Search Results (207)

G-quadruplexes have been identified as a promising anti-cancer target because of their ability to modulate the stability of mRNAs encoding oncogenes, tumor suppressor genes, and other potential therapeutic targets. Deregulation of DNA damage and Unfolded Protein Response pathways in cancer cells may create vulnerabilities that can be exploited therapeutically. Previous studies have shown variations in the relative expression of DDR and UPR components in canine lymphoma and leukemia cell lines CLBL-1, CLB70, and GL-1. In the present study, we report the presence of G-quadruplex structures in these canine cell lines. Downregulation of the expression of DDR and UPR components at the mRNA level was observed in the CLBL-1 and CLB70 cell lines after stabilization of G4 structures using the ligand PhenDC3. In contrast, in GL-1 cells, important components of the DDR pathway, such as PARP1, GADD45A, and PIK3CB were upregulated in response to PhenDC3 treatment. Downregulation of DDIT4 mRNA expression, which encodes an important UPR component, was detected in the CLBL-1 and GL-1 cell lines after PhenDC3 exposure. These results suggest that G4 structures can be used to manipulate the expression of potential targets to treat lymphoma in dogs. A substantial enrichment of DNA replication and pyrimidine metabolism pathways was found in the GL-1 cell line after G4 stabilization. This finding suggests that PhenDC3 may induce DNA replication stress in this cell line. Collectively, these results support the feasibility of employing canine cancer cells as a model system to investigate the role of G-quadruplex structures in cancer. Full article

Rabies, a zoonotic infectious disease causing central nervous system inflammation, remains a threat to public health in regions with limited medical resources. Vaccination effectively reduces rabies incidence and mortality, underscoring the need for vaccines that are cost-effective, immunogenic, protective, and safe. This study constructed a recombinant rabies virus (rRABV)-overexpressing glucocorticoid-induced tumor necrosis factor receptor ligand (GitrL), named rLBNSE-GitrL, using a reverse genetic operating system. rLBNSE-GitrL exhibited similar in vitro phenotypic characteristics and immune safety as the parent RABV (rLBNSE). This recombinant virus stimulated the production of a greater number of activated dendritic cells (DCs) compared to rLBNSE. The enhanced innate immune response induced by rLBNSE-GitrL may be mediated through the activation of innate immune-related signaling pathways, such as the tumor necrosis factor (TNF), and chemokine signaling pathways, and the upregulation of a series of innate immune-related genes, including MMP2, IL-6, CXCL9, TIMP1, IL-17d, and TNF-α. Consequently, rLBNSE-GitrL elicited significantly higher levels of RABV vaccine-induced virus-neutralizing antibodies (VNA), IgG, and IgM compared to rLBNSE as early as 3 days post-immunization (dpi), thereby improving the protective effect in mice. Collectively, the overexpression of GitrL facilitated the induction of early and potent antibody responses following RABV immunization. Full article

This paper presents the design and implementation of a blockchain-secured system for monitoring driver sobriety and real-time geolocation. The proposed platform integrates a Modular Sensor Battery (MSB) for detecting alcohol concentration in exhaled air, a centralized Data Collection Platform (DC Platform) for real-time data visualization and storage, and a complementary physiological monitoring device—the IoT Fit-Bit Smart Band (IFSB)—which captures heart rate and blood oxygen saturation as alternative indicators when breath-based sensing may be compromised. The MSB, the DC Platform, integration with the IoT FitBit Smart Band, and the blockchain-based data management architecture represent the authors’ direct contribution to both the conceptual design and technical implementation. These elements are introduced as part of a unified, fully integrated system designed to enable non-invasive sobriety monitoring and secure data integrity in vehicular contexts. To ensure data authenticity, a custom Ethereum smart contract stores cryptographic hashes of sensor readings, enabling decentralized, tamper-evident verification without exposing sensitive medical information. The system was validated in a controlled experimental environment, confirming its operational robustness and demonstrating its potential to improve road safety through secure, real-time sobriety detection and geolocation tracking. Full article

