Search Results (360)

Ports located within dense urban environments face a major challenge in achieving deep decarbonization without compromising the reliability and safety of critical maritime operations. This study develops and validates a resilience-oriented control and sizing typology for Hybrid Renewable Energy Systems (HRESs), supporting the transition of a medium-sized Mediterranean port toward a Nearly Zero Energy Port (nZEP). The framework integrates five years of measured electrical demand at 15 min resolution to capture stochastic load variability, seasonal effects, and safety-critical peak events. Thirty-five HRES configurations are simulated using HOMER Pro, assessing photovoltaic and wind generation combined with alternative Energy Storage System (ESS) technologies under two grid-interface control strategies: Net Metering (NM) and non-NM curtailment-based operation. Conventional Lead–Acid batteries are compared with inherently safer Vanadium Redox Flow Batteries (VRFBs), while autonomy constraints of 24 h and 48 h are imposed to represent operational resilience. System performance is evaluated through a multi-criteria framework encompassing economic viability (Levelized Cost of Energy), environmental impact (Lifecycle Assessment-based carbon footprint), and operational reliability. Results indicate that NM-enabled HRES architectures significantly outperform non-NM configurations by exploiting the external grid as an active balancing layer. The optimal NM configuration achieves a Levelized Cost of Energy of 0.063 €/kWh under a 24 h autonomy constraint, while reducing operational carbon intensity to approximately 70 gCO₂,eq/kWh, corresponding to a reduction exceeding 90% relative to baseline grid-dependent operation. In contrast, non-NM systems require substantial storage and generation oversizing to maintain resilience, resulting in higher curtailment losses and Levelized Cost of Energy values of 0.12–0.15 €/kWh. Across both control regimes, VRFB-based systems consistently exhibit superior robustness and safety performance compared to Lead–Acid alternatives. The proposed typology provides a transferable framework for resilient and low-carbon port microgrid design under real-world operational constraints. Full article

According to the UN World Water Development Report 2024, global food production has more than doubled over the past three decades, placing increasing pressure on freshwater systems due to climate change, urban expansion, and intensified pollution events. This study presents a Monitoring and Mitigation Framework (MMF) for real-time river contamination detection, contamination source identification, and estimation of Chemical Oxygen Demand (COD) concentrations at the source. The framework is based on Inverse Estimation (IE) algorithms using feed-forward neural networks trained on approximately 85,000 simulated pollution events for the Ebro River (Spain). Each event represents a 52 h contamination episode monitored at two locations with a 10 min sampling interval, covering a wide range of COD concentrations. For low-concentration scenarios (<1000 mg/L), the TensorFlow-based regression model achieved a Mean Absolute Relative Error (MARE) of 0.26% and a Mean Square Relative Error (MSRE) of 1.82%, while for higher concentrations (>1000 mg/L), the scikit-learn implementation provided superior performance with MARE below 1.85%. Source location identification achieved an accuracy of 81%, increasing to 97% when allowing adjacent river sections. Overall, the MMF is a scalable, low-cost, real-time decision-support tool for water authorities such as the Confederación Hidrográfica del Ebro (CHE) to detect, track, and mitigate pollution events. Full article

3D printing offers great potential for rapid and cost-effective fabrication of microfluidic lab-on-a-chip systems. Through a comparative approach, we implemented staggered herringbone mixer (SHM), Tesla mixer, and split and recombine mixer (SAR), along with a basic unperturbed channel into one chip and performed comparative mixing efficiency experiments. We also introduced a phaseguide-based, T-shaped stop structure at the Y-shaped inlets for bubble-free and parallel filling. The structures were analyzed with two poorly mixable dye solutions at flow rates ranging from 1 µL/min to 200 µL/min. The mixing efficiency was evaluated using optical gray value analysis and compared against diffusion-based mixing. The fluid-aligning phaseguides in the 3D-printed system were shown to work. Among the three different mixing structures tested, SHM exhibited the best mixing efficiency at all tested flow rates. Uniformly designed SHM structures contain a region of poor mixing between the two zones of turbulence. In a non-uniform design, fluid breakers were placed between two SHM units to redirect poorly mixed fluids to the edges, resulting in 100% mixing efficiency across all measured flow rates. These results, especially SHM with fluid breakers, support the development of cost-effective injection-molded lab-on-a-chip systems with improved mixing functionalities at close range instead of simple long-length meandric systems. Full article

