Search Results (2,682)

Background and Objectives: Cyanoacrylate (CA) tissue adhesives have gained increasing attention as alternatives to sutures in oral surgery and periodontology. The objective of this scoping review is to assess their clinical applications and effectiveness in wound closure and postoperative management. Materials and Methods: The review was conducted following the JBI methodology and PRISMA-ScR guidelines. A comprehensive search was performed in PubMed, Scopus, and Web of Science to identify randomized controlled trials published between 2015 and 2025 evaluating the use of CAs in oral surgery and periodontal procedures. Results: A total of 19 studies were included. Cyanoacrylate adhesives demonstrated comparable or superior outcomes to other wound healing strategies in terms of operative time, postoperative pain reduction, and early wound healing. Their use was particularly beneficial in free gingival grafts and palatal donor site management. However, the findings across studies were not always consistent, and some trials did not report statistically significant differences. The use of long-chain CA formulations is associated with minimal toxicological risk, though these adhesives demonstrate intrinsic hemostatic and antimicrobial effects. Conclusions: Cyanoacrylate tissue adhesives represent a valid alternative to sutures in several dental surgical contexts, especially in procedures involving mucogingival grafts. Further high-quality clinical studies are needed to clarify their long-term outcomes and broaden their indications in dentistry. Full article

As the global energy system accelerates its transition toward a low-carbon economy, renewable energy sources (RESs), such as wind and photovoltaic power, are rapidly replacing traditional fossil fuels. These RESs are becoming a critical element of deeply decarbonized power systems (DDPSs). However, the inherent non-stationarity, multi-scale volatility, and uncontrollability of RES output significantly increase the risk of source–load imbalance, posing serious challenges to the reliability and economic efficiency of power systems. Scenario generation technology has emerged as a critical tool to quantify uncertainty and support dispatch optimization. Nevertheless, conventional scenario generation methods often fail to produce highly credible wind and solar output scenarios. To address this gap, this paper proposes a novel renewable energy scenario generation method based on a multi-resolution diffusion model. To accurately capture fluctuation characteristics across multiple time scales, we introduce a diffusion model in conjunction with a multi-scale time series decomposition approach, forming a multi-stage diffusion modeling framework capable of representing both long-term trends and short-term fluctuations in RES output. A cascaded conditional diffusion modeling framework is designed, leveraging historical trend information as a conditioning input to enhance the physical consistency of generated scenarios. Furthermore, a forecast-guided fusion strategy is proposed to jointly model long-term and short-term dynamics, thereby improving the generalization capability of long-term scenario generation. Simulation results demonstrate that MDDPM achieves a Wasserstein Distance (WD) of 0.0156 in the wind power scenario, outperforming DDPM (WD = 0.0185) and MC (WD = 0.0305). Additionally, MDDPM improves the Global Coverage Rate (GCR) by 15% compared to MC and other baselines. Full article

Lead (Pb) exposure poses a significant public health concern due to its neurotoxic effects. While mitochondrial dysfunction is implicated in lead neurotoxicity, the precise molecular mechanisms, particularly the role of non-coding RNA-mediated competing endogenous RNA networks, remain underexplored. SH-SY5Y neuroblastoma cells were treated with 10 μM lead acetate. Cell viability was assessed by Cell Counting Kit-8 (CCK-8). Mitochondrial ultrastructure and quantity were analyzed via transmission electron microscopy (TEM). Key mitochondrial dynamics proteins were examined by Western blot. Comprehensive transcriptome sequencing, including long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), microRNAs (miRNAs) and mRNAs, was performed followed by functional enrichment and ceRNA network construction. Selected RNAs and hub genes were validated using quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR). Lead exposure significantly reduced SH-SY5Y cell viability and induced mitochondrial damage (decreased quantity, swelling, fragmentation). Western blot confirmed an imbalance in mitochondrial dynamics, as indicated by decreased mitofusin 2 (MFN2), increased total and phosphorylated dynamin-related protein 1 (DRP1). Transcriptomic analysis revealed widespread differential expression of lncRNAs, circRNAs, miRNAs, and mRNAs. Enrichment analysis highlighted mitochondrial function and oxidative stress pathways. A ceRNA network identified five key hub genes: SLC7A11, FOS, HMOX1, HGF, and NR4A1. All validated RNA and hub gene expression patterns were consistent with sequencing results. Our study demonstrates that lead exposure significantly impairs mitochondrial quantity and morphology in SH-SY5Y cells, likely via disrupted mitochondrial dynamics. We reveal the potential regulatory mechanisms of lead-induced neurotoxicity involving ceRNA networks, identifying hub genes crucial for cellular stress response. This research provides a foundational framework for developing therapeutic strategies against lead-induced neurotoxicity. Full article

