Saved Queries

by Vitaliy Levoniuk, Andriy Chaban, Paweł Czaja, Aleksander Dydycz, Andrzej Szafraniec, Roman Kwiecień and Małgorzata Górska

Energies 2025, 18(24), 6599; https://doi.org/10.3390/en18246599 (registering DOI) - 17 Dec 2025

Abstract

Based on the modified Hamilton–Ostrogradsky principle, a mathematical model of a distributed-parameter high-voltage HVAC line that includes lightning shield wires is proposed. A partial differential equation of a five-wire power line is produced as a result. Therefore, a methodology for looking for boundary conditions of a long line equation in the five-wire version is proposed here. A mathematical model is introduced as an example of a section of a power line that consists of a high-voltage long line that includes shield wires operating in an equivalent concentrated-parameter power system presented in its circuit version. The system is described with both partial and ordinary derivative differential equations. Poincaré boundary conditions of the third type are applied to solve the state equations of the object discussed. A discrete line model is thus presented, described with ordinary differential equations based on the well-known straight-line method. Transient processes across the system are analysed exactly at the moment of a lightning strike against a shield wire in the middle section of the line. To this end, a mathematical lightning strike model is developed by means of cubic spline interpolation. The original system of differential equations is integrated into the implicit Euler method, considering the Seidel method. The end results of the computer simulation are presented graphically and analysed. The results show the effectiveness of the proposed method of analysing transients across ultra-high-voltage lines that include lightning protection wires and can serve as accurate calculations of power supply lightning protection at the stages of design and production. Full article

(This article belongs to the Special Issue Modeling, Simulation, Analysis, and Design of Linear and/or Nonlinear Electrical Circuits Under Various Operating Conditions)

22 pages, 1545 KB

Open AccessArticle

The Diffusion of Risk Management Assistance for Wildland Fire Management in the United States

by Tyler A. Beeton, Tyler Aldworth, Melanie M. Colavito, Nicolena vonHedemann, Ch’aska Huayhuaca and Michael D. Caggiano

Fire 2025, 8(12), 478; https://doi.org/10.3390/fire8120478 (registering DOI) - 17 Dec 2025

Abstract

The wildland fire management system is increasingly complex and uncertain, which challenges suppression actions and increases stress on an already strained system. Researchers and managers have called for the use of strategic, risk-informed decision making and decision support tools (DSTs) in wildfire management to manage complexity and mitigate uncertainty. This paper evaluated the use of an emerging wildfire DST, the Risk Management Assistance (RMA) dashboard, during the 2021 and 2022 wildfire seasons. We used a mixed-method approach, consisting of an online survey and in-depth interviews with fire managers. Our objectives were the following: (1) to determine what factors at multiple scales facilitated and frustrated the adoption of RMA; and (2) to identify actionable recommendations to facilitate uptake of RMA. We situate our findings within the diffusion of innovations literature and use-inspired research. Most respondents indicated RMA tools were easy to use, accurate, and relevant to decision-making processes. We found evidence that the tools were used throughout the fire management cycle. Previous experience with RMA and training in risk management, trust in models, leadership support, and perceptions of current and future fire risk affected RMA adoption. Recommendations to improve RMA included articulating how the tools integrate with existing wildland fire DSTs, new tools that consider dynamic forecasting of risk, and both formal and informal learning opportunities in the pre-season, during incidents, and in post-fire reviews. We conclude with research and management considerations to increase the use of RMA and other DSTs in support of safe, effective, and informed wildfire decision making. Full article

(This article belongs to the Section Fire Social Science)

