Search Results (11,641)

High-speed imaging offers a non-intrusive approach for monitoring falling-film flows over horizontal tube bundles, but reflective images are difficult to quantify because grayscale variations are jointly affected by film geometry, interfacial curvature, surface slope, viewing angle, and local highlights. This study proposes an interpretable visual-proxy sensing framework for comparative state assessment of such flows. Isothermal water experiments were conducted on a five-row horizontal tube bundle over Re_Γ = 184 − 960. For each condition, grayscale frames were acquired at fps and analyzed within five fixed row-wise regions of interest. The image sequence was transformed by temporal-median background subtraction, local spatiotemporal mapping, moving-average detrending, and median-absolute-deviation normalization. The resulting normalized map M_n and dynamic renewal field G were used to extract four scalar descriptors: noise-corrected apparent renewal intensity I_R, high-frequency fraction R_HF, spectral peak frequency f_p, and burst-event rate F_B. Results show that M_n and G capture the transition from sparse column flow to more continuous sheet flow and reveal row-dependent activity organization. The descriptors provide complementary information on renewal intensity, frequency composition, dominant time scale, and intermittent events. Zero-response, noise-correction, and sensitivity tests confirm that the framework avoids structured pseudo-waves and maintains stable row-wise comparisons. The method provides a low-calibration visual sensing tool for relative falling-film state assessment. Full article

Economic dispatch in grid-connected microgrids is challenged by the variability of renewable generation, the uncertainty of demand, and the need to simultaneously satisfy technical and economic constraints under different operating conditions. This study proposes an integrated predictive economic dispatch strategy for power grids with interconnected microgrids, structured as a unified optimization framework. The approach integrates nodal electrical modeling, Optimal Power Flow (OPF)-based optimization, multi-scenario analysis, and post-optimization feasibility verification based on performance indicators within a single decision-support structure. The methodology is applied to a modified 14-node power grid interconnected with a microgrid, where simulations are conducted under three representative load scenarios (100%, 70%, and 40%) and two operational configurations (hybrid and renewable-only), enabling a comprehensive assessment of system behavior. Results show that the hybrid configuration consistently outperforms the renewable-only case, achieving loss reductions of up to 7.3 MW, increases in spinning reserve exceeding 50 MW, and a transition from net power import to export of approximately 50 MW under high demand. Additionally, the microgrid plays an active operational role, dynamically switching between import and export modes based on load levels and the generation mix. The proposed framework enables identification of operationally efficient and technically feasible configurations by incorporating bidirectional power exchange, electrical constraints, and reserve requirements. The main contribution lies in integrating technical, operational, and interaction variables within a single deterministic Optimal Power Flow (OPF)-based assessment scheme to support decision-making in interconnected microgrid-based power grids. Full article

Objectives: Secondary progressive multiple sclerosis (SPMS) is a natural transition from relapsing-remitting multiple sclerosis (RRMS) in many cases. However, whether and how these phenotypes differ on an individual basis is not fully understood, limiting timely diagnosis and management for SPMS. This study aimed to investigate how deep learning using 3-dimensional (3D) frameworks including VGG19, ResNet152, and DenseNet-121 helped differentiate SPMS from RRMS based on routine clinical datasets, and what brain areas mostly contributed to this differentiation using model explanation techniques. Methods: We examined 140 participants (70 each for RRMS and SPMS) as part of an ongoing study comprising prospectively collected clinical and imaging data from routine healthcare. The data was curated to improve consistency and completeness using different strategies and were then randomly split by subject into training (n = 120) and held-out testing (n = 20). The former was used for model development through five-fold cross validation. Deep learning used T1-weighted, T2-weighted, and FLAIR brain MRI, with optional clinical variables (n = 6). A 3D gradient-weighted class activation mapping (Grad-CAM) technique was applied to identify brain areas of significance followed by ablation studies for additional insight. Results: Among the 3D frameworks validated, VGG19 was deemed the best. Based on MRI and the best 3D VGG19 model, different data curation strategies showed largely similar results. Additionally, the models combining clinical variables with MRI achieved equivalent or slightly greater performance than MRI-only models, with an average testing area under the receiver operating characteristic curve of 0.84 when datasets were fused at the flatten layer, best at 0.92, versus 0.82 and 0.89. Model explanation indicated brain regions of significance in distinguishing SPMS from RRMS individuals, including bilateral frontal lobes, left occipital and temporal lobes, and cerebellum. Conclusions: Overall findings suggest the potential of 3D deep learning models such as VGG19 for distinguishing SPMS from RRMS using routine brain MRI and clinical data, which, along with 3D Grad-CAM, could facilitate discovery of new biomarkers underlying disease worsening. Full article

