Search Results (285)

This study examines the impact of photovoltaic (PV) modeling fidelity utilizing single-diode (SDM), double-diode (DDM), and triple-diode (TDM) representations on the precision of hydrogen production (H₂P) estimates when integrated with various electrolyzer technologies, specifically proton exchange membrane (PEM), alkaline (AEL), and solid oxide electrolysis cells (SOECs). Precise evaluation of solar-powered green hydrogen (H₂) systems necessitated a dependable estimate of PV power under authentic working circumstances. Hourly site-specific irradiance and ambient temperature (T_a) data for Riyadh, Saudi Arabia, were used to calculate PV power outputs, which were then sent to physically based electrolyzer models regulated by electrochemical voltage relationships and Faraday’s law. The findings indicate that while all PV models display the same seasonal patterns, SDM somewhat overestimates yearly PV energy in comparison to DDM and TDM, with relative errors around 0.03%. These discrepancies somewhat affect H₂ yield estimations but do not change the relative ranking of electrolyzer technology. Among the assessed options, SOEC consistently produced the highest H₂ output, generating approximately 21.8% more H₂ than PEM and 9.1% more than AEL, with annual yields of 62.46–62.47 g for PEM, 69.70–69.71 g for AEL, and 76.04–76.05 g for SOEC across the SDM, DDM, and TDM frameworks under equivalent solar power inputs. The findings indicate that the selection of electrolyzer technology significantly impacts H₂P more than the choice of a PV model, while high-fidelity PV modeling is crucial for a physically realistic and precise system-level assessment of integrated PV-H₂ energy systems. Full article

This study presents the successful expression, purification, and functional characterization of the human TRPM8 ion channel, a key player in temperature sensing and pain modulation. Using a modified bacterial expression protocol and DDM-based solubilization, TRPM8 was purified via HPLC-SEC and analyzed for its membrane-binding properties. FRET-based assays with synthetic lipid rafts revealed a strong and selective affinity of TRPM8 for cholesterol-containing membranes, suggesting cholesterol’s role in modulating TRPM8 localization and activity. These findings provide quantitative in vitro evidence of TRPM8–cholesterol interactions and establish a robust model system for future structural and functional studies of membrane-associated proteins. Full article

High-resolution mass spectrometry (HRMS) enables non-targeted detection of drugs and metabolites in complex matrices. Phase II metabolites—especially glucuronides—are often the only detectable biomarkers in late or postmortem samples but are underrepresented in commercial libraries. This work pursued the prediction of phase II-glucuronide conjugates in diluted urine samples by non-targeted/targeted LC-HRMS workflows. A simply “dilute-and-shoot” qualitative UHPLC-HRMS/MS method (Q Exactive HF, ddMS²) was integrated with Compound Discoverer^® software for data processing. The workflow incorporated predictive strategies such as exact mass suspect lists, Structured Query Language (SQL)-based filters, compound-class and diagnostic neutral-loss rules (including the characteristic loss of 176.0321 Da for glucuronides) and MS/MS confirmation using both in-house and public spectral libraries. An additional part of the application’s performance assessment involved its validation for diluted urine sample. A qualitative validated method for more than two hundred drugs in urine samples was performed, including the method’s selectivity/specificity, limit of identification, matrix effects, and potential carryover. Most analytes fulfilled the qualitative acceptance criteria, with more than 60% successfully identified at a concentration of at least 2.5 ng/mL. Matrix effects were within acceptable limits for most compounds, and no severe ion suppression was observed. A non-targeted workflow was applied to real forensic samples (n = 16), allowing a reduction of approximately 66,800 detected features to 225 glucuronide candidates, while a targeted workflow based on exact mass lists yielded 31 high-confidence identifications. Characteristic neutral losses and diagnostic fragment ions led to the tentative identification of some glucuronide phase II metabolites such as mirtazapine–glucuronide, morphine-6–glucuronide, and glucuronide conjugates of benzodiazepines and synthetic opioids. In conclusion, the integration of biotransformation knowledge with HRMS-based predictive filtering allows for the efficient and hydrolysis-free detection of glucuronide metabolites, thereby extending detection windows and enhancing toxicological interpretation in complex forensic scenarios. This adaptable and library-independent workflow also facilitates retrospective data mining, making it suitable for the identification of emerging substances and newly characterized metabolites. Full article