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34 pages, 1681 KB

Open AccessReview

Sarcopenia in the Aging Process: Pathophysiological Mechanisms, Clinical Implications, and Emerging Therapeutic Approaches

by Larissa Parreira Araújo, Ana Clara Figueiredo Godoy, Fernanda Fortes Frota, Caroline Barbalho Lamas, Karina Quesada, Claudia Rucco Penteado Detregiachi, Adriano Cressoni Araújo, Maria Angélica Miglino, Elen Landgraf Guiguer, Rafael Santos de Argollo Haber, Eliana de Souza Bastos Mazuqueli Pereira, Virgínia Cavallari Strozze Catharin, Vitor Cavallari Strozze Catharin, Lucas Fornari Laurindo and Sandra Maria Barbalho

Int. J. Mol. Sci. 2025, 26(24), 12147; https://doi.org/10.3390/ijms262412147 (registering DOI) - 17 Dec 2025

Abstract

In the face of population aging, sarcopenia has emerged as a significant muscle disorder characterized by the progressive loss of muscle mass, strength, and function. Chronic inflammation, oxidative stress, and mitochondrial dysfunction contribute to sarcopenia and help explain its association with comorbidities such as type 2 diabetes, obesity, and neurodegenerative diseases. Despite extensive research, there remains a need to integrate current knowledge on interventions that target these interconnected mechanisms. This review synthesizes recent evidence on the effects of resistance exercise, nutritional supplementation (high-protein intake, leucine, vitamin D, omega-3 fatty acids), and probiotic use on muscle function and inflammatory status in older adults with sarcopenia. Literature was critically analyzed to evaluate the efficacy of multicomponent strategies. The reviewed studies consistently report that combining resistance training with anti-inflammatory nutrition and targeted supplementation improves muscle strength, reduces pro-inflammatory cytokines, and supports mitochondrial function. These findings suggest that an integrated, multicomponent approach represents a promising strategy for attenuating the progression of sarcopenia and reducing its associated comorbidities. Full article

(This article belongs to the Special Issue Molecular Mechanisms of the Aging Process: 2nd Edition)

24 pages, 1572 KB

Open AccessArticle

Voltage Security-Constrained Energy Storage Planning Model Considering Multi-Agent Collaborative Optimization in High-Renewable Power Systems

by Han Jiang, Linsong Liu, Jinming Hou, Jiawei Wu, Tingke He and Xiaomeng Ai

Energies 2025, 18(24), 6597; https://doi.org/10.3390/en18246597 (registering DOI) - 17 Dec 2025

Abstract

Enhancing system strength to ensure voltage security has become a critical challenge for power systems with high penetration of renewable energy (RE). As China accelerates its clean-energy transition, the conventional grid dominated by synchronous generators is evolving into a dual-high system characterized by both high shares of wind–solar generation and extensive power-electronic interfaces. This shift fundamentally alters the mechanisms of voltage support, rendering traditional short circuit ratio (SCR) index inadequate for describing grid strength. To address this gap, this study proposes a multi-renewable-station short circuit ratio (MRSCR) index that quantitatively evaluates the voltage support strength of RE-dominated systems, and further analyzes the mechanism by which multiple agents on the generation and grid sides affect MRSCR, enhancing the generality and applicability of the proposed index. The MRSCR is further formulated as a voltage security constraint and integrated into an energy storage planning model considering multi-agent collaborative optimization. The proposed model jointly optimizes the siting and capacity configuration of grid-forming energy storage under voltage security constraints. Case studies on the IEEE 14-bus system and a real provincial grid show that incorporating the MRSCR indicator effectively enhances the system’s voltage support performance and operational resilience, achieving these improvements with only a 5.45% increase in daily operating cost compared with baseline planning results. The framework provides a practical offline tool for energy storage planning, enabling both enhanced renewable integration and improved voltage security. Full article

(This article belongs to the Section F1: Electrical Power System)

35 pages, 1453 KB

Open AccessArticle

Data-Driven Method for Predicting S-N Curve Based on Structurally Sensitive Fatigue Parameters

by Andrey Kurkin, Alexander Khrobostov, Vyacheslav Andreev and Olga Andreeva

Metals 2025, 15(12), 1384; https://doi.org/10.3390/met15121384 (registering DOI) - 17 Dec 2025

