Specific Scenarios Research Articles

Analyzing the likelihood of OAM mode energy transfer in turbulent environments with stochastic Cn2

Abstract Orbital angular momentum (OAM) is a crucial property of electromagnetic waves used in various applications such as free space communication, light detection and ranging, and remote sensing. However, turbulence can disrupt OAM-based systems by causing energy transfer between OAM modes, significantly impacting emerging fields in the optical spectrum. While traditional analyses assumed a constant turbulence parameter (Cn 2), recent research highlights its stochastic nature in specific scenarios. Our study introduces a new model that considers the stochastic nature of Cn 2. By incorporating this characteristic, our approach provides better predictions of system performance and valuable insights for accurately characterizing and designing communication and remote sensing systems in weak turbulent environments, enhancing their reliability and efficiency.

Exploring interventions for improving rural digital governance performance: A simulation study of the data-driven institutional pressure transmission mechanism

Considering the technological and institutional nature, along with the inherent complexity, of the rural digital governance context, a data-driven institutional pressure transmission (D-IPT) mechanism is developed in this study. This mechanism aims to facilitate effective regulation of villagers' behaviours, thereby improving rural digital governance performance. Firstly, we proposed a conceptual model based on complex adaptive systems theory and institutional theory to demonstrate how this mechanism can ensure an improvement of the emergent rural digital governance performance. Subsequently, we translated the conceptual model into a computational representation, thereby constructing a theory-informed simulation model. A behavioural dataset consists of 1,255,206 rural households from 119 villages over a span of 18 months was used to instantiate and validate the simulation model based on a specific scenario. Then, we designed five simulation experiments to investigate interventions aimed at harnessing the D-IPT mechanism more effectively to improve the rural digital governance performance. The results show that interventions targeting interaction efficiency, interaction frequency, and institutional environment configuration significantly affect the role of the D-IPT mechanism in enhancing rural governance performance. Finally, we discussed the theoretical and practical implications of the D-IPT mechanism.

Analysis of Aliyun-based serverless on MapReduce efficiency

In the context of the current era of big data, traditional Hadoop and cluster-based MapReduce frameworks are unable to meet the demands of modern research. This paper presents a MapReduce framework based on the AliCloud Serverless platform, which has been developed with the objective of optimizing word frequency counting in large-scale English texts. Leveraging AliCloud's dynamic resource allocation and elastic scaling, we have created an efficient and flexible text data processing system. This paper details the design and implementation of the Map and Reduce phases and analyses the impact of vCPU and memory specifications, as well as parallel resource allocation on system performance. Experimental results show that increasing vCPU specifications significantly improves processing capacity and execution efficiency. While the impact of memory specifications is relatively minor, it can positively influence performance in specific scenarios. Parallel processing markedly enhances system performance. Experiments on "Harry Potter and the Sorcerer's Stone" validate the framework's performance across various configurations. This study offers valuable insights for the design and optimization of serverless-based MapReduce frameworks, as well as suggesting future enhancements. These include the implementation of advanced parallel computing strategies, improved error handling, and refined data preprocessing, which collectively aim to boost system performance and stability.

FACTFINDERS FOR PATIENT SAFETY: Minimizing risks with cervical epidural injections