Photovoltaic (PV) systems are increasingly integral to global energy solutions, but their long-term reliability is challenged by various operational faults. In this article, we propose an advanced hybrid diagnostic framework combining a Deep Belief Network (DBN) for feature pattern extraction and a Light Gradient Boosting Machine (LightGBM) for classification to detect and diagnose PV panel faults. The proposed model is trained and validated on the QASP PV Fault Detection Dataset, a real-time SCADA-based dataset collected from 255 W panels at the Quaid-e-Azam Solar 100 MW Power Plant (QASP), Pakistan’s largest solar facility. The dataset encompasses seven classes: Healthy, Open Circuit, Photovoltaic Ground (PVG), Partial Shading, Busbar, Soiling, and Hotspot Faults. The DBN captures complex non-linear relationships in SCADA parameters such as DC voltage, DC current, irradiance, inverter power, module temperature, and performance ratio, while LightGBM ensures high accuracy in classifying fault types. The proposed model is trained and evaluated on a real-world SCADA-based dataset comprising 139,295 samples, with a 70:30 split for training and testing, ensuring robust generalization across diverse PV fault conditions. Experimental results demonstrate the robustness and generalization capabilities of the proposed hybrid (DBN–LightGBM) model, outperforming conventional machine learning methods and showing an accuracy of 98.21% classification accuracy, 98.0% macro-F1 score, and significantly reduced training time compared to Transformer and CNN-LSTM baselines. This study contributes to a reliable and scalable AI-driven solution for real-time PV fault monitoring, offering practical implications for large-scale solar plant maintenance and operational efficiency. Full article

This study investigates the impact of reducing waiting times for port berth on improving the Carbon Intensity Indicator (CII) ratings of Korean-flagged container ships. As the International Maritime Organization (IMO)’s CII regulation mandates corrective actions for poorly rated ships for Greenhouse Gas (GHG) reduction in international shipping, the analysis focuses on container ships with projected D or E ratings by 2035. Using Automatic Identification System (AIS) data from ships, this study identifies annual waiting times and simulates changes in CII ratings under scenarios of reduced waiting times (30%, 50%, 70%, and 100%). The relationship between ship speed and fuel consumption was established by analyzing the recent literature, and the CII improvement was evaluated based on IMO Data Collection System (DCS) 2022 data. The results show that a 30% reduction in waiting time can lower CO₂ emissions by 12.18% and improve the CII rating by one or two levels for approximately half of the sample ships. However, a 50% reduction or more is required to maintain improved ratings beyond 2030. The findings highlight the significance of just-in-time (JIT) practices in minimizing latency and enhancing regulatory compliance. The policy recommendations advocate for prioritizing port call optimization and recommend the adoption of JIT as a measure to achieve the IMO’s GHG reduction targets. Full article

This paper presents a literature review of low-power hybrid wind–photovoltaic (PV) systems and introduces a 3 m diameter prototype rotor featuring twelve PV-coated pivoting blades stiffened by a peripheral rim. Existing solutions—foldable umbrella concepts, Darrieus rotors with PV-integrated blades, and morphing blades—are surveyed, and current gaps in simultaneous wind + PV co-generation on a single moving structure are highlighted. Key performance indicators such as power coefficient (C_p), DC ripple, cell temperature difference (ΔT), and levelised cost of energy (LCOE) are defined, and an integrated assessment methodology is proposed based on blade element momentum (BEM) and computational fluid dynamics (CFD) modelling, dynamic current–voltage (I–V) testing, and failure modes and effects analysis (FMEA) to evaluate system performance and reliability. Preliminary results point to moderate aerodynamic penalties (ΔC_p ≈ 5–8%), PV output during rotation equal to 15–25% of the nominal PV power (P_PV), and an estimated 70–75% reduction in blade–root bending moment when the peripheral ring converts each blade from a cantilever to a simply supported member, resulting in increased blade stiffness. Major challenges include the collective pitch mechanism, dynamic shading, and wear of rotating components (slip rings); however, the suggested technical measures—maximum power point tracking (MPPT), string segmentation, and redundant braking—keep performance within acceptable limits. This study concludes that the concept shows promise for distributed microgeneration, provided extensive experimental validation and IEC 61400-2-compliant standardisation are pursued. This paper has a dual scope: (i) a concise literature review relevant to low-Re flat-blade aerodynamics and ring-stiffened rotor structures and (ii) a multi-fidelity aero-structural study that culminates in a 3 m prototype proposal. We present the first evaluation of a hybrid wind–PV rotor employing untwisted flat-plate blades stiffened by a peripheral ring. Using low-Re BEM for preliminary loading, steady-state RANS-CFD (k-ω SST) for validation, and elastic FEM for sizing, we assemble a coherent load/performance dataset. After upsizing the hub pins (Ø 30 mm), ring (50 × 50 mm), and spokes (Ø 40 mm), von Mises stresses remain < 25% of the 6061-T6 yield limit and tip deflection ≤ 0.5%·R acrosscut-in (3 m s⁻¹), nominal (5 m s⁻¹), and extreme (25 m s⁻¹) cases. CFD confirms a broad efficiency plateau at λ = 2.4–2.8 for β ≈ 10° and near-zero shaft torque at β = 90°, supporting a three-step pitch schedule (20° start-up → 10° nominal → 90° storm). Cross-model deviations for C_p, torque, and pressure/force distributions remain within ± 10%. This study addresses only the rotor; off-the-shelf generator, brake, screw-pitch, and azimuth/tilt drives are intended for later integration. The results provide a low-cost manufacturable architecture and a validated baseline for full-scale testing and future transient CFD/FEM iterations. Full article

