Search Results (767)

Chronic hepatitis B virus (HBV) infection remains a global public health challenge, and the currently approved medications can not achieve a cure. Synthetic triterpenoids have shown promising therapeutic potential for liver pathologies. In our search for novel antiviral agents against HBV, we found that two triterpenoids, 2-cyano-3,12-dioxooleana-1,9-dien-28-oic acid (CDDO) and CDDO-ethyl amide (CDDO-EA), significantly inhibited HBV DNA replication. Further mechanistic investigation indicated that these two compounds did not significantly alter the levels of total HBV pgRNA, but dramatically reduced extracellular pgRNA and intracellular encapsidated pgRNA in a dose-dependent manner. Western blot analysis indicated minimal effects on core protein expression. Interestingly, using a particle gel assay, we observed that CDDO and CDDO-EA promoted the formation of empty capsids with no alteration in electrophoretic mobility. Moreover, we demonstrated that both compounds modulated the phosphorylation status of the core protein. Further cellular thermal shift assay (CETSA), surface plasmon resonance (SPR) assay, and molecular docking analyses collectively suggested that CDDO and CDDO-EA could bind directly to the dimer–dimer interfaces of HBV core protein. Finally, a synergistic effect was observed between CDDO-EA and lamivudine in reducing intracellular and extracellular HBV DNA levels. Our findings indicate that triterpenoids CDDO and CDDO-EA are new mechanistically type of HBV capsid assembly modulators and warranted for further development as lead compounds against HBV. Full article

Social housing plays a key role in the Colombian residential market, showing a growing commitment to sustainability: currently, a high percentage of EDGE-certified homes belong to this segment. However, in hot and humid areas such as Montería, most VIS homes have deficiencies in their thermal envelopes and poor roof insulation, which leads to a heavy reliance on air conditioning. This study addresses the lack of practical and replicable methodologies for improving energy efficiency in social housing located in hot–humid climates. The research aims to develop and apply a methodological framework that integrates architectural rehabilitation strategies with quantitative evaluation using the EDGE App tool. The proposed approach was implemented in Montería, Colombia, through a case study that combines diagnostic analysis of existing housing conditions, simulation of energy-saving measures, and assessment of environmental and economic performance. A real home in Montería was used as a reference, and more than 600 simulations were carried out considering different orientations and passive strategies. Through a Pareto analysis, the three most efficient measures were identified: natural ventilation, high-solar-reflectance roofing, and moderate reduction in the U-value. Together, these measures reduced energy consumption by up to 50%, with minimal increases in construction costs (≤1.2% of the commercial value). It was also found that excessive insulation can induce unwanted nighttime heating demands, highlighting the need for adjustments to the climatic context. The results confirm the technical and economic feasibility of mass rehabilitation of VIS in hot and humid climates using standard passive measure packages, consolidating the role of the EDGE App as a key tool for guiding sustainable design, investment, and environmental certification decisions. Full article

We consider the problem of designing a Private Information Retrieval (PIR) scheme for n files replicated on k servers that can collude and return incorrect answers. Our goal is to correctly retrieve a specific message while keeping its identity private from the database servers. We focus on minimizing download costs and propose PIR schemes with minimal download costs and the smallest file size (proportional to the number of involved servers). Motivated by the possible presence of stragglers, we extend our previous conference results and propose a scheme in which the number of participating servers may vary. Full article