This paper investigates the performance of cooperative multiple-input multiple-output (MIMO) systems under practical operating impairments, with particular emphasis on imperfect channel state information (CSI) and relay decoding errors. Although Decode-and-Forward (DF) relaying can provide diversity gains under ideal assumptions, these gains may significantly degrade in practical wireless environments affected by channel uncertainty. The analysis demonstrates that imperfect CSI introduces residual interference, leading to SINR saturation and BER error floors in the high-SNR regime. In cooperative systems, this degradation becomes more severe due to relay error propagation, where erroneously detected relay symbols introduce additional structured interference at the destination. Consequently, cooperative transmission may underperform direct MIMO communication within specific SNR operating regimes. To characterize this behavior, the concept of a harmful cooperation region is introduced, describing the operating regime in which the combined effects of CSI uncertainty and relay decoding errors render cooperation detrimental. To mitigate these limitations, a reliability-aware relay activation mechanism is proposed, enabling selective relay participation according to channel quality. By suppressing unreliable relay transmissions, the proposed approach significantly reduces error propagation and improves BER performance, particularly in the medium-to-high SNR regime. In addition, the impact of antenna scaling is investigated. The results reveal a robustness transition in which larger MIMO configurations exhibit improved resilience to CSI imperfections due to increased spatial diversity and improved channel conditioning. Overall, the findings demonstrate that cooperative transmission under imperfect CSI is inherently SNR-dependent and that robust system design requires the joint consideration of channel uncertainty, relay reliability, and system dimension. Full article

Background: Dalbavancin (DBV) is a long-acting lipoglycopeptide with activity against Gram-positive pathogens approved for the treatment of acute bacterial skin and skin structure infections (ABSSSI). Its pharmacological profile supports use in infections requiring prolonged therapy, yet its role in osteomyelitis without implantable devices (OM-WoID) remains off-label. This study aims to describe real-world DBV use in a large tertiary care hospital, focusing on its effectiveness in OM-WoID. Methods: This is a monocentric, retrospective analysis including all patients receiving DBV at ASST Spedali Civili di Brescia, Italy, from April 2017 to July 2023. The statistical analysis focused on patients who received DBV for either ABSSSI or OM-WoID, with the latter transitioning to DBV after traditional daily intravenous therapy. Clinical, microbiological, and treatment data were extracted from electronic records and stored in REDCap. Effectiveness was defined as infection resolution or improvement; treatment failure encompassed clinical worsening, recurrence or suppressive therapy. Predictors of failure were identified through univariate and stepwise multivariate logistic regression. Results: During the study period, 157 patients (63.0% male; mean age 62.5 ± 20 years) received at least one dose of DBV, predominantly for off-label indications (66.2%). Early discharge was the most common reason for switching to DBV (66.3%). Focusing specifically on patients treated for ABSSSI (53) and OM-WoID (43), treatment success was achieved in 81.1% of ABSSSI and 90.7% of OM-WoID cases. In the stepwise multivariate logistic regression, older age was independently associated with an increased risk of treatment failure (OR 1.07, 95% CI 1.01–1.13; p = 0.028), while the presence of multimorbidity significantly reduced the risk (OR 0.07, 95% CI 0.01–0.77; p = 0.029). Discussion: Our study offers a comprehensive real-world analysis of DBV use in both approved and off-label indications. Although current clinical experience with DBV remains limited, DBV emerges as a valuable step-down option for the management of invasive Gram-positive infections in our setting. Consistent with previous evidence, older age independently increased the risk of treatment failure, whereas multimorbidity appeared protective, likely due to selection bias and the more intensive monitoring, earlier interventions, and tailored management such patients often receive. Our results support a broader range of approved indications for DBV to allow earlier discharge and more efficient use of healthcare resources. Full article

