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29 pages, 38992 KB

Open AccessArticle

Constrained and Unconstrained Control Design of Electromagnetic Levitation System with Integral Robust–Optimal Sliding Mode Control for Mismatched Uncertainties

by Amit Pandey, Dipak M. Adhyaru, Gulshan Sharma and Kingsley A. Ogudo

Energies 2026, 19(2), 350; https://doi.org/10.3390/en19020350 (registering DOI) - 10 Jan 2026

Abstract

In real life, almost all systems are nonlinear in nature. The electromagnetic levitation system (EMLS) is one such system that has a wide range of applications due to its frictionless, fast, and affordable technique. Optimal control and sliding mode control (SMC) techniques are [...] Read more.

In real life, almost all systems are nonlinear in nature. The electromagnetic levitation system (EMLS) is one such system that has a wide range of applications due to its frictionless, fast, and affordable technique. Optimal control and sliding mode control (SMC) techniques are often used controllers for EMLS. However, these techniques can achieve the required levitation but lag in having perfect set-point tracking and robustness against uncertainties. To get over these drawbacks, this article proposes the design of unconstrained mismatched uncertainties, constrained mismatched uncertainties, and integral sliding mode control with mismatched uncertainties for the current-controlled-type electromagnetic levitation system (CC-EMLS). The modeled equations of CC-EMLS are transfomed in terms of the mismatched uncertainties, and the required control action is obtained with and without constraints on the control input. The quadratic performance function is suggested for the unconstrained control scheme and is solved using the Hamilton–Jacobi–Bellman (HJB) equation. The non-quadratic cost function is designed for the constrained control method, and the HJB equation is utilized to obtain the solution. Both control schemes provide robustness to the system, but deviations in the set point are observed in tracking the position of the ball when the changes in the payload occur in the system. Therefore, integral sliding mode control with robust–optimal (IOSMC) gain is proposed for the CC-EMLS to overcome the steady-state error in the other two schemes. The stability is proven using the direct method of Lyapunov stability. The essential studies based on the simulation are carried out to showcase the performance of the proposed control schemes. The integral performance indicators are compared for all three proposed control schemes to highlight the efficacy, robustness, and efficiency of the designed controllers. Full article

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15 pages, 15035 KB

Open AccessArticle

A Comprehensive Digital Workflow for Enhancing Dental Restorations in Severe Structural Wear

by Abdulrahman Alshabib, Jake Berger, Edgar Garcia, Carlos A. Jurado, Guilherme Cabral, Adriano Baldotto, Hilton Riquieri, Mohammed Alrabiah and Franciele Floriani

Bioengineering 2026, 13(1), 77; https://doi.org/10.3390/bioengineering13010077 (registering DOI) - 10 Jan 2026

Abstract

Patients with severe structural tooth wear present significant restorative challenges, including compromised oral function and the loss of essential anatomical landmarks such as marginal ridges, incisal edges, cusps, occlusal planes, and vertical dimension of occlusion (VDO). Successful management requires meticulous diagnosis, comprehensive treatment [...] Read more.

Patients with severe structural tooth wear present significant restorative challenges, including compromised oral function and the loss of essential anatomical landmarks such as marginal ridges, incisal edges, cusps, occlusal planes, and vertical dimension of occlusion (VDO). Successful management requires meticulous diagnosis, comprehensive treatment planning, and careful selection of restorative materials with appropriate biomechanical properties. Digital technologies have become integral to this process, particularly for enhancing diagnostic accuracy, material selection, and tooth preparation design within a fully digital workflow. This clinical case report illustrates a complete digital approach, beginning with an initial intraoral scan merged with a digital wax-up STL file featuring varying translucency dimensions to guide tooth preparation. This workflow enabled precise planning of tooth reduction, accurate assessment of available interocclusal space, and determination of material thickness requirements prior to irreversible procedures. Additionally, the integration of digital visualization improved patient communication, treatment predictability, and interdisciplinary collaboration. Overall, this case highlights the value of CAD/CAM technology in supporting complex oral rehabilitation for patients with advanced tooth wear, demonstrating its capacity to enhance efficiency, precision, and outcome quality in full-mouth zirconia ceramic restorations. Full article

(This article belongs to the Special Issue New Tools for Multidisciplinary Treatment in Dentistry, 2nd Edition)

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32 pages, 9391 KB

Open AccessArticle

From “Data Silos” to “Collaborative Symbiosis”: How Digital Technologies Empower Rural Built Environment and Landscapes to Bridge Socio-Ecological Divides: Based on a Comparative Study of the Yuanyang Hani Terraces and Yu Village in Anji

by Weiping Zhang and Yian Zhao

Buildings 2026, 16(2), 296; https://doi.org/10.3390/buildings16020296 (registering DOI) - 10 Jan 2026

Abstract

Rural areas are currently facing a deepening “social-ecological divide,” where the fragmentation of natural, economic, and cultural data—often trapped in “data silos”—hinders effective systemic governance. To bridge this gap, in this study, the Rural Landscape Information Model (RLIM), an integrative framework designed to [...] Read more.

Rural areas are currently facing a deepening “social-ecological divide,” where the fragmentation of natural, economic, and cultural data—often trapped in “data silos”—hinders effective systemic governance. To bridge this gap, in this study, the Rural Landscape Information Model (RLIM), an integrative framework designed to reconfigure rural connections through data fusion, process coordination, and performance feedback, is proposed. We validate the framework’s effectiveness through a comparative analysis of two distinct rural archetypes in China: the innovation-driven Yu Village and the heritage-conservation-oriented Hani Terraces. Our results reveal that digital technologies drive distinct empowerment pathways moderated by regional contexts: (1) In the data domain, heterogeneous resources were successfully integrated into the framework in both cases (achieving a Monitoring Coverage > 80%), yet served divergent strategic ends—comprehensive territorial management in Yu Village versus precision heritage monitoring in the Hani Terraces. (2) In the process domain, digital platforms restructured social interactions differently. Yu Village achieved high individual participation (Participation Rate ≈ 0.85) via mobile governance apps, whereas the Hani Terraces relied on cooperative-mediated engagement to bridge the digital divide for elderly farmers. (3) In the performance domain, the interventions yielded contrasting but positive economic-ecological outcomes. Yu Village realized a 25% growth in tourism revenue through “industrial transformation” (Ecology+), while the Hani Terraces achieved a 12% value enhancement by stabilizing traditional agricultural ecosystems (Culture+). This study contributes a verifiable theoretical model and a set of operational tools, demonstrating that digital technologies are not merely instrumental add-ons but catalysts for fostering resilient, collaborative, and context-specific rural socio-ecological systems, ultimately offering scalable governance strategies for sustainable rural revitalization in the digital era. Full article

(This article belongs to the Special Issue Digital Technologies in Construction and Built Environment)

26 pages, 7320 KB

Open AccessArticle

Atmospheric Drivers and Spatiotemporal Variability of Pan Evaporation Across China (2002–2018)

by Shuai Li and Xiang Li

Atmosphere 2026, 17(1), 73; https://doi.org/10.3390/atmos17010073 (registering DOI) - 10 Jan 2026

Abstract

Pan evaporation (PE) is widely used as an indicator of atmospheric evaporative demand and is relevant to irrigation demand and climate-related hydrological changes. Using daily records from 759 meteorological stations across China during 2002–2018, this study investigated the temporal trends, spatial patterns, and [...] Read more.