Background/Objectives: Chagas disease, caused by Trypanosoma cruzi, remains a major neglected tropical disease with limited therapeutic options restricted to benznidazole and nifurtimox, both associated with significant toxicity and reduced efficacy during chronic infection. Seeking novel, safe, and sustainable chemotherapeutic candidates, two new saturated cardanol-derived phospholipid analogs—LDT10 and LDT119—were rationally designed based on the molecular scaffold of miltefosine and biosourced from cashew nut shell liquid (CNSL). This study aimed to evaluate the pharmacokinetic properties of these compounds in silico and assess their antiparasitic activity, cytotoxicity, and morphological and ultrastructural effects on all developmental forms of T. cruzi in vitro. Materials and Methods: In silico ADMET predictions (SwissADME, pkCSM) were performed to determine bioavailability, pharmacokinetic behavior, CYP inhibition, mutagenicity, and hepatotoxicity. Antiproliferative activity was evaluated in epimastigotes, trypomastigotes, and intracellular amastigotes using dose–response assays and flow cytometry. Cytotoxicity was assessed in HEPG2 and HFF-1 cells using resazurin-based viability assays. Morphological and ultrastructural alterations were investigated through scanning (SEM) and transmission (TEM) electron microscopy. Reactive oxygen species (ROS) generation was quantified with H₂DCFDA after 4 h and 24 h of exposure. Results: In silico analyses indicated favorable drug-like profiles, high intestinal absorption (>89%), absence of mutagenicity or hepatotoxicity, and non-penetration of the blood–brain barrier. LDT10 was not a P-gp substrate, and LDT119 acted as a P-gp inhibitor, suggesting reduced efflux and higher intracellular retention. Both compounds inhibited epimastigote proliferation with low IC₅₀ values (LDT10: 0.81 µM; LDT119: 1.2 µM at 48 h) and reduced trypomastigote viability (LD₅₀ LDT10: 2.1 ± 2 µM; LDT119: 1.8 ± 0.8 µM). Intracellular amastigotes were highly susceptible (IC₅₀ LDT10: 0.48 µM; LDT119: 0.3 µM at 72 h), with >90% inhibition at higher concentrations. No cytotoxicity was observed in mammalian cells up to 20 µM. SEM revealed membrane wrinkling, pore-like depressions, rounded cell bodies, and multiple flagella, indicating cell division defects. TEM showed Golgi disorganization, autophagic vacuoles, mitochondrial vesiculation, and abnormal kinetoplast replication, while host cells remained structurally preserved. Both compounds induced significant ROS production in trypomastigotes after 24 h in a dose-dependent manner. Conclusions: LDT10 and LDT119 exhibited potent and selective in vitro activity against all developmental stages of T. cruzi, with low micromolar to submicromolar IC₅₀/LD₅₀ values, minimal mammalian cytotoxicity, and extensive morphological and ultrastructural damage consistent with disruption of phospholipid biosynthesis pathways. Combined with favorable in silico pharmacokinetic predictions, these CNSL-derived phospholipid analogs represent promising candidates for future Chagas disease chemotherapy and warrant further in vivo evaluation. Full article

Skin meshing is widely used to treat extensive burn injuries due to its cost-efficiency and capacity to cover large wound areas. As biomimetics focuses on deriving engineering principles from biological structure–function relationships, this review examines how to optimize skin-meshing expansion and investigates factors contributing to reported discrepancies between clinical and manufacturer-reported expansion ratios. The biology and mechanical behavior of skin layer are discussed, emphasizing the anisotropic properties govern by collagen fiber orientation associated with Langer’s lines in the dermis. The epidermis and hypodermis show isotropic properties and therefore have minimal influence on load-bearing capacity. Surveying 111 studies, the review evaluates which constitutive equations employed for skin modelling is suitable to replicate mechanical behavior of skin meshing undergoing large expansion. Elastic models fail to capture large expansion ratios. Viscoelastic and QLV are excluded due to negligible sliding of collagen fibers at slow strain rates and limited importance of hysteresis. Consequently, hyperelastic models are recognized as more suitable for predicting large deformations. Among these, the structural GOH model, which represents fiber dispersion through a probability-density function, demonstrates strong agreement with experimental data using few parameters; its damage extensions improve prediction of mesh tearing. Additionally, emerging auxetic mesh geometries with negative Poisson ratios are examined, highlighting their potential to achieve greater expansion when combined with suitable structural anisotropic constitutive models, e.g., GOH. Full article

