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The Role of Kupffer Cells and Liver Macrophages in the Pathogenesis of Metabolic Dysfunction-Associated Steatotic Liver Disease
No full textMetabolic dysfunction-associated steatotic liver disease (MASLD) is a continuum of hepatic pathological manifestations of the metabolic syndrome. Pathogenesis is not clearly understood despite recent progress, but Kupffer cells and bone marrow-derived macrophages (BMDMs) have a fundamental role. In this review, the multiple pathophysiological aspects of MASLD are presented, including genetics, insulin resistance, lipotoxicity, and inflammation. The participation of innate and adaptive immunity, as well as the implications of the recently described trained immunity, is presented. The interplay of the liver with the gut microbiota is also analyzed. A recent adipocentric theory and the various mechanisms of hepatocyte death are also described. The fundamental role of Kupffer cells and other liver macrophages is discussed in detail, including their extreme phenotypic plasticity in both the normal and the MASLD liver. The functional differentiation between pro-inflammatory and anti-inflammatory subpopulations and their protective or detrimental involvement is further described, including the participation of Kupffer cells and BMDMs in all aspects of MASLD pathogenesis. The role of macrophages in the development of advanced MASLD, including fibrosis and hepatocellular carcinoma, is analyzed and the lack of explanation for the transition from MASLD to MASH is recognized. Finally, current modalities of drug treatment are briefly presented and the effects of different drugs on macrophage polarization and functions are discussed
Enhanced Social Group Optimization Algorithm for the Economic Dispatch Problem Including Wind Power
No full textThe economic dispatch (ED) problem is a major challenge in power system optimization. In this article, an Enhanced Social Group Optimization (ESGO) algorithm is presented for solving the economic dispatch problem with or without wind units, considering various characteristics related to valve-point effects, ramp-rate constraints, prohibited operating zones, and transmission power losses. The Social Group Optimization (SGO) algorithm models the social dynamics of individuals within a group—through mechanisms of collective learning, behavioral adaptation, and information exchange—and leverages these interactions to guide the population efficiently towards optimal solutions. ESGO extends SGO along three complementary directions: redefining the update relations of the original SGO, introducing stochastic operators into the heuristic mechanisms, and dynamically updating the generated solutions. These modifications aim to achieve a more robust balance between exploration and exploitation, enable flexible adaptation of search steps, and rapidly integrate improved-fitness solutions into the evolutionary process. ESGO is evaluated in six distinct cases, covering systems with 6, 40, 110, and 220 units, to demonstrate its ability to produce competitive solutions as well as its performance in terms of stability, convergence, and computational efficiency. The numerical results show that, in the vast majority of the analyzed cases, ESGO outperforms SGO and other known or improved metaheuristic algorithms in terms of cost and stability. It incorporates wind generation results at an operating cost reduction of approximately 10% compared to the thermal-only system, under the adopted linear wind power model. Moreover, relative to the size of the analyzed systems, ESGO exhibits a reduced average execution time and requires a small number of function evaluations to obtain competitive solutions
Advances in Near-Infrared Organic Photodetectors: Molecular Design, Exciton Dynamics, and Device Integration
No full textNear-infrared organic photodetectors (NIR-OPDs) are emerging as versatile platforms for flexible and low-cost optical sensing, yet achieving high-performance in the NIR region remains difficult remains challenging due to intrinsic trade-offs at both the material and device levels, due to the inherent balance required among bandgap narrowing, exciton dissociation, charge transport, and dark-current suppression. This review provides a concise overview of OPD operating mechanisms and the performance metrics governing sensitivity and noise. We highlight recent molecular-engineering strategies—core fluorination, asymmetric π-bridge design, fused-ring rigidification, and polymer backbone/side-chain tuning—that effectively enhance intermolecular ordering, reduce energetic disorder, and extend NIR absorption. Progress in all-polymer detectors and ambipolar phototransistors further demonstrates improved stability and broadened detection capability. Additionally, emerging applications, including NIR communication, biosignal monitoring, flexible imaging, and biometric recognition, showcase the expanding utility of NIR-OPDs. Remaining challenges include pushing detection beyond 1200 nm, simplifying synthesis, and improving long-term stability. Overall, advances in low-bandgap molecular design and device engineering continue to accelerate the practical adoption of NIR-OPDs