The growing demand for sustainable energy alternatives highlights the need for decision support tools in biodiesel supply chains. This study proposes a mixed-integer programming (MIP) model for tactical planning in the palm oil biodiesel supply chain, focusing on refining, blending, and distribution. The model incorporates economies of scale, inventory, and transport constraints and is enhanced with valid inequalities (VI) and a warm-start heuristic procedure (WS) to improve computational efficiency. Computational experiments on simulated instances with up to 6273 variables and 47 million iterations demonstrated robust performance, achieving solutions within 15 min. The model also reduced time-to-first-feasible (TTFF) solutions by 60–75% and CPU times by 17–21% compared to the baseline, confirming its applicability in realistic contexts. The proposed model provides actionable insights for managers by supporting decisions on facility scaling, product allocation, and profitability under supply–demand constraints. Beyond palm oil biodiesel, the formulation and its VI + WS enhancement provide a transferable blueprint for tactical planning in other process industry and renewable energy supply chains, where (i) multi-echelon flow conservation holds and (ii) discrete operating scales couple throughput with fixed/variable cost structures, enabling fast scenario analyses under changing prices, demand, and capacities. Full article

To address the high-temperature and high-cost challenges of the conventional dry oxidation process in boron diffusion for n-type tunnel oxide passivated contact solar cells, this study proposes a dry–wet–dry mixed oxidation drive-in process for fabricating p-type emitters in TOPCon solar cells. Through systematic investigation of oxidation temperature, O₂/H₂O flow ratio, and oxidation time effects on emitter performance, it is found that mixed oxidation at 1000 °C achieves comparable sheet resistance and doping profiles to dry oxidation at 1050 °C. For our newly developed mixed oxidation process, in which the oxidation temperature is 1000 °C, oxidation time is 80 min with O₂/H₂O flow ratio of 20:1, the same photoelectric conversion efficiency has been achieved. Comparing the data, the mixed oxidation process forms a dry/wet/dry three-layer SiO₂ structure, reducing the oxidation temperature by 50 °C while achieving an average efficiency of 26.02%, comparable to high-temperature dry oxidation. This process not only reduces the thermal budget of quartz tubes and extends equipment service life but also provides a feasible solution for the low-temperature manufacturing of high-efficiency TOPCon solar cells, showing significant industrial application prospects. Full article

As an emerging threat to global food security, wheat blast necessitates the development of a rapid and field-deployable detection system to facilitate early diagnosis, enable effective management, and prevent its further spread to new regions. In this study, we aimed to validate and improve a Recombinase Polymerase Amplification coupled with PCRD lateral flow detection (RPA-PCRD strip assay) kit for the rapid and specific identification of Magnaporthe oryzae pathotype Triticum (MoT) in field samples. The assay demonstrated exceptional sensitivity, detecting as low as 10 pg/µL of target DNA, and exhibited no cross-reactivity with M. oryzae Oryzae (MoO) isolates and other major fungal phytopathogens under the genera of Fusarium, Bipolaris, Colletotrichum, and Botrydiplodia. The method successfully detected MoT in wheat leaves as early as 4 days post-infection (DPI), and in infected spikes, seeds, and alternate hosts. Furthermore, by combining a simplified polyethylene glycol-NaOH method for extracting DNA from plant samples, the entire RPA-PCRD strip assay enabled the detection of MoT within 30 min with no specialized equipment and high technical skills at ambient temperature (37–39 °C). When applied to field samples, it successfully detected MoT in naturally infected diseased wheat plants from seven different fields in a wheat blast hotspot district, Meherpur, Bangladesh. Training 52 diverse stakeholders validated the kit’s field readiness, with 88% of trainees endorsing its user-friendly design. This method offers a practical, low-cost, and portable point-of-care diagnostic tool suitable for on-site genomic surveillance, integrated management, seed health testing, and quarantine screening of wheat blast in resource-limited settings. Furthermore, the RPA-PCRD platform serves as an early warning modular diagnostic template that can be readily adapted to detect a wide array of phytopathogens by integrating target-specific genomic primers. Full article