This series of FactFinders presents a brief summary of the evidence and outlines recommendations to minimize risks associated with cervical epidural injections.Evidence in support of the following facts is presented.Minimizing Risks with Cervical Interlaminar Epidural Steroid Injections – 1) CILESIs should be performed at C6-C7 or below, with C7-T1 as the preferred access point due to the more generous dorsal epidural space at this level compared to the more cephalad interlaminar segments. This reduces the risk of the minor complication of dural puncture and the major complication of spinal cord injury due to inadvertent needle placement. 2) LF gaps are most prevalent in the midline cervical spine. This can result in diminished tactile feedback with loss of resistance (LOR), increasing the risk for inadvertent dural puncture or spinal cord injury. Based on current evidence, needle placement in the paramedian portion of the interlaminar space is safest to avoid LF gaps. 3) An optimal AP trajectory view and the physician's ability to discern engagement in the LF and subsequent LOR are crucial. Confirmation of minimal needle insertion depth relative to the ventral margin of the lamina with either a lateral or contralateral oblique (CLO) safety view is critical to minimize the risk of inadvertently inserting the needle too ventral. 4) There have been closed claims and case reports of patients who have suffered catastrophic neurologic injuries while receiving CILESIs under deep sedation. If sedation is administered, the least amount necessary should be utilized to ensure the patient can provide verbal feedback during the procedure. 5) CILESIs are an elective procedure; therefore, necessity and likelihood of benefit must be foremost considerations. Current guidelines recommend holding ACAP therapy before CILESIs due to the potentially catastrophic complications associated with epidural hematoma (EH) formation. However, there is also a risk of severe systemic complications with ceasing ACAP in specific clinical scenarios. The treating physician is obligated to determine if the procedure is indicated and can ultimately decide to delay the intervention or not perform the procedure if the benefit does not outweigh the risks.Minimizing Risks with Cervical Transforaminal Epidural Steroid Injections – the Role of Preprocedural Review of Advanced Imaging -- Variations in vascular anatomy may warrant a modified approach to CTFESI. Preprocedural review of cross-sectional imaging can provide critical information for safe injection angle planning specific to individual patients and may help to decrease the risk of unintended vascular events with potentially catastrophic outcomes.Safety of Multi-level or Bilateral Fluoroscopically-Guided Cervical Transforaminal Epidural Steroid Injections -- Safe performance of a CTFESI procedure requires the ability to detect inadvertent arterial injection. Contrast medium placed into the epidural space and/or along the exiting spinal nerves during an initial CTFESI may obscure the detection of inadvertent cannulation of a radiculomedullary artery by a subsequent CTFESI. While no available literature directly addresses the potential risk that exists with a multi-level or bilateral CTFESI, caution is still warranted.

Addressing localized thermal comfort needs of the human body through advanced personal thermal management garments design and evaluation

Personal thermal management clothing has garnered significant attention in recent years for offering innovative solutions to address extreme environments. By examining the “human-clothing-environment” system, we have analyzed the contribution and developmental trajectory of personal thermal management clothing, considering the diverse human needs and application environments. Local variations in human physiology necessitate localized thermal comfort solutions provided by personal thermal management clothing. Despite advancements, many Personal thermal management garments still encounter challenges such as bulky size, heavy weight, intricate donning process, subpar performance, limited temperature control, and low durability. Furthermore, aspects like comfort, ergonomics, and aesthetics, tailored to specific usage scenarios, have often been overlooked. Enhancing the standardized design and evaluation processes for personal thermal management clothing items is crucial to better cater to individual preferences and usage demands, offering recommendations for development to align with individual requirements, enhance effectiveness, and promote energy conservation.

Dried plasma spot as an innovative approach to therapeutic drug monitoring of apixaban: Development and validation of a novel liquid chromatography-tandem mass spectrometry method.

Apixaban, a direct oral anticoagulant drug (DOAC), typically does not require routine therapeutic drug monitoring (TDM), yet recent guidelines propose its use in specific clinical scenarios. While various antifactor Xa (anti-FXa) chromogenic assays serve as useful proxies for measuring plasma exposure to apixaban in emergencies, they lack specificity compared with chromatographic methods. This research project is intended to the development and validation of a standardized protocol of liquid chromatography-tandem mass spectrometry (LC-MS/MS) in conformity with the ICH guidelines M10 for the measurement of apixaban in both plasma and dried plasma spots (DPSs). Samples preparation included protein precipitation after the addition of a deuterated internal standard (IS), and the chromatographic separation was carried out on a Thermo Scientific™ Accucore™ Polar Premium column (50 mm × 2.1mm, i.d. 2.6 m). The newly developed LC-MS/MS method for apixaban mesurement from both plasma and DPS resulted linear over a wideconcentration range (31.25-500 ng/mL), accurate, and reproducible without matrix effects, allowing for specific and rapid quantification. Stability was assessed on quality controls and a real sample, allowing the setting up of a robust TDM protocol that was applied to five anonymized plasma samples obtained from adult patients undergoing apixaban treatment at steady-state. In conclusion our novel LC-MS/MS method is adequate for accurate apixaban quantitation from both plasma and DPS matrixes, andmay thus facilitate the guidelines suggested implementation of apixaban TDM, even in peripheral hospitals through shipment of DPS at reference laboratories.