Pan evaporation (PE) is widely used as an indicator of atmospheric evaporative demand and is relevant to irrigation demand and climate-related hydrological changes. Using daily records from 759 meteorological stations across China during 2002–2018, this study investigated the temporal trends, spatial patterns, and climatic controls of PE across seven major climate zones. Multiple decomposition techniques revealed a dominant annual cycle and a pronounced peak in 2018, while a decreasing interannual trend was observed nationwide. Spatial analyses showed a clear north–south contrast, with the strongest declines occurring in northern China. A random forest (RF) model was employed to quantify the contributions of climatic variables, achieving high predictive performance. RF results indicated that the dominant drivers of PE varied substantially across climate zones: sunshine duration (as a proxy for solar radiation) and air temperature mainly controlled PE in humid regions, while wind speed and relative humidity (RH) exerted stronger influences in arid and semi-arid regions. The widespread decline in northern China is consistent with concurrent changes in wind speed and sunshine duration, together with humidity conditions, which modulate evaporative demand at monthly scales. These findings highlight substantial spatial heterogeneity in PE responses to climate forcing and provide insights for drought assessment and water resource management in a warming climate. Full article

(This article belongs to the Section Climatology)

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19 pages, 1826 KB

Open AccessArticle

Evaluation of the Efficacy of an Artificial Intelligence-Based Assessment and Correction System in the Rehabilitation of Patients Following Anterior Cruciate Ligament Reconstruction Surgery

by Tingting Zhu, Ying Huang, Jingjing Pu, Chaolong Wang, Min Ruan, Ping Lu, Xiaojiang Yang, Nirong Bao, Yueying Chen and Aiqin Zhang

J. Clin. Med. 2026, 15(2), 575; https://doi.org/10.3390/jcm15020575 (registering DOI) - 10 Jan 2026

Abstract

Background: Arthroscopic anterior cruciate ligament (ACL) reconstruction is widely recognised as the primary treatment for ACL injuries. However, with the increasing incidence of sports-related injuries and growing demand for rehabilitation services, conventional rehabilitation models—largely reliant on therapists’ experience and subjective assessment—are increasingly insufficient [...] Read more.

Background: Arthroscopic anterior cruciate ligament (ACL) reconstruction is widely recognised as the primary treatment for ACL injuries. However, with the increasing incidence of sports-related injuries and growing demand for rehabilitation services, conventional rehabilitation models—largely reliant on therapists’ experience and subjective assessment—are increasingly insufficient to meet the clinical need for precise and individualised rehabilitation programmes. This study aimed to evaluate the effectiveness of a rehabilitation protocol incorporating an artificial intelligence (AI)-based assessment and correction system on functional recovery following ACL reconstruction. Methods: Using convenience sampling, 80 patients undergoing ACL reconstruction between June to December 2024 were recruited for this randomised controlled trial. Participants were randomly assigned to either a control group (n = 40), which received conventional functional exercise training, or a trial group (n = 40), which received rehabilitation intervention guided by an AI-based assessment and correction system. Knee function scores (Lysholm score, IKDC score), Berg Balance Scale (BBS) scores, joint range of motion (ROM), and rehabilitation exercise compliance scores were collected and analysed 1, 2, 3, and 4 months postoperatively. Results: Compared with the control group, the trial group demonstrated significantly greater improvements in Lysholm score, IKDC score, BBS score, and active knee joint ROM (p < 0.05) at postoperative assessment points. Additionally, rehabilitation exercise adherence was significantly higher in the trial group compared to the control group (p < 0.05). Conclusions: Rehabilitation protocols integrating AI-based assessment and correction systems effectively enhance knee function recovery, joint mobility and balance ability following ACL reconstruction. Moreover, these protocols significantly improve rehabilitation exercise adherence, demonstrating superior efficacy compared to conventional rehabilitation approaches. This digital rehabilitation model represents an efficient and promising intervention for postoperative ACL rehabilitation. Full article

(This article belongs to the Section Clinical Rehabilitation)

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8 pages, 193 KB

Open AccessProtocol

Effectiveness of Metformin in Preventing Type 2 Diabetes in Children and Adolescents with Overweight or Obesity: A Protocol for a Systematic Review and Meta-Analysis

by Neil Wills, Neeki Derhami, Aadya Makhija, Hayley Patrick, Ava Pourtousi, Jade Asfour, Liam McAlister, Tiago Jeronimo dos Santos and Marina Ybarra

Obesities 2026, 6(1), 4; https://doi.org/10.3390/obesities6010004 (registering DOI) - 10 Jan 2026

Abstract

Type 2 diabetes is increasingly prevalent among children and adolescents with overweight or obesity, and although lifestyle interventions remain first-line preventive strategies, long-term adherence and effectiveness are often limited. Metformin has demonstrated efficacy in delaying type 2 diabetes onset in adults at high [...] Read more.

Type 2 diabetes is increasingly prevalent among children and adolescents with overweight or obesity, and although lifestyle interventions remain first-line preventive strategies, long-term adherence and effectiveness are often limited. Metformin has demonstrated efficacy in delaying type 2 diabetes onset in adults at high risk, but its preventive role in pediatric populations remains unclear. This systematic review and meta-analysis aims to evaluate the effectiveness of metformin, alone or in combination with lifestyle interventions, in preventing or delaying type 2 diabetes among children and adolescents with overweight or obesity. The protocol is registered in PROSPERO (CRD42024615622), MEDLINE (PubMed), Embase, Cochrane Library, Scopus, and Web of Science and will be searched from inception to June 2025. Eligible studies include randomized controlled trials, quasi-experimental studies, and prospective cohort studies involving individuals under 18 years of age. The primary outcome is incidence of type 2 diabetes, with secondary outcomes including fasting plasma glucose, HbA1c, insulin resistance, BMI z-score, adherence, and adverse events. Where appropriate, random-effects meta-analyses will be conducted. This review will synthesize current evidence on metformin for pediatric type 2 diabetes prevention and inform future preventive strategies and clinical decision-making. Full article

15 pages, 1266 KB

Open AccessArticle

Efficient and Lightweight Differentiable Architecture Search

by Min Zhou, Wenqi Du, Jianming Li and Xin Li

Electronics 2026, 15(2), 314; https://doi.org/10.3390/electronics15020314 (registering DOI) - 10 Jan 2026

Abstract

While Neural Architecture Search (NAS) has revolutionized the automation of deep learning model design, gradient-based approaches like DARTS often suffer from high computational overheads, the collapse of skip-connections, and optimization instability. To address these limitations, we propose Efficient and Lightweight Differentiable Architecture Search [...] Read more.