Abstract

Under cyclic loading, almost immediately after its onset, a surface layer forms where hardening and softening processes occur. The interaction of plastic deformation traces with each other, and with other structural elements, leads to the formation of a characteristic microstructure on the surface of a component subjected to cyclic loading. The set of factors (conditions) acting during cyclic loading determines the nature of slip band accumulation, the integral structurally sensitive fatigue parameter, expressed as the slope of the left side of the fatigue curve linearized in logarithmic coordinates, and the location of the breaking point on the fatigue curve in the high-cycle region. A combined review of numerous data on the fatigue of metals, obtained under various combinations of factors, and the generalization of these results through a normalization procedure for obtaining the relative (recalculated) parameters of fatigue, allows us to derive a universal method for “S-N” curve prediction. However, extensive generalization decreases the prediction accuracy for specific cases; therefore, it is proposed to form limited generalized dependencies corresponding to specific operating conditions. This paper evaluates the accuracy of fatigue limit prediction using generalized and limited-generalized relationships of fatigue recalculated parameters for various fatigue curves obtained from independent experimental data. Full article

(This article belongs to the Special Issue Fatigue, Fracture, and Multiaxial Integrity of Metallic Structure Materials: From Microstructure to Data-Driven Assessment)

20 pages, 9150 KB

Open AccessArticle

A Cascade Deep Learning Approach for Design and Control Optimization of a Dual-Frequency Induction Heating Device

by Arash Ghafoorinejad, Paolo Di Barba, Fabrizio Dughiero, Michele Forzan, Maria Evelina Mognaschi and Elisabetta Sieni

Energies 2025, 18(24), 6598; https://doi.org/10.3390/en18246598 (registering DOI) - 17 Dec 2025

Abstract

A cascade deep learning approach is proposed for optimizing the design and control of a dual-frequency induction heating system used in semiconductor manufacturing. The system is composed of two independent power inductors, fed at different frequencies, to achieve a homogeneous temperature profile along a graphite susceptor surface, crucial for enhancing layer quality and integrity. The optimization process considers both electrical (current magnitudes and frequencies) and geometrical parameters of the coils, which influence the power penetration and subsequent temperature distribution within the graphite disk. A two-step procedure based on deep neural networks (DNNs) is employed. The first step, namely optimal design, identifies the optimal operating frequencies and geometrical parameters of the two coils. The second step, namely optimal control, determines the optimal current magnitudes. The DNNs are trained using a database generated through finite element (FE) analysis. This deep learning-based cascade approach reduces computational time and multiphysics simulations compared to classical methods by reducing the dimensionality of parameter mapping. Therefore, the proposed method proves to be effective in solving high-dimensional multiphysics inverse problems. From the application point of view, achieving thermal uniformity (±7% fluctuation at 1100 °C) improves layer quality, increases efficiency, and reduces operating costs of epitaxy reactors. Full article

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26 pages, 393 KB

Open AccessReview

Essential and Toxic Elements in Cardiovascular Disease: Pathophysiological Roles and the Emerging Contribution of Hair Mineral Analysis

by Zofia Gramala, Oliwia Kalus, Joanna Maćkowiak, Katarzyna Zalewska, Michał Karpiński, Antoni Staniewski, Zofia Szymańska, Maciej Zieliński, Malwina Grobelna, Paweł Zawadzki, Ryszard Staniszewski, Aleksandra Krasińska-Płachta, Paulina Mertowska, Mansur Rahnama-Hezavah, Ewelina Grywalska and Tomasz Urbanowicz

Int. J. Mol. Sci. 2025, 26(24), 12145; https://doi.org/10.3390/ijms262412145 (registering DOI) - 17 Dec 2025

