bluuebunny/crossref_metadata_2025 huggingface dataset:
Digital Implant-Supported Restoration Planning Placed in Autologous Graft Using Titanium Implants Produced by Additive Manufacturing
Authors: Louro Rafael Seabra , Moraschini Vittorio , Melhem-Elias Fernando , Sturzinger George Patrick Sotero , Amad Renata Augusto , Shibli Jamil A.
Published: June 2024

This clinical report presents a technique to reconstruct extensively resected mandibles using a combination of autologous bone grafts and additive manufacturing techniques. Mandibular defects, often arising from trauma, tumors, or congenital anomalies, can severely impact both function and aesthetics. Conventional reconstruction methods have their limitations, often resulting in suboptimal outcomes. In these reports, we detail clinical cases where patients with different mandibular defects underwent reconstructive surgery. In each instance, autologous grafts were harvested to ensure the restoration of native bone tissue, while advanced virtual planning techniques were employed for precise graft design and dental implant placement. The patients experienced substantial improvements in masticatory function, speech, and facial aesthetics. Utilizing autologous grafts minimized the risk of rejection and complications associated with foreign materials. The integration of virtual planning precision allowed customized solutions, reducing surgical duration and optimizing implant positioning. These 2 cases underscores the potential of combining autologous grafts with virtual planning precision and dental implants produced by additive manufacturing for mandible reconstruction.
Using the Modified Apical Access Technique to Treat Peri-Implant Mucosa Defects: Description of the Technique and Three-Dimensional Quantitative Measurement of Buccal Augmented Tissue
Authors: Quispe-López Norberto , Marques Tiago , Guadilla Yasmina , Flores-Fraile Javier , Garrido-Martínez Pablo , Montero Javier
Published: June 2024

The importance of augmenting the peri-implant soft- and hard-tissue architecture is now widely accepted. However, while most contemporary research supports this premise, clinicians are encountering peri-implant soft tissue defects with increasing frequency, which they are therefore required to reconstruct. These complications can result from the difficulty of establishing an appropriate diagnosis and treatment plan or from suboptimal clinical situations (implant malposition, insufficient vestibular alveolar bone thickness or inadequate mucosal thickness). In this context, it is the peri-implant soft-tissue phenotype that most influences esthetic and health-related results in the short and long term. This article describes two clinical cases in which a modification of the apical access technique is presented that may be useful in clinical scenarios requiring large gains in mucosal thickness. Use of the modified bilaminar apical access with de-epithelialized free gingival graft technique showed promising results, with a significant increase in mucosal thickness and satisfactory outcomes in esthetics and peri-implant health.
Innovative Approach on Teaching and Learning with Technical Aids for STEM Education at the Primary Level
Authors: Guncaga Jan , Korenova Lilla , Záhorec Ján , Ostradicky Peter
Published: June 2024

Education is a constantly evolving field that encompasses various approaches to teaching and learning. In our paper, we focused on qualitative research conducted with future primary level teachers using a STEAM (Science, Technology, Engineering, Arts and Mathematics) approach. The research involved classroom observation, analysis of the student work, and obtaining interpretations from the students via report protocols and focused interviews. We examined the students’ learning and problem-solving strategies within STEAM-based activities as well as their perspectives on its use in primary education. Students participated in the research activity in two stages. In the first stage, further referred to as Activity 1, they followed a predetermined algorithm, instructions to construct an electronic device. The instructions for this device were developed to serve as a resource for primary education and to prepare the students for the second stage. In the second stage, further known as Activity 2, the students were tasked with creating a new electronic device together with providing the instructions. The new device was required to have a practical application. Following the completion of these activities, we collected and analyzed the procedural reflections and didactic interpretations from students. Within these interpretations, we also sought their opinions on how STEAM projects like these could help develop various aspects of STEAM competencies in children such as technical skills and knowledge, algorithmic thinking, and device architecture as well as mathematical and scientific thinking.
A Virtual Reality Environment Based on Infrared Thermography for the Detection of Multiple Faults in Kinematic Chains
Authors: Alvarado-Hernandez Alvaro Ivan , Checa David , Osornio-Rios Roque A. , Bustillo Andres , Antonino Daviu Jose A.
Published: June 2024

Kinematic chains are crucial in numerous industrial settings, playing a key role in various processes. Over recent years, several methods have been developed to monitor and maintain these systems effectively. One notable method is the analysis of infrared thermal images, which serves as a non-invasive and effective approach for identifying various electromechanical issues. Additionally, Virtual Reality (VR) is a burgeoning technology that, despite its limited use in industrial contexts, offers a cost-effective and accessible solution for the training and education of industrial workers on specialized engineering subjects. Nevertheless, most virtual environments are based on numerical simulations. This paper presents the design and development of a Virtual Reality training module for the detection of fourteen electromechanical fault cases in a kinematic chain. The VR training tool developed is based on actual thermographic data derived from experiments conducted on an authentic kinematic chain. During these experiments, thermal images were captured using an low-cost infrared sensor. The thermographic images were processed by calculating the histogram and fifteen statistical indicators, which served to differentiate fault cases in the VR application. A comprehensive evaluation was carried out with a group of vocational students specialized in electrical and automation installations to determine the effectiveness and practicality of the VR training module.
Hardness Classification Using Cost-Effective Off-the-Shelf Tactile Sensors Inspired by Mechanoreceptors
Authors: Sharma Yash , Ferreira Pedro , Justham Laura
Published: June 2024