While Neural Architecture Search (NAS) has revolutionized the automation of deep learning model design, gradient-based approaches like DARTS often suffer from high computational overheads, the collapse of skip-connections, and optimization instability. To address these limitations, we propose Efficient and Lightweight Differentiable Architecture Search (EL-DARTS). EL-DARTS constructs a compact and redundancy-reduced search space, integrates a partial channel strategy to lower memory usage, employs a Dynamic Coefficient Scheduling Strategy to balance edge importance, and introduces entropy regularization to sharpen operator selection. Experiments on CIFAR-10 and ImageNet demonstrate that EL-DARTS substantially improves both search efficiency and accuracy. Remarkably, it attains a 2.47% error rate on CIFAR-10, requiring merely 0.075 GPU-days for the search. On ImageNet, the discovered architecture achieves a 26.2% top-1 error while strictly adhering to the mobile setting (<600 M MACs). These findings confirm that EL-DARTS effectively stabilizes the search process and pushes the efficiency frontier of differentiable NAS. Full article

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16 pages, 879 KB

Open AccessArticle

Moments of Real, Respectively of Complex Valued Functions, Approximation and Applications

by Cristian Octav Olteanu

Mathematics 2026, 14(2), 272; https://doi.org/10.3390/math14020272 (registering DOI) - 10 Jan 2026

Abstract

The first aim of this study is to point out new aspects of approximation theory applied to a few classes of holomorphic functions via Vitali’s theorem. The approximation is made with the aid of the complex moments of the involved functions, which are [...] Read more.

The first aim of this study is to point out new aspects of approximation theory applied to a few classes of holomorphic functions via Vitali’s theorem. The approximation is made with the aid of the complex moments of the involved functions, which are defined similarly to the moments of a real-valued continuous function. By applying uniform approximation of continuous functions on compact intervals via Korovkin’s theorem, the hard part concerning uniform approximation on compact subsets of the complex plane follows according to Vitali’s theorem. The theorem on the set of zeros of a holomorphic function is also applied. In the end, the existence and uniqueness of the solution for a multidimensional moment problem are characterized in terms of limits of sums of quadratic expressions. This is the application appearing at the end of the title. Consequences resulting from the first part of the paper are pointed out with the aid of functional calculus for self-adjoint operators. Full article

(This article belongs to the Special Issue Nonlinear Approximation Theory in Banach Spaces)

13 pages, 1081 KB

Open AccessArticle

Activity of Natural Substances and n-Undecyl-α/β-l-Fucopyranoside Against the Formation of Pathogenic Biofilms by Pseudomonas aeruginosa

by Christian Dietrich Vogel, Anne Christine Aust, Raffael Christoph Wende, Undraga Schagdarsurengin and Florian Wagenlehner

Antibiotics 2026, 15(1), 76; https://doi.org/10.3390/antibiotics15010076 (registering DOI) - 10 Jan 2026

Abstract

Background/Objectives: Emerging biofilms of uropathogenic bacteria, particularly P. aeruginosa, on medical devices such as urinary catheters, lead to complications in the treatment of urinary tract infections (UTI). Considering the spread of antibiotic resistance, the search for alternative efficient control options for [...] Read more.

Background/Objectives: Emerging biofilms of uropathogenic bacteria, particularly P. aeruginosa, on medical devices such as urinary catheters, lead to complications in the treatment of urinary tract infections (UTI). Considering the spread of antibiotic resistance, the search for alternative efficient control options for biofilms is of great medical interest. Methods: Curcumin, 1-monolaurin, n-undecyl-α/β-l-fucopyranoside, and the fungal metabolite terrein were investigated for their influence on biofilm formation by P. aeruginosa on latex catheter pieces in artificial urine (AU), monitoring the number of colony-forming units per cm Latex-Catheter (CFU/cm Latex-Catheter). Results: Significant inhibition of P. aeruginosa biofilm formation [55.6% CFU reduction/cm²] was observed with the fungal metabolite terrein at 256 µg/mL AU. At a concentration of 512 µg/mL AU, terrein achieved almost complete inhibition of biofilm formation. n-undecyl-α/β-l-fucopyranoside inhibited biofilm formation [58.3% CFU reduction/cm²] by P. aeruginosa ATCC 27853 at 512 µg/mL AU. Compared to that, it caused an increase in biofilm formation [87.0% CFU increase/cm²] by P. aeruginosa PA 01 at 256 µg/mL AU. This study is limited by the fact that no investigations into the possible cytotoxicity of the two active substances, terrein and n-undecyl-α/β-l-fucopyranoside, on healthy eukaryotic cells have been carried out. Conclusions: Natural substances may be a promising approach to prevent the formation of P. aeruginosa biofilms. For antibacterial applications, fungal metabolites, such as terrein, offer a novel approach to prevent biofilms in urological practice. Full article

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27 pages, 1311 KB

Open AccessReview

Peptide-Functionalized Iron Oxide Nanoparticles for Cancer Therapy: Targeting Strategies, Mechanisms, and Translational Opportunities

by Andrey N. Kuskov, Lydia-Nefeli Thrapsanioti, Ekaterina Kukovyakina, Anne Yagolovich, Elizaveta Vlaskina, Petros Tzanakakis, Aikaterini Berdiaki and Dragana Nikitovic

Molecules 2026, 31(2), 236; https://doi.org/10.3390/molecules31020236 (registering DOI) - 10 Jan 2026

Abstract

Therapeutic peptides have emerged as promising tools in oncology due to their high specificity, favorable safety profile, and capacity to target molecular hallmarks of cancer. Their clinical translation, however, remains limited by poor stability, rapid proteolytic degradation, and inefficient biodistribution. Iron oxide nanoparticles [...] Read more.

Therapeutic peptides have emerged as promising tools in oncology due to their high specificity, favorable safety profile, and capacity to target molecular hallmarks of cancer. Their clinical translation, however, remains limited by poor stability, rapid proteolytic degradation, and inefficient biodistribution. Iron oxide nanoparticles (IONPs) offer a compelling solution to these challenges. Owing to their biocompatibility, magnetic properties, and ability to serve as both drug carriers and imaging agents, IONPs have become a versatile platform for precision nanomedicine. The integration of peptides with IONPs has generated a new class of hybrid systems that combine the biological accuracy of peptide ligands with the multifunctionality of magnetic nanomaterials. Peptide functionalization enables selective tumor targeting and deeper tissue penetration, while the IONP core supports controlled delivery, MRI-based tracking, and activation of therapeutic mechanisms such as magnetic hyperthermia. These hybrids also influence the tumor microenvironment (TME), facilitating stromal remodeling and improved drug accessibility. Importantly, the iron-driven redox chemistry inherent to IONPs can trigger regulated cell death pathways, including ferroptosis and autophagy, inhibiting opportunities to overcome resistance in aggressive or refractory tumors. As advances in peptide engineering, nanotechnology, and artificial intelligence accelerate design and optimization, peptide–IONP conjugates are poised for translational progress. Their combined targeting precision, imaging capability, and therapeutic versatility position them as promising candidates for next-generation cancer theranostics. Full article

(This article belongs to the Special Issue Exploring the Therapeutic Potential of Peptides in Anti-Cancer Treatments)

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19 pages, 706 KB

Open AccessArticle

Urban–Rural Environmental Regulation Convergence and Enterprise Export: Micro-Evidence from Chinese Timber Processing Industry

by Kangze Zheng, Yufen Zhong, Yu Huang and Weiming Lin

Forests 2026, 17(1), 95; https://doi.org/10.3390/f17010095 (registering DOI) - 10 Jan 2026

Abstract

Environmental regulations serve as a critical determinant of industrial competitiveness in the global market. Recent policy shifts have driven a gradual convergence of rural environmental standards with urban norms, fostering a dynamic landscape of “top-down competition” between urban and rural regulatory frameworks. While [...] Read more.