The article presents a long-term assessment of the surface water quality of six small rivers in the East Kazakhstan region (Breksa, Tikhaya, Ulba, Glubochanka, Krasnoyarka, and Oba) based on hydrochemical monitoring data from the Kazhydromet State Enterprise for the period 2017–2024. A unified water quality classification system was applied, along with statistical methods, including multiple linear regression. The Glubochanka and Krasnoyarka rivers were identified as the most polluted (reaching classes 4–5), with multiple exceedances of Zn (up to 2.96 mg/dm³), Cd (up to 0.8 mg/dm³), and Cu (up to 0.051 mg/dm³). The most stable and highest water quality was recorded in the Oba River, where from 2021 to 2024, water consistently corresponded to Class 2. Regression models of water quality class as a function of time and annual precipitation were constructed to assess the influence of climatic factors. Statistical analysis revealed no consistent linear correlation between average annual precipitation and water quality (correlation coefficients ranging from −0.49 to +0.37), indicating a complex interplay between climatic and anthropogenic factors. Significant relationships were found for the Breksa (R² = 0.903), Glubochanka (R² = 0.602), and Tikhaya (R² = 0.555) rivers, suggesting an influence of temporal and climatic factors on water quality. In contrast, the Oba (R² = 0.130), Ulba (R² = 0.100), and Krasnoyarka (R² = 0.018) rivers exhibited low coefficients, indicating the predominance of other, likely local, sources of pollution. It was found that summer periods are characterized by the highest pollution due to low water flow, while episodes of acid runoff occur in spring. A decrease in pH below 7.0 was first recorded in 2023–2024 in the Ulba and Tikhaya rivers. Forecasts to 2030 suggest relative stability in water quality under current climatic conditions; however, by 2050, the risk of water quality deterioration is expected to rise due to increased precipitation and extreme weather events. This study presents, for the first time, a systematic long-term analysis of small rivers in the East Kazakhstan region, offering deeper insight into the dynamics of surface water quality and providing a scientific foundation for developing adaptive strategies for the protection and sustainable use of water resources under climate change and anthropogenic pressure. The results emphasize the importance of prioritizing rivers with high variability in water quality for regular monitoring and the development of adaptive conservation measures. The research holds strong applied significance for shaping a sustainable water use strategy in the region. Full article

Evaluating the implant site immediately after implant removal is crucial for assessing its condition and ensuring morphological stability. Immediate reconstruction at the time of implant removal has been proposed as a strategy to preserve alveolar ridge width. This study aims to evaluate whether immediate alveolar bone reconstruction at the time of implant removal provides comparable or superior dimensional stability of the alveolar ridge compared to staged reconstruction approaches. The null hypothesis of this study is that there is no significant difference in alveolar bone dimensions between immediate and staged reconstructions following implant removal. This retrospective study included seven participants, consisting of six males and one female. The participants were categorized into three groups based on the treatment approach following implant removal. In Group 1, no bone grafting was performed after implant removal. In Group 2, bone grafting was conducted following implant removal, with an adequate healing period before implant placement. In Group 3, bone grafting was performed simultaneously with implant removal. Cone-beam computed tomography (CBCT) imaging was conducted before implant removal (T0), after implant removal or bone grafting (T1), and after implant placement (T2). All removed implants were successfully replaced with new ones, regardless of bone grafting. In terms of alveolar ridge width at 1 mm below the crest, Group 1 exhibited the greatest reduction (ΔT1 − T0 = −5.1 ± 3.7 mm), while Group 2 showed a mild increase (+1.1 ± 2.6 mm), and Group 3 had a moderate decrease (−1.3 ± 1.0 mm). This suggests that delayed bone grafting can better preserve or enhance bone volume during healing. A reduction in buccal ridge height between T1 and T0 (ΔT1 − T0) was observed, particularly in Group 1. In contrast, an increase in buccal ridge height was most pronounced in Group 2. Although immediate reconstruction (Group 3) did not result in statistically significant gains, it achieved successful implant placement without complications and reduced the total treatment duration, which might be beneficial from a clinical efficiency and patient satisfaction standpoint. Therefore, staged bone grafting (Group 2) appears to offer greater dimensional stability, particularly in maintaining ridge height, whereas immediate reconstruction (Group 3) remains a clinically viable alternative for stable healing in select cases, especially when shorter treatment timelines are prioritized. Full article