Abstract

Hair mineral analysis (HMA) has emerged as a promising non-invasive method for assessing long-term exposure to trace elements and metals, potentially complementing traditional biochemical and clinical markers of cardiovascular risk. This review synthesizes current evidence on the relationships between hair elemental profiles and cardiovascular disease (CVD), with an emphasis on toxic metals (As, Hg, Pb, Cd, Ni, Al) and essential micronutrients (Mg, Mn, Zn, Cu, Fe, Cr, Li). The reviewed studies consistently show that patients with CVD exhibit elevated levels of toxic elements and reduced concentrations of protective ones, reflecting oxidative stress, inflammation, and endothelial dysfunction as mechanistic links. Methodologically, the review highlights inductively coupled plasma mass spectrometry (ICP-MS) with collision/reaction cell technology and microwave digestion as gold-standard analytical approaches, while underscoring the urgent need for harmonized protocols, validated washing procedures, and certified reference materials. The interpretation of HMA requires consideration of temporal dynamics, external contamination, and regional variability. Although current evidence supports the research utility of HMA, its clinical integration remains limited by the absence of reference ranges and prospective validation. HMA may hold future value in environmental risk stratification and primary prevention in exposed populations, but further standardization and large-scale longitudinal studies are necessary to define its diagnostic and prognostic relevance in cardiovascular medicine. Full article

(This article belongs to the Special Issue The Role of Trace Elements in Nutrition and Health, 2nd Edition)

23 pages, 11937 KB

Open AccessArticle

Numerical Analysis of the Three-Dimensional Interaction Between Nanosecond-Pulsed Actuation and Pulsed H₂ Jets in Supersonic Crossflow

by Keyu Li, Jiangfeng Wang and Yuxuan Gu

Aerospace 2025, 12(12), 1113; https://doi.org/10.3390/aerospace12121113 (registering DOI) - 17 Dec 2025

Abstract

A combined flow control method, integrating nanosecond pulsed surface dielectric barrier discharge (NS-SDBD) with pulsed jets, is proposed to address the challenge of low mixing efficiency in supersonic combustion. Numerical validation and mechanism analysis were conducted by solving the three-dimensional unsteady Reynolds-averaged Navier–Stokes (RANS) equations, coupled with the shear stress transport (SST) k–ω turbulence model. The simulations were carried out under a Mach 2.8 inflow condition with a 50 kHz pulsed frequency for H₂ jets. The results demonstrate that, compared to the steady jet case, the combined control scheme increases the combustion product mass flow rate by 27.1% and enhances combustion efficiency by 26.8%. The average temperature in the wake region increases by 65 K, while the total pressure recovery coefficient shows only a marginal change. The pressure disturbance center evolves along the outer edge of the counter-rotating vortex pair (CVP) and is eventually absorbed by the vortex core. This process generates favorable velocity and vorticity perturbations, which enhance O₂ entrainment into the CVP and increase the average wake temperature. Meanwhile, the strengthened reflected shock induces favorable velocity perturbations in the upper shear layer of the wake and further elevates the local temperature. Full article

(This article belongs to the Section Aeronautics)

18 pages, 1372 KB

Open AccessArticle

A Knowledge-Guide Data-Driven Model with Selective Wavelet Kernel Fusion Neural Network for Gearbox Intelligent Fault Diagnosis

by Nan Zhuang, Zhaogang Ren, Dongyao Yang, Xu Tian and Yingwu Wang

Sensors 2025, 25(24), 7656; https://doi.org/10.3390/s25247656 (registering DOI) - 17 Dec 2025

Abstract

The gearbox is a critical component in modern industrial systems, directly determining the operational reliability of machinery. Therefore, effective fault diagnosis is essential to ensure its proper functioning. Modern diagnostic approaches often employ accelerometers to monitor vibration signals and apply data-driven techniques for fault identification, achieving considerable success. However, deep learning-based methods still face limitations due to their “black-box” nature and lack of interpretability. To address these issues, this paper proposes a knowledge-guided selective wavelet kernel fusion neural network. By integrating diagnostic domain knowledge into data-driven modeling, the proposed method enhances both the interpretability and diagnostic performance of intelligent fault diagnosis systems. First, a multi-kernel convolutional module is designed based on domain knowledge and embedded into a Modern Temporal Convolutional Network. Then, an attention-based selective wavelet kernel fusion strategy is introduced to adaptively fuse kernels according to the distribution of different datasets. Finally, the effectiveness of the proposed method is validated on two public datasets. Experimental results demonstrate that the approach not only provides prior interpretability, which overcoming the black-box limitation of deep learning, but also further improves diagnostic accuracy. Full article