Previous studies have emphasized the role of task difficulty in decision performance while relatively neglecting the decision maker’s subjective initiative and intrinsic learning process during task execution. This study manipulated the rule hierarchy factor, which reflects external task difficulty, and the block factor, which reflects the accumulation of intrinsic learning, and used analysis of variance (ANOVA), the drift-diffusion model (DDM), and random forest algorithms to systematically examine how task difficulty and learning jointly influence decision behavior and its underlying mechanisms. A total of 40 participants were recruited, and after strict exclusion criteria were applied, 34 valid datasets were included in the final analysis. The results showed that although rule hierarchy had a significant impact on decision performance in the early stage of the task (the first two blocks), this effect gradually diminished as task repetitions increased. Furthermore, the results revealed a clear dissociation in predictive mechanisms: intrinsic cognitive factors (specifically, evidence accumulation efficiency and decision bias) were the primary predictors of decision accuracy, whereas external task difficulty (rule hierarchy) acted as the dominant predictor for decision speed (reaction time). These findings provide a new perspective for understanding the dynamic relationship between external task demands and intrinsic learning processes, highlighting the necessity of distinguishing between accuracy and speed metrics in personalized education, training, and human–computer interaction design. Full article

In communication-centric integrated sensing and communication (ISAC) systems, passive radars exploit existing communication signals of opportunity for sensing. To compute delay-Doppler or range–velocity maps (DDMs and RVMs, respectively), modern orthogonal frequency division multiplexing (OFDM)-based sensing systems use the channel frequency response (CFR) originally estimated in communication receivers for equalization. In OFDM-based passive radars utilizing 4G LTE or 5G NR waveforms, CFR estimation typically relies only on reference signals. However, simulation-based studies that assume a priori knowledge of user data symbols indicate potential performance gains when incorporating user data and other downlink channels. In this work, we present an experimental evaluation of an OFDM-based passive radar that jointly utilizes all commonly present components of the 5G NR downlink waveform: synchronization signals (PSS and SSS), broadcast and control channels (PBCHs and PDCCHs, respectively), data channels (PDSCHs), and reference signals (PBCH DM-RSs, PDCCH DM-RSs, PDSCH DM-RSs, and CSI-RSs). Our results show that utilizing user data from fully occupied 5G downlink signals, under the assumption of full knowledge of PDSCH locations, significantly improves both the probability of detection (POD) and the peak height, measured by the peak-to-noise-floor ratio (PNFR), compared with pilot-only sensing. Since perfect knowledge of the user data payload is not assumed, we estimate the transmission bit error rate (BER) and analyze its impact on sensing performance. Finally, we investigate more realistic scenarios in which only a subset of PDSCH resource element locations is known, as in practical 5G deployments, and evaluate how partial data location knowledge affects the POD and PNFR under different BER conditions. Full article