Environmental regulations serve as a critical determinant of industrial competitiveness in the global market. Recent policy shifts have driven a gradual convergence of rural environmental standards with urban norms, fostering a dynamic landscape of “top-down competition” between urban and rural regulatory frameworks. While the economic consequences of regional regulatory disparities are well-documented, the specific impacts of this regulatory convergence remain insufficiently explored. To address this gap, this study constructs a novel index to measure the convergence of environmental regulations between urban districts and rural counties at the prefecture level. Utilizing an unbalanced panel dataset of 5600 county-level timber processing enterprises, the Heckman two-stage model is employed for empirical analysis. The results demonstrate that the convergence of urban and rural environmental regulations significantly enhances both the export probability and export intensity of county-level firms, with these effects exhibiting persistence and cumulative growth over time. These findings remain robust across a series of validation tests, including instrumental variable estimation, double machine learning, and alternative model specifications. Mechanism analysis reveals that regulatory convergence promotes exports primarily by improving access to green credit and enhancing peer quality within the industry. Furthermore, heterogeneity tests indicate that the positive effects are most pronounced for start-ups and firms in the decline stage, as well as for enterprises located in eastern China, those outside the Yangtze River Economic Belt, and those subject to minimal government intervention. This study provides critical micro-level evidence that helps enterprises navigate the evolving policy landscape and supports the formulation of strategies to boost export trade amidst the integration of environmental regulations. Full article

(This article belongs to the Special Issue Toward the Future of Forestry: Education, Technology, and Governance)

17 pages, 21797 KB

Open AccessArticle

Numerical Investigation of Micromechanical Failure Evolution in Rocky High Slopes Under Multistage Excavation

by Tao Zhang, Zhaoyong Xu, Cheng Zhu, Wei Li, Yu Nie, Yingli Gao and Xiangmao Zhang

Appl. Sci. 2026, 16(2), 739; https://doi.org/10.3390/app16020739 (registering DOI) - 10 Jan 2026

Abstract

High rock slopes are extensively distributed in areas of major engineering constructions, such as transportation infrastructure, hydraulic projects, and mining operations. The stability and failure evolution mechanism during their multi-stage excavation process have consistently been a crucial research topic in geotechnical engineering. In [...] Read more.

High rock slopes are extensively distributed in areas of major engineering constructions, such as transportation infrastructure, hydraulic projects, and mining operations. The stability and failure evolution mechanism during their multi-stage excavation process have consistently been a crucial research topic in geotechnical engineering. In this paper, a series of two-dimensional rock slope models, incorporating various combinations of slope height and slope angle, were established utilizing the Discrete Element Method (DEM) software PFC2D. This systematic investigation delves into the meso-mechanical response of the slopes during multi-stage excavation. The Parallel Bond Model (PBM) was employed to simulate the contact and fracture behavior between particles. Parameter calibration was performed to ensure that the simulation results align with the actual mechanical properties of the rock mass. The research primarily focuses on analyzing the evolution of displacement, the failure modes, and the changing characteristics of the force chain structure under different geometric conditions. The results indicate that as both the slope height and slope angle increase, the inter-particle deformation of the slope intensifies significantly, and the shear band progressively extends deeper into the slope mass. The failure mode transitions from shallow localized sliding to deep-seated overall failure. Prior to instability, the force chain system exhibits an evolutionary pattern characterized by “bundling–reconfiguration–fracturing,” serving as a critical indicator for characterizing the micro-scale failure mechanism of the slope body. Full article

(This article belongs to the Section Civil Engineering)

14 pages, 987 KB

Open AccessArticle

Impact of UGT1A1*28 Allele on the Safety and Effectiveness of Sacituzumab Govitecan in Metastatic Triple-Negative Breast Cancer: Real-World Evidence

by Fernando do Pazo-Oubiña, Betel del Rosario García, Marta Miarons, Eva M. Legido Perdices, Elena Prado Mel, Ruth Ramos Díaz and Fernando Gutiérrez Nicolás

J. Clin. Med. 2026, 15(2), 574; https://doi.org/10.3390/jcm15020574 (registering DOI) - 10 Jan 2026

Abstract

Background: The UGT1A1 gene is associated with the toxicity caused by SN38, the cytotoxic component of Sacituzumab govitecan (SG) used in the treatment of metastatic triple-negative breast cancer (mTNBC), among other approved indications. In this study, we aimed to analyze the effect of [...] Read more.

Background: The UGT1A1 gene is associated with the toxicity caused by SN38, the cytotoxic component of Sacituzumab govitecan (SG) used in the treatment of metastatic triple-negative breast cancer (mTNBC), among other approved indications. In this study, we aimed to analyze the effect of UGT1A1*28 allele on the safety and, secondarily, the effectiveness of SG in mTNBC. Methods: This was a multicenter, ambispective study that included patients treated with SG for mTNBC. Genotyping for UGT1A1*28 was performed using real-time polymerase chain reaction (PCR). Adverse events (AEs) of grade ≥ 2 during the first three cycles were compared between patients who were homozygous mutant (UGT1A1*28/*28) and those with wild-type (WT) or heterozygous genotypes. Effectiveness between the two groups was also compared using progression-free survival (PFS) and overall survival (OS) assessed with the Kaplan–Meier method. Results: A total of 81 patients were included: 37.0% were WT, 55.6% heterozygous, and 7.4% homozygous mutant. All UGT1A1 *28/*28 patients experienced grade ≥ 2 AEs (100% vs. 69.3%; p = 0.109), with a statistically significant association in the case of febrile neutropenia (33.3% vs. 6.7%; p = 0.025), and a trend towards higher rates of anemia and diarrhea (50.0% vs. 17.3%; p = 0.053). Genotype did not influence PFS or OS; however, dose reductions were associated with better survival outcomes. Conclusions: This real-world study shows a correlation between toxicity and the presence of the UGT1A1*28 mutation in patients treated with SG for mTNBC. Improving treatment tolerability through dose reductions may enhance SG effectiveness. These findings support the implementation of UGT1A1 genotyping in routine clinical practice. Full article

(This article belongs to the Special Issue Breast Cancer: Clinical Diagnosis and Personalized Therapy)

15 pages, 2186 KB

Open AccessArticle

A Short-Term Wind Power Forecasting Method Based on Multi-Decoder and Multi-Task Learning

by Qiang Li, Yongzhi Liu, Xinyue Yan, Haipeng Zhang, Siyu Wang and Ran Li

Energies 2026, 19(2), 349; https://doi.org/10.3390/en19020349 (registering DOI) - 10 Jan 2026

Abstract

In short-term power forecasting for wind farms, factors such as weather conditions and geographic location lead to certain correlations in the power output of different wind farms, resulting in complex coupling relationships between them. Traditional wind power forecasting methods often predict each wind [...] Read more.