Perception is essential for robotic systems, enabling effective interaction with their surroundings through actions such as grasping and touching. Traditionally, this has relied on integrating various sensor systems, including tactile sensors, cameras, and acoustic sensors. This study leverages commercially available tactile sensors for hardness classification, drawing inspiration from the functionality of human mechanoreceptors in recognizing complex object properties during grasping tasks. Unlike previous research using customized sensors, this study focuses on cost-effective, easy-to-install, and readily deployable sensors. The approach employs a qualitative method, using Shore hardness taxonomy to select objects and evaluate the performance of commercial off-the-shelf (COTS) sensors. The analysis includes data from both individual sensors and their combinations analysed using multiple machine learning approaches, and accuracy as the primary evaluation metric was considered. The findings illustrate that increasing the number of classification classes impacts accuracy, achieving 92% in binary classification, 82% in ternary, and 80% in quaternary scenarios. Notably, the performance of commercially available tactile sensors is comparable to those reported in the literature, which range from 50% to 98% accuracy, achieving 92% accuracy with a limited data set. These results highlight the capability of COTS tactile sensors in hardness classification giving accuracy levels of 92%, while being cost-effective and easier to deploy than customized tactile sensors.
High-Capacity Multiple-Input Multiple-Output Communication for Internet-of-Things Applications Using 3D Steering Nolen Beamforming Array
Authors: Zhang Hanxiang , Yan Hao , Liu Powei , Pour Saeed Zolfaghary , Arigong Bayaner
Published: June 2024

In this paper, a novel 2D Nolen beamforming phased array with 3D scanning capability to achieve high channel capacity is presented for multiple-input multiple-output (MIMO) Internet-of-Things (IoT) applications. The proposed 2D beamforming phased array is designed by stacking a fundamental building block consisting of a 3 × 3 tunable Nolen matrix, which applies a small number of phase shifters with a small tunning range and reduces the complexity of the beam-steering control mechanism. Each 3 × 3 tunable Nolen matrix can achieve a full 360° range of progressive phase delay by exciting all three input ports, and nine individual radiation beams can be generated and continuously steered on azimuth and elevation planes by stacking up three tunable Nolen matrix in horizontal and three in vertical to maximize signal-to-noise ratio (SNR) in the corresponding spatial directions. To validate the proposed design, the simulations have been conducted on the circuit network and assessed in a fading channel environment. The simulation results agree well with the theoretical analysis, which demonstrates the capability of the proposed 2D Nolen beamforming phased array to realize high channel capacity in MIMO-enabled IoT communications.
Research on Control Strategy of PMSG-PWM Power Generation System with Tidal Energy
Authors: Ran Yukuan , Qiao Mingzhong , Xia Yihui
Published: June 2024

Aiming at the characteristics of machine-side output instability and the wide-range variation in the tidal energy PMSG-PWM power generation system, we propose a control scheme with a high anti-interference capability applied to the PWM rectifier. The outer voltage loop adopts sliding mode control (SMC) with the variable exponential convergence law based on expanded state observer (ESO). By compensating the perturbation observation value into the sliding mode controller in advance, it improves the robustness and response speed of the system and suppresses the direct current (DC)-side voltage jitter phenomenon. The current inner loop combines model prediction (MP) with direct power control (DPC). By introducing time-delay compensation, it improves the control accuracy, reduces the machine-side harmonic content, and enables the system to maintain unit power factor operation despite external disturbances. Simulation and experiment results show that, under the wide range of PMSG output, the PMSG-PWM power generation system adopting the “ESO_SMC+MDPDC” scheme has a fast dynamic regulation of DC output voltage, obvious vibration suppression effect, low harmonic content of the current on the PMSG side, and fast current tracking speed, which verify the feasibility of applying this system to the field of tidal energy generation.
Contrastive Learning Joint Regularization for Pathological Image Classification with Noisy Labels
Authors: Guo Wenping , Han Gang , Mo Yaling , Zhang Haibo , Fang Jiangxiong , Zhao Xiaoming
Published: June 2024