The Phylogenomic Approach Suggests That Butyrophilins Have Ligands Beyond Gamma–Delta Receptors
No full textSince γδ T cells are present in all jawed vertebrates, we wondered whether butyrophilins, proteins that play a key role in the activation of these cells, were also present in these organisms. Our analyses revealed the presence of genes encoding butyrophilins across all jawed vertebrates, including in squamates, a reptilian clade that is nonetheless reported in the literature to have lost γδ T cells. The conservation of butyrophilins in this group, despite the absence of their only known cellular partner, suggests that they may fulfill an alternative function, possibly through interaction with another ligand. Given their strong conservation across jawed vertebrates, it is reasonable to hypothesize that this alternative ligand may also be present in humans
Urban Heat Islands: Their Influence on Building Heating and Cooling Energy Demand Throughout Local Climate Zones
No full textThe thermal influence of Urban Heat Islands (UHIs) is not limited to periods of high temperature but persists throughout the year. The present study utilizes hourly data collected over a period of one year from a network of hygrothermal monitoring stations with a high density, which were deployed across the city of Cáceres (Spain). The network was designed in accordance with the World Meteorological Organization’s guidelines for urban measurements (employing radiation footprints and surface roughness) and ensures representation of each Local Climate Zone (LCZ), characterized by those factors (such as building typology and density, urban fabric, vegetation, and anthropogenic activity, among others) that influence potential solar radiation absorption. The magnitude of the heat island effect in this city has been determined to be approximately 7 °C in summer and winter at the first hours of the morning. In order to assess the energy impact of UHIs, Cooling and Heating Degree Days (CDD and HDD) were calculated for both summer and winter periods across the different LCZs. Following the implementation of rigorous quality control procedures and the utilization of gap-filling techniques, the analysis yielded discrepancies in energy demand of up to 10% between LCZs within the city. The significance of incorporating UHIs into the design of building envelopes and climate control systems is underscored by these findings, with the potential to enhance both energy efficiency and occupant thermal comfort. This methodology is particularly relevant for extrapolation to larger and denser urban environments, where the intensification of UHI effects exerts a direct impact on energy consumption and costs. The following essay will provide a comprehensive overview of the relevant literature on the subject
Color Image Encryption Based on Phase-Only Hologram Encoding Under Dynamic Constraint and Phase Retrieval Under Structured Light Illumination
No full textThis paper introduces a color image encryption technique based on phase-only hologram (POH) encoding with dynamic constraint and phase retrieval under structured light illumination (SLI). During encryption, the color plaintext is first encoded into a POH. This hologram is then transformed into an amplitude distribution through phase-amplitude conversion. Subsequently, using an iterative phase retrieval algorithm under structured light, the amplitude is encrypted into a visible ciphertext image, while a POM set is produced. The resulting ciphertext exhibits a visible image pattern, rather than noise-like appearance, providing ultrahigh imperceptibility. Moreover, the dynamic constraint in hologram encoding ensures balanced quality across color channels, leading to high-quality decrypted images with correct keys. The incorporation of a structured phase mask and the POM set expands the key space and boosts security. In decryption, the decryption structured light (DSL) illuminates the ciphertext and the neural network sequentially to generate a reconstructed amplitude. This amplitude is converted into a phase distribution via amplitude-phase conversion, which then acts as the POH for color holographic reconstruction, yielding the decrypted image. Numerical simulations demonstrate the method’s feasibility, high security, and strong robustness
Lysozyme Functionalized Alginate-Chitosan Beads and Films for Different Release Applications
No full textThe main goal of this work was to develop nanoparticles of lysozyme (Lys) for biological and biomedical applications. The developed biosystems were based on Lys-loaded calcium alginate 2% and chitosan 1% beads and films with different concentrations of each polymer. Encapsulation efficiency was 100%. The ratio of adsorbed Lys on the films, Lys activity, and the release profile of Lys were measured using water and buffer solution at pH similar to the environment of cancer cells, at a controlled temperature of 37 °C and a constant speed, to assess the efficacy of the encapsulation process. Lys antimicrobial activity was assessed using Micrococcus lysodeikticus. Moreover, the anti-inflammatory and antioxidant properties of the developed biosystems were also evaluated. The anti-inflammatory activity of Lys released from calcium alginate 2%-chitosan 1% beads loaded with Lys was about 99%. These findings highlight the potential of the developed beads and films for biomedical applications, particularly in antimicrobial and anti-inflammatory therapies