Attempting to detect and improve the management of Ketosis, the objective of this study was to determine and confirm the relationship between hours of activity, rumination time, conductivity, and milk production with the presence of ketosis in cows during the transition period in dairy cows in the Comarca Lagunera region, the heart of the dairy cattle production in Mexico. Data were collected in a large scale dairy cattle study. High-precision electronic collar sensors, high-precision electronic scales, and online electronic weighing sensors were employed to determine activity and ruminating time, milk electrical conductivity, and milk yield, respectively. All data were collected and integrated using an electronic peripheral management and control software. Using urinary ketone bodies measured by qualitative strips as the biomarker for ketosis, 10.50% of the cows were found to be positive for ketosis, while the remaining 89.50% were negative. The mean and standard error for activity time (AT), ruminating time (RT), milk electrical conductivity (CE) and milk yield (MY) in normal (N) vs ketotic (P) cows were: AT N 61.38, ± 0.39, AT P 39.08 ± 0.49; RT N 530.85 ± 2.94, RT P 295.24 ± 10.69; CE N 5.68 ± 0.03, CE P 9.13 ± 0.11; and MY N 38.87 ± 0.29, MY P 20.34 ± 0.54. Exploratory Factor Analysis (EFA) was conducted for the purpose of uncovering the underlying structure of the data by identifying latent constructs that influence the observed variables. The EFA estimated two factors which explained 62% of the variation observed. The Factor 1 (MR1) comprising the variables MY and EC, and Factor 2 (MR2), which consists the variables AT and RT. High-precision measurement sensors along multivariable analyses could facilitate the establishment of a correlation between ketosis and variables associated with the physiology, well-being, and productivity of bovines in the transition period. It further open the possibility of early detection of metabolic diseases such as ketosis. Full article

Background/Objectives: Interpretation of high-dimensional molecular profiling data requires computational strategies that go beyond single-layer analyses to resolve coordinated biological programs. Although multi-omics integration has advanced latent structure discovery, translating these cross-omics signals into interpretable, functionally meaningful molecular states remains a central challenge. Methods: Here, we present an interpretable, pathway-centric multi-omics integration framework that combines expression, DNA methylation, and copy-number alteration data to capture nonlinear cross-omics interactions and enable biologically grounded representations of molecular states. We apply this framework towards oesophageal adenocarcinoma, a malignancy that remains relatively underexplored in integrative multi-omics and systems-level analyses, despite its rising incidence and poor prognosis. Results: Using samples spanning Barrett’s oesophagus and oesophageal adenocarcinoma from the Oesophageal Cancer Clinical and Molecular Stratification consortium, we demonstrate the framework’s ability to resolve coordinated oncogenic, metabolic, immune, and cell-cycle programs that evolve across disease states. Conclusions: Together, this work establishes a scalable and interpretable computational strategy for pathway-based multi-omics integration, along with providing a generalisable approach for molecular state assignment in complex biological systems. Full article

AMn₇O₁₂ perovskites (with A = divalent elements) show complex structural and magnetic transitions including incommensurate orbital density waves and coupled/decoupled modulated spin helicity originating from charge-ordered Mn³⁺/Mn⁴⁺ cations with the 3:1 ratio at the B perovskite sites and unusual apically compressed Jahn–Teller distortions of MnO₆ octahedra. The same Mn³⁺:Mn⁴⁺ ratio can be achieved in RCuMn₆O₁₂ compositions, where R is a trivalent rare-earth cation. Therefore, the comparison in behavior of AMn₇O₁₂ and RCuMn₆O₁₂ is of interest. In this work, the A-site-ordered quadruple perovskite NdCuMn₆O₁₂ was prepared by a high-pressure high-temperature method. Its structural properties were investigated by synchrotron powder X-ray diffraction between 100 K and 350 K and laboratory powder X-ray diffraction between 5 K and 300 K. It shows a first-order structural phase transition from Im-3 symmetry (at high temperatures) to R-3 symmetry near 292 K. The structural transition is accompanied by charge (Mn³⁺/Mn⁴⁺) and unusual orbital (on the Jahn–Teller active Mn³⁺ cations located in MnO₆ octahedra) orders. However, no additional structural/orbital modulations were found at lower temperatures in comparison with AMn₇O₁₂. Magnetic properties were investigated by temperature- and field-dependent magnetization and specific heat measurements, where a ferrimagnetic transition was found near 120 K. In addition, low-temperature magnetic anomalies were observed near 20 K, probably originating from the Nd sublattice. Full article