Flow sensing is essential in biomedical engineering, industrial process control, and environmental monitoring. Conventional sensors, while accurate, are often constrained by high fabrication costs, complex processes, and limited design flexibility, restricting their use in disposable or rapidly customizable applications. This paper presents a novel ultra-low-cost airflow sensor fabricated entirely through fused deposition modeling 3D printing. The device employs a cantilever-based structure printed with PETg filament, followed by the deposition of a conductive ABS piezoresistive layer in a two-step process requiring no curing or post-processing. Experimental characterization reveals that the sensor operates in an ultra-low pressure range of 0.88–26.68 Pa, corresponding to flow velocities of 1.2–6.6 m/s. The sensor achieves a sensitivity of 967 Ω/Pa, a resolution of 9.27 Pa, and a detection limit of 83.27 Pa, with a total resistance change of approximately 51.5 kΩ. This kilo-ohm-scale response enables direct readout via a digital multimeter without requiring Wheatstone bridges or instrumentation amplifiers. The minimalist design, combined with sub-5 min fabrication time and material cost below $0.05, positions this sensor as an accessible platform for disposable, scalable, and resource-constrained flow monitoring applications in both biomedical and industrial contexts. Full article

The competitive adsorption of two model herbicides, 2,4-dichlorophenoxyacetic acid (2,4-D) and 4-chloro-2-methylphenoxyacetic acid (MCPA), onto Activated Carbons (ACs) derived from pomegranate peels through chemical activation with phosphoric acid (H₃PO₄) was investigated in fixed-bed column mode. The prepared activated carbon (AC-PA) exhibited a high apparent surface area (up to 1409 m²/g) and a predominantly microporous structure. Morphological and chemical analyses (micrographic observation, X-ray difraction, N₂ adsorption–desorption) confirmed the presence of well-developed pore networks and surface oxygenated functionalities. Column adsorption experiments were performed under varying flow rates (0.25–3 mL/min) for both single and binary solutions. The breakthrough data were modeled using the Thomas and Yoon–Nelson equations, achieving high determination coefficients (R² = 0.91–0.99). Lower flow rates favored higher adsorption capacities, reaching 193.61 mg/g for 2,4-D at 0.25 mL/min. Under similar conditions (flow rate of 1.5 mL min⁻¹), the AC provided a better adsorption for 2,4-D than for MCPA in single systems, which was attributed to stronger affinity based on its greater hydrophobicity and prominence to dispersive interactions. In binary systems, competitive effects shifted the results and a noticeable roll-up phenomenon was observed for 2,4-D, attributed to its displacement by MCPA along the bed; this made the adsorbent more effective for MCPA in binary mixtures than in single ones. These findings highlight the potential of pomegranate-based activated carbon as a cost-effective and sustainable adsorbent for herbicide removal in continuous water treatment systems. Full article

Compressed air systems are widely used in industry, and air leaks that occur over time lead to significant and unnecessary energy losses. This study aims to quantify the energy-saving potential of compressed air leaks in a manufacturing plant and to develop machine learning (ML) regression models for sustainable leak management. A total of 230 leak points were identified by measuring three periods using an ultrasonic device. Using the measured acoustic emission level (dB) and probe distance (x) as inputs, the leak flow rate, annual energy-saving potential, cost loss, and carbon footprint were calculated. As a result of the repairs, energy consumption improved by 8% compared to the initial state. Three regression models were compared to predict leak flow: Linear Regression, Bagging Regression Trees, and Multivariate Adaptive Regression Splines. Among the models evaluated, the Bagging Regression Trees model demonstrated the best prediction performance, achieving an R² value of 0.846, a mean squared error (MSE) of 389.85 (L/min²), and a mean absolute error (MAE) of 12.13 L/min in the independent test set. Compared to previous regression-based approaches, the proposed ML method contributes to sustainable production strategies by linking leakage prediction to energy performance indicators. Full article