In short-term power forecasting for wind farms, factors such as weather conditions and geographic location lead to certain correlations in the power output of different wind farms, resulting in complex coupling relationships between them. Traditional wind power forecasting methods often predict each wind farm independently, without considering these coupling relationships. To address this issue, this paper proposes a multi-task Transformer model based on multiple decoders, which accounts for the intrinsic connections between different wind farms, enabling joint power forecasting across multiple sites. The proposed model adopts a single encoder-multiple decoder structure, where a unified encoder processes all input data, and multiple decoders perform prediction tasks for each wind farm separately. Testing on actual wind farm data from the Inner Mongolia region of China shows that, compared to other forecasting models, the proposed model significantly improves the accuracy of power predictions for different wind farms. Full article

(This article belongs to the Special Issue Challenges and Research Trends of Integrated Zero-Carbon Power Plant)

42 pages, 20313 KB

Open AccessArticle

Comparative Study on Multi-Objective Optimization Design Patterns for High-Rise Residences in Northwest China Based on Climate Differences

by Teng Shao, Kun Zhang, Yanna Fang, Adila Nijiati and Wuxing Zheng

Buildings 2026, 16(2), 298; https://doi.org/10.3390/buildings16020298 (registering DOI) - 10 Jan 2026

Abstract

As China’s urbanization rate continues to rise, the scale of high-rise residences also grows, emerging as one of the main sources of building energy consumption and carbon emissions. It is therefore crucial to conduct energy-efficient design tailored to local climate and resource endowments [...] Read more.

As China’s urbanization rate continues to rise, the scale of high-rise residences also grows, emerging as one of the main sources of building energy consumption and carbon emissions. It is therefore crucial to conduct energy-efficient design tailored to local climate and resource endowments during the schematic design phase. At the same time, consideration should also be given to its impact on economic efficiency and environmental comfort, so as to achieve synergistic optimization of energy, carbon emissions, and economic and environmental performance. This paper focuses on typical high-rise residences in three cities across China’s northwestern region, each with distinct climatic conditions and solar energy resources. The optimization objectives include building energy consumption intensity (BEI), useful daylight illuminance (UDI), life cycle carbon emissions (LCCO₂), and life cycle cost (LCC). The optimization variables include 13 design parameters: building orientation, window–wall ratio, horizontal overhang sun visor length, bedroom width and depth, insulation layer thickness of the non-transparent building envelope, and window type. First, a parametric model of a high-rise residence was created on the Rhino–Grasshopper platform. Through LHS sample extraction, performance simulation, and calculation, a sample dataset was generated that included objective values and design parameter values. Secondly, an SVM prediction model was constructed based on the sample data, which was used as the fitness function of MOPSO to construct a multi-objective optimization model for high-rise residences in different cities. Through iterative operations, the Pareto optimal solution set was obtained, followed by an analysis of the optimization potential of objective performances and the sensitivity of design parameters across different cities. Furthermore, the TOPSIS multi-attribute decision-making method was adopted to screen optimal design patterns for high-rise residences that meet different requirements. After verifying the objective balance of the comprehensive optimal design patterns, the influence of climate differences on objective values and design parameter values was explored, and parametric models of the final design schemes were generated. The results indicate that differences in climatic conditions and solar energy resources can affect the optimal objective values and design variable settings for typical high-rise residences. This paper proposes a building optimization design framework that integrates parametric design, machine learning, and multi-objective optimization, and that explores the impact of climate differences on optimization results, providing a reference for determining design parameters for climate-adaptive high-rise residences. Full article

(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)

9 pages, 295 KB

Open AccessProtocol

Mapping Socioecological Interconnections in One Health Across Human, Animal, and Environmental Health: A Scoping Review Protocol

by Jessica Farias Dantas Medeiros, Leonor Maria Pacheco Santos, Sindy Maciel Silva, Jorge Otávio Maia Barreto, Johnathan Portela da Silva Galdino, Eveline Fernandes Nascimento Vale, Kary Desiree Santos Mercedes, Mayara Suelirta da Costa, Juliana Michelotti Fleck, Karine Suene Mendes Almeida, Verônica Cortez Ginani, Wildo Navegantes de Araújo, Diule Vieira de Queiroz and Christina Pacheco

Int. J. Environ. Res. Public Health 2026, 23(1), 98; https://doi.org/10.3390/ijerph23010098 (registering DOI) - 10 Jan 2026

Abstract

The One Health framework highlights the interconnectedness of human, animal, and environmental health, requiring interdisciplinary and multisectoral collaboration to address complex global health challenges. This scoping review protocol aims to guide the systematic mapping on how studies and policy initiatives have incorporated socioecological [...] Read more.

The One Health framework highlights the interconnectedness of human, animal, and environmental health, requiring interdisciplinary and multisectoral collaboration to address complex global health challenges. This scoping review protocol aims to guide the systematic mapping on how studies and policy initiatives have incorporated socioecological interconnections within the One Health paradigm, following the Joanna Briggs Institute guidance and the PRISMA Scr checklist. The experimental design includes searches in PubMed, Scopus, Web of Science, LILACS, Health Systems Evidence, Social Systems Evidence, and Google Scholar for the period from 2004 to 2025. The strategy, developed with librarian support and peer reviewed, includes terms in English, Portuguese, and Spanish. Pilot searches retrieved 5333 PubMed and 470 LILACS records. Eligible documents must explicitly present two or more of the six One Health dimensions: policies to strengthen health systems; antimicrobial resistance; food safety; environmental health; emerging and re-emerging zoonotic epidemics and pandemics; endemic zoonotic, neglected tropical and vector-borne diseases. A standardized tool was developed for data extraction, synthesizing in narrative, tabular, and graphical formats. The protocol’s utilization will provide comprehensive mapping of practices and policies, identifying achievements, barriers, and knowledge gaps to inform future strategies and strengthen global health governance. Full article

19 pages, 2937 KB

Open AccessArticle

GAD-YOLO: A Sight-Distance Adaptive Detection Algorithm for General Aviation Aircraft Skin Damage

by Tao Wu, Jifei Zhong, Zhanhai Wang, Chen Chen and Zhenghong Xia

Algorithms 2026, 19(1), 61; https://doi.org/10.3390/a19010061 (registering DOI) - 10 Jan 2026

Abstract

To address the challenges in detecting surface damage on general aviation aircraft skin—such as feature degradation under varying imaging distances, significant target scale variations, and low recognition accuracy—this paper proposes GAD-YOLO, a sight-distance adaptive detection algorithm. First, a P2 small-target detection layer is [...] Read more.

