Search Results (6,983)

Integrated energy systems (IESs) are increasingly being deployed and expanded, which integrate various energy infrastructures to enable flexible conversion and utilization among different energy forms. To facilitate collaboration among operators of varying scales and fully leverage the economic and environmental benefits of multi-integrated energy systems (MIESs), this study develops a peer-to-peer (P2P) energy sharing framework for MIES based on asymmetric Nash bargaining. First, an IoT-based P2P energy sharing architecture for MIES is proposed, which incorporates coordinated electricity–heat–gas multi-energy synergy within IES models. Carbon capture systems (CCS) and power-to-gas (P2G) units are integrated with carbon trading mechanisms to reduce carbon emissions. Then, an MIES energy sharing operational model is established using Nash bargaining theory, subsequently decoupled into two subproblems: alliance benefit maximization and individual IES benefit distribution optimization. For subproblem 2, an asymmetric bargaining method employing natural exponential functions quantifies participant contributions, enabling fair distribution of cooperative benefits. Finally, the alternating direction method of multipliers (ADMM) is employed to solve both subproblems distributively, effectively preserving participant privacy. The effectiveness of the proposed method is verified by case simulation, demonstrating reduced operational costs across all IESs alongside equitable benefit allocation proportional to energy-sharing contributions. Carbon emission amounts are simultaneously reduced. Full article

Background: Pancreatic ductal adenocarcinoma (PDAC) exhibits marked resistance to immunotherapy. Beyond its characteristically low tumor mutational burden, post-translational modifications (PTMs) remodel the immunopeptidome and promote immune escape through reversible, enzyme-driven programs. Subject Matter: We synthesize evidence that aberrant glycosylation, O-GlcNAcylation, phosphorylation, and citrullination constitute core determinants of antigen visibility operating within spatially discrete tumor niches and a desmoplastic stroma. In hypoxic regions, HIF-linked hexosamine metabolism and OGT activity stabilize immune checkpoints and attenuate antigen processing; at tumor margins, sialylated mucins engage inhibitory Siglec receptors on innate and adaptive lymphocytes; within the stroma, PAD4-dependent NET formation enforces T cell exclusion. We also delineate technical barriers to discovering PTM antigens labile chemistry, low stoichiometry, and method-embedded biases and outline practical solutions: ETD/EThcD/AI-ETD fragmentation, PTM-aware database searching and machine-learning models, and autologous validation in patient-derived organoid–T cell co-cultures. Finally, we highlight therapeutic strategies that either immunize against PTM neoepitopes or inhibit PTM machinery (e.g., PAD4, OGT, ST6GAL1), with stromal remodeling as an enabling adjunct. Conclusions: PTM biology, spatial omics, and patient sample models can uncover targetable niches and speed up PDAC vaccination, TCR, and enzyme-directed treatment development. Full article

The inherent multiscale nature of objects poses a fundamental challenge in remote sensing object detection. To address this, feature pyramids have been widely adopted as a key architectural component. However, the effectiveness of these pyramids critically depends on the sampling operations used to construct them, highlighting the need to move beyond traditional fixed-kernel methods. While conventional interpolation approaches (e.g. nearest-neighbor and bilinear) are computationally efficient, their content-agnostic nature often leads to detail loss and artifacts. Recent dynamic sampling operators improve performance through content-aware mechanisms, yet they typically incur substantial computational and parametric costs, hindering their applicability in resource-constrained scenarios. To overcome these limitations, we propose Lurker, a learned and unified resampling kernel that supports both upsampling and downsampling within a consistent framework. Lurker constructs a compact source kernel space and employs bilinear interpolation to generate adaptive kernels, enabling content-aware feature reassembly while maintaining a lightweight parameter footprint. Extensive experiments on the DIOR and DOTA datasets demonstrate that Lurker achieves a favorable trade-off between detection accuracy and efficiency, outperforming existing resampling methods in terms of both accuracy and parameter efficiency, making it especially suitable for remote sensing object detection applications. Full article