Motivation: We aim to study the boundary stability and persistence of positive odes in mathematical epidemiology models by importing structural tools from chemical reaction networks. This is largely a review work, which attempts to congregate the fields of mathematical epidemiology (ME), and chemical reaction networks (CRNs), based on several observations. We started by observing that epidemiologic strains, defined as disjoint blocks in either the Jacobian on the infected variables, or as blocks in the next generating matrix (NGM), coincide in most of the examples we studied, with either the set of critical minimal siphons or with the set of minimal autocatalytic sets (cores) in an underlying CRN. We leveraged this to provide a definition of the disease-free equilibrium (DFE) face/infected set as the union of either all minimal siphons, or of all cores (they always coincide in our examples). Next, we provide a proposed definition of ME models, as models which have a unique boundary fixed point on the DFE face, and for which the Jacobian of the infected subnetwork admits a regular splitting, which allows defining the famous next generating matrix. We then define the interaction graph on minimal siphons (IGMS), whose vertices are minimal siphons, and whose edges indicate the existence of reactions producing species in one siphon from species in another. When this graph is acyclic, we say the model exhibits an Acyclic Minimal Siphon Decomposition (AMSD). For AMSD models whose minimal siphons partition the infection species, we show that the NGM is block triangular after permutation, which implies the classical max structure of the reproduction number $R_0$ for multi-strain models. In conclusion, using irreversible reaction networks, minimal siphons and acyclic siphon decompositions, we provide a natural bridge from CRN to ME. We implement algorithms to compute IGMS and detect AMSD in our Epid-CRN Mathematica package (which already contain modules to identify minimal siphons, criticality, drainability, self-replicability, etc.). Finally, we illustrate on several multi-strain ME examples how the block structure induced by AMSD, and the ME reproduction functions, allow expressing boundary stability and persistence conditions by comparing growth numbers to 1, as customary in ME. Note that while not addressing the general Persistence Conjecture mentioned in the title, our work provides a systematic method for deriving boundary instability conditions for a significant class of structured models. Full article

IoT applications are increasingly common, yet they often rely on expensive, externally managed authentication services. This paper introduces a novel, self-contained authentication method for IoT applications which leverages fog computing principles to lower operational costs and infrastructure complexity. The proposed system, fogauth, combines device serial numbers with cryptographically generated UUIDs to establish secure identification without third-party services. A static cloud-side architecture coupled with a lightweight, locally hosted API enables secure authentication through object-storage operations. Performance testing demonstrates comparable security performance to commercial cloud-based authentication while reducing long-term operational costs and maintaining latency at below 2 minutes in production conditions. fogauth provides a scalable and economically viable alternative for companies seeking to reduce cloud dependency and minimize long-term costs associated with IoT application security. To support reproducibility, a complete open-source implementation and validation dataset are provided, allowing independent replication and extension of the system. Full article

Thermoelectric systems configured in multistring arrays of thermoelectric generators (TEGs) represent a promising solution for energy harvesting in environments with non-uniform thermal gradients. However, the presence of multiple maximum power points (MPPs) in such configurations poses significant challenges to energy extraction efficiency. This study presents a comprehensive performance evaluation of four maximum power point tracking (MPPT) algorithms, Perturb and Observe (P&O), Incremental Conductance (InC), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA), applied to multistring thermoelectric generator (TEG) arrays under multiple and asymmetric thermal gradients. The simulated systems, modeled in MATLAB/Simulink, replicate real-world thermoelectric configurations by employing series-parallel topologies and eleven distinct thermal scenarios, including uniform, localized, and sinusoidal temperature distributions. The key contribution of this work lies in demonstrating the superior capability of metaheuristic algorithms (PSO and GA) to locate the global maximum power point (GMPP) in complex thermal environments, outperforming classical methods (P&O and InC), which consistently converged to local maxima under multi-peak conditions. Notably, PSO achieved the best average convergence time (0.23 s), while the GA recorded the fastest response (0.05 s) in the most challenging multi-peak scenarios. Both maintained high tracking accuracy (error ≈ 0.01%) and minimized power ripple, resulting in conversion efficiencies exceeding 97%. The study emphasizes the crucial role of algorithm selection in maximizing energy harvesting performance in practical TEG applications such as embedded systems, waste heat recovery, and autonomous sensor networks. Future directions include physical validation through prototypes, incorporation of dynamic thermal modeling, and development of hybrid or AI-enhanced MPPT strategies. Full article