With the continuous expansion and increasing operational complexity of high-speed railway networks, there is a growing need for more flexible and efficient EMU (Electric Multiple Unit) routing strategies. To address these challenges, in this paper, we propose a multi-day flexible circulation model that minimizes total connection time and deadheading mileage. A multi-commodity network flow model is formulated, incorporating constraints such as first-level maintenance intervals, storage capacity, train coupling/decoupling operations, and train types, with across-day consistency. To solve this complex model efficiently, a heuristic decomposition algorithm is designed to separate the problem into daily service chain generation and EMU assignment. A real-world case study in the Beijing–Baotou high-speed corridor demonstrates the effectiveness of the proposed approach. Compared to a fixed strategy, the flexible strategy reduces EMU usage by one unit, lowers deadheading mileage by up to 16.4%, and improves maintenance workload balance. These results highlight the practical value of flexible EMU deployment for large-scale, multi-day railway operations. Full article

This study evaluates the performance of nine satellite and model-based daily surface soil moisture products, encompassing sixteen algorithm versions across mainland China to support sustainable land and water management. The assessment utilizes 2018 in situ measurements from over 2400 stations in China’s Automatic Soil Moisture Monitoring Network. All products were standardized to a 0.25° × 0.25° grid in the WGS-84 coordinate system through reprojection and resampling for consistent comparison. Daily averaged station observations were matched to product pixels using a 10 km radius buffer, with the mean station value as the reference for each time series after rigorous quality control. Results reveal distinct performance rankings, with SMAP-based products, particularly the SMAP_IB descending orbit variant, achieving the lowest unbiased root mean square deviation (ubRMSD) and highest correlation with in situ data. Blended products like ESA CCI and NOAA SMOPS, alongside reanalysis datasets such as ERA5 and MERRA2, outperformed SMOS and China’s FY3 products. The SoMo.ml product showed the broadest spatial coverage and strong temporal consistency, while FY3-based products showed limitations in spatial reliability and seasonal dynamics capture. These findings provide critical insights for selecting appropriate soil moisture datasets to enhance sustainable agricultural practices, optimize water resource allocation, monitor ecosystem resilience, and support climate adaptation strategies, therefore advancing sustainable development across diverse geographical regions in China. Full article

This study proposes a computational framework that transforms eye-tracking analysis from statistical description to cognitive structure modeling, aiming to reveal the organizational features embedded in the viewing process. Using the designers’ observation of a traditional Chinese landscape painting as an example, the study draws on the goal-oriented nature of design thinking to suggest that such visual exploration may exhibit latent structural tendencies, reflected in patterns of fixation and transition. Rather than focusing on traditional fixation hotspots, our four-dimensional framework (Region, Relation, Weight, Time) treats viewing behavior as structured cognitive networks. To operationalize this framework, we developed a data-driven computational approach that integrates fixation coordinate transformation, K-means clustering, extremum point detection, and linear interpolation. These techniques identify regions of concentrated visual attention and define their spatial boundaries, allowing for the modeling of inter-regional relationships and cognitive organization among visual areas. An adaptive buffer zone method is further employed to quantify the strength of connections between regions and to delineate potential visual nodes and transition pathways. Three design-trained participants were invited to observe the same painting while performing a think-aloud task, with one participant selected for the detailed demonstration of the analytical process. The framework’s applicability across different viewers was validated through consistent structural patterns observed across all three participants, while simultaneously revealing individual differences in their visual exploration strategies. These findings demonstrate that the proposed framework provides a replicable and generalizable method for systematically analyzing viewing behavior across individuals, enabling rapid identification of both common patterns and individual differences in visual exploration. This approach opens new possibilities for discovering structural organization within visual exploration data and analyzing goal-directed viewing behaviors. Although this study focuses on method demonstration, it proposes a preliminary hypothesis that designers’ gaze structures are significantly more clustered and hierarchically organized than those of novices, providing a foundation for future confirmatory testing. Full article