The annotation of pathological images often introduces label noise, which can lead to overfitting and notably degrade performance. Recent studies have attempted to address this by filtering samples based on the memorization effects of DNNs. However, these methods often require prior knowledge of the noise rate or a small, clean validation subset, which is extremely difficult to obtain in real medical diagnosis processes. To reduce the effect of noisy labels, we propose a novel training strategy that enhances noise robustness without prior conditions. Specifically, our approach includes self-supervised regularization to encourage the model to focus more on the intrinsic connections between images rather than relying solely on labels. Additionally, we employ a historical prediction penalty module to ensure consistency between successive predictions, thereby slowing down the model’s shift from memorizing clean labels to memorizing noisy labels. Furthermore, we design an adaptive separation module to perform implicit sample selection and flip the labels of noisy samples identified by this module and mitigate the impact of noisy labels. Comprehensive evaluations of synthetic and real pathological datasets with varied noise levels confirm that our method outperforms state-of-the-art methods. Notably, our noise handling process does not require any prior conditions. Our method achieves highly competitive performance in low-noise scenarios which aligns with current pathological image noise situations, showcasing its potential for practical clinical applications.
Processing and Integration of Multimodal Image Data Supporting the Detection of Behaviors Related to Reduced Concentration Level of Motor Vehicle Users
Authors: Smoliński Anton , Forczmański Paweł , Nowosielski Adam
Published: June 2024

This paper introduces a comprehensive framework for the detection of behaviors indicative of reduced concentration levels among motor vehicle operators, leveraging multimodal image data. By integrating dedicated deep learning models, our approach systematically analyzes RGB images, depth maps, and thermal imagery to identify driver drowsiness and distraction signs. Our novel contribution includes utilizing state-of-the-art convolutional neural networks (CNNs) and bidirectional long short-term memory (Bi-LSTM) networks for effective feature extraction and classification across diverse distraction scenarios. Additionally, we explore various data fusion techniques, demonstrating their impact on improving detection accuracy. The significance of this work lies in its potential to enhance road safety by providing more reliable and efficient tools for the real-time monitoring of driver attentiveness, thereby reducing the risk of accidents caused by distraction and fatigue. The proposed methods are thoroughly evaluated using a multimodal benchmark dataset, with results showing their substantial capabilities leading to the development of safety-enhancing technologies for vehicular environments. The primary challenge addressed in this study is the detection of driver states not relying on the lighting conditions. Our solution employs multimodal data integration, encompassing RGB, thermal, and depth images, to ensure robust and accurate monitoring regardless of external lighting variations
Research and Application of the Median Filtering Method in Enhancing the Imperceptibility of Perturbations in Adversarial Examples
Authors: He Yiming , Dong Yanhua , Sun Hongyu
Published: June 2024

In the field of object detection, the adversarial attack method based on generative adversarial network efficiently generates adversarial examples, thereby significantly reducing time costs. However, this approach overlooks the imperceptibility of perturbations in adversarial examples, resulting in poor visual performance and insufficient invisibility of the generated adversarial examples. To further enhance the imperceptibility of perturbations in adversarial examples, a method utilizing median filtering is proposed to address these generated perturbations. Experimental evaluations were conducted on the Pascal VOC dataset. The results demonstrate that, compared to the original image, there is an increase of at least 17.2% in the structural similarity index (SSIM) for generated adversarial examples. Additionally, the peak signal-to-noise ratio (PSNR) increases by at least 27.5%, while learned perceptual image patch similarity (LPIPS) decreases by at least 84.6%. These findings indicate that the perturbations in generated adversarial examples are more difficult to detect, with significantly improved imperceptibility and closer resemblance to the original image without compromising their high aggressiveness.
Human-Centric Digitization in Montenegro: Progress through 17 Years of National Independence and Future Trends
Authors: Kara Peter A. , Ognjanovic Ivana , Hölscher Dirk , Šćekić Lazar , Kovacevic Pavle , Mantas John , Šendelj Ramo , Laković Luka , Maksimovic Ana , Rakočević Nada , Roganović Milovan , Reich Christoph , Simon Aniko , Bokor Laszlo
Published: June 2024

Montenegro restored its national independence in 2006, and in the 17 years since then, the country has made significant progress in digital transformation, which is especially important for its accession to the European Union. In this paper, this period of 17 years of Montenegrin digital transformation is reviewed. The work aims to provide comprehensive coverage of the digitization processes across all relevant sectors, including healthcare, education, telecommunications and internet, personal and public transportation, tourism, agriculture, energy and sustainability, and public administration. The human-centric nature of digitization is emphasized where relevant, as well as related future trends. This paper also highlights barriers and limitations, such as the lack of consideration for cooperative intelligent transportation systems in national strategies, and user-related issues in digital public administration.
Linear Consensus Protocol Based on Vague Sets and Multi-Attribute Decision-Making Methods
Authors: Yang Dong , Tsai Wei-Tek
Published: June 2024

This paper proposes a linear consensus protocol QuickBFT based on Vague sets and multi-attribute decision-making methods. QuickBFT simplifies the communication process based on the HotStuff protocol, reduces the four-stage communication to three-stage communication, and reduces the consensus delay. Furthermore, we introduce the Vague set and multi-attribute decision-making theory into the consensus protocol and propose a new leader node selection algorithm, which can prevent Byzantine nodes from becoming leader nodes, thereby improving the protocol performance when the leader node is attacked. Experimental results show that the throughput of QuickBFT is slightly higher than that of the HotStuff protocol without Byzantine nodes, and the consensus delay is reduced by 20%. In the presence of Byzantine nodes, the throughput of QuickBFT is increased by 80% compared with the HotStuff protocol, and the consensus delay is reduced by 60%.
Development of an On-Shaft Vibration Sensing Module for Machine Wearable Rotor Imbalance Monitoring
Authors: Meng Zhaozong , Liu Yirou , Chen Yang , Cheng Zhen , Feng Guojin , Zhang Hao , Gao Nan , Zhang Zonghua
Published: June 2024