Staged multi-cluster fracturing in horizontal wells is a key technology for efficiently developing unconventional oil and gas reservoirs. Extreme Limited-Entry Fracturing (ELF) and Temporary Plugging Fracturing (TPF) are effective techniques to enhance the uniformity of fracture stimulation within a stage. However, in fractured reservoirs, the propagation morphology of multiple intra-stage fractures and fluid distribution patterns becomes significantly more complex under the influence of ELF and TPF. This complexity results in a lack of theoretical guidance for optimizing field operational parameters. This study establishes a competitive propagation model for multiple hydraulic fractures (HFs) within a stage under ELF and TPF conditions in fractured reservoirs based on the Displacement Discontinuity Method (DDM) and fluid mechanics theory. The accuracy of the model was verified by comparing it with laboratory experimental results and existing numerical simulation results. Using this model, the influence of ELF and TPF on intra-stage fracture propagation morphology and fluid partitioning was investigated. Results demonstrate that extremely limited-entry perforation and ball-sealer diversion effectively mitigate the additional flow resistance induced by both the stress shadow effect and the connection of natural fractures (NFs), thereby mitigating uneven fluid distribution and imbalanced fracture propagation among clusters. ELF artificially creates extremely high perforation friction by drastically reducing the number of perforations or the perforation diameter, thereby forcing the fracturing fluid to enter multiple perforation clusters relatively uniformly. Compared to the unlimited-entry scheme (16 perforations/cluster), the limited-entry scheme (5 perforations/cluster) yielded a 37.84% improvement in fluid distribution uniformity and reduced the coefficient of variation (CV) for fracture length and fluid intake by 54.28% and 44.16%, respectively. The essence of the TPF is non-uniform perforation distribution, which enables the perforation clusters with large fluid intake to obtain more temporary plugging balls (TPBs), so that their perforation friction can be increased and their fluid intake can be reduced, thereby diverting the fluid to the perforation clusters with small fluid intake. Deploying TPBs (50% of total perforations) at the mid-stage of fracturing (50% time) increased fluid distribution uniformity by 37.86% and reduced the CV of fracture length and fluid intake by 72.54% and 58.39%, respectively. This study provides methodological and modeling foundations for systematic optimization of balanced stimulation parameters in fractured reservoirs. Full article

Accurate parameter extraction is essential for reliable photovoltaic (PV) modeling and performance assessment. This study proposes an improved Particle Swarm Optimization (IPSO) algorithm and presents a comparative evaluation against particle swarm optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Artificial Bee Colony (ABC), simulated annealing (SA), and Nelder–Mead (NM) for estimating the parameters of single-, double-, and triple-diode PV models. All algorithms are tested using identical experimental I–V data and evaluated in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Bias Error (MBE), coefficient of determination (R²⁾, and computational time. The proposed IPSO significantly enhances convergence accuracy and stability for SDMs and DDMs, achieving very low best-case RMSE values with R² exceeding 0.9999. For the more complex TDM, IPSO attains the lowest best-case error, while DE and ABC exhibit superior robustness in terms of mean error and variance. Overall, the results demonstrate the effectiveness of the proposed IPSO and highlight the trade-off between accuracy and robustness when selecting optimization algorithms for PV parameter extraction. Full article

The disjunction effect in the prisoner’s dilemma game shows that humans tend to cooperate more under uncertain condition (U) than under the two complementary known conditions—one being competitive (D) and the other being cooperative (C)—a well-known violation of the classical decision principle. Our study explores the potential role of prosociality in the disjunction effect. We measured prosocial trait via the SVO Slider Measure, and prosocial bias via the drift diffusion model (DDM). By using the SVO Slider Measure (for prosocial trait) and the DDM starting-point bias parameter (for prosocial bias), we found that the variation in prosocial bias between uncertain and certain conditions substantially contributes to the disjunction effect. At the aggregate level, prosocial bias significantly decreased from U to D (competitive) but did not differ between U and C (cooperative). At the individual level, participants showed heterogeneous bias changes across prosocial-trait groups: intermediate participants had the largest bias shifts. This heterogeneity underlies the observed inverted U-shaped relationship between prosocial trait and effect size of the disjunction effect. Our study fills a critical gap by clarifying how prosocial inclination influences the disjunction effect. Full article