The proposed forest monitoring photo trap ecosystem integrates a cost-effective architecture for observation and transmission using Internet of Things (IoT) technologies and long-range digital radio systems such as LoRa (Chirp Spread Spectrum—CSS) and nRF24L01 (Gaussian Frequency Shift Keying—GFSK). To address low-bandwidth links, a novel approach based on the Monte Carlo sampling algorithm enables progressive, bandwidth-aware image transfer and its thumbnail’s reconstruction on edge devices. The system transmits only essential data, supports remote image deletion/retrieval, and minimizes site visits, promoting environmentally friendly practices. A key innovation is the integration of no-reference image quality assessment (NR IQA) to determine when thumbnails are ready for operator review. Due to the computational limitations of the Raspberry Pi 3, the PIQE indicator was adopted as the operational metric in the quality stabilization module, whereas deep learning-based metrics (e.g., HyperIQA, ARNIQA) are retained as offline benchmarks only. Although single-pass inference may meet initial timing thresholds, the cumulative time–energy cost in an online pipeline on Raspberry Pi 3 is too high; hence these metrics remain offline. The system was validated through real-world field tests, confirming its practical applicability and robustness in remote forest environments. Full article

Background/Objectives: Palmitoylethanolamide (PEA) is an endogenous lipid mediator with endocannabinoid-like activity. Despite its therapeutic potential in muscle-related inflammatory disorders, including sarcopenia, its clinical use is limited by poor solubility and bioavailability. To overcome these issues, we developed hybrid nanoparticles combining poly(lactic-co-glycolic acid) (PLGA) and lipids to enhance PEA encapsulation and ok delivery. Methods: PEA-loaded hybrid nanoparticles (PEA-Hyb-np) were produced via a modified single-emulsion solvent evaporation method using stearic acid and Gelucire^® 50/13 as lipid components. Characterization included particle size, morphology, PDI, and zeta potential, as well as DSC, FT-IR, and XRD analyses. For the biological evaluation in a C2C12 myoblasts cell culture, coumarin-6-labeled nanoparticles were employed. Results: PEA-Hyb-np showed mean particle sizes of ~150 nm, with internal lipid–polymer phase separation. This structure enabled high encapsulation efficiency (79%) and drug loading (44.2 mg/g). Drug release in physiological and non-physiological media was enhanced due to drug amorphization, confirmed by DSC, FT-IR, and XRD analyses. Cytocompatibility studies showed no toxicity and improved cell viability compared to unloaded nanoparticles. Cellular uptake studies by confocal microscopy and flow cytometry demonstrated efficient and time-dependent internalization. Conclusions: PEA-Hyb-np represent a promising delivery platform to improve the solubility, bioavailability, and therapeutic efficacy of PEA for muscle-targeted applications. Full article

Background and Objectives: This study aimed to assess the week-by-week predictive value of Acute:Chronic Workload Ratios (ACWRs) for non-contact injury risk in professional soccer players. Materials and Methods: A cohort of 40 elite players was monitored using GPS over two competitive seasons. Binomial logistic regression and ROC curve analyses were performed on ACWR metrics—including total distance, moderate-to high-speed running, sprinting, acceleration, and deceleration—during the four weeks prior to injury (W4 to W1). p-values were further adjusted for multiple comparisons using the false discovery rate (FDR) correction (q < 0.05). Results: Significant predictive models emerged mainly for ACWR metrics related to moderate-speed running (15–20 km/h), sprinting (>25 km/h), and acceleration/deceleration. The ACWR for 15–20 km/h (DSR15–20) demonstrated the highest predictive accuracy, particularly in Week 3 (AUC = 0.811, p = 0.004). Sprinting (DSR>25) was also significantly associated with injury occurrence across Weeks 1–4 (AUC = 0.709–0.755, p = 0.011–0.024). Acceleration (ACC) and deceleration (DEC) metrics showed significant associations prior to correction—ACC in Weeks 3–4 (AUC = 0.737–0.755, p = 0.020–0.026) and DEC in Weeks 3–4 (AUC = 0.720–0.741, p = 0.029–0.043)—but these associations did not retain significance following FDR adjustment (q = 0.052–0.086). In contrast, total distance (ACWR TD) and high-speed running (20–25 km/h) were weaker predictors, reaching only marginal or nonsignificant levels (e.g., Week 3, AUC = 0.675, p = 0.054). After FDR correction, only DSR15–20 and DSR>25 remained statistically significant (q < 0.05), confirming them as robust predictors of non-contact injury risk. Multivariable models adjusted for age and playing position confirmed these findings, with DSR15–20 and DSR>25 retaining their predictive value independent of confounding factors. Injury risk thresholds were established through Estimated Marginal Means (EMMs), defining the “Sweet Spot” and “Danger Zone” for each metric, whereas the “Low Load” zone was treated as exploratory. Conclusions: This weekly ACWR monitoring approach enables practical injury risk profiling, helping training staff optimize load management and minimize non-contact injury risk in elite soccer. Full article