Design and utilization of a decision support tool to advance energy efficiency in industries

The industrial sector contributes significant environmental impact due to greenhouse gas (GHG) emissions. It is necessary to investigate the energy flow from resources, and the possibilities for lowering energy use through energy management. Energy modeling and visualization have been identified as effective and powerful methods for investigating energy-saving potential and improving energy efficiency. In this study, the Sankey diagram-based decision support tool is designed and developed to illustrate the energy flow and the potential for energy savings from various processes in different industrial sectors. The tool is developed in Python and presented as a web-based dashboard, making it accessible to various users. The web-based Sankey diagram tool allows the user to comprehend the present state of energy consumption, costs, and CO2 emissions, as well as the potential for enhancing system flexibility and efficiency in the industry. The opportunities for heat recovery, renewable energy sources, and energy storage technologies are discussed as an assumed case study in 4 specific scenarios. Finally, the findings validate the significance of energy-efficient solutions in industry utilizing the proposed tool. This underscores the importance of adopting sustainable practices within industries, ultimately contributing to the enhancement of the sector's overall sustainability and resilience.

Teaching mathematics in college: The need for a set of professionally oriented tasks

The purpose of the study is to solve this problem, which consists of substantiating, developing, and implementing a methodological system for preparing students for professionally oriented mathematics education in college. Professionally oriented mathematics education in colleges is advisable, as it helps students see the practical benefits and importance of mathematics in their future professional activities. This approach allows us to better prepare students for their future professions. Research problem: what are the leading directions, principles, content, and forms of methodological preparation of students for professionally oriented mathematics education in college? In achieving this goal, we distinguish a complex of professionally oriented tasks in the fact that they reflect specific scenarios and difficulties that specialists face in their professional activities, and this allows students to gain experience in specific tasks from their field. Research methods: interviewing and testing students, teachers; observation, study, and generalization of pedagogical experience; development of an author's methodology for preparing students for professionally oriented mathematics education in college. We identified criteria to verify the effectiveness of the training methodology, assessing the students’ assimilation of subject knowledge and their increased motivation for learning. The results of the pedagogical experiment show that the process of teaching mathematics in a college with a professionally oriented methodological learning system, implemented through the use of a set of professionally oriented tasks, contributes to an increase in the level of mathematical training and educational motivation of students.

Deep learning based Compton backscatter imaging with scattered X-ray spectrum data: A Monte Carlo study

Compton backscatter imaging (CBI) is a non-destructive testing (NDT) technique with the Compton effect. In CBI, the radiation source and detector are positioned on the same side of the object, which means that the imaging is almost not limited by the size of the object. The characteristic of single-sided imaging gives CBI substantial potential for applications in specific scenarios. In field archaeology, CBI can rapidly acquire subsurface images with the millimeter-level resolution, effectively enhancing archaeological excavation efficiency while better protecting artifacts. Therefore, CBI offers substantial potential in archaeological excavation. To address the problem of severe noise in CBI, we employed a scintillation detector to receive backscattered X-rays. Transformers directly reconstruct multi-channel projections to swiftly and accurately obtain precise reconstruction images. Monte Carlo simulation experiments confirm this method’s suitability for detecting a variety of common archaeological materials. Experimental results indicate that compared with count mode, multi-channel projections can effectively improve the reconstruction quality of CBI. Moreover, CBI can clearly image various materials of buried artifacts, proving the method’s versatility and practical value in NDT field.

ShouldAR: Detecting Shoulder Surfing Attacks Using Multimodal Eye Tracking and Augmented Reality

Shoulder surfing attacks (SSAs) are a type of observation attack designed to illicitly gather sensitive data from "over the shoulder" of victims. This attack can be directed at mobile devices, desktop screens, Personal Identification Number (PIN) pads at an Automated Teller Machine (ATM), or written text. Existing solutions are generally focused on authentication techniques (e.g., logins) and are limited to specific attack scenarios (e.g., mobile devices or PIN Pads). We present ShotjldAR, a mobile and usable system to detect SSAs using multimodal eye gaze information (i.e., from both the potential attacker and victim). ShouldAR uses an augmented reality headset as a platform to incorporate user eye gaze tracking, rear-facing image collection and eye gaze analysis, and user notification of potential attacks. In a 24-participant study, we show that the prototype is capable of detecting 87.28% of SSAs against both physical and digital targets, a two-fold improvement on the baseline solution using a rear-facing mirror, a widely used solution to the SSA problem. The ShouldAR approach provides an AR-based, active SSA defense that applies to both digital and physical information entry in sensitive environments.