Recognizing the critical role of polar Sea Ice Concentration (SIC) in climate feedback mechanisms, this study presents the first comprehensive investigation of China’s Fengyun-3E(FY-3E) GNOS-II Global Navigation Satellite System Reflectometry (GNSS-R) for bipolar SIC retrieval. Specifically, reflected signals from multiple Global Navigation Satellite Systems (GNSS) are utilized to extract characteristic parameters from Delay Doppler Maps (DDMs). By integrating regional partitioning and dynamic thresholding for sea ice detection, a Random Forest Regression (RFR) model incorporating a rolling-window training strategy is developed to estimate SIC. The retrieved SIC products are generated at the native GNSS-R observation resolution of approximately 1 × 6 km, with each SIC estimate corresponding to an individual GNSS-R observation time. Owing to the limited daily spatial coverage of GNSS-R measurements, the retrieved SIC results are further aggregated into monthly composites for spatial distribution analysis. The model is trained and validated across both polar regions, including targeted ice–water boundary zones. Retrieved SIC estimates are compared with reference data from the OSI SAF Special Sensor Microwave Imager Sounder (SSMIS), demonstrating strong agreement. Based on an extensive dataset, the average correlation coefficient (R) reaches 0.9450 in the Arctic and 0.9602 in the Antarctic for the testing set, with corresponding Root Mean Squared Error (RMSE) of 0.1262 and 0.0818, respectively. Even in the more challenging ice–water transition zones, RMSE values remain within acceptable ranges, reaching 0.1486 in the Arctic and 0.1404 in the Antarctic. This study demonstrates the feasibility and accuracy of GNSS-R-based SIC retrieval, offering a robust and effective approach for cryospheric monitoring at high latitudes in both polar regions. Full article

Hot-fluid injection in thermal-enhanced oil recovery (thermal-EOR, TEOR) imposes temperature-driven volumetric strains that can substantially alter in situ stresses, fracture geometry, and wellbore/reservoir integrity, yet existing TEOR modeling has not fully captured coupled thermo-poroelastic (thermo-hydro-mechanical) effects on fracture aperture, fracture-tip behavior, and stress rotation within a displacement discontinuity method (DDM) framework. This study aims to examine the influence of sustained hot-fluid injection on stress redistribution, hydraulic-fracture deformation, and fracture stability in thermal-EOR by accounting for coupled thermal, hydraulic, and mechanical interactions. This study develops a fully coupled thermo-poroelastic DDM formulation in which fracture-surface normal and shear displacement discontinuities, together with fluid and heat influx, act as boundary sources to compute time-dependent stresses, pore pressure, and temperature, while internal fracture fluid flow (Poiseuille-based volume balance), heat transport (conduction–advection with rock exchange), and mixed-mode propagation criteria are included. A representative scenario considers an initially isothermal hydraulic fracture grown to 32 m, followed by 12 months of hot-fluid injection, with temperature contrasts of ΔT = 0–100 °C and reduced pumping rate. Results show that the hydraulic-fracture aperture increases under isothermal and modest heating (ΔT = 25 °C) and remains nearly stable near ΔT = 50 °C, but progressively narrows for ΔT = 75–100 °C despite continued injection, indicating potential injectivity decline driven by thermally induced compressive stresses. Hot injection also tightens fracture tips, restricting unintended propagation, and produces pronounced near-fracture stress amplification and re-orientation: minimum principal stress increases by 6 MPa for ΔT = 50 °C and 10 MPa for ΔT = 100 °C, with principal-stress rotation reaching 70–90° in regions adjacent to the fracture plane and with markedly elevated shear stresses that may promote natural-fracture activation. These findings show that temperature effects can directly influence injectivity, fracture containment, and the risk of unintended fracture or natural-fracture activation, underscoring the importance of temperature-aware geomechanical planning and injection-strategy design in field operations. Incorporating these effects into project design can help operators anticipate injectivity decline, improve fracture containment, and reduce geomechanical uncertainty during long-term hot-fluid injection. Full article