A reliable Digital Elevation Model (DEM) is a key input for land use planning and risk management, particularly in floodplains where low-resolution models often fail to represent subtle topographic variations. In many regions worldwide, high-precision elevation data are unavailable, necessitating the development of methods to enhance existing global digital elevation models (DEM). This study proposes a practical and replicable methodology to improve the vertical accuracy of global DEMs in flat terrains with limited data availability. The approach is based on correcting the altimetric differences between the DEM and GNSS-RTK-surveyed topographic points, incorporating land cover classification to refine adjustments. The methodology was tested in the Ranchería River delta in Riohacha, La Guajira, Colombia, using four global DEMs: FABDEM, SRTM, ASTER, and ALOS. Results showed a significant reduction in root mean square error (RMSE), with improvements of up to 76.691% for ASTER, 55.882% for FABDEM, 55.932% for SRTM, and 36.842% for ALOS. The proposed method requires minimal computational resources and no advanced programming. Due to minimal data requirements, it makes it a scalable and replicable solution for similar floodplain environments. These enhancements in local altimetric accuracy could help to improve the reliability of hydrodynamic modeling, with direct implications for flood risk management and decision-making in vulnerable flatland areas. Full article

The growing share of variable renewable energy sources in power systems is increasing the need for short-term operational flexibility—particularly from large industrial electricity consumers. This study proposes a practical, two-stage optimization framework to unlock this flexibility in cement manufacturing and support participation in electricity balancing markets. In Stage 1, a mixed-integer linear programming model minimizes electricity procurement costs by optimally scheduling the raw milling subsystem, subject to technical and operational constraints. In Stage 2, a flexibility assessment model identifies and evaluates profitable deviations from this baseline, targeting participation in Spain’s manual Frequency Restoration Reserve market. The methodology is validated through a real-world case study at a Spanish cement plant, incorporating photovoltaic (PV) generation and battery energy storage systems (BESS). The results show that flexibility services can yield monthly revenues of up to €800, with limited disruption to production processes. Additionally, combined PV + BESS configurations achieve electricity cost reductions and investment paybacks as short as six years. The proposed framework offers a replicable pathway for integrating demand-side flexibility into energy-intensive industries—enhancing grid resilience, economic performance, and decarbonization efforts. Full article

Drip fertigation (DF) is a sustainable agricultural management technique that optimizes water and nutrient usage, enhances crop productivity, and reduces environmental impact. Herein, we compared the effects of DF and conventional fertilization (CF) with a basal fertilizer on yield, soil inorganic nitrogen dynamics, N₂O emissions, and nitrogen leaching during facility-grown eggplant cultivation. The experiment was conducted in a greenhouse from September 2023 to May 2024, with treatments arranged in three rows and three replicates. Soil, gas, and water samples were collected and analyzed throughout the growing season. The results revealed that the DF treatment produced yields comparable to those obtained with the CF treatment while significantly reducing nitrogen and phosphorus inputs. DF effectively prevented excessive nitrogen accumulation in the soil and reduced nitrogen loss through leaching and gas emissions. N₂O emissions were significantly lower by more than 60% under DF than under CF. Precise nutrient management in DF suppressed nitrification and denitrification processes, mitigating N₂O emissions. DF also significantly reduced nitrogen leaching by more than 70% compared with that in CF. These findings demonstrate that DF effectively enhances agricultural sustainability by improving nutrient use efficiency, reducing greenhouse gas emissions, and minimizing nitrogen leaching during the cultivation of facility-grown eggplant. Full article

In recent years, the manufacturing industry and power sector have collectively accounted for nearly 60% of global carbon emissions, presenting a formidable obstacle to achieving net-zero targets by 2050. To address the urgent need for industrial decarbonization, this paper proposes a profit-driven framework for low-carbon manufacturing that synergistically integrates green certificates, demand response, distributed generation, and carbon capture, utilization, and storage (CCUS) technologies. A comprehensive optimization model is formulated to enable manufacturers to maximize profits through strategic participation in electricity, carbon, green certificate, and industrial manufacturing product markets simultaneously. By solving this optimization problem, manufacturers can derive optimal production decisions. The framework’s effectiveness is demonstrated through a case study on lithium-ion battery manufacturing, which reveals promising outcomes: meaningful profit growth, substantial carbon emission reductions, and only minimal impacts on production output. Furthermore, the proposed demand response strategy achieves significant reductions in electricity consumption during peak hours, while the integration of distributed generation systems markedly decreases reliance on the main grid. The incorporation of CCUS extends the clean operation periods of thermal power units, generating additional revenue from carbon trading and CO₂ utilization. In summary, the proposed model represents the first unified profit-maximizing optimization framework for low-carbon manufacturing industries, shifting from traditional cost minimization to profitability optimization, addressing gaps in fragmented low-carbon strategies, and providing a replicable blueprint for carbon-neutral operations while enhancing profitability. Full article