Biophysics-based, patient-specific modeling remains challenging for clinical translation, particularly for cerebrospinal fluid (CSF) flow where anatomical detail and computational cost are tightly coupled. We present a computational framework for steady net CSF redistribution in an MRI-derived cranial CSF domain reconstructed from T₂-weighted imaging, including the ventricular system, cranial subarachnoid space, and periarterial pathways, to the extent resolvable by clinical MRI. Cranial CSF spaces were segmented in 3D Slicer and a steady Darcy formulation with prescribed CSF production/absorption was solved in COMSOL Multiphysics^®. Geometrical and flow descriptors were quantified using region-based projection operations. We assessed discretization cost–accuracy trade-offs by comparing first- and second-order finite elements. First-order elements produced a 1.4% difference in transmantle pressure and a <10% difference in element-wise mass-weighted velocity metric for 90% of elements, while reducing computation time by 75% (20 to 5 min) and peak memory usage five-fold (150 to 30 GB). This proof-of-concept framework provides a computationally tractable baseline for studying steady net CSF pathway redistribution and sensitivity to boundary assumptions, and may support future patient-specific investigations in pathological conditions such as subarachnoid hemorrhage, hydrocephalus and brain tumors. Full article

Reverse electrodialysis (RED) enables the efficient conversion of the chemical potential difference between seawater and freshwater into electricity while simultaneously facilitating hydrogen production for integrated energy utilization. Nevertheless, the widespread deployment of RED remains constrained by the reliance on ruthenium–iridium-coated electrodes, which are expensive and resource-limited. This study proposes the adoption of titanium-based redox electrodes as a replacement for traditional precious metal electrodes and employs a novel spike structure to accelerate hydrogen bubble detachment. The electrochemical performance of titanium electrodes in an RED hydrogen production system was systematically evaluated experimentally. The influences of several parameters on the RED system performance were systematically examined under these operating conditions, including the ruthenium–iridium catalytic layer, operating temperature (15 to 45 °C), electrode rinse solution (ERS) concentration (0.1 to 0.7 M), and flow rate (50 to 130 mL·min⁻¹). Experimental results demonstrate that optimized titanium redox electrodes maintain high electrocatalytic activity while significantly reducing system costs. Under optimal conditions, the hydrogen yield of the Ti redox electrode reached 89.7% of that achieved with the mesh titanium plate coated oxide iridium and oxide ruthenium as electrodes, while the electrode cost was reduced by more than 60%. This is also one of the cost-cutting solutions adopted by RED for its development. Full article

Next Generation Sequencing is a constantly evolving technology whose applicability is increasingly expanding into the field of routine food analysis. In this context, metabarcoding has proven to be a powerful tool for detecting food fraud due to its ability to taxonomically classify even highly fragmented DNA from processed products. While Illumina sequencing platforms, representing second-generation sequencing technologies, are widely used for such applications, fourth-generation sequencing devices such as Oxford Nanopore Technologies’ MinION offer advantages in terms of flexibility, scalability, and simplified handling. In this study, we evaluate the transferability of an established Illumina-based metabarcoding method for the detection of pistachio adulteration in processed foods to the MinION platform of Oxford Nanopore Technology. In more detail, we transferred the established method from Illumina on both MinION and Flongle flow cells to assess sequencing accuracy, quantification potential and practical aspects such as cost-efficiency and workflow. Our results highlight the applicability of the MinION sequencing platform as a reliable and cost-effective alternative to Illumina protocols for routine food authenticity testing, enabling faster processing and broader accessibility without significantly compromising accuracy. Full article

High-pressure water jet technology is widely utilized for cleaning marine artificial structures due to its portability, efficiency, and environmental friendliness, yet traditional jets underperform in submerged environments. Gas-assisted water jet technology has predominantly been applied to rock breaking—where vertical forces are prioritized—with insufficient research into flow regime evolution, limiting its utility for cleaning applications. This study introduces a supercavitating high-pressure water jet aimed at improving underwater cleaning efficiency while lowering economic costs. Employing ANSYS Fluent—with the RNG k-ε turbulence model and mixture model—validated via high-speed camera experiments, we explored the flow regime evolution of both unconstrained and semi-constrained impinging jets. The key findings of this paper are as follows: The cavity evolves with a periodic “necking-bubbling” pattern, whose intensity correlates positively with gas outlet velocity and supply rate; moderate gas supply—with 120 L/min identified as optimal through orthogonal analysis—effectively delays water jet breakup. For semi-constrained jets, the wall-adjacent gas flow also exhibits “necking-bubbling”; small-angle impact (30° versus 60°) reduces near-wall shear vortices, enhancing gas cavity stability on the target plate. This study bridges the gap between gas-assisted jet technology and underwater cleaning requirements, offering theoretical insights and optimized parameters for efficient, low-cost marine structure cleaning. It thereby supports the sustainable exploitation of marine resources and the stable operation of key marine facilities. Full article