(This article belongs to the Special Issue Deep Learning Based Intelligent Fault Diagnosis)

23 pages, 6780 KB

Open AccessArticle

Improving the Flexibility of Combined Heat and Power (CHP) Units by the Integration of Molten Salt Thermal Energy Storage

by Wei Su, Lin Li, Luyun Wang, Cuiping Ma, Congyu Wang, Xiaohan Ren and Jian Liu

Energies 2025, 18(24), 6595; https://doi.org/10.3390/en18246595 (registering DOI) - 17 Dec 2025

Abstract

Molten salt thermal energy storage (TES) provides an efficient solution to improve the flexibility of combined heat and power (CHP) plants. This study investigated two operation modes of TES: the Power-Augmenting TES Mode (Mode 1), which enhances power generation flexibility, and the Heating-Augmenting TES Mode (Mode 2), which improves the flexibility of industrial steam supply. Based on a validated thermodynamic model, the flexibility, energy efficiency, exergy efficiency, and economic performance of the integrated system are evaluated. Results show that Mode 1 offers stronger peak-shaving capability, while Mode 2 achieves comparable peak-topping performance and is more suitable for high industrial heating load scenarios due to its inherent heat–power decoupling effect. Mode 2 exhibits more pronounced energy efficiency improvement, whereas both modes reach identical maximum exergy efficiency. Additionally, the integration of molten salt TES significantly enhances profitability, increasing annual profit to 97.3 million RMB under Mode 1 and 85.4 million RMB under Mode 2 from a baseline of 79.7 million RMB. While Mode 1 generates higher profit at lower heating loads, Mode 2 becomes progressively more advantageous as industrial heating load increases. Full article

(This article belongs to the Special Issue Challenges and Research Trends of Energy Conservation and Efficient Utilization for Thermal Energy)

12 pages, 2116 KB

Open AccessArticle

A Design of High-Precision and Low-Noise High-Current Power Amplifier

by Meng Li, Zishu He, Yu Cao, Binghui He, Bin Liu and Jian Ren

Electronics 2025, 14(24), 4956; https://doi.org/10.3390/electronics14244956 (registering DOI) - 17 Dec 2025

Abstract

Addressing the limitations of existing power amplifiers, particularly in terms of accuracy and noise performance, a high-voltage and high-current power amplifier has been developed. The input stage utilizes a rail-to-rail circuit structure, allowing the amplifier to deal with the full swing of input signals from the negative to the positive power supply. The output stage features an innovative class AB configuration with a bias structure, effectively reducing the crossover distortion typically associated with traditional circuits. This design improves linearity, achieving an output range that extends to the rails, while also enhancing the power supply rejection ratio and optimizing noise performance. Furthermore, over-temperature protection and current limiting circuits have been integrated to safeguard the system against permanent damage under extreme conditions. The power amplifier circuit was simulated and validated using Cadence 61 Spectre software. With a power supply of ±30 V, the amplifier achieved an output current of 560 mA, a low-frequency gain of 138 dB, a bandwidth of 24 MHz, and a noise level of 4.8 nV/

\sqrt{H z}

. The slew rate was measured at 14.2 V/μs. Compared to existing literature, significant advancements have been achieved in terms of gain, bandwidth, and noise performance. Full article

(This article belongs to the Section Circuit and Signal Processing)