Rotor imbalance is considered to be one of the main mechanical faults of rotating machinery; which may result in bearing damage and even catastrophic system failure. Recent progress in the Internet of Things (IoT) has promoted the application of novel sensing and computing techniques in the industry, and it is promising to employ novel IoT techniques for imbalance detection to avoid potential failures. Existing sensing techniques suffer from the impact of bearing structure dynamics, loss of accuracy during their lifetime, and security risks introduced by the sensor cabling and supports, which may, in turn, interfere with the machine operations due to inappropriate design and installation. This investigation provides an on-shaft machine wearable vibration sensing technique for effectively monitoring the running state of rotors while minimizing the interference with their operations. In this work, key investigations include the following: (1) theoretical modeling and an analysis of rotor imbalance, and its measurement with an on-shaft micro-electromechanical system (MEMS) accelerometer; (2) the development of a wirelessly powered, cordless on-shaft vibration measurement (OSVM) sensor for unobtrusive sensing of the vibration of rotating shafts; (3) the in-sensor computing design for optimizing the distribution of computing resources and decreasing data transmission. The tests and evaluation of the proposed techniques were conducted with a rotor test rig to demonstrate their feasibility. The presented investigation is a typical example of applying new sensing and computing paradigms to improve the flexibility and convenience of applications, which is a good reference for related investigations and practices.
Special Issue: Emerging E-Health Applications and Medical Information Systems
Authors: Kotsilieris Theodore , Karanikas Haralampos , Tsanas Athanasios , Anagnostopoulos Ioannis
Published: June 2024

Over the last few decades, research on e-Health applications and medical information systems has significantly grown over time due to the need to reinforce health applications’ governance and access potential as well as medical information systems’ interoperability, satisfaction, performance, and usefulness [...]
Tethered Swimming: Historical Notes and Future Prospects
Authors: Morouço Pedro , Tavares Dina , Silva Hugo Plácido
Published: June 2024

Swimming coaches know that a swimmer’s assessment must be specific and ecological. Thus, it is critical to select and employ adequate methodologies. The tethered swimming method can be useful and valid, in addition to being simple to apply. Regular use of this methodology can give coaches tools to intervene with their swimmers and increase performance. The main objective of this manuscript was to analyze the potential for measuring the propulsive forces exerted in water as a biomechanical tool for evaluating and training competitive swimmers. The key results demonstrated that this methodology allows (i) the assessment of upper limb bilateral kinetic asymmetries; (ii) the evaluation of the contribution of the upper and lower limb actions, inferring about the (un)balance between strength and coordination; (iii) the examination of the relationship between the intracyclic variations in speed and force; (iv) the evaluation of the effective application of force to the speed of high-level swimmers. Furthermore, this manuscript suggests advances using mathematical modeling and artificial intelligence (AI) that will provide significant insights into swimming performances. AI developments will promote its integration into sports optimization, and swimming will be no exception.
DNA Hypomethylation Underlies Epigenetic Swapping between AGO1 and AGO1-V2 Isoforms in Tumors
Authors: Fain Jean S. , Wangermez Camille , Loriot Axelle , Denoue Claudia , De Smet Charles
Published: June 2024

Human tumors progress in part by accumulating epigenetic alterations, which include gains and losses of DNA methylation in different parts of the cancer cell genome. Recent work has revealed a link between these two opposite alterations by showing that DNA hypomethylation in tumors can induce the expression of transcripts that overlap downstream gene promoters and thereby induce their hypermethylation. Preliminary in silico evidence prompted us to investigate if this mechanism applies to the locus harboring AGO1, a gene that plays a central role in miRNA biogenesis and RNA interference. Inspection of public RNA-Seq datasets and RT-qPCR experiments show that an alternative transcript starting 13.4 kb upstream of AGO1 (AGO1-V2) is expressed specifically in testicular germ cells, and becomes aberrantly activated in different types of tumors, particularly in tumors of the esophagus, stomach, and lung. This expression pattern classifies AGO1-V2 into the group of “Cancer-Germline” (CG) genes. Analysis of transcriptomic and methylomic datasets provided evidence that transcriptional activation of AGO1-V2 depends on DNA demethylation of its promoter region. Western blot experiments revealed that AGO1-V2 encodes a shortened isoform of AGO1, corresponding to a truncation of 75 aa in the N-terminal domain, and which we therefore referred to as “∆NAGO1”. Interestingly, significant correlations between hypomethylation/activation of AGO1-V2 and hypermethylation/repression of AGO1 were observed upon examination of tumor cell lines and tissue datasets. Overall, our study reveals the existence of a process of interdependent epigenetic alterations in the AGO1 locus, which promotes swapping between two AGO1 protein-coding mRNA isoforms in tumors.
Early Smoke Recognition Algorithm for Forest Fires
Authors: Wang Yue , Piao Yan , Wang Qi , Wang Haowen , Qi Nan , Zhang Hao
Published: June 2024