Background/Objectives: Pacifier use is a widespread soothing practice during infancy, but extended use has been linked to adverse dental outcomes, particularly malocclusion. This review aimed to evaluate the association between pacifier use and dental developmental issues in infants and toddlers and to identify key influencing factors. Methods: A scoping review using PubMed, Scopus, Web of Science, and Dentistry and Oral Sciences Source was performed. Peer-reviewed, full-text articles published in English between 2014 and 2024 were screened by two independent reviewers using predefined criteria. Eligible studies were thematically analyzed. Results: From 262 records, 35 studies met the inclusion criteria. Pacifier use was consistently associated with an increased prevalence of malocclusions, including anterior open bite, posterior crossbite, and overjet. The risk and severity of dental issues were strongly influenced by the duration, frequency, and intensity of pacifier use. Prolonged use beyond three years significantly increased the likelihood of structural changes requiring intervention. Conclusion: There is strong evidence linking pacifier use to negative dental developmental outcomes, particularly when use is prolonged or frequent. Early intervention, caregiver education, and timely weaning are critical to minimizing long-term oral health risks. Future research should explore pacifier design, objective measures of use, and how socioeconomic factors may influence pacifier use patterns and oral health outcomes. Understanding these relationships can support the development of more targeted and equitable prevention strategies. Full article

Background: Atypical subtrochanteric femoral fractures (ASFs), frequently linked to long-term bisphosphonate use, present significant fixation challenges due to impaired bone healing. While intramedullary (IM) nailing is the standard treatment, delayed union or nonunion remains common. This study aimed to evaluate whether supplementing IM nailing with lateral plate augmentation improves radiographic healing in patients with ASFs. Methods: This retrospective comparative study included 12 elderly female patients with ASFs treated between October 2013 and October 2023. Five patients underwent IM nailing alone (IM group), while seven received IM nailing with additional lateral plate fixation (Plate + IM group). Fracture healing was assessed using the modified Radiographic Union Score for Tibial fractures (mRUST) at 3, 6, and 12 months postoperatively. Intergroup comparisons were performed using the Mann–Whitney U test. Results: The median mRUST scores in the IM group were 4 (IQR 3.5–4), 6 (IQR 4.5–6.5), and 8 (IQR 7–9) at 3, 6, and 12 months, respectively. In the Plate + IM group, the scores were 5 (IQR 4–6), 8 (IQR 8–8), and 10 (IQR 10–11), respectively. The Plate + IM group demonstrated significantly higher mRUST scores at all assessed time points (3 months: p = 0.018; 6 months: p = 0.003; 12 months: p = 0.006). No implant failures or postoperative infections occurred in either group during the 12-month follow-up period. One patient (20%) in the IM group developed fracture nonunion, while no nonunion cases were observed in the Plate + IM group. Conclusions: Lateral plate augmentation as an adjunct to IM nailing may promote faster and more consistent radiographic healing in atypical subtrochanteric femoral fractures. This dual-fixation strategy may offer a biomechanically more robust option for patients at risk of delayed union, potentially contributing to a lower risk of nonunion, though further prospective studies are required to confirm this finding. Full article

Intelligent fault diagnosis for rotary machinery often suffers performance degradation under domain shifts between training and deployment environments. To address this, we propose a robust fault diagnosis framework incorporating three key components: (1) an order-frequency-based preprocessing method to normalize rotational variations, (2) a U-Net variational autoencoder (U-NetVAE) to enhance adaptation through reconstruction learning, and (3) a test-time training (TTT) strategy enabling unsupervised target domain adaptation without access to source data. Since existing works rarely evaluate under true domain shift conditions, we first construct a unified cross-domain benchmark by integrating four public datasets with consistent class and sensor settings. The experimental results show that our method outperforms conventional machine learning and deep learning models in both F1-score and recall across domains. Notably, our approach maintains an F1-score of 0.47 and recall of 0.51 in the target domain, outperforming others under identical conditions. Ablation studies further confirm the contribution of each component to adaptation performance. This study highlights the effectiveness of combining mechanical priors, self-supervised learning, and lightweight adaptation strategies for robust fault diagnosis in the practical domain. Full article