Rotational energy harvesters are often constrained by narrow operating bandwidths and sensitivity to specific rotational regimes, limiting their effectiveness under variable-speed conditions. This work presents an orientation-adaptive dual-mode piezoelectric rotational energy harvester capable of broadband energy extraction across diverse rotational and vibration environments. The proposed design combines gravity-induced magnetic excitation at low rotational speeds with centripetal-force-induced nonlinear dynamics at higher rotational speeds, enabling passive transition between operating modes without active tuning. A coupled nonlinear electromechanical model is developed to investigate the interactions among gravitational forcing, magnetic coupling, centripetal loading and piezoelectric transduction. Numerical simulations reveal the transition from gravity-dominated mono-stable behaviour to broadband nonlinear operation as rotational speed increases. Experimental validation is conducted using representative vibration profiles from aerospace, automotive, civil infrastructure and industrial environments. The results demonstrate clear orientation-dependent performance, with the downward cantilever configuration achieving a maximum average power output of 57.8 μW under aerospace elevation excitation, whilst the upward configuration exhibits improved robustness under broadband random vibrations. The proposed orientation-adaptive framework provides a compact, stator-independent solution for broadband rotational energy harvesting under realistic operating conditions. Full article

Under the “dual carbon” goals, Taiyuan, a prefecture-level administrative unit and energy-intensive region in Shanxi Province, China, has experienced changes in soil carbon storage and soil carbon flux under rapid urbanization and industrialization. To clarify the spatial patterns of soil carbon storage and flux, 26 field sampling sites, including 78 soil samples, were analyzed using laboratory measurements and an optimized tunable diode laser absorption spectroscopy–geographic information system (TDLAS–GIS) integrated monitoring approach. This study investigated the spatial patterns of soil carbon storage and flux and discussed their potentially associated factors, providing an exploratory workflow for regional carbon monitoring. The results showed clear spatial heterogeneity, with an average soil organic carbon (SOC) content of 10.86 g/kg. High-SOC areas were mainly located in the southern and southwestern plains, while lower SOC levels occurred in urban expansion zones and highly disturbed surfaces. The western mountainous areas were important ecological barriers but were not the highest measured SOC zones. At the site level, arable land and forestland showed higher mean SOC values than grassland, with average SOC contents of 12.47, 12.07, and 8.27 g/kg, respectively, although these land-use-related differences were not statistically significant. Soil carbon flux was relatively higher in some mountainous regions and industrial–ecological transition areas but lower in several urban expansion areas. The results suggest that urbanization and industrial activity may be associated with changes in SOC and soil-atmosphere CO₂ exchange. This study describes the spatial variation characteristics of soil carbon storage and flux, establishes a reproducible TDLAS–GIS workflow for regional carbon monitoring, and provides exploratory support for ecological sustainability, sustainable land management, and the “dual carbon” strategy in northern resource-based cities. Full article

Renewable integration is increasingly important for sustainable off-grid computing. The inherent variability of solar output frequently produces unusable midday surpluses. Leveraging recent Artificial Intelligence (AI) advances and established literature, we evaluate an AI-driven demand-response framework for scaling Large Language Models (LLMs) training servers using real-time solar energy data, Solcast forecasts, and battery storage records collected from Battery Management Systems (BMS), Maximum Power Point Tracking (MPPT) units, and smart inverters. An n8n AI Agent using the Ollama chat model gpt-oss:20b assesses surplus solar energy, activating selected servers to utilize otherwise wasted capacity. Workloads consistently align with solar availability, demonstrating 99% operational reliability, sub-second responsiveness, and accurate surplus-energy detection. This research demonstrates how Artificial Intelligence can repurpose surplus solar output into usable computational capacity, thereby contributing to a broader transition toward renewable-powered infrastructures. Full article

Non-isocyanate polyurethanes (NIPUs) produced by the aminolysis of cyclic carbonates are often presented as safer and more sustainable alternatives to conventional polyurethanes. Their monomer sourcing and synthetic pathways are by now fairly well explored, but the physical principles controlling their properties remain much less understood. This perspective challenges the notion that these materials follow the paradigm of conventional polyurethanes. Emphasis is placed on the hydroxyl group formed next to the urethane moiety, which distinguishes these materials from conventional polyurethanes and makes them more precisely poly(hydroxy urethanes). The available evidence indicates that this pendent hydroxyl is not a minor structural detail but a central actor affecting hydrogen bonding, microphase separation, and through them, many macroscopic physical properties of NIPUs, such as glass transition, mechanical response, water uptake and reprocessability. In addition, it enables thermally activated bond-exchange reactions, which dynamically change chain connectivity and, in networks, topology. As a result, concepts borrowed from conventional segmented polyurethanes cannot be transferred directly to non-isocyanate ones. Instead, a new, physics-oriented predictive framework is the necessary next step for the rational design of non-isocyanate polyurethanes. Such a framework should take bond-exchange reactions into account and connect molecular structure and thermal history with the macroscopic physical properties. Full article