Forest fires require rapid and precise early smoke detection to minimize damage. This study focuses on employing smoke recognition methods for early warning systems in forest fire detection, identifying smoke as the primary indicator. A significant hurdle lies in the absence of a large-scale dataset for real-world early forest fire smoke detection. Early smoke videos present characteristics such as smoke plumes being small, slow-moving, and/or semi-transparent in color, and include images where there is background interference, posing critical challenges for practical recognition algorithms. To address these issues, this paper introduces a real-world early smoke monitoring video dataset as a foundational resource. The proposed 4D attention-based motion target enhancement network includes an important frame sorting module which adaptively selects essential frame sequences to improve the detection of slow-moving smoke targets. Additionally, a 4D attention-based motion target enhancement module is introduced to mitigate interference from smoke-like objects and enhance recognition of light smoke during the initial stages. Moreover, a high-resolution multi-scale fusion module is presented, incorporating a small target recognition layer to enhance the network’s ability to detect small smoke targets. This research represents a significant advancement in early smoke detection for forest fire surveillance, with practical implications for enhancing fire management.
An Individual Tree Detection and Segmentation Method from TLS and MLS Point Clouds Based on Improved Seed Points
Authors: Chen Qiuji , Luo Hao , Cheng Yan , Xie Mimi , Nan Dandan
Published: June 2024

Individual Tree Detection and Segmentation (ITDS) is a key step in accurately extracting forest structural parameters from LiDAR (Light Detection and Ranging) data. However, most ITDS algorithms face challenges with over-segmentation, under-segmentation, and the omission of small trees in high-density forests. In this study, we developed a bottom–up framework for ITDS based on seed points. The proposed method is based on density-based spatial clustering of applications with noise (DBSCAN) to initially detect the trunks and filter the clusters by a set threshold. Then, the K-Nearest Neighbor (KNN) algorithm is used to reclassify the non-core clustered point cloud after threshold filtering. Furthermore, the Random Sample Consensus (RANSAC) cylinder fitting algorithm is used to correct the trunk detection results. Finally, we calculate the centroid of the trunk point clouds as seed points to achieve individual tree segmentation (ITS). In this paper, we use terrestrial laser scanning (TLS) data from natural forests in Germany and mobile laser scanning (MLS) data from planted forests in China to explore the effects of seed points on the accuracy of ITS methods; we then evaluate the efficiency of the method from three aspects: trunk detection, overall segmentation and small tree segmentation. We show the following: (1) the proposed method addresses the issues of missing segmentation and misrecognition of DBSCAN in trunk detection. Compared to using DBSCAN directly, recall (r), precision (p), and F-score (F) increased by 6.0%, 6.5%, and 0.07, respectively; (2) seed points significantly improved the accuracy of ITS methods; (3) the proposed ITDS framework achieved overall r, p, and F of 95.2%, 97.4%, and 0.96, respectively. This work demonstrates excellent accuracy in high-density forests and is able to accurately segment small trees under tall trees.
Radial Growth of Dahurian Larch (Larix gmelinii) Responses to Climate and Competition
Authors: Li Ziyu , Zhang Quanzhi , Wei Yongkang , Zhao Kunyi
Published: June 2024

The phenomenon of divergent responses in tree ring radial growth to climate change has been observed in the mid- and high-latitude regions of the Northern Hemisphere amidst global warming. However, the stability of the responses of the primary conifer species to climate factors in the mountainous regions of northeastern China remains unclear. Additionally, it is uncertain whether the radial growth in this area consistently responds to different competition indices. In our study, we developed tree ring width chronologies for Dahurian larch (Larix gmelinii) and analysed the radial growth responses to the regional climate from 1980 to 2012. This analysis was conducted by correlating meteorological data from different physiological stages of trees. We also evaluated the reliability of competition indices in predicting tree growth by constructing multiple linear regression models. Furthermore, we explored the relationship between the Basal Area Increment (BAI) of each tree and the competition indices in the sample plot over the previous five years. Our results showed that the temperature during the Non-Structural Carbohydrate (NSC) accumulation period and the NSC consumption period was significantly negatively correlated with the Ring Width Index (RWI) of Dahurian larch. Conversely, the RWI was significantly positively correlated with precipitation during these periods. The Standardised Precipitation Evapotranspiration Index (SPEI) at any period had a significantly positive correlation with the RWI. These findings suggest that future radial growth is likely to continue to be constrained by rising temperatures and water scarcity. Our findings also demonstrated that radial growth was less negatively impacted by competition from nearby trees and more influenced by the initial size of the tree. Compared to models that included other indices, models that included the BAL index as a single explanatory variable demonstrated superior statistical effectiveness. Our research suggests that before evaluating the competitive effects of the neighbourhood on radial growth, it is necessary to consider the stand diversity of tree species and the parameters related to spatial structure when selecting an appropriate competition index.
Effects of Forest Management on Beetle (Coleoptera) Communities in Beech Forests (Fagus sylvatica) in the Apennines of Central Italy (Tuscany)
Authors: Parisi Francesco , Mazziotta Adriano , Chirici Gherardo , D’amico Giovanni , Vangi Elia , Francini Saverio , Travaglini Davide
Published: June 2024