Peri-implant infections pose a significant challenge in dental implantology. This study aimed to develop and characterize a chitosan–vancomycin coating for titanium surfaces, focusing on drug loading, release kinetics, antimicrobial performance, and cytocompatibility. Grade 4 titanium discs were coated with a chitosan film using the dip-coating technique and subsequently loaded with vancomycin through immersion in an aqueous solution. Coating morphology was examined by scanning electron microscopy (SEM). Vancomycin loading was quantified by spectrophotometry, and release kinetics were monitored over 144 h (6-day). Antimicrobial activity was assessed through agar diffusion assays against Staphylococcus aureus. Cytocompatibility was evaluated using human mesenchymal stem cells (hMSCs), whose metabolic activity, adhesion, and morphology were assessed over a 19-day culture period by resazurin assay and SEM. SEM analysis revealed a uniformly distributed, smooth, and crack-free chitosan film, which remained stable after drug loading. The coating exhibited a biphasic release profile, characterized by an initial burst followed by sustained release over six days, which maintained antimicrobial activity, as confirmed by inhibition zones. hMSCs adhered and proliferated on the coated surfaces, displaying normal morphology despite a transient reduction in metabolic activity on vancomycin-containing films. These findings support the potential of chitosan–vancomycin coatings as localized antimicrobial strategies for implant applications, warranting further in vivo and mechanical evaluations. Full article

Background/Objectives: Klebsiella pneumoniae is a major Gram-negative pathogen associated with community-acquired pneumonia (CAP) and a critical contributor to antimicrobial resistance (AMR). Culture-based diagnostics remain the clinical standard but may underestimate microbial diversity and resistance gene profiles. This pilot study compared pathogen detection and antimicrobial resistance gene (ARG) repertoires in matched K. pneumoniae pure cultures and primary sputum samples using targeted next-generation sequencing (tNGS). Methods: We analyzed 153 sputum samples from patients with CAP. Among 48 culture-positive cases, 22 (14% overall; 54% culture-positive) yielded K. pneumoniae. MALDI-TOF MS, phenotypic drug susceptibility testing, and tNGS were conducted on both culture isolates and matched sputum specimens. Microbial composition, ARG diversity, and method concordance were evaluated, with focused analysis of discordant and fatal cases. Results: K. pneumoniae was detected in 14.4% of all CAP cases and accounted for 54.2% of culture-positive samples. Identification rates differed across methods: 35% by MALDI-TOF MS, 45% by culture tNGS, and 29% by sputum tNGS. Sputum tNGS revealed substantially higher microbial diversity than cultures (3.04 vs. 1.42 species per sample) and detected more than sixfold unique ARGs (38 vs. 7), including clinically relevant determinants that were absent from culture isolates. Concordance was high between MALDI-TOF MS and culture tNGS (κ = 0.712), but low between sputum and culture tNGS (κ = 0.279). Among twelve K. pneumoniae isolates included in AMR analysis, all showed resistance to β-lactams, and two-thirds exhibited MDR/XDR phenotypes. Genotypic screening identified seven ARGs, but major ESBL and carbapenemase genes were not detected, suggesting the presence of alternative resistance mechanisms. Overall, sputum tNGS provided additional etiological and resistome information not captured by cultivation and complemented classical diagnostics in CAP involving K. pneumoniae. Conclusions: Culture-based diagnostics and tNGS provide complementary insights into the detection and resistance profiling of K. pneumoniae in CAP, with sputum tNGS revealing broader microbial and resistome information than pure cultures, while classical methods remain essential for species confirmation and phenotypic AST. An integrated diagnostic approach combining both methodologies may improve pathogen detection, guide antimicrobial therapy, and enhance AMR surveillance in K. pneumoniae-associated CAP. Full article

Background: Comprehensive and accurate compound composition characterization in natural sources has high relevance in food and nutrition, health and medicine, environmental and agriculture research areas, though profiling of plant metabolites is a challenging task due to the structural complexity of natural products. This study delves into the identification and characterization of compounds within the Plantago genus, leveraging state-of-the-art analytical techniques. Methods: Utilizing an ultra-high-performance liquid chromatography (UHPLC) system in conjunction with Orbitrap™ IQ-X™ Tribrid™ mass spectrometer (MS), we employed a Phenyl-Hexyl HPLC column alongside optimized extraction protocols to analyze both husk and leaf samples. To maximize compound identification, we implemented data-dependent acquisition (DDA) methods including MS² (ddMS2), MS³ (ddMS3), AcquireX™ deep scan, and real-time library search (RTLS). Results: Our results demonstrate a significant increase in the number of putatively yet confidently assigned compounds, with 472 matches in P. lanceolata leaves and 233 in P. ovata husk identified through combined acquisition methods. The inclusion of an additional fragmentation level (MS³) noticeably enhanced the confidence in compound annotation, facilitating the differentiation of isomeric compounds. Furthermore, the application of low-energy fragmentation (10 normalized collision energy (NCE) for higher-energy collisional dissociation (HCD)) improved the detection and grouping of MS¹ fragments by 55% in positive mode and by 16% in negative mode, contributing to a more comprehensive analysis with minimal loss in compound identification. Conclusions: These advancements underscore the potential of our methodologies in expanding the chemical profile of plant materials, offering valuable insights into natural product analysis and dereplication of untargeted data. Full article