A domain feature decoupling network for rotating machinery fault diagnosis under unseen operating conditions

Operating conditions reflect the mission evolution of rotating machinery in specific application scenarios. The monitoring data under different operating conditions exhibit disparate statistic distributions in terms of amplitude and frequency properties. In the modern operation and maintenance, domain adaptation approaches, which align the feature distributions of source and target domains, are commonly employed to address cross-domain diagnosis challenges. However, the availability of the target domain is serendipitous rather than guaranteed. To meet the engineering requirements, a domain feature decoupling network (DFDN) is proposed to achieve the fault diagnosis of rotating machinery under unseen operating conditions. The core concept of DFDN is to decouple the extracted features into domain-related features and fault-related features. These features are then adaptively integrated into domain-invariant features using a significance fusion method, thereby enabling the feature learning and knowledge transfer from seen source domains to unseen target domains. Furthermore, a self-learning joint optimization strategy is developed for DFDN to facilitate the efficient acquisition of generalized diagnosis knowledge. Two bearing datasets from different test rigs are considered to assess the domain generalization performance of DFDN. The experimental results demonstrate the superiority and potential of this method in coping with zero-shot fault diagnosis under unseen operating conditions.

Influence of emissivity on infrared camouflage performance

The rapid development of infrared detection technology has generated an urgent demand for infrared camouflage, sparking widespread interest in low-emissivity materials. Novel material designs and advanced micro/nanofabrication technologies make it possible to realize materials with extremely low emissivity. However, a lower infrared emissivity does not always mean a better camouflage performance. There is a lack of sufficient discussion on how to determine an appropriate emissivity for a specific working condition to achieve effective infrared camouflage. Here, through outdoor experiments, we demonstrated that for a specific scenario, an appropriate emissivity always exists that can make the infrared characteristics of the target effectively blend into its background, and deviations from the emissivity result in deteriorated camouflage performance. Further, we established a heat transfer model to conduct quantitative analysis on the influence of emissivity on infrared camouflage performance in terms of surface temperature and radiative temperature in various conditions. In addition, we proposed a general method for determining the optimal emissivity of infrared camouflage, defined as the emissivity value at which the radiative temperatures of the target and the background are equal. To facilitate practical application of this method, we developed a user-friendly MATLAB app named “Optimal Emissivity Calculator” to calculate the optimal emissivity. It was found that for a vehicle’s engine compartment surface at approximately 340.0 K, the optimal emissivity is 0.4 with a background temperature of 300.0 K. This work highlights the significance of selecting appropriate emissivity for infrared camouflage and provides a reference for designing the emissivity of infrared camouflage materials.

Study on the Estimation of Leaf Area Index in Rice Based on UAV RGB and Multispectral Data

Leaf area index (LAI) is a key variable for monitoring crop growth. Compared with traditional measurement methods, unmanned aerial vehicle (UAV) remote sensing offers a cost-effective and efficient approach for rapidly obtaining crop LAI. Although there is extensive research on rice LAI estimation, many studies suffer from the limitations of models that are only applicable to specific scenarios with unclear applicability conditions. In this study, we selected commonly used RGB and multispectral (Ms) data sources, which contain three channels of color information and five multi-band information, respectively, combined with five different spatial resolutions of data at intervals of 20–100 m. We evaluated the effectiveness of models using single- and multi-feature variables for LAI estimation in rice. In addition, texture and coverage features other than spectra were introduced to further analyze their effects on the inversion accuracy of the LAI. The results show that the accuracy of the model established with multi-variables under single features is significantly higher than that of the model established with single variables under single features. The best results were obtained using the RFR (random forest regression) model, in which the model’s R2 is 0.675 and RMSE is 0.886 for multi-feature VIs at 40 m. Compared with the analysis results of Ms and RGB data at different heights, the accuracy of Ms data estimation results fluctuates slightly and is less sensitive to spatial resolution, while the accuracy of the results based on RGB data gradually decreases with the increase in height. The estimation accuracies of both Ms and RGB data were improved by adding texture features and coverage features, and their R2 improved by 9.1% and 7.3% on average. The best estimation heights (spatial resolution) of the two data sources were 40 m (2.2 cm) and 20 m (0.4 cm), with R2 of 0.724 and 0.673, and RMSE of 0.810 and 0.881. This study provides an important reference for the estimation of rice LAI based on RGB and Ms data acquired using the UAV platform.

Elevating Detection Performance in Optical Remote Sensing Image Object Detection: A Dual Strategy with Spatially Adaptive Angle-Aware Networks and Edge-Aware Skewed Bounding Box Loss Function.