To address the challenges in detecting surface damage on general aviation aircraft skin—such as feature degradation under varying imaging distances, significant target scale variations, and low recognition accuracy—this paper proposes GAD-YOLO, a sight-distance adaptive detection algorithm. First, a P2 small-target detection layer is integrated into the shallow network to enhance the capture of fine damage details. Second, an HMFHead detection head is introduced to mitigate scale variation effects through progressive semantic fusion and edge-aware constraints. Third, an LDown downsampling module is designed to construct a multi-scale feature fusion architecture. This module reduces redundancy via cross-level interaction and a lightweight kernel design, thereby decreasing the number of parameters and computational cost. Additionally, a DySample-based dynamic sampling operator is proposed to preserve local details through proximity-aware sampling while enriching the contextual semantics of distant damage features, effectively improving recognition performance. Experiments on a self-constructed general aviation aircraft skin damage dataset show that GAD-YOLO achieves 87.4% precision, 80.4% recall, 86.6% mAP@0.5, and 59.7% mAP@0.5:0.95. These results outperform the YOLOv11n baseline by 2.0%, 9.4%, 6.7%, and 7.6%, respectively. The proposed method significantly improves detection performance and provides a valuable reference for intelligent inspection and maintenance in general aviation. Full article

(This article belongs to the Special Issue Applications of Intelligent Optimization Algorithms in Integrated Transportation Systems)

26 pages, 6534 KB

Open AccessArticle

Nonlinear and Congestion-Dependent Effects of Transport and Built-Environment Factors on Urban CO₂ Emissions: A GeoAI-Based Analysis of 50 Chinese Cities

by Xiao Chen, Yubin Li, Xiangyu Li and Huang Zheng

Buildings 2026, 16(2), 297; https://doi.org/10.3390/buildings16020297 (registering DOI) - 10 Jan 2026

Abstract

Understanding how transport conditions and the built environment shape urban CO₂ emissions is critical for low-carbon urban development. This study analyses CO₂ emission intensity across fifty major Chinese cities using integrated ODIAC emissions, VIIRS night-time lights, traffic performance indicators, built-environment morphology, [...] Read more.

Understanding how transport conditions and the built environment shape urban CO₂ emissions is critical for low-carbon urban development. This study analyses CO₂ emission intensity across fifty major Chinese cities using integrated ODIAC emissions, VIIRS night-time lights, traffic performance indicators, built-environment morphology, population/POI structure, and socioeconomic controls. We develop a GeoAI workflow that couples XGBoost modelling with SHAP interpretation, congestion-based city grouping, and 1 km grid-level GNNWR to map intra-urban spatial non-stationarity. The global model identifies night-time light intensity as the strongest predictor, followed by population density and building density. SHAP results reveal pronounced nonlinearities, with high sensitivity at low–medium levels and diminishing marginal effects as activity and density increase. Although transport indicators are less influential in the aggregate model, their roles differ across congestion regimes: in low-congestion cities, emissions align more consistently with overall activity intensity, whereas in high-congestion cities they respond more strongly to population distribution, motorisation, and built-form intensity, with less stable relationships. Grid-level GNNWR further shows that key mechanisms are spatially uneven within cities, with local effects concentrating in specific cores and corridors or fragmenting across multiple subareas. These findings demonstrate that emission drivers are context-dependent across and within cities. Accordingly, uncongested cities may gain more from activity-related energy-efficiency measures, while highly congested cities may require congestion-sensitive land-use planning, spatial-structure optimisation, and motorisation control. Integrating explainable GeoAI with regime differentiation and spatial heterogeneity mapping provides actionable evidence for targeted low-carbon planning. Full article

(This article belongs to the Special Issue Climate Change and the Built Environment: Pathways to Resilience, Sustainability, and Equity)

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28 pages, 2342 KB

Open AccessArticle

Machine Learning-Based Blood Pressure Prediction Using Cardiovascular Disease Data: A Comprehensive Comparative Study

by Irina Naskinova, Mikhail Kolev, Dilyana Karova and Mariyan Milev

Electronics 2026, 15(2), 312; https://doi.org/10.3390/electronics15020312 (registering DOI) - 10 Jan 2026

Abstract

Hypertension remains one of the most pressing public health challenges worldwide, affecting more than one billion individuals and serving as a principal risk factor for cardiovascular morbidity and mortality. Whilst blood pressure measurement constitutes a routine component of clinical practice, the capacity to [...] Read more.

Hypertension remains one of the most pressing public health challenges worldwide, affecting more than one billion individuals and serving as a principal risk factor for cardiovascular morbidity and mortality. Whilst blood pressure measurement constitutes a routine component of clinical practice, the capacity to predict blood pressure values from readily obtainable patient characteristics could substantially enhance preventive care strategies and facilitate timely intervention. The present study examines whether machine learning methodologies can reliably forecast blood pressure measurements utilizing cardiovascular risk factors in conjunction with demographic and anthropometric data. We have analyzed data from 68,616 individuals following rigorous quality assessment of 70,000 patient records obtained from Kaggle’s cardiovascular disease repository. Beyond the 10 original variables, we engineered additional features encompassing demographic patterns, body composition indices, clinical risk indicators, and their interactions. Nine distinct predictive models were systematically evaluated, spanning from elementary baseline approaches through to sophisticated gradient boosting ensembles. CatBoost demonstrated superior performance, yielding systolic blood pressure predictions with a root mean squared error (RMSE) of 14.37 mmHg and coefficient of determination (R²) of 0.265, alongside diastolic blood pressure predictions with RMSE of 8.57 mmHg and R² of 0.187. These modest explained variance values—substantially below unity—reveal a fundamental limitation: blood pressure proves remarkably resistant to prediction from the demographic, anthropometric, and clinical variables typically available in epidemiological datasets. These findings illuminate a sobering reality regarding blood pressure prediction from routinely collected clinical data. The observation that standard variables account for merely one-quarter of blood pressure variance should temper expectations for machine learning applications within this domain, whilst simultaneously underscoring the necessity for richer data sources or novel biomarkers to achieve clinically meaningful predictive accuracy. Full article

(This article belongs to the Special Issue Recent Advances and Applications of Machine Learning in Pattern Recognition)

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27 pages, 6740 KB

Open AccessArticle

A Rock-on-a-Chip Approach to Investigate Flow Behavior for Underground Gas Storage Applications

by Marialuna Loffredo, Cristina Serazio, Nicolò Santi Vasile, Eloisa Salina Borello, Matteo Scapolo, Donatella Barbieri, Andrea Mantegazzi, Fabrizio Candido Pirri, Francesca Verga, Christian Coti and Dario Viberti

Energies 2026, 19(2), 348; https://doi.org/10.3390/en19020348 (registering DOI) - 10 Jan 2026

Abstract

Large-scale storage solutions play a critical role in the ongoing energy transition, with Underground Hydrogen Storage (UHS) emerging as a possible option. UHS can benefit from existing natural gas storage expertise; however, key differences in hydrogen’s behavior compared to CH₄ must be [...] Read more.