In European mountains most beech forest areas have been managed for timber production. This practice has reduced the availability of biomass for the whole forest-dwelling species assemblage and of deadwood for the saproxylic community. Despite most of Italy’s beech stands having a long history of management, its effects on forest species remain poorly understood. To address this gap, we studied beetle abundance and diversity in five beech-dominated forests with increasing management intensity in central Italy’s Apennines (Tuscany). We assessed if forests with similar management intensity exhibited comparable patterns in beetle diversity, abundance, and commonness versus rarity. Three forests were managed with even-aged shelterwood; one was managed with continuous cover forestry; and one was old-growth. We found 25 beetle families and 195 species across all sites with similar total abundance and richness. However, the representation of the most abundant families varied among sampling sites (ANOVA test: always significant for the total abundance of the most abundant families: F ≥ 2.77, d.f. = 4, p ≤ 0.038). The old-growth forest harbored more threatened species than managed sites. Saproxylic assemblages were similar between the recently cut site and the old-growth forest, and between shelterwood and continuous cover sites. While the similarity gradient among the whole species assemblages reflected geographical proximity, the similarity gradient among saproxylic assemblages reflected the successional proximity among forest management systems. Our research underscores the effects of management on beetle diversity, offering insights for sustainable forestry.
Consolidation and Dehydration Effects of Mildly Degraded Wood from Luoyang Canal No. 1 Ancient Ship
Authors: Yang Weiwei , Ma Wanrong , Liu Xinyou , Wang Wei
Published: June 2024

To ensure the conservation of waterlogged archaeological wood, sustainable, safe, and effective methods must be implemented, with consolidation and dehydration being crucial for long-term preservation to maintain dimensional stability and structural integrity. This study compares the permeability of 45% methyltrimethoxysilane (MTMS) and 45% trehalose solutions to evaluate the dimensional changes, hygroscopicity, and mechanical properties of treated wood. Since the collected samples (from an ancient ship, Luoyang Canal No. 1) were mildly degraded, the drying method had a slight impact on the properties of archaeological wood. Consolidated with trehalose and MTMS agents, the longitudinal compressive strength of the waterlogged wood’s cell walls increased by 66.8% and 23.5%, respectively. Trehalose proved to be more advantageous in filling pores and reducing overall shrinkage, while MTMS significantly reduced the hygroscopicity and surface hydrophilicity of the wood substance. Overall, the MTMS treatment has a smaller effect on the appearance of samples, making it more suitable for the consolidation of mildly degraded waterlogged archaeological wood.
A Forest Fire Smoke Monitoring System Based on a Lightweight Neural Network for Edge Devices
Authors: Huang Jingwen , Yang Huizhou , Liu Yunfei , Liu Han
Published: June 2024

Forest resources are one of the indispensable resources of the earth, which are the basis for the survival and development of human society. With the swift advancements in computer vision and artificial intelligence technology, the utilization of deep learning for smoke detection has achieved remarkable results. However, the existing deep learning models have poor performance in forest scenes and are difficult to deploy because of numerous parameters. Hence, we introduce an optimized forest fire smoke monitoring system for embedded edge devices based on a lightweight deep learning model. The model makes full use of the multi-scale variable attention mechanism of Transformer architecture to strengthen the ability of image feature extraction. Considering the needs of application scenarios, we propose an improved lightweight network model LCNet for feature extraction, which can reduce the parameters and enhance detecting ability. In order to improve running speed, a simple semi-supervised label knowledge distillation scheme is used to enhance the overall detection capability. Finally, we design and implement a forest fire smoke detection system on an embedded device, including the Jetson NX hardware platform, high-definition camera, and detection software system. The lightweight model is transplanted to the embedded edge device to achieve rapid forest fire smoke detection. Also, an asynchronous processing framework is designed to make the system highly available and robust. The improved model reduces three-fourths of the parameters and increases speed by 3.4 times with similar accuracy to the original model. This demonstrates that our system meets the precision demand and detects smoke in time.
Response of Vegetation Coverage to Climate Drivers in the Min-Jiang River Basin along the Eastern Margin of the Tibetan Plat-Eau, 2000–2022
Authors: Liu Shuyuan , Gu Yicheng , Wang Huan , Lin Jin , Zhuo Peng , Ao Tianqi
Published: June 2024