Accurate modeling and performance analysis of brushless DC (BLDC) motors are essential for high-efficiency control in modern drive systems. In this article, a BLDC motor was modeled using system identification techniques. In addition, experimental data were collected from the BLDC motor, including its speed response to various input signals. Using system identification tools, particularly those provided by MATLAB/Simulink R2024b, an approximation model of the BLDC motor was constructed to represent the motor’s dynamic behavior. The identified model was experimentally validated using various input signals, demonstrating its accuracy and generalizability under different operating conditions. Additionally, a series of mechanical load tests was conducted using the AVL eddy-current dynamometer to evaluate performance under practical operating conditions. Fixed load torques were applied across a range of motor speeds, and multiple torque levels were tested to assess the motor’s dynamic response. Electrical power, mechanical power, and efficiency of the entire system were computed for each case to assess overall system performance. Moreover, the real-time state of charge (SOC) of Lithium-ion (Li-ion) battery was estimated using the Coulomb counting method to analyze the impact of Li-ion battery energy level on the BLDC motor efficiency. The study offers valuable insights into the motor’s dynamic and energetic behavior, forming a foundation for robust control design and real-time application development. Full article

The development and utilization of large-scale offshore wind power (OWP) are critical measures for achieving global energy transition. To address the demands of future large-scale OWP centralized development and transmission, this study systematically investigates the influencing factors and construction principles for topology selection in offshore wind power high-voltage direct current (HVDC) transmission systems delivering power to load centers. First, under the context of expanding the offshore wind power transmission scale, the necessity of transmitting OWP via HVDC overhead lines directly to load centers after landing is theoretically discussed. Five key topological influencing factors are then analyzed: offshore wind power collection schemes, multi-terminal HVDC network configurations, DC fault isolation mechanisms, offshore converter station architectures, and voltage source converter HVDC (VSC-HVDC) receiving terminal landing modes. Corresponding topology construction principles for direct HVDC transmission to load centers are proposed to guide system design. Finally, the feasibility of the proposed principles is validated through a case study of a multi-terminal HVDC system integrated into an actual regional power grid, demonstrating practical applicability. Full article

The International Maritime Organization (IMO) has set a goal to reach net-zero greenhouse gas emissions from international shipping by or around 2050. The ship energy efficiency framework has played a positive role over the past decade in improving carbon intensity and reducing greenhouse gas emissions by employing the technical and operational energy efficiency metrics as effective appraisal tools. To quantify the ship energy efficiency performance and review the existing energy efficiency framework, this paper analyzed the data for the reporting year of 2023 extracted from the European Union (EU) monitoring, reporting, and verification (MRV) system, and investigated the operational profiles and energy efficiency for the ships calling at EU ports. The results show that the data accumulated in the EU MRV system could provide powerful support for conducting ship energy efficiency appraisals, which could facilitate the formulation of decarbonization policies for global shipping and management decisions for stakeholders. However, data quality, ship operational energy efficiency metrics, and co-existence with the IMO data collection system (DCS) remain issues to be addressed. With the improvement of IMO DCS system and the implementation of IMO Net-Zero Framework, harmonizing the two systems and avoiding duplicated regulation of shipping emissions at the EU and global levels are urgent. Full article