Vibrations induced by marine environmental loads can compromise the operational performance of offshore platforms and, in severe cases, result in structural instability or overturning. This study proposes a biomimetic nonlinear X-shaped vibration isolation system (NXVIS) to suppress earthquake-induced vibration response in offshore platforms. Compared with traditional passive vibration isolators, the key innovations of the NXVIS include: (1) the proposed NXVIS can be tailored to different load requirements and resonant frequencies to accommodate diverse offshore platforms and environmental loads; (2) By adjusting isolator parameters (e.g., link length and spring stiffness, etc.), the anti-vibration system can achieve different types of nonlinear stiffness and a large-stroke quasi-zero stiffness (QZS) range, enabling ultra-low frequency (ULF) vibration control without compromising load capacity. To evaluate the effectiveness of the designed NXVIS for vibration suppression of jacket offshore platforms under seismic loads, numerical analysis was performed on a real offshore platform subjected to seismic loads. The results show that the proposed nonlinear vibration isolation solution significantly reduces the dynamic response of deck displacement and acceleration under seismic loads, demonstrating effective low-frequency vibration control. This proposed NXVIS provides a novel and effective method for manipulating beneficial nonlinearities to achieve improved anti-vibration performance. Full article

To ensure end-to-end Quality of Service (QoS) management in 5G-TSN converged networks, this paper proposes a dynamic weighted QoS mapping method based on Weighted Fuzzy C-Means and Three-Way Decisions (WFCM-TDwQM). The WFCM algorithm is employed to cluster Time-Sensitive Networking (TSN) flows based on their QoS attributes, reducing computational complexity. A three-way decision-based method is used to assign a reasonable and approximate set of 5G QoS Identifier (5QI) values to each cluster. Finally, dynamic weights are adjusted by considering QoS similarity and the residual load rate, enabling the system to adapt to network load changes. The experimental results show that, compared with three other mapping algorithm combinations, WFCM-TDwQM not only ensures end-to-end QoS consistency but also achieves better load balancing under varying network loads. Moreover, its mapping performance is evaluated under different network scenarios. Full article

Fractal antennas employ self-similar geometries to generate scaled multiple resonances within compact structures, thereby achieving broadband performance. However, many reported designs remain constrained by narrow impedance bandwidths or demonstrate only multiband characteristics. To address these limitations, we present a novel pentagonal fractal antenna with ultra-wideband performance suitable for C, X, Ku and K-band applications. The key innovation lies in a double-pentagonal fractal configuration, created by embedding a secondary pentagonal ring within the conventional pentagonal radiator. This design significantly enhances the impedance bandwidth and enables ultra-wideband operation. The proposed antenna was validated through both electromagnetic simulations and experimental measurements. Results show a measured −10 dB impedance bandwidth of 3.84–22.4 GHz, corresponding to a fractional bandwidth of 141.5%. The antenna dimensions are only 0.384 × 0.525 × 0.01λ₀³. A peak gain of 10.2 dBi was achieved, with the gain varying between 2.88 and 10.2 dBi across the operating frequency range. Owing to these characteristics, the proposed antenna is well-suited for diverse wireless communication systems, including Wi-Fi, ultra-wideband communication, 5G mid-band and emerging 6G technologies. Full article