This paper investigates the valorization of peanut shell powder (PSP), an abundant agro-industrial residue, as a biofiller for the development of sustainable 3D printable PLA-based composites for food packaging applications. A low-filled biocomposite containing 2.5 wt.% PSP was successfully processed into filament with dimensional tolerances suitable for fused deposition modeling printing. Thermal and melt flow analyses demonstrated that PSP marginally reduced the thermal stability of PLA while preserving its thermal transition temperatures and increasing the melt flow rate up to 51%. Differential scanning calorimetry revealed a slight increase in crystallinity in biocomposite filament compared to neat PLA pellets, mainly associated with thermo-mechanical processing of the extrusion, while the lower crystallinity degree relative to PLA extrudate suggested a negligible nucleating effect of PSP. To optimize print quality, different extrusion temperatures and infill flow rates were evaluated. The best mechanical performance was achieved at 200 °C and 130% flow rate, where reduced inter-filament porosity (5.2%) resulted in improved tensile strength and stiffness compared with the other printing conditions. Although mechanical properties remained lower than neat PLA, the material proved suitable for non-structural packaging applications. Prototype packaging boxes were fabricated and tested for the storage of fresh-cut melon. Compared with neat PLA packaging, the PLA-PSP system better preserved fruit firmness over 10 days, inhibited fungal growth, and delayed visible deterioration, highlighting the potential active role of PSP in food preservation. Anaerobic biodegradation tests conducted under mesophilic conditions confirmed that the addition of PSP did not hinder PLA biodegradability and slightly enhanced methane production. Overall, the results demonstrate that peanut shell waste can be effectively upcycled into functional 3D-printable biocomposites for sustainable packaging solutions. Full article

To address the difficulty in controlling the filtration performance of water-based drilling fluids under high-temperature and high-salinity conditions during the drilling of deep and ultra-deep wells, a salt-responsive micro-crosslinked polymer gel filtration loss reducer, designated LZX, was developed. The synthesis employed 2-acrylamido-2-methylpropane sulfonic acid (AMPS), N,N-dimethylacrylamide (DMAA), dimethyldiallylammonium chloride (DMDAAC), and a betaine monomer containing an unsaturated double bond as monomers, with polyethylene glycol diacrylate (PEGDA) introduced as a crosslinker. Experimental results showed that the product structure matched the design expectations, and the thermal decomposition temperature of the main molecular chain exceeded 290 °C, indicating good thermal stability. At 220 °C under saturated salt conditions, a dosage of 2.5 wt% LZX maintained the API filtration loss at 5.8 mL and the HPHT filtration loss at 28.6 mL. Comparative experiments at different temperatures demonstrated that LZX exhibited superior filtration control performance compared to the commercial high-temperature filtration reducer Driscal Temp and Driscal D. The micro-crosslinked structure of LZX enhanced the rigidity of the molecular chains, raising the upper limit of its thermal resistance. Rheological and viscosity-average molecular weight measurements revealed that LZX exhibited typical antipolyelectrolyte behavior in high-salinity environments—the molecular chains tended to extend and the filtration reduction capability was accordingly maintained—preliminarily achieving a functional transition from passive salt tolerance to active salt responsiveness. LZX is expected to support the construction of high-performance water-based drilling fluids with high temperature and high salt resistance for future deep-earth drilling. Full article

Background: Pharmaceutical innovation plays a vital role in advancing global health. This study evaluates the landscape of pharmaceutical innovation in the European Union (EU) over the 2011–2020 decade. Methods: A retrospective analysis was performed on new medicinal products containing new active substances (NAS) authorized between 2011 and 2020 through the centralized procedure. Products were categorized into first-in-class, advance-in-class, and addition-to-class. Trends in therapeutic areas (ATC codes), orphan designation, technology platforms, administration routes, and dosage forms were analyzed. Results: Across the decade, 357 new medicinal products received authorization. Of these, 56% were designated as first-in-class and 28% as advance-in-class, indicating that 84% of new products contributed substantive therapeutic innovation. Small molecules remained the predominant technology (63.5%), yet the decade also witnessed a pronounced expansion in monoclonal antibodies and the introduction of cell and gene therapies (Advanced Therapy Medicinal Products, ATMPs). The increased use of subcutaneous delivery systems, particularly for monoclonal antibodies, suggests a broader shift toward patient-centered administration routes. Conclusions: Between 2011 and 2020, pharmaceutical innovation in the European Union (EU) was characterized by a high rate of breakthrough innovations and a transition to more sophisticated biologic therapies. The EU could boost innovation further by refining expedited approval pathways (e.g., PRIME). Full article