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28 pages, 5197 KB

Open AccessArticle

Enhancing Object Detection for Autonomous Vehicles in Low-Resolution Environments Using a Super-Resolution Transformer-Based Preprocessing Framework

by Mokhammad Mirza Etnisa Haqiqi, Ajib Setyo Arifin and Arief Suryadi Satyawan

World Electr. Veh. J. 2025, 16(12), 678; https://doi.org/10.3390/wevj16120678 (registering DOI) - 17 Dec 2025

Abstract

Low-resolution (LR) imagery poses significant challenges to object detection systems, particularly in autonomous and resource-constrained environments where bandwidth and sensor quality are limited. To address this issue, this paper presents an integrated framework that enhances object detection performance by incorporating a Super-Resolution (SR) preprocessing stage prior to detection. Specifically, a Dense Residual Connected Transformer (DRCT) is employed to reconstruct high-resolution (HR) images from LR inputs, effectively restoring fine-grained structural and textural information essential for accurate detection. The reconstructed HR images are subsequently processed by a YOLOv11 detector without requiring architectural modifications. Experimental evaluations demonstrate consistent improvements across multiple scaling factors, with an average increase of 13.4% in Mean Average Precision (mAP)@50 at ×2 upscaling and 9.7% at ×4 compared with direct LR detection. These results validate the effectiveness of the proposed SR-based preprocessing approach in mitigating the adverse effects of image degradation. The proposed method provides an improved yet computationally challenging solution for object detection. Full article

(This article belongs to the Section Automated and Connected Vehicles)

29 pages, 5168 KB

Open AccessArticle

Effects of Dual-Operator Modes on Team Situation Awareness: A Non-Dyadic HMI Perspective in Intelligent Coal Mines

by Xiaofang Yuan, Xinxiang Zhang, Jiawei He and Linhui Sun

Appl. Sci. 2025, 15(24), 13222; https://doi.org/10.3390/app152413222 (registering DOI) - 17 Dec 2025

Abstract

Under the context of non-dyadic human–machine interaction in intelligent coal mines, this study investigates the impact of different dyadic collaboration modes on Team Situation Awareness (TSA). Based on a simulated coal mine monitoring task, the experiment compares four working modes—Individual Operation, Supervised Operation, Cooperative Operation, and Divided-task Operation—across tasks of varying complexity. TSA was assessed using both objective (SAGAT) and subjective (SART) measures, alongside parallel evaluations of task performance and workload (NASA-TLX). The results demonstrate that, compared to Individual or Supervised Operation, both Cooperative and Divided-task Operation significantly enhance TSA and task performance. Cooperative Operation improves information integration and comprehension, while Divided-task Operation enhances response efficiency by enabling focused attention on role-specific demands. Moreover, dyadic collaboration reduces cognitive workload, with the task-sharing mode showing the lowest cognitive and temporal demands. The findings indicate that clear task structuring and real-time information exchange can alleviate cognitive bottlenecks and promote accurate environmental perception. Theoretically, this study extends the application of non-dyadic interaction theory to intelligent coal mine scenarios and empirically validates a “Collaboration Mode–TSA–Performance” model. Practically, it provides design implications for adaptive collaboration frameworks in high-risk, high-complexity industrial systems, highlighting the value of dynamic role allocation in optimizing cognitive resource utilization and enhancing operational safety. Full article

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22 pages, 450 KB

Open AccessReview

Exploring the Security of Mobile Face Recognition: Attacks, Defenses, and Future Directions

by Elísabet Líf Birgisdóttir, Michał Ignacy Kunkel, Lukáš Pleva, Maria Papaioannou, Gaurav Choudhary and Nicola Dragoni

Appl. Sci. 2025, 15(24), 13232; https://doi.org/10.3390/app152413232 (registering DOI) - 17 Dec 2025