In this paper, we address the problem of intelligent operation of Distribution Static Synchronous Compensators (D-STATCOMs) in power distribution systems to reduce energy losses and CO₂ emissions while improving system operating conditions. In addition, we consider the entire set of constraints inherent in the operation of such networks in an environment with D-STATCOMs. To solve such a problem, we used three master–slave methodologies based on sequential programming methods. In the proposed methodologies, the master stage solves the problem of intelligent D-STATCOM operation using the continuous versions of the Monte Carlo (MC) method, the population-based genetic algorithm (PGA), and the Particle Swarm Optimizer (PSO). The slave stage, for its part, evaluates the solutions proposed by the algorithms to determine their impact on the objective functions and constraints representing the problem. This is accomplished by running an Hourly Power Flow (HPF) based on the method of successive approximations. As test scenarios, we employed the 33- and 69-node radial test systems, considering data on power demand and CO₂ emissions reported for the city of Medellín in Colombia (as documented in the literature). Furthermore, a test system was adapted in this work to the demand characteristics of a feeder located in the city of Talca in Chile. This adaptation involved adjusting the conductors and voltage limits to include a test system with variations in power demand due to seasonal changes throughout the year (spring, winter, autumn, and summer). Demand curves were obtained by analyzing data reported by the local network operator, i.e., Compañía General de Electricidad. To assess the robustness and performance of the proposed optimization approach, each scenario was simulated 100 times. The evaluation metrics included average solution quality, standard deviation, and repeatability. Across all scenarios, the PGA consistently outperformed the other methods tested. Specifically, in the 33-node system, the PGA achieved a 24.646% reduction in energy losses and a 0.9109% reduction in CO₂ emissions compared to the base case. In the 69-node system, reductions reached 26.0823% in energy losses and 0.9784% in CO₂ emissions compared to the base case. Notably, in the case of the Talca feeder—particularly during summer, the most demanding season—the PGA yielded the most significant improvements, reducing energy losses by 33.4902% and CO₂ emissions by 1.2805%. Additionally, an uncertainty analysis was conducted to validate the effectiveness and robustness of the proposed optimization methodology under realistic operating variability. A total of 100 randomized demand profiles for both active and reactive power were evaluated. The results demonstrated the scalability and consistent performance of the proposed strategy, confirming its effectiveness under diverse and practical operating conditions. Full article

This study develops two machine learning models to predict the medal performance of countries at the 2028 Olympic Games while systematically analyzing and quantifying the impacts of the host effect and exceptional coaching on medal gains. The dataset encompasses records of total medals by country, event categories, and athletes’ participation from the Olympic Games held between 1896 and 2024. We use K-means clustering to analyze medal trends, categorizing 234 nations into four groups (${\alpha }_1$, ${\alpha }_2$, ${\alpha }_3$, ${\alpha }_4$). Among these, ${\alpha }_1$, ${\alpha }_2$, ${\alpha }_3$ represent medal-winning countries, while ${\alpha }_4$ consists of non-medal-winning nations. For the $\alpha$1, $\alpha$2, and $\alpha$3 groups, 2–3 representative countries from each are selected for trend analysis, with the United States serving as a case study. This study extracts ten factors that may influence medal wins from the dataset, including participant data, the number of events, and medal growth rates. Factor analysis is used to reduce them into three principal components: Factor analysis condenses ten influencing factors into three principal components: the event scale factor (F1), the medal trend factor (F2), and the gender and athletic ability factor (F3). An ARIMA model predicts the factor coefficients for 2028 as 0.9539, 0.7999, and 0.2937, respectively. Four models (random forest, BP Neural Network, XGBoost, and SVM) are employed to predict medal outcomes, using historical data split into training and testing sets to compare their predictive performance. The research results show that XGBoost is the optimal medal predicted model, with the United States projected to win 57 gold medals and a total of 135 medals in 2028. For non-medal-winning countries (${\alpha }_4$), a three-layer fully connected neural network (FCNN) is constructed, achieving an accuracy of 85.5% during testing. Additionally, a formula to calculate the host effect and a Bayesian linear regression model to assess the impact of exceptional coaching on athletes’ medal performance are proposed. The overall trend of countries in the $\alpha$1 group is stable, but they are significantly affected by the host effect; the trend in the $\alpha$2 group shows an upward trend; the trend in the $\alpha$3 group depend on the athletes’ conditions and whether the events they excel in are included in that year’s Olympics. In the $\alpha$4 group, the probabilities of the United Arab Republic (UAR) and Mali (MLI) winning medals in the 2028 Olympic Games are 77.47% and 58.47%, respectively, and there are another four countries with probabilities exceeding 30%. For the eight most recent Olympic Games, the gain rate of the host effect is 74%. Great coaches can bring an average increase of 0.2 to 0.5 medals for each athlete. The proposed models, through an innovative integration of clustering, dimensionality reduction, and predictive algorithms, provide reliable forecasts and data-driven insights for optimizing national sports strategies. These contributions not only address the gap in predicting first-time medal wins for non-medal-winning nations but also offer guidance for policymakers and sports organizations, though they are constrained by assumptions of stable historical trends, minimal external disruptions, and the exclusion of unknown athletes. Full article

