eng globally or in the ECs. AAV-VEGF was stereotactically injected into the brain to induce brain focal angiogenesis and brain AVM. We found that icre caused more Eng deletion in the brain, indicated by a lower level of Eng proteins (p < 0.001) and fewer Eng-positive ECs (p = 0.01) than mice with WT cre. Mice with icre-mediated Eng deletion have more abnormal vessels (p = 0.02), CD68+ macrophages (p = 0.002), and hemorrhage (p = 0.04) and less vascular pericyte and smooth muscle coverage than mice with WT cre. In addition, arteriovenous shunts were detected in the intestines of icre mice, a phenotype that has not been detected in WT cre mice before. RNA-seq analysis showed that 8 out of the 10 top upregulated pathways identified by gene ontology (GO) analysis are related to inflammation. Therefore, the increase in Eng deletion in ECs exacerbates AVM severity, which is associated with enhanced inflammation. Strategies that can reduce Eng-negative ECs could be used to develop new therapies to reduce AVM severity for HHT1 patients.
Analysis of Intervertebral Disc Degeneration Induced by Endplate Drilling or Needle Puncture in Complement C6-Sufficient and C6-Deficient Rabbits
Authors: Kuhn Amelie , Huber-Lang Markus , Weckbach Sebastian , Riegger Jana , Teixeira Graciosa Q. , Rasche Volker , Fiedler Jörg , Neidlinger-Wilke Cornelia , Brenner Rolf E.
Published: July 2024

Previous studies indicate an implication of the terminal complement complex (TCC) in disc degeneration (DD). To investigate the functional role of TCC in trauma-induced DD in vivo, the model of endplate (EP) drilling was first applied in rabbits using a C6-deficient rabbit strain in which no TCC formation was possible. In parallel the model of needle puncture was investigated. Using a minimally invasive surgical intervention, lumbar rabbit intervertebral discs (IVDs) were treated with EP drilling or needle puncture. Degenerative effects of both surgical interventions were assessed by Pfirrmann grading and T2 quantification of the IVDs based on high-resolution MRI (11.7 T), as well as radiographic determination of disc height index. Pfirrmann grading indicated significant degenerative effects after EP drilling. Contrary to our assumption, no evidence was found that the absence of TCC formation in C6-deficient rabbits reduces the development of DD compared to C6-sufficient animals. EP drilling was proven to be suitable for application in rabbits. However, results of the present study do not provide clear evidence of a central functional role of TCC within DD and suggest that TCC deposition in DD patients may be primarily considered as a marker of complement activation during DD progression.
KIF1A, R1457Q, and P1688L Mutations Induce Protein Abnormal Aggregation and Autophagy Impairment in iPSC-Derived Motor Neurons
Authors: Zhao Mingri , Wang Junling , Liu Miao , Xu Yaoyao , Huang Jiali , Zhang Yiti , He Jianfeng , Gu Ao , Liu Mujun , Liu Xionghao
Published: July 2024

Mutations in the C-terminal of KIF1A (Kinesin family member 1A) may lead to amyotrophic lateral sclerosis (ALS) through unknown mechanisms that are not yet understood. Using iPSC reprogramming technology and motor neuron differentiation techniques, we generated iPSCs from a healthy donor and two ALS patients with KIF1A mutations (R1457Q and P1688L) and differentiated them into spinal motor neurons (iPSC-MN) to investigate KIF1A-related ALS pathology. Our in vitro iPSC-iMN model faithfully recapitulated specific aspects of the disease, such as neurite fragmentation. Through this model, we observed that these mutations led to KIF1A aggregation at the proximal axon of motor neurons and abnormal accumulation of its transport cargo, LAMP1, resulting in autophagy dysfunction and cell death. RNAseq analysis also indicated that the functions of the extracellular matrix, structure, and cell adhesion were significantly disturbed. Notably, using rapamycin during motor neuron differentiation can effectively prevent motor neuron death.
SNHG1: Redefining the Landscape of Hepatocellular Carcinoma through Long Noncoding RNAs
Authors: Fonseca Tiago S. , Martins Rui Miguel , Rolo Anabela P. , Palmeira Carlos M.
Published: July 2024

Hepatocellular carcinoma (HCC) represents a global health concern, ranking as the sixth most common malignancy worldwide and the third leading cause of cancer-related mortality. Despite advances in research, the diagnosis and prognosis of such malignancy remain challenging. Alpha-fetoprotein, the current serum biomarker used in the management of HCC, has limited sensitivity and specificity, making early detection and effective management more difficult. Thus, new management approaches in diagnosis and prognosis are needed to improve the outcome and survival of HCC patients. SNHG1 is a long noncoding RNA mainly expressed in the cell and cytoplasm of cells and is consistently upregulated in tissues and cell lines of HCC, where it acts as an important regulator of various processes: modulation of p53 activity, sponging of microRNAs with consequent upregulation of their target mRNAs, regulation of fatty acid, iron and glucose metabolism, and interaction with immune cells. The deregulation of these processes results in abnormal cell division, angiogenesis, and apoptosis, thus promoting various aspects of tumorigenesis, including proliferation, invasion, and migration of cells. Clinically, a higher expression of SNHG1 predicts poorer clinical outcomes by significantly correlating with bigger, less differentiated, and more aggressive tumors, more advanced disease stages, and lower overall survival in HCC patients. This article comprehensively summarizes the current understanding of the multifaceted roles of SNHG1 in the pathogenesis of HCC, while also highlighting its clinicopathological correlations, therefore concluding that it has potential as a biomarker in HCC diagnosis and prognosis.