Ecological zonation research is typically conducted in the eastern margin of the Tibetan Plateau. In order to enhance the structure and function of regional ecosystems and monitor their quality, it is crucial to investigate shifts in the coverage of vegetation and the factors that contribute to these shifts. The goal of this study is to assess the spatial and temporal variations in vegetation covering and the partitioning of its drivers in the Minjiang River Basin on the eastern edge of the Tibetan Plateau between 2000 and 2022. The Mann-Kendall test, Hurst index, Theil-Sen median trend analysis, and other techniques were used to look at the features of temporal and geographical changes in regional vegetation coverage as well as potential development trends. The climatic influences leading to the spatial differentiation of vegetation NDVI (Normalized Difference Vegetation Index) were quantified through partial and complex correlation analyses of NDVI with temperature and precipitation. The results of the study showed that (1) the NDVI of the watershed performed well with a stable upward trend, indicating that the vegetation growth was generally good; (2) the spatial analysis showed that the coefficient of variation of the NDVI reached 0.092, which highlighted the stability of the vegetation change in the region; (3) the future development trend of the vegetation coverage in the watershed is low, and there is a certain degree of ecological risk; and (4) the main driver of the vegetation coverage is the non-climate factor, distributed in most parts of the watershed; (5) the climate driver shows localized influence, especially concentrated in the southwest, downstream and part of the upstream areas of the watershed.
Repeated Previous Transarterial Treatments Negatively Affect Survival in Patients with Hepatocellular Carcinoma Receiving Sorafenib
Authors: Stefanini Bernardo , Ielasi Luca , Casadei-Gardini Andrea , Piscopo Michele , Tortora Raffaella , Lani Lorenzo , Pressiani Tiziana , Sansone Vito , Sacco Rodolfo , Magini Giulia , Renzulli Matteo , Foschi Francesco Giuseppe , Piscaglia Fabio , Tovoli Francesco , Granito Alessandro
Published: June 2024

Background: Transarterial chemoembolisation (TACE) and radioembolisation (TARE) can lead to the deterioration of liver function, especially in cases of a high tumour burden, potentially lessening the benefits of subsequent systemic treatments. We aimed to verify whether a high number of previous transarterial treatments modified the outcomes of patients who received sorafenib as a frontline systemic treatment. Methods: A retrospective analysis of a large multicenter dataset containing prospectively collected data of sorafenib-treated patients was conducted. Results: Data from 696 patients were analysed, with 139 patients having received >two transarterial procedures before starting sorafenib. A propensity score matched 139 identified pairs of patients. Having received >two locoregional treatments was independently associated with a shorter survival (hazard ratio 1.325, 95% confidence interval 1.018–1.725, p = 0.039). This pattern was confirmed amongst responders to sorafenib, but not in progressors. A trend toward a higher rate of the permanent discontinuation of sorafenib due to liver failure (18.7 vs. 10.8%, p = 0.089) and a lower rate of eligibility for second-line treatments (24.5 vs. 17.3%, p = 0.184) was observed in patients who had received >two transarterial procedures. Conclusions: Repeated endovascular treatments negatively impacted the survival of HCC patients, especially sorafenib-responders. An early switch to systemic therapies should be considered in cases that are unlikely to respond.
De Novo Pathogenic Variant in FBRSL1, Non OMIM Gene Paralogue AUTS2, Causes a Novel Recognizable Syndromic Manifestation with Intellectual Disability; An Additional Patient and Review of the Literature
Authors: Bukvic Nenad , De Rinaldis Marta , Chetta Massimiliano , Trabacca Antonio , Bassi Maria Teresa , Marsano René Massimiliano , Holoubkova Lenka , Rivieccio Maria , Oro Maria , Resta Nicoletta , Kerkhof Jennifer , Sadikovic Bekim , Viggiano Luigi
Published: June 2024

FBRSL1, together with FBRS and AUTS2 (Activator of Transcription and Developmental Regulator; OMIM 607270), constitutes a tripartite AUTS2 gene family. AUTS2 and FBRSL1 are evolutionarily more closely related to each other than to FBRS (Fibrosin 1; OMIM 608601). Despite its paralogous relation to AUTS2, FBRSL1’s precise role remains unclear, though it likely shares functions in neurogenesis and transcriptional regulation. Herein, we report the clinical presentation with therapeutic approaches and the molecular etiology of a patient harboring a de novo truncating variant (c.371dupC) in FBRSL1, leading to a premature stop codon (p.Cys125Leufs*7). Our study extends previous knowledge by highlighting potential interactions and implications of this variant, alongside maternal and paternal duplications, for the patient’s phenotype. Using sequence conservation data and in silico analysis of the truncated protein, we generated a predicted domain structure. Furthermore, our in silico analysis was extended by taking into account SNP array results. The extension of in silico analysis was performed due to the possibility that the coexistence of FBRSL1 truncating variant contemporary with maternal and paternal duplication could be a modifier of proband’s phenotype and/or influence the novel syndrome clinical characteristics. FBRSL1 protein may be involved in neurodevelopment due to its homology with AUTS2, together with distinctive neuronal expression profiles, and thus should be considered as a potential modulation of clinical characteristics in a novel syndrome. Finally, considering that FBRSL1 is apparently involved in neurogenesis and in transcriptional regulatory networks that orchestrate gene expression, together with the observation that different genetic syndromes are associated with distinct genomic DNA methylation patterns, the specific episignature has been explored.
Mustn1 in Skeletal Muscle: A Novel Regulator?
Authors: Kim Charles J. , Hadjiargyrou Michael
Published: June 2024