Large-scale storage solutions play a critical role in the ongoing energy transition, with Underground Hydrogen Storage (UHS) emerging as a possible option. UHS can benefit from existing natural gas storage expertise; however, key differences in hydrogen’s behavior compared to CH₄ must be characterized at the pore scale to optimize the design and the management of these systems. This work investigates two-phase (gas–water) flow behavior using microfluidic devices mimicking reservoir rocks’ pore structure. Microfluidic tests provide a systematic side-by-side comparison of H₂–water and CH₄–water disstrategies for UHS to manage trapping and optimize recovery. Full article

(This article belongs to the Special Issue Advanced Underground Energy Storage Technologies)

16 pages, 1106 KB

Open AccessArticle

Inhibitory Effect of Trichoderma longibrachiatum on Growth of Fusarium Species and Accumulation of Fumonisins

by Ruiqing Zhu, Ying Li, María Viñas, Qing Kong, Manlin Xu, Xia Zhang, Xinying Song, Kang He and Zhiqing Guo

J. Fungi 2026, 12(1), 49; https://doi.org/10.3390/jof12010049 (registering DOI) - 10 Jan 2026

Abstract

Fusarium spp. cause devastating crop diseases and produce carcinogenic mycotoxins such as fumonisins, threatening global food safety and human health. In this study, Trichoderma longibrachiatum A25011, isolated from apples in Aksu, Xinjiang, exhibited significant antagonistic activity with mycelial growth inhibition rates of 54.52% [...] Read more.

Fusarium spp. cause devastating crop diseases and produce carcinogenic mycotoxins such as fumonisins, threatening global food safety and human health. In this study, Trichoderma longibrachiatum A25011, isolated from apples in Aksu, Xinjiang, exhibited significant antagonistic activity with mycelial growth inhibition rates of 54.52% against F. verticillioides 48.62% against F. proliferatum, and 58.22% against F. oxysporum in confrontation assays. Enzyme activity detection revealed high chitinase (583.21 U/mg protein) and moderate cellulase (43.92 U/mg protein) production, which may have the capacity to degrade fungal cell walls. High-Performance Liquid Chromatography–Mass Spectrometry (HPLC-MS/MS) analyses enabled the quantification of fungal hormones including gibberellin A3 (GA3, 2.44 mg/L), cytokinins (cis-zeatin riboside (CZR): 0.69 mg/L; trans-zeatin riboside (TZR) : 0.004 mg/L; kinetin: 0.006 mg/L), and auxins (indole-3-acetic acid (IAA) : 0.35 mg/L; abscisic acid: 0.06 mg/L). Application of a T. longibrachiatum A25011 spore suspension around the roots of peanut plants enhanced growth by 13.20% (height), 5.65% (stem and leaf biomass), and 39.13% (root biomass). Notably, A25011 reduced F. proliferatum-derived fumonisin accumulation in rice-based cultures by 93.58% (6 d) and 99.35% (10 d), suggesting biosynthetic suppression. The results demonstrated that T. longibrachiatum strain A25011 exhibited excellent biocontrol capability against Fusarium spp., proving its dual role in simultaneously suppressing fungal growth and fumonisin accumulation while promoting plant growth. T. longibrachiatum A25011 could be applied as a multifunctional biocontrol agent in sustainable agriculture in the future. Full article

(This article belongs to the Special Issue Advances in the Control of Plant Fungal Pathogens)

14 pages, 1746 KB

Open AccessArticle

Resistance Patterns in Gram-Negative Bacilli Isolated in a Secondary Care Hospital: A Therapeutic Challenge in Western Mexico

by César Ricardo Cortez-Álvarez, Benjamín de Jesús Gutiérrez-García, Pablo Ulises Romero-Mendoza, María del Rosario Cabral-Medina, Monserratt Abud-Gonzalez, Susana Olivia Guerra-Martínez, Livier Amalia Gutiérrez-Morales, María Luisa Muñoz-Almaguer, Santiago José Guevara-Martínez, Daniel Osmar Suárez-Rico, Marco Pérez-Cisneros and Martin Zermeño-Ruiz

Microbiol. Res. 2026, 17(1), 17; https://doi.org/10.3390/microbiolres17010017 (registering DOI) - 10 Jan 2026

Abstract

Antimicrobial resistance (AMR) continues to represent a significant global public health concern. Gram-negative bacilli (GNB) are the primary causative agents of severe nosocomial infections and possess a notable capacity to develop resistance mechanisms that restrict therapeutic options. The objective of this study was [...] Read more.

Antimicrobial resistance (AMR) continues to represent a significant global public health concern. Gram-negative bacilli (GNB) are the primary causative agents of severe nosocomial infections and possess a notable capacity to develop resistance mechanisms that restrict therapeutic options. The objective of this study was to characterize the antimicrobial susceptibility profiles of GNB isolated at a secondary-level hospital in Guadalajara, Mexico, with the aim of identifying predominant resistance patterns and the most effective therapeutic alternatives. A descriptive, retrospective, cross-sectional study was conducted using clinical isolates of Acinetobacter spp., Pseudomonas spp., Escherichia coli, Klebsiella spp., Morganella morganii, Proteus spp., and Enterobacter spp. collected during 2024. The identification and susceptibility testing were carried out using the VITEK^® 2 automated system, and the results were interpreted in accordance with CLSI guidelines. High resistance rates were observed in Acinetobacter spp. and Pseudomonas spp., particularly to carbapenems (>50% and >40%, respectively). Escherichia coli and Klebsiella spp. demonstrated resistance to third-generation cephalosporins and trimethoprim/sulfamethoxazole, exhibiting high susceptibility to amikacin and carbapenems (>90%). New-generation β-lactam/β-lactamase inhibitor combinations, such as ceftazidime/avibactam and ceftolozane/tazobactam, have demonstrated high efficacy against resistant strains. Overall, GNB isolates in this secondary-level hospital demonstrated elevated resistance levels, particularly to β-lactams and carbapenems, which pose a significant therapeutic challenge. Nevertheless, amikacin, carbapenems, and new-generation β-lactams persist as valuable therapeutic options. In order to contain the spread of multidrug-resistant organisms, it is imperative to strengthen local surveillance, optimize antibiotic stewardship, and reinforce infection control measures. Full article

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33 pages, 10526 KB

Open AccessReview

Recent Developments in the Catalytic Enantioselective Sakurai Reaction

by Hélène Pellissier

Reactions 2026, 7(1), 6; https://doi.org/10.3390/reactions7010006 (registering DOI) - 10 Jan 2026

Abstract

The Sakurai reaction constitutes a valuable tool for carbon–carbon bond formation. The use of nontoxic allylic reagents as well as the atom economy of the global process has prompted the development of enantioselective (aza)-variants based on the use of chiral organo- and metal [...] Read more.