The high-temperature thermal stability of dexamethasone (DEX) was systematically investigated under nitrogen and air atmospheres using non-isothermal thermogravimetry at heating rates of 0.1–20 °C·min⁻¹. The thermal decomposition was found to initiate below the melting temperature, proceeding via a three-step pathway that generated a complex mixture of volatile and condensed by-products (~10% solid residuum at 550 °C). Kinetic modeling was realized using the single-curve multivariate kinetic analysis (sc-MKA), and was based on the autocatalytic framework with temperature-dependent parameters, combined with consequent reaction mechanisms. An excellent agreement of the theoretical model with the experimental data enabled reliable predictive extrapolations to pharmaceutical processing conditions. Whereas the onset of degradation was observed at ~180–190 °C, significant decomposition rates (>1% mass loss during first 5 min) were only reached above 220 °C, well above the processing windows of most pharmaceutical polymers. Consequently, dexamethasone can be considered thermally stable for hot-melt extrusion and fused deposition modeling, except in high-temperature-processing applications involving polymers such as, e.g., polylactic acid, polyvinyl alcohol, or thermoplastic polyurethanes. Importantly, the study highlights that reliable kinetic predictions require measurements across a broad heating-rate range and in both oxidizing and inert atmospheres, with special emphasis on low heating rates (≤0.2 °C·min⁻¹), which proved critical for capturing early-stage degradation. These findings provide a rigorous kinetic framework for ensuring safe incorporation of DEX into advanced pharmaceutical and medical device formulations. Full article

This study evaluated the effects of enzymatically produced collagen hydrolysate and Salicornia perennans extract on the quality, oxidative stability, and nutritional composition of canned canine meat pâtés. Two formulations were prepared: a control 2% NaCl, no hydrolysate and an experimental sample containing 3% collagen hydrolysate sheep:camel:bovine = 1:1:1, 1% Salicornia perennans extract, and 1% NaCl. Physicochemical, textural, amino-acid, fatty-acid, and oxidative parameters were monitored over 10 days of storage. The treated pâtés showed similar proximate composition moisture 76.1%, protein 9.2%, metabolizable energy (ME) 102 kcal·100 g⁻¹; p > 0.05 but exhibited enhanced functional stability, with reduced water loss syneresis 1.8 vs. 3.1%; p < 0.05 and improved cohesiveness 0.46 vs. 0.41; p < 0.05. Amino-acid enrichment included higher aspartic acid +33%; p < 0.05, methionine +53%; p < 0.05, and tryptophan +39%; p < 0.05, while the lipid profile showed lower SFA 52.8 vs. 56.4%; p < 0.05, higher n-3 PUFA 1.5 vs. 0.8%; p < 0.05, and a reduced n-6:n-3 ratio 3.8 vs. 5.6; p < 0.05. During storage, oxidative markers decreased: TBARS −45%, carbonyls −14%, acid value −18%, and color stability improved by +2.0 pp. These findings confirm the synergistic antioxidant and structuring effects of collagen-derived peptides and Salicornia polyphenols, as evidenced by a 45% reduction in TBARS, 14% lower protein carbonyls, and 18% lower acid value relative to the control (p < 0.05). This synergy enabled a sodium-reduced, clean-label formulation with improved technological performance, oxidative resistance, and shelf-life stability for functional wet dog foods. In addition, it enhanced the color and visual appeal—key attributes that influence both animal palatability and the purchasing decisions of pet owners. Full article