Abstract

Biometric authentication on smartphones has advanced rapidly in recent years, with face recognition becoming the dominant modality due to its convenience and easy integration with modern mobile hardware. However, despite these developments, smartphone-based facial recognition systems remain vulnerable to a broad spectrum of attacks. This survey provides an updated and comprehensive examination of the evolving attack landscape and corresponding defense mechanisms, incorporating recent advances up to 2025. A key contribution of this work is a structured taxonomy of attack types targeting smartphone facial recognition systems, encompassing (i) 2D and 3D presentation attacks; (ii) digital attacks; and (iii) dynamic attack patterns that exploit acquisition conditions. We analyze how these increasingly realistic and condition-dependent attacks challenge the robustness and generalization capabilities of modern face anti-spoofing (FAS) systems. On the defense side, the paper reviews recent progress in liveness detection, deep-learning- and transformer-based approaches, quality-aware and domain-generalizable models, and emerging unified frameworks capable of handling both physical and digital spoofing. Hardware-assisted methods and multi-modal techniques are also examined, with specific attention to their applicability in mobile environments. Furthermore, we provide a systematic overview of commonly used datasets, evaluation metrics, and cross-domain testing protocols, identifying limitations related to demographic bias, dataset variability, and controlled laboratory conditions. Finally, the survey outlines key research challenges and future directions, including the need for mobile-efficient anti-spoofing models, standardized in-the-wild evaluation protocols, and defenses robust to unseen and AI-generated spoof types. Collectively, this work offers an integrated view of current trends and emerging paradigms in smartphone-based face anti-spoofing, supporting the development of more secure and resilient biometric authentication systems. Full article

(This article belongs to the Collection Innovation in Information Security)

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23 pages, 1680 KB

Open AccessArticle

Comprehensive Insights into Obesity and Type 2 Diabetes from Protein Network, Canonical Pathway, Phosphorylation and Antimicrobial Peptide Signatures of Human Serum

by Petra Magdolna Bertalan, Erdenetsetseg Nokhoijav, Ádám Pap, George C. Neagu, Miklós Káplár, Zsuzsanna Darula, Gergő Kalló, Laszlo Prokai and Éva Csősz

Proteomes 2025, 13(4), 67; https://doi.org/10.3390/proteomes13040067 (registering DOI) - 17 Dec 2025

Abstract

Background: Obesity is a major risk factor for type 2 diabetes (T2D); however, the molecular links between these conditions are not fully understood. Methods: We performed an integrative serum proteomics study on samples from 134 individuals (healthy controls, patients with obesity and/or T2D) using both data-independent (DIA) and data-dependent (DDA) liquid chromatography-mass spectrometry approaches, complemented by phosphopeptide enrichment, kinase activity prediction, network and pathway analyses to get more information on the different proteoforms involved in the pathophysiology of the diseases. Results: We identified 235 serum proteins, including 13 differentially abundant proteins (DAPs) between groups. Both obesity and T2D were characterized by activation of complement and coagulation cascades, as well as alterations in lipid metabolism. Ingenuity Pathway Analysis^® (IPA) revealed shared canonical pathways, while phosphorylation-based regulation differentiated the two conditions. Elevated hemopexin (HPX), vitronectin (VTN), kininogen-1 (KNG1) and pigment epithelium-derived factor (SERPINF1), along with decreased adiponectin (ADIPOQ) and apolipoprotein D (APOD), indicated a pro-inflammatory, pro-coagulant serum profile. Network analyses of antimicrobial and immunomodulatory peptides (AMPs) revealed strong overlaps between immune regulation and lipid metabolism. Phosphoproteomics and kinase prediction highlighted altered CK2 and AGC kinase activities in obesity, suggesting signaling-level modulation. Conclusions: Our comprehensive proteomic and phosphoproteomic profiling reveals overlapping yet distinct molecular signatures in obesity and T2D, emphasizing inflammation, complement activation and phosphorylation-driven signaling as central mechanisms that potentially contribute to disease progression and therapeutic targeting. Full article

(This article belongs to the Special Issue Proteomics in Diabetes: From Mechanisms to Biomarkers)

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