Skeletal muscle is a complex organ essential for locomotion, posture, and metabolic health. This review explores our current knowledge of Mustn1, particularly in the development and function of skeletal muscle. Mustn1 expression originates from Pax7-positive satellite cells in skeletal muscle, peaks during around the third postnatal month, and is crucial for muscle fiber differentiation, fusion, growth, and regeneration. Clinically, Mustn1 expression is potentially linked to muscle-wasting conditions such as muscular dystrophies. Studies have illustrated that Mustn1 responds dynamically to injury and exercise. Notably, ablation of Mustn1 in skeletal muscle affects a broad spectrum of physiological aspects, including glucose metabolism, grip strength, gait, peak contractile strength, and myofiber composition. This review summarizes our current knowledge of Mustn1’s role in skeletal muscle and proposes future research directions, with a goal of elucidating the molecular function of this regulatory gene.
Transcriptomic Insights into the Developmental Dynamics of Eimeria acervulina: A Comparative Study of a Precocious Line and the Wild Type
Authors: Zhang Ning , Li Xiaojin , Liu Jie , Chen Linlin , Zhang Sixin , Liu Xianyong , Tang Xinming , Suo Xun , Zhang Yuanyuan
Published: June 2024

Coccidiosis, a parasitic disease caused by single or multiple Eimeria species, leads to significant economic losses in the poultry industry. The Eimeria life cycle includes schizogony, gametogony, and sporogony. To investigate the dynamics of gene expression and regulatory networks during the development of Eimeria acervulina, we employed time-course transcriptomics to rigorously compare the gene expression patterns between a precocious line (PL) and the wild type (WT) of E. acervulina. The results revealed that the PL enters into gametogony 12 h earlier than the WT, and both the PL and WT exhibited distinct clustering patterns during the development phase. A weighted gene co-expression network analysis (WGCNA) identified genes specifically expressed at four distinct developmental stages, schizogony, gametogony, sporulated oocysts, and unsporulated oocysts, clarifying the key biological processes at each stage. This study used global transcriptome profiling to elucidate molecular variations throughout the E. acervulina life cycle, providing critical insights into molecular characterization and valuable resources for investigating other apicomplexan parasites of public health importance.
Mechanics of Rainfall-Induced Landslides after a Prolonged Dry Period Based on Laboratory Tests and Numerical Models Incorporating Soil-Water Characteristic Curves
Authors: Bhadiyadra Kishan , Ong Dominic E. L.
Published: June 2024

In India, particularly within its Northeastern territories, landslides triggered by rainfall following dry periods are a major concern, consistently causing extensive damage to both life and infrastructure. This study focuses on mitigating their impact through preemptive measures, with an emphasis on analyzing slope stability to determine critical intervention points. The investigation includes experimental tests on soil samples to assess key parameters, such as soil matric suction and unconfined compressive strength, alongside an analysis of slope failures during the 2017 monsoon in Mizoram’s Lunglei district. Employing Soil-Water Characteristic Curves (SWCC) derived from ASTM D5298-10 standards and a microwave drying technique for preparing soil samples, the research evaluates the condition of the slopes before and after monsoonal rains. This study utilizes a blend of numerical modeling and empirical laboratory investigations to explore the factors contributing to slope instability. The findings underscore the necessity of advanced landslide warning systems, suggesting that a deeper understanding of rainfall-induced slope failures could significantly enhance disaster preparedness and reduce potential damages.
Sustainable Geotourism in the Chiusella Valley (NW Italian Alps): A Tool for Enhancing Alpine Geoheritage in the Context of Climate Change
Authors: Negri Arianna , Storta Elena , Khoso Rasool Bux , Colizzi Agnese Maria , Acquaotta Fiorella , Palomba Mauro , Giardino Marco
Published: June 2024

The Chiusella Valley (NW Italian Alps) is a key area for both the history of the alpine orogeny and its environmental context. It presents major structural features (including the Traversella neoalpine intrusion and a section of the Insubric line) and evidence of past climate changes in the region. Even if the Chiusella Valley was previously renowned for mining activities and most recently considered an alpine rural area with minor tourist attractions, its important geoheritage could offer alternatives to traditional mountain activities, which are facing adversity from increasing temperatures. This paper emphasises the role of geotourism in both enhancing sustainable development and raising awareness of climate change. For this purpose, the geodiversity of the Chiusella Valley has been analysed and several geosites have been identified. The research methodology includes field surveys, analysis of an existing educational activities and scientific literature, and assessment of geosites by quantitative analysis of five groups of indicators, including scientific, cultural, and educational values. The geosite selection within the Chiusella Valley reveals memories of past and present climate changes but also supports the development of targeted geotourism activities in the area. Additionally, a specific location has been identified for hosting indoor activities showcasing climate change action. These valuable contributions to sustainable geotourism provide opportunities for exploring the Alps in the vicinity of the Po Plain urban areas, while minimizing the environmental impact and facilitating educational activities on geodiversity and geoheritage.