Plant–herbivore–parasitoid systems are poorly studied in the tropics. Enicospilus carmenae Campos and Palacio sp. nov. are described, originating from southern Mexico in the Yucatan Peninsula and establishing a new tri-trophic interaction. This species is a koinobiont larval endoparasitoid of the American silkworm moth caterpillar Zanola verago (Cramer) (Lepidoptera: Apatelodidae) feeding on the shrub Piper neesianum C.DC. (Piperaceae) in a semi-evergreen forest. The host plant P. neesianum had no herbivore records to date, and a single collection event yielded the rearing of a new species of Enicospilus (Ichneumonidae, Ophioninae). Morphological, molecular (COI), biological, ecological, and geographical data are integrated to delineate the new species. Full article

Emerging evidence highlights the gut microbiota as a pivotal determinant of pharmacological efficacy. While Enterococcus faecalis (E. faecalis)-derived tyrosine decarboxylases (tyrDCs) are known to decarboxylate levodopa (L-dopa), compromising systemic bioavailability, the causal mechanisms underlying microbiota-mediated pharmacodynamic variability remain unresolved. In our study, we employed antibiotic-induced microbiota depletion and fecal microbiota transplantation (FMT) to interrogate microbiota-L-dopa interactions in MPTP-induced Parkinson’s disease (PD) mice. The study demonstrated that antibiotic-mediated microbiota depletion enhances L-dopa bioavailability and striatal dopamine (DA) level, correlating with improved motor function. To dissect clinical heterogeneity in the L-dopa response, PD patients were stratified into moderate responders and good responders following standardized L-dopa challenges. In vitro bioconversion assays revealed greater L-dopa-to-DA conversion in fecal samples from moderate responders versus good responders. FMT experiments confirmed mice receiving good-responder microbiota exhibited enhanced L-dopa bioavailability, higher striatal DA concentrations, and a heightened therapeutic effect of L-dopa relative to moderate-responder recipients. Collectively, our study provided evidence that the gut microbiota directly modulates L-dopa metabolism and microbial composition determines interindividual therapeutic heterogeneity. Targeted microbial modulation—through precision antibiotics or donor-matched FMT—is a viable strategy to optimize PD pharmacotherapy, supporting the potential for microbiota-targeted adjuvant therapies in PD management. Full article

A diode-based rectifier (DR) is an attractive transmission technology for offshore wind farms, which reduces the volume of large bulk platforms. A novel parallel–series DC wind farm based on a distributed DR is proposed, which meets the requirements of high voltage and high power with an isolation capability from other units. The coupling mechanism between a modular multilevel converter (MMC) and a DR has been built, and the coordinate control strategy for the whole system has been proposed based on the MMC triple control targets with intermediate variables. Under the proposed control strategy, the system automatically operates at maximum power point tracking (MPPT). The feasibility of topology and the effectiveness of the control strategy are verified under start-up, power fluctuation, onshore alternating current (AC) fault, and direct current (DC) fault based on the power systems computer-aided design (PSCAD)/electromagnetic transients including direct current (EMTDC) simulation. Full article