In optical remote sensing image object detection, discontinuous boundaries often limit detection accuracy, particularly at high Intersection over Union (IoU) thresholds. This paper addresses this issue by proposing the Spatial Adaptive Angle-Aware (SA3) Network. The SA3 Network employs a hierarchical refinement approach, consisting of coarse regression, fine regression, and precise tuning, to optimize the angle parameters of rotated bounding boxes. It adapts to specific task scenarios using either class-aware or class-agnostic strategies. Experimental results demonstrate its effectiveness in significantly improving detection accuracy at high IoU thresholds. Additionally, we introduce a Gaussian transform-based IoU factor during angle regression loss calculation, leading to the development of Edge-aware Skewed Bounding Box Loss (EAS Loss). The EAS loss enhances the loss gradient at the final stage of angle regression for bounding boxes, addressing the challenge of further learning when the predicted box angle closely aligns with the real target box angle. This results in increased training efficiency and better alignment between training and evaluation metrics. Experimental results show that the proposed method substantially enhances the detection accuracy of ReDet and ReBiDet models. The SA3 Network and EAS loss not only elevate the mAP of the ReBiDet model on DOTA-v1.5 to 78.85% but also effectively improve the model's mAP under high IoU threshold conditions.

MBFT: A Modular Byzantine Fault Tolerance Protocol for high adaptability

With the substantial increase in permissioned blockchain applications, various application scenarios for Byzantine Fault Tolerance (BFT) protocols are emerging. However, due to the inherent complexity of BFT, the performance improvement of BFT protocols in one aspect generally comes at the expense of degradation in others. The contradiction between the diverse requirements and the inherent characteristics of BFT makes it challenging for traditional BFT protocols to effectively address the requirements of different scenarios in practice, and the rising number of protocols and implementations often overwhelm practitioners. To propose a solution to this dilemma and improve the adaptability and effectiveness of BFT protocols in different scenarios, a novel Modular Byzantine Fault Tolerance protocol (MBFT) has been proposed, whose core idea is to utilizes modular methods to quickly and flexibly construct satisfactory BFT protocols that meet the requirements of specific scenarios. MBFT deconstructs the single-layer leader-based BFT protocol into three independent phases and provides two typical modules with different characteristics for each phase. By combining different modules, BFT protocols with different characteristics and guarantee safety, liveness and even Byzantine democracy can be obtained. The safety, liveness and Byzantine democracy of MBFT are theoretically proved, the fault resistance capability of MBFT and the relationship between the level of cluster clock synchronization and Byzantine democracy are analyzed. Extensive experimental results highlight that MBFT has high adaptability and excellent performance compared with some state-of-the-art protocols.

A novel quantum security multi-party extremum protocol in a d-dimensional quantum system

Secure multi-party extremum computation (SMEC) is a specific application scenario of secure multi-party computation, which allows multiple participants to compute the extremum of data without disclosing private information. The extremum includes maximum, minimum, sum of extremums, and difference of extremums. SMEC has wide applications in financial transactions, market analysis, sports events, healthcare, etc. Current protocol research mainly exists in the classical domain and cannot withstand quantum computing attacks. To address this issue, we propose a novel QSME protocol based on a d-dimensional quantum system, capable of computing the maximum and minimum values among multi-party data under unconditional security, and can compute the sum and difference of extremums without disclosing the maximum and minimum values, to adapt to complex application scenarios. The article proposes a coding method for a d-dimensional quantum system to further enhance security, provides correctness analysis, security analysis, robustness analysis, and comparative analysis, and proposes an experimental method for a d-dimensional quantum system to verify the effectiveness of the protocol, demonstrating strong practicality.

Individualized dose calculation for internal exposure on radionuclide intake: GPU acceleration approach

Objective. The rapid and accurate assessment of internal exposure dose is a crucial safeguard for personnel health and safety. This study aims to investigate a precise and efficient GPU Monte Carlo simulation approach for internal exposure dose calculation. It directly calculates doses from common radioactive nuclides intake, like 60Co for occupational exposure, allowing personalized assessments. Approach. This study developed a GPU-accelerated Monte Carlo program for internal exposure on radionuclide intake, successfully realizing photoelectronic coupled transport, nuclide simulation, and optimized acceleration. The generation of internal irradiation sources and sampling methods were achieved, along with the establishment of a personalized phantom construction process. Three irradiation scenarios were simulated to assess computational accuracy and efficiency, and to investigate the influence of posture variations on internal dose estimations. Main results. Using the International Commission on Radiological Protection (ICRP) voxel-type phantom, the internal dose of radionuclides in individual organs was calculated, exhibiting relative deviation of less than 3% in comparison to organ dose results interpolated by Specific Absorbed Fractions in ICRP Publication 133. Employing the Chinese reference phantom for calculating internal irradiation dose from the intake of various radionuclides, the use of GPU Monte Carlo program significantly shortened the simulation time compared to using CPU programs, by a factor of 150–500. Internal dose estimation utilizing a seated Chinese phantom revealed up to a 75% maximum difference in organ dose compared to the same phantom in a standing posture. Significance. This study presents a rapid GPU-based simulation method for internal irradiation doses, capable of directly simulating dose outcomes from nuclide intake and accommodating individualized phantoms for more realistic and expeditious calculations tailored to specific internal irradiation scenarios. It provides an effective and feasible tool for precisely calculating internal irradiation doses in real-world scenarios.