Characterization and Analysis of Hybrid Fractal Antennas for Multiband Communication and Radar Applications
No full textThis work introduces the development and performance analysis of a hybrid fractal antenna combining a Koch snowflake outer geometry with a center slot patterned as a Sierpinski rectangular carpet. The antenna is fabricated on an FR4 board (εr=4.7, tanδ=0.0197) with dimensions 40×60×0.8 mm3. Electromagnetic simulations are performed using Ansys HFSS v15, revealing seven distinct resonances at 2.11, 3.06, 5.78, 6.94, 8.48, 9.23, and 9.56 GHz. The corresponding impedance bandwidths are 90, 37, 67, 100, 90, 130, and 220 MHz, with return losses of −14, −12, −16, −10, −30, −16, and −17 dB, and VSWR values ranging from 1.06 to 1.80. The gains at these resonances are 3.92, 8.24, 6.90, 11.66, 19.38, 16.76, and 12.06 dBi. Frequency allocation analysis indicates compatibility with UMTS/LTE (2.11 GHz), S-band 5G and radar (3.06 GHz), ISM/UNII-3 Wi-Fi and ITS (5.78 GHz), C-band satellite uplink (6.94 GHz), and X-band radar/satellite downlink (8.48–9.56 GHz). The proposed geometry demonstrates wide multi-band coverage, making it a strong candidate for integration into multi-standard communication and radar platforms requiring compact, broadband, and high-directivity performance
Real-Time Sensorless Speed Control of PMSMs Using a Runge–Kutta Extended Kalman Filter
No full textPermanent magnet synchronous motors (PMSMs) are widely preferred in modern applications due to their high efficiency, high torque-to-inertia ratio, high power factor, and rapid dynamic response. Achieving optimal PMSM performance requires precise control, which depends on accurate estimation of motor speed and rotor position. This information is traditionally obtained through sensors such as encoders; however, these devices increase system cost and introduce size and integration constraints, limiting their use in many PMSM-based applications. To overcome these limitations, sensorless control strategies have gained significant attention. Since PMSMs inherently exhibit nonlinear dynamic behavior, accurate modeling of these nonlinearities is essential for reliable sensorless operation. In this study, a Runge–Kutta Extended Kalman Filter (RKEKF) approach is developed and implemented to enhance estimation accuracy for both rotor position and speed. The developed method utilizes the applied stator voltages and measured phase currents to estimate the motor states. Experimental validation was conducted on the dSPACE DS1104 platform under various operating conditions, including forward and reverse rotation, acceleration, low- and high-speed operation, and loaded operation. Furthermore, the performance of the developed RKEKF under load was compared with the conventional Extended Kalman Filter (EKF), demonstrating its improved estimation capability. The real-time feasibility of the developed RKEKF was experimentally verified through execution-time measurements on the dSPACE DS1104 platform, where the conventional EKF and the RKEKF required 47 µs and 55 µs, respectively, confirming that the proposed approach remains suitable for real-time PMSM control while accommodating the additional computational effort associated with Runge–Kutta integration
Efficient Quantization of Pretrained Deep Networks via Adaptive Block Transform Coding
No full textThis work investigates the effectiveness of block transform coding (BTC) as a lightweight, training-free quantization strategy for compressing the weights of pretrained deep neural networks. The proposed method applies a rule-based block transform with variance and root mean square error (RMSE)-driven stopping criteria, enabling substantial reductions in bit precision while preserving the statistical structure of convolutional and fully connected layer weights. Unlike uniform 8-bit quantization, BTC dynamically adjusts bit usage across layers and achieves significantly lower distortion for the same compression budget. We evaluate BTC across many pretrained architectures and tabular benchmarks. Experimental results show that BTC consistently reduces storage to 4–7.7 bits per weight while maintaining accuracy within 2–3% of the 32-bit floating point (FP32) baseline. To further assess scalability and baseline strength, BTC is additionally evaluated on large-scale ImageNet models and compared against a calibrated percentile-based uniform post-training quantization method. The results show that BTC achieves a substantially lower effective bit-width while incurring only a modest accuracy reduction relative to calibration-aware 8-bit quantization, highlighting a favorable compression–accuracy trade-off. BTC also exhibits stable behavior across successive post-training quantization (PTQ) configurations, low quantization noise, and smooth RMSE trends, outperforming naïve uniform quantization under aggressive compression. These findings confirm that BTC provides a scalable, architecture-agnostic, and training-free quantization mechanism suitable for deployment in memory- and computing-constrained environments