In the process of collecting operational data for the performance analysis of water-cooled centrifugal chillers, missing values are inevitable due to various factors such as sensor errors, data transmission failures, and failure of the measurement system. When a substantial amount of missing data is present, the reliability of data analysis decreases, leading to potential distortions in the results. To address this issue, it is necessary to either minimize missing occurrences by utilizing high-precision measurement equipment or apply reliable imputation techniques to compensate for missing values. This study focuses on two water-cooled turbo chillers installed in Tower A, Seoul, collecting a total of 118,464 data points over 3 years and 4 months. The dataset includes chilled water inlet and outlet temperatures ($T_1$ and $T_2$) and flow rate (${\dot{V}}_1$) and cooling water inlet and outlet temperatures ($T_3$ and $T_4$) and flow rate (${\dot{V}}_3$), as well as chiller power consumption (${\dot{W}}_c$). To evaluate the performance of various imputation techniques, we introduced missing values at a rate of 10–30% under the assumption of a missing-at-random (MAR) mechanism. Seven different imputation methods—mean, median, linear interpolation, multiple imputation, simple random imputation, k-nearest neighbors (KNN), and the dynamically clustered KNN (DC-KNN)—were applied, and their imputation performance was validated using MAPE and CVRMSE metrics. The DC-KNN method, developed in this study, improves upon conventional KNN imputation by integrating clustering and dynamic weighting mechanisms. The results indicate that DC-KNN achieved the highest predictive performance, with MAPE ranging from 9.74% to 10.30% and CVRMSE ranging from 12.19% to 13.43%. Finally, for the missing data recorded in July 2023, we applied the most effective DC-KNN method to generate imputed values that reflect the characteristics of the studied site, which employs an ice thermal energy storage system. Full article

Raman spectroscopy is one of the best techniques for obtaining information concerning the physical–chemical interactions between a lipid and a solvent. Phospholipids in water are the main elements of cell membranes and, by means of their chemical and physical structures, their cells can interact with other biological molecules (i.e., proteins and vitamins) and express their own biological functions. Phospholipids, due to their amphiphilic structure, form biomimetic membranes which are useful for studying cellular interactions and drug delivery. Synthetic systems such as DPhPC-based liposomes replicate the key properties of biological membranes. Among the different models, phospholipid mimetic membrane models of lamellar vesicles have been greatly supported. In this work, a biomimetic system, a deuterium solution (50 mM) of the synthetic phospholipid 1,2-diphytanoyl-sn-glycero-3-phosphocholine (DPhDC), is studied using μ-Raman spectroscopy in a wide temperature range from −181.15 °C up to 22.15 °C, including the following temperatures: −181.15 °C, −146.15 °C, −111.15 °C, −76.15 °C, −61.15 °C, −46.15 °C, −31.15 °C, −16.15 °C, −1.15 °C, 14.15 °C, and 22.15 °C. Based on the Raman evidence, phase transitions as a function of temperature are shown and grouped into five classes, where the corresponding Raman modes describe the stretching of the (C−N) bond in the choline head group (gauche) and the asymmetric stretching of the (O−P−O) bond. The acquisition temperature of each Raman spectrum characterizes the rocking mode of the methylene of the acyl chain. These findings enhance our understanding of the role of artificial biomimetic lipids in complex phospholipid membranes and provide valuable insights for optimizing their use in biosensing applications. Although the phase stability of DPhPC is known, the collected Raman data suggest subtle molecular rearrangements, possibly due to hydration and second-order transitions, which are relevant for membrane modeling and biosensing applications. Full article

To ensure the sustainable development of the surrounding environment and the sustainable operation of landfills, detecting landfill leakage is of great significance. In landfills lacking a leakage monitoring system, the inability to detect and locate damaged High-Density Polyethylene (HDPE) membranes can lead to the contamination of soil and groundwater by landfill leachate. To address this issue, this study proposes a resistivity tomography inversion model based on the external-electrode power supply mode. Utilizing the resistivity difference between the leakage zone and the surrounding soil, electrodes are arranged symmetrically for both power supply and measurement. Upon applying direct current (DC) excitation, potential data are collected, with the finite volume method employed for inversion and the Gauss–Newton method integrated with an adaptive particle swarm optimization algorithm for parameter fitting. Experimental results show that the combined algorithm provides better clarity in edge recognition of low-resistance models compared with single algorithms. The maximum deviation between inferred leakage coordinates and the actual location is 10.1 cm, while the minimum deviation is 6.4 cm, satisfying engineering requirements. This method can effectively locate point sources and line sources, providing an accurate solution for subsequent leakage point filling and improving repair efficiency. Full article