The Sakurai reaction constitutes a valuable tool for carbon–carbon bond formation. The use of nontoxic allylic reagents as well as the atom economy of the global process has prompted the development of enantioselective (aza)-variants based on the use of chiral organo- and metal catalysts. This review collects the recent developments in catalytic enantioselective Sakurai reactions published since the beginning of 2011, including methodologies based on the use of chiral organocatalysts, metal/boron catalysts and multicatalyst systems. It is divided into three parts, dealing successively with enantioselective organocatalytic (aza)-Sakurai reactions, enantioselective metal/boron-catalyzed Sakurai reactions and enantioselective multicatalyzed (aza)-Sakurai reactions. It shows that, although still widely developed with aromatic aldehydes, the enantioselective catalytic Sakurai reaction has considerably matured in the last decade. Full article

(This article belongs to the Special Issue Feature Papers in Reactions in 2025)

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14 pages, 1218 KB

Open AccessArticle

Finding Influencers Based on Social Interaction and Graph Structure in Social Media

by Jongtae Lim, Hwanyong Choi, Sanghyun Choi, Kyoungsoo Bok and Jaesoo Yoo

Appl. Sci. 2026, 16(2), 738; https://doi.org/10.3390/app16020738 (registering DOI) - 10 Jan 2026

Abstract

With the development of online social media, influencer detection methods on these platforms have become an important area of study. However, existing influencer detection methods often place significant emphasis on the number of followers, which can lead to a drawback in maintaining the [...] Read more.

With the development of online social media, influencer detection methods on these platforms have become an important area of study. However, existing influencer detection methods often place significant emphasis on the number of followers, which can lead to a drawback in maintaining the influence of users who have not been very active recently. In this paper, we propose an influencer detection method that takes both social interactions and the graph structure of social media into account. By considering both social interactions and graph structure, the proposed method prevents influence scores of users who have not been recently active from remaining disproportionately high. To demonstrate the superiority of the proposed method, we conducted a performance comparison with existing methods. Full article

(This article belongs to the Special Issue AI-Based Data Science and Database Systems)

25 pages, 1336 KB

Open AccessArticle

Motion Accuracy and Dynamic Responses of Dual-Manipulator on Spacecraft Considering Clearance Joints

by Yiling Kuang, Zhengfeng Bai and Cheng Wei

Aerospace 2026, 13(1), 75; https://doi.org/10.3390/aerospace13010075 (registering DOI) - 10 Jan 2026

Abstract

Clearance in joints caused by assemblage, manufacturing errors, and wear is inevitable, which will affect the dynamic performance of the dual-manipulator system on spacecraft. The motion of the dual-manipulator system with clearances in imperfect joints is the motion of dual-manipulator system with ideal [...] Read more.

Clearance in joints caused by assemblage, manufacturing errors, and wear is inevitable, which will affect the dynamic performance of the dual-manipulator system on spacecraft. The motion of the dual-manipulator system with clearances in imperfect joints is the motion of dual-manipulator system with ideal joints. In this paper, the dynamic responses and motion accuracy ofdual-manipulator system on spacecraft considering clearance effects are investigated numerically. The imperfect joint with clearance is considered as contact force constraint and the mathematical model of revolute joint with clearance is established, where the normal contact force is established using nonlinear continuous contact force model and the friction effect is considered using modified Coulomb friction model. Then, a dual-manipulator system on spacecraft with clearance joints is used as the numerical example to implement the investigation. The effects of clearances on dynamic responses and motion accuracy of the dual-manipulator system are presented and discussed via different case studies. Numerical simulation results indicate that clearances present significant effects on the dynamic performances of dual-manipulator system due to contact and impact in clearance joints. The motion accuracy and stability of the dual-manipulator system are obviously reduced. The investigation in this work clearly shows the effects of clearances on dynamic performance of the dual-manipulator system on spacecraft, which is the basis for robust control system design of dual-manipulator system. Full article

(This article belongs to the Section Astronautics & Space Science)

16 pages, 896 KB

Open AccessArticle

Off-the-Shelf Masked Ultrasonic Atomization for Hydrophilic Droplet Microarrays and Gradient Screening

by Xiaochen Lai, Xicheng Wang, Yanfei Sun, Yong Zhu and Mingpeng Yang

Appl. Sci. 2026, 16(2), 737; https://doi.org/10.3390/app16020737 (registering DOI) - 10 Jan 2026

Abstract

Droplet microarrays are increasingly used for miniaturized, high-throughput biochemical assays, yet their fabrication commonly relies on complex lithographic processes, custom masks, or specialized coatings. Here we present a simple method for generating hydrophilic arrays on hydrophobic plastic substrates by combining ultrasonic atomization with [...] Read more.

Droplet microarrays are increasingly used for miniaturized, high-throughput biochemical assays, yet their fabrication commonly relies on complex lithographic processes, custom masks, or specialized coatings. Here we present a simple method for generating hydrophilic arrays on hydrophobic plastic substrates by combining ultrasonic atomization with off-the-shelf perforated masks. A fine mist of poly(vinyl alcohol) (PVA) solution is directed through commercial diamond sieves onto polypropylene (PP) sheets and polystyrene (PS) sheets, forming hydrophilic spots surrounded by the native hydrophobic background. Static contact angle measurements confirm a strong local contrast in wettability (from 100.85 ± 0.91° on untreated PP to 39.96 ± 0.71° on patterned spots, from 95.68 ± 3.61° on untreated PS to 52.00 ± 0.85° on patterned spots), while Image analysis shows droplet CVs of 6–8% in aqueous dye solutions for 1.2–2.0 mm masks; in complex media (LB), droplet uniformity decreases. By mounting the moving mask on a motorized stage, we generate one-dimensional reagent gradients simply by controlling the moving mask motion during atomization. We further demonstrate biological compatibility by culturing Escherichia coli in LB droplets containing resazurin, and by performing localized antibiotic screening using a moving mask-guided streptomycin gradient. The resulting droplet-wise viability data yield an on-chip dose–response curve with an IC₅₀ of 5.1 µg · mL⁻¹ (95% CI: 4.5–5.6 µg·mL⁻¹), obtained from a single array. Covering droplets with Electronic Fluorinated Fluid maintains volumes within 5% of their initial value over 24 h. Compared with conventional droplet microarray fabrication, the proposed method eliminates custom mask production and cleanroom steps, is compatible with standard plastic labware, and intrinsically supports spatial gradients. These attributes make masked ultrasonic atomization a practical platform for high-throughput microfluidic assays, especially in resource-limited settings. Full article

(This article belongs to the Section Additive Manufacturing Technologies)

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