This study presents the development and experimental validation of a novel wind turbine emulator (WTE) based on a doubly fed induction generator (DFIG). The proposed architecture employs an induction motor (IM) driven by a variable frequency drive (VFD) to emulate wind turbine dynamics, offering a cost-effective and low-maintenance alternative to traditional DC motor-based systems. The contribution of this work lies, therefore, not in the hardware topology itself, but in the complete real-time software implementation of the control system using C language and RTLib, which enables higher sampling rates, faster PWM updates, and improved execution reliability compared with standard Simulink/RTI approaches. The proposed control structure integrates tip–speed ratio (TSR)-based maximum power point tracking (MPPT) with flux-oriented vector control of the DFIG, fully coded in C to provide optimized real-time performance. Experimental results confirm the emulator’s ability to accurately replicate real wind turbine behavior under varying wind conditions. The test bench demonstrates fast dynamic response, with rotor currents settling in 11–18 ms, and active/reactive powers stabilizing within 25–30 ms. Overshoots remain below 10%, and steady-state errors are limited to ±1 A for currents and ±100 W/±50 VAR for powers, ensuring precise power regulation. The speed tracking error is approximately 0.61 rad/s, validating the system’s ability to follow dynamic references with high accuracy. Additionally, effective decoupling between active and reactive loops is achieved, with minimal cross-coupling during step changes. Full article

Glucosidase II (GluII) is a heterodimeric enzyme localized in the endoplasmic reticulum (ER), essential for the sequential trimming of glucose residues during N-linked glycosylation. This critical function facilitates glycoprotein folding via the calnexin/calreticulin chaperone system, maintaining ER homeostasis. Dysregulation or inhibition of GluII has been implicated in various pathological processes, including cancer, viral infections, and glycoprotein misfolding disorders. This review summarizes the current knowledge of GluII’s structure and function, highlights a wide range of natural and synthetic GluII inhibitors—including iminosugar derivatives (e.g., deoxynojirimycin (DNJ), castanospermine (CAST)), non-iminosugar compounds (e.g., bromoconduritol, catechins), and mechanism-based cyclophellitol analogues—and evaluates their biological effects and therapeutic potential. The cellular impact of GluII inhibition is explored in the context of ER stress, unfolded protein response (UPR), tumor cell apoptosis, and viral replication. Key challenges in developing selective GluII inhibitors are discussed, with a focus on strategies to minimize off-target effects, including prodrug design, allosteric modulation, and emerging genetic approaches such as microRNA (miRNA)-mediated downregulation of GluII subunits. Taken together, these insights underscore the therapeutic relevance of GluII as a druggable target and pave the way for the rational design of next-generation inhibitors in oncology, infectious diseases, and metabolic disorders. Full article

This study develops a GIS-based, heritage-sensitive urban lighting framework for Niğde, Türkiye, integrating Sentinel-2 MSI Level-2A imagery (10 m), ASTER DEM, and municipal cadastral data. Five spatial criteria—land cover, parks, protected heritage assets, population distribution, and government institutions—were classified through supervised mapping, visibility analysis, and architectural integrity assessment. All layers were standardized and combined using a weighted-overlay approach, supported by sensitivity testing across three weighting scenarios to ensure model robustness. Priority zones are concentrated in the historic core, where cultural landmarks, central parks, and high-density residential areas overlap. Peripheral agricultural and rural zones exhibited minimal lighting needs. Field verification and expert consultation demonstrated 82% correspondence between modeled and observed priority and visibility patterns, while a structured nighttime audit and ecological checklist provided additional empirical grounding for lighting sufficiency, glare risks, and biodiversity considerations. Results emphasize context-specific lighting that strengthens cultural identity, improves pedestrian comfort and nighttime legibility, and reduces unnecessary energy use and light pollution. This approach offers a replicable workflow aligned with CIE 150:2017 and IES RP-8-18 guidance. Future work may incorporate dynamic population mobility, AHP-based weighting, and adaptive smart-lighting systems to scale the methodology across similar medium-sized heritage cities seeking balanced aesthetic, cultural, and ecological nighttime environments. Full article