As low-carbon supply chains increasingly integrate green transition strategies with digital transformation, coordinating high-cost green technology investments with data-driven marketing (DDM) becomes a complex managerial task. While these dual investments are essential for market growth, the inherent tension between economic efficiency and fairness concerns often triggers strategic friction phenomenon whose impact under cap-and-trade regulations remains insufficiently explored. This paper investigates the strategic implications of fairness concerns in a low-carbon supply chain in which a manufacturer invests in carbon emission reduction and a retailer engages in data-driven marketing (DDM), under a cap-and-trade regulation. We formulate four Stackelberg game models—Neutral Benchmark (NF), Retailer Fairness (RF), Manufacturer Fairness (MF), and Bilateral Fairness (BF)—to analyze the interplay between behavioral equity and economic efficiency. The main analytical results indicate that (1) fairness concerns universally function as an “efficiency tax” on the supply chain system, where the rational benchmark consistently yields the highest system efficiency. In contrast, bilateral fairness concerns lead to the worst performance due to double friction effects. (2) Counter-intuitively, the retailer can “weaponize” fairness concerns to extract surplus from the leader. Specifically, in environments with high carbon emission reduction costs, a fairness-concerned retailer compels the manufacturer to grant significant wholesale price concessions, thereby achieving higher profits than in a purely rational setting. (3) The manufacturer’s fairness creates a “Benevolence Trap” for the follower; to balance equity, a fair manufacturer tends to underinvest in green technologies, which severely contracts market demand and, unlike the retailer fairness scenario, fails to yield economic benefits for the retailer. (4) A critical “regime-switching” dynamic exists regarding the carbon trading price. While the retailer benefits from fairness strategies in nascent carbon markets, a pivot to rationality becomes optimal as carbon prices surge and efficiency dividends dominate. These findings offer novel managerial insights for supply chain members to navigate behavioral complexities and for policymakers to align incentive mechanisms. Full article

Advanced alkaline water electrolysis (AWE) in “zero-gap” configuration is a promising approach for low-temperature hydrogen production, but its efficiency strongly depends on the design and surface chemistry of nickel-based electrodes. Here, we present a simple dip-and-drying method (DDM) to modify commercial nickel foam with a Ni–FeOOH/PTFE microporous catalytic layer and evaluate its electrochemical performance in 1 M KOH and in a laboratory zero-gap cell with a Zirfon^® Perl 500 UTP diaphragm, through circulating 25 wt.% KOH. The FeSO₄-assisted DDM treatment generates mixed Ni–Fe oxyhydroxide surface species, while PTFE imparts control hydrophobicity, enhancing both catalytic activity and gas-release behavior. Annealing the electrode (DDM-NF-CAT-A) results in a cell voltage of 2.45 V at 1 A·cm⁻² and 80 °C, demonstrating moderate performance comparable to other Ni-based electrodes prepared via low-complexity methods, though below that of optimized state-of-the-art zero-gap systems. Short-term durability tests (80 h at 0.5 A·cm⁻²) indicate stable operation, but long-term industrial performance was not assessed. These findings illustrate the potential of the DDM approach as a simple, low-cost route to structured nickel foam electrodes and provide a foundation for further optimization of catalyst loading, microstructure, and long-term stability for practical AWE applications. Full article