This review critically examines advancements in sensing, control, and networking technologies for agricultural robots (AgRobots) and their impact on modern farming. AgRobots—including Unmanned Aerial Vehicles (UAVs), Unmanned Ground Vehicles (UGVs), Unmanned Surface Vehicles (USVs), and robotic arms—are increasingly adopted to address labour shortages, sustainability challenges, and rising food demand. This paper reviews sensing technologies such as cameras, LiDAR, and multispectral sensors for navigation, object detection, and environmental perception. Control approaches, from classical PID (Proportional-Integral-Derivative) to advanced nonlinear and learning-based methods, are analysed to ensure precision, adaptability, and stability in dynamic agricultural settings. Networking solutions, including ZigBee, LoRaWAN, 5G, and emerging 6G, are evaluated for enabling real-time communication, multi-robot coordination, and data management. Swarm robotics and hybrid decentralized architectures are highlighted for efficient collective operations. This review is based on the literature published between 2015 and 2025 to identify key trends, challenges, and future directions in AgRobots. While AgRobots promise enhanced productivity, reduced environmental impact, and sustainable practices, barriers such as high costs, complex field conditions, and regulatory limitations remain. This review is expected to provide a foundation for guiding research and development toward innovative, integrated solutions for global food security and sustainable agriculture. Full article

Serum-free suspension culture technology for animal cells involves the division and proliferation of cells in serum-free medium as single cells or cell clusters within shaking flasks or bioreactors. This approach enables large-scale cell culture, enhances the yield and quality of biopharmaceuticals, reduces costs, and broadens the applications of animal cells. Serum-free suspension culture of adherent cells (e.g., Madin–Darby canine kidney (MDCK), Chinese hamster ovary (CHO), Vero, baby hamster kidney (BHK-21), and human embryonic kidney (HEK293) cells) has been successfully achieved through direct or indirect adaptation, medium optimization, and genetic engineering. Additionally, novel suspension cell lines, such as duck embryonic stem (EB66) and human retinoblastoma (PER.C6) cells, have been developed as potential new substrates for biopharmaceutical production. This review examines animal cell suspension culture technology and its applications in viral vaccines, recombinant proteins, and monoclonal antibodies, providing insights into the development and utilization of this important technology. Full article

Silicon (Si) anodes offer ultrahigh theoretical capacity (~4200 mAh g⁻¹) for next-generation lithium-ion batteries but suffer from severe mechanical degradation due to repetitive volume expansion (>300%). Conventional electrode-centric strategies face scalability limitations, shifting focus to electrolyte engineering as a critical solution. This review synthesizes recent advances in liquid electrolyte design for stabilizing Si anodes, emphasizing three key pillars: (i) Lithium salts that enable anion-derived inorganic-rich solid electrolyte interphase (SEI) layers with high fracture toughness; (ii) Solvent systems including carbonates, ethers, and phosphonates, where fluorination and steric hindrance tailor SEI elasticity; (iii) Functional additives (F/B/Si-containing) that form mechanically compliant interphases and scavenge detrimental species. Innovative architectures—high-concentration electrolytes (HCEs), localized HCEs (LHCEs), and weakly solvating electrolytes—are critically assessed for their ability to decouple ion transport from volume strain. The perspective highlights the imperative of hybrid solid–liquid interfaces to enable commercially viable Si anodes. Full article

This study investigates the impact of build orientation on thermal distortion, residual stress behaviour, and process efficiency in LPBF. Four orientation strategies, optimized for surface area, support volume, print time, and overheating, were generated in Siemens NX and evaluated using Atlas 3D to predict build-stage and post-support removal distortion. Experimental validation through 3D scanning enabled detailed surface deviation comparisons with simulation outputs. Results showed that support volume and print time optimizations led to the lowest in-process distortion but exhibited higher deformation after support removal, driven by residual stress relaxation. In contrast, the surface area-optimized orientation displayed greater distortion during printing but more stable post-processing behaviour. The overheating-optimized build resulted in the largest total distortion. Atlas 3D predictions aligned closely with scan data, particularly in identifying critical zones on sloped and unsupported surfaces. Sustainability and cost analysis revealed that the surface area strategy had the highest impact in reducing CO₂ emissions and production costs (~€832 and ~900 g CO₂/part), while support volume and print time orientations reduced cost by more than 20% and halved emissions. Energy consumption followed the same trend, with support volume and print time optimisations requiring only ~2 kWh/part compared to nearly 5 kWh/part for surface area, and overheating minimisation. These findings underscore the importance of integrating distortion simulation, cost, and environmental criteria into orientation selection to achieve balanced, high-performance LPBF manufacturing. Full article