Incremental Learning for LiDAR Attack Recognition Framework in Intelligent Driving Using Gaussian Processes

The perception system plays a crucial role by integrating LiDAR and various sensors to perform localization and object detection, which ensures the security of intelligent driving. However, existing research indicates that LiDAR is vulnerable to sensor attacks, which lead to inappropriate driving strategies and need effective attack recognition methods. Previous LiDAR attack recognition methods rely on fixed anomaly thresholds obtained from depth map data distributions in specific scenarios as static anomaly boundaries, which lead to reduced accuracy, increased false alarm rates, and a lack of performance stability. To address these problems, we propose an adaptive LiDAR attack recognition framework capable of adjusting to different driving scenarios. This framework initially models the perception system by integrating the vehicle dynamics model and object tracking algorithms to extract data features, subsequently employing Gaussian Processes for the probabilistic modeling of these features. Finally, the framework employs sparsification computing techniques and a sliding window strategy to continuously update the Gaussian Process model with window data, which achieves incremental learning that generates uncertainty estimates as dynamic anomaly boundaries to recognize attacks. The performance of the proposed framework has been evaluated extensively using the real-world KITTI dataset covering four driving scenarios. Compared to previous methods, our framework achieves a 100% accuracy rate and a 0% false positive rate in the localization system, and an average increase of 3.43% in detection accuracy in the detection system across the four scenarios, which demonstrates superior adaptive capabilities.

Evaluation of deep learning for predicting rice traits using structural and single-nucleotide genomic variants

BackgroundStructural genomic variants (SVs) are prevalent in plant genomes and have played an important role in evolution and domestication, as they constitute a significant source of genomic and phenotypic variability. Nevertheless, most methods in quantitative genetics focusing on crop improvement, such as genomic prediction, consider only Single Nucleotide Polymorphisms (SNPs). Deep Learning (DL) is a promising strategy for genomic prediction, but its performance using SVs and SNPs as genetic markers remains unknown.ResultsWe used rice to investigate whether combining SVs and SNPs can result in better trait prediction over SNPs alone and examine the potential advantage of Deep Learning (DL) networks over Bayesian Linear models. Specifically, the performances of BayesC (considering additive effects) and a Bayesian Reproducible Kernel Hilbert space (RKHS) regression (considering both additive and non-additive effects) were compared to those of two different DL architectures, the Multilayer Perceptron, and the Convolution Neural Network, to explore their prediction ability by using various marker input strategies. We found that exploiting structural and nucleotide variation slightly improved prediction ability on complex traits in 87% of the cases. DL models outperformed Bayesian models in 75% of the studied cases, considering the four traits and the two validation strategies used. Finally, DL systematically improved prediction ability of binary traits against the Bayesian models.ConclusionsOur study reveals that the use of structural genomic variants can improve trait prediction in rice, independently of the methodology used. Also, our results suggest that Deep Learning (DL) networks can perform better than Bayesian models in the prediction of binary traits, and in quantitative traits when the training and target sets are not closely related. This highlights the potential of DL to enhance crop improvement in specific scenarios and the importance to consider SVs in addition to SNPs in genomic selection.

Management of Peri-implant Mucosal Dehiscences: A Scoping Review.

Several treatment-oriented classifications for the management of peri-implant marginal mucosal defects (PMMDs) have been published to date. While each classification provides valuable insights into key diagnostic and therapeutic aspects, there is a marked heterogeneity regarding the recommended clinical guidelines to achieve success in specific scenarios. The purpose of this review was to critically analyze and organize the similarities and differences enclosed in the available classifications linked with treatment recommendations on the management of PMMDs at single implant non-molar sites with the purpose of providing an overview of recommended interdisciplinary treatment options to facilitate clinical decision-making processes.