Background: Optimal nutrition and training regimens are essential for athletes to maximize performance and recovery. Probiotic supplementation, through the modulation of the gut microbiota, and omega-3 fatty acids, known for their anti-inflammatory properties, may enhance physiological adaptations when combined with targeted training. This study evaluated the effects of probiotics and omega-3 supplementation, alongside ultra-short race pace training (USRPT), on performance metrics in competitive sprint swimmers. Methods: In this double-blind, placebo-controlled study, 60 male sprint swimmers (age: 19.2 ± 3.6 years; height: 182.2 ± 5.2 cm; weight: 81.6 ± 4.4 kg) with a minimum of five years of training experience, were randomly assigned to six groups (n = 10 per group): (1) Control (CON), (2) USRPT only, (3) Placebo + USRPT (PLA + USRPT), (4) Probiotics + USRPT (PRO + USRPT), (5) Omega-3 + USRPT (OMEGA + USRPT), and (6) Probiotics + Omega-3 + USRPT (PRO + OMEGA + USRPT). Over the eight-week intervention, the participants in PRO + USRPT consumed one multi-strain probiotic capsule daily (4.5 × 10¹¹ CFU) and a placebo capsule. Those in OMEGA + USRPT ingested 1000 mg of fish oil after lunch (500 mg EPA and 180 mg DHA per capsule) paired with a placebo capsule. The combined supplementation group (PRO + OMEGA + USRPT) received both probiotic and omega-3 capsules. The PLA + USRPT group consumed two starch capsules daily. The USRPT protocol was implemented across all the training groups, where the swimmers performed 17 sets of 25 m and 12.5 m sprints based on weekly recorded race times. Performance assessments included pre- and post-test measurements of sprint times (50 m and 100 m freestyle), vertical jump tests (both in water and on dry land), and other strength and endurance metrics (reaction time, agility T-test, sprint index, fatigue index, and velocity). Results: The combined intervention of probiotics and omega-3 with USRPT produced the greatest improvements in performance. The PRO + OMEGA + USRPT group reduced 50 m freestyle time by 1.92% (p = 0.002, pEta² = 0.286) and 100 m freestyle time by 2.48% (p = 0.041, pEta² = 0.229), demonstrating significant Time × Group interactions consistent with a synergistic effect. Additionally, the sprint index improved (pEta² = 0.139, p = 0.013) and reaction time decreased (pEta² = 0.241, p = 0.009) in the combined group, indicating enhanced anaerobic capacity and neuromuscular responsiveness compared to single interventions. Conclusions: This study suggests that combining probiotics and omega-3 supplementation with USRPT leads to synergistic improvements in sprint swimming performance, enhancing anaerobic power and recovery beyond what is achieved with individual interventions. This integrated approach may provide a practical strategy for competitive swimmers seeking to optimize their performance. Future studies should incorporate mechanistic markers, longer intervention durations, and diverse athlete populations to clarify further and extend these findings. Full article

Consensus protocols for a multi-agent networked system consist of strategies that align the states of all agents that share information according to a given network topology, despite challenges such as communication limitations, time-varying networks, and communication delays. The special orthogonal group $SO\left(n\right)$ plays a key role in applications from rigid body attitude synchronization to machine learning on Lie groups, particularly in fields like physics-informed learning and geometric deep learning. In this paper, N-agent consensus protocols are proposed on the Lie group $SO\left(4\right)$ and the corresponding tangent bundle $TSO\left(4\right)$, in which the state spaces are $SO{\left(4\right)}^N$ and $TSO{\left(4\right)}^N$, respectively. In particular, when using communication topologies such as a ring graph for which the local stability of non-consensus equilibria is retained in the closed loop, a consensus protocol that leverages a reshaping strategy is proposed to destabilize non-consensus equilibria and produce consensus with almost global stability on $SO{\left(4\right)}^N$ or $TSO{\left(4\right)}^N$. Lyapunov-based stability guarantees are obtained, and simulations are conducted to illustrate the advantages of these proposed consensus protocols. Full article