To address the limitations of existing power system low-carbon dispatching studies—such as over-reliance on generation-side carbon mitigation, price-oriented demand response (DR) failing to guide carbon reduction, and the low solution efficiency of traditional carbon emission flow (CEF)-based two-stage models—this paper proposes a data-driven CEF framework integrated with a bi-level economic and low-carbon dispatching model. First, a data-driven CEF calculation method is developed: It eliminates the need for complex power flow post-processing while maintaining calculation accuracy through multiple linear regression. On this basis, a bi-level optimization model is constructed: The upper level focuses on optimizing the economic and low-carbon objectives of power grid operation, while the lower level regulates industrial, commercial, and residential load aggregators (LAs) via carbon-intensity-oriented DR strategies and economic compensation mechanisms. Finally, a sample-based optimization algorithm combined with convex relaxation is proposed to solve the model, avoid the static setting of power flow and carbon intensity, and improve solution efficiency. Case studies demonstrate the following: the proposed method reduces the calculation time of node carbon intensity from 5 min to less than 100 ms, with the coefficient of determination (R²) ranging from 0.969 to 0.998; compared with the two-stage method, it achieves a 4.26% reduction in total scheduling cost, a 3.80% decrease in total carbon emissions, a 53.27% drop in carbon trading cost, and a 21.6% shortening in iteration time. These results verify that the proposed method can effectively enhance the source−load interaction and improve the accuracy and efficiency of low-carbon scheduling. This study provides a feasible technical path for the low-carbon transition of new-type power systems. Full article

Objectives: The aim of this study was to evaluate the stability of azacitidine (AZA) under clinical storage conditions (room temperature vs. refrigeration) to identify practical protocols that minimize waste and improve cost-effectiveness. Methods: AZA solutions (1 mg/mL) were stored at 23 ± 2 °C or 4 °C. Stability was assessed using a validated high-performance liquid chromatography (HPLC) method. Chromatographic separation was achieved on a Hypersil ODS C₁₈ column (250 mm × 4.6 mm, 5 μm) using an isocratic mobile phase of 50 mM potassium phosphate buffer (pH 7.0)-acetonitrile (98:2, v/v) at a flow rate of 1.0 mL/min, with UV detection at 245 nm and a 20 μL injection volume. The method demonstrated specificity for AZA and its main degradation product (DP), with LOD and LOQ of 12.56 μg/mL and 62.8 μg/mL, respectively. Linearity (R² = 0.9928), precision (RSD% < 5 for mid/high levels), and accuracy (mean recovery 96%) were established. Results: Azacitidine degraded rapidly at room temperature, with >85% loss within 24 h. In contrast, refrigeration at 4 °C significantly delayed degradation, with only ~26% loss observed over the same 24 h period. Chromatographic analysis confirmed the formation of a primary degradation product (tentatively identified as the open-ring hydrolytic species N-(formylamidino)-N′-β-D-ribofuranosylurea based on its chromatographic behavior and literature data), consistent with the known hydrolytic pathway. The applied HPLC-UV method offered an optimal balance of specificity and practicality for monitoring this main degradation trend under clinical storage conditions, distinguishing it from more complex techniques used primarily for structural elucidation. Conclusions: The pronounced instability of reconstituted AZA underscores the critical importance of strict adherence to immediate-use protocols. Refrigeration provides only a limited stability window. Based on our kinetic data, maintaining the reconstituted solution within an acceptable degradation limit (e.g., ≤10% loss) at 4 °C would require administration within a very short timeframe, supporting current handling guidelines to ensure therapeutic efficacy and minimize economic waste. Full article