Search Results (207,427)

CO₂ flooding has become a strategic tool for enhanced oil recovery and reservoir management in mature fields. This technique, however, is rarely utilized in asphaltenic crude oil systems, due to the likely occurrence of high asphaltene precipitation. The effect of asphaltene concentrations and CO₂ injection pressures has mostly been the focus of studies in determining asphaltene precipitation rates. However, asphaltene precipitation is not the only direct factor to be considered in predicting the extent of damage in an asphaltenic crude oil system. In this study, a compositional reservoir simulation was conducted using Eclipse 300 to investigate the injection pressure at which asphaltene-induced formation damage can be avoided during both miscible and immiscible CO₂ flooding in an asphaltenic crude system. Simulation results indicate that asphaltene-induced permeability reduction exceeded 35% in most affected zones, with a corresponding drop in injectivity of 28%. Cumulative oil recovery improved by 19% compared to base cases without CO₂ injection, achieving peak recovery after approximately 4200 days of simulation time. As CO₂ was injected below the minimum miscibility pressure of 2079.2 psi, a significantly lower asphaltene precipitation was observed near the injector. This could be attributed to the stripping of lighter hydrocarbon components (C2–C7+) occurring in the transition zone at the gas–oil interface. Injecting CO₂ at pressures above the minimum miscibility pressure resulted in precipitation occurring throughout the entire reservoir at 3200 psia and 1000 bbls per day injection rates. An increase in the injection rate at pressures above the minimum miscibility pressure increased the rate of precipitation. However, a further increase in the injection rate from 1000 bbl per day to 4200 bbl per day resulted in a decrease in asphaltene. The pressure drop in the water phase caused by pore throat increase demonstrated that water injection was effective in removing asphaltene deposits and restoring permeability. This work provides critical insights into optimizing CO₂ injection strategies to enhance oil recovery while minimizing asphaltene-induced formation damage in heavy oil reservoirs. Full article

Wheel slipping during trajectory tracking presents significant challenges for wheeled mobile robots (WMRs), degrading accuracy and stability on low-friction or dynamic terrain. Effective control requires addressing unknown slipping parameters while balancing tracking precision and energy efficiency. To address this challenge, a control framework integrating a sliding mode observer (SMO), an improved particle swarm optimization (PSO) algorithm, and a linear quadratic regulator (LQR) is proposed. First, a dynamic model incorporating longitudinal slipping is established. Second, an SMO is designed to estimate the slipping ratio in real-time, with chattering suppressed using a low-pass filter. Finally, an improved PSO algorithm featuring a nonlinear cosine-decreasing inertia weight strategy optimizes the LQR weighting matrices (Q/R) online to both minimize tracking errors and control energy consumption. Simulations including both circular and sine wave trajectories demonstrate that the SMO achieves rapid and accurate slipping ratio estimation, while the PSO-optimized LQR significantly enhances tracking accuracy, achieves smoother control inputs, and maintains stability under varying slipping conditions. Full article

As bike-sharing systems become increasingly integral to sustainable urban mobility, understanding their economic viability requires moving beyond conventional linear models to capture complex operational dynamics. This study develops an interpretable analytical framework to uncover non-linear relationships governing bike-sharing economic performance in Xi’an, China, utilizing one-month operational data across 202 Transportation Analysis Zones (TAZs). Combining spatial analysis with explainable machine learning (XGBoost–SHAP), we systematically examine how operational factors and built environment characteristics interact to influence economic outcomes, achieving superior predictive performance (R² = 0.847) compared to baseline linear regression models (R² = 0.652). The SHAP-based interpretation reveals three key findings: (1) bike-sharing performance exhibits pronounced spatial heterogeneity that correlates strongly with urban functional patterns), with commercial districts and transit-adjacent areas demonstrating consistently higher economic returns. (2) Gradual positive relationships emerge across multiple factors—including bike supply density (maximum SHAP contribution +1.0), commercial POI distribution, and transit accessibility—with performance showing consistent but moderate improvements rather than dramatic threshold effects. (3) Significant interaction effects are quantified between key factors, with bike supply density and commercial POI density exhibiting strong synergistic relationships (interaction values 1.5–2.0), particularly in areas combining high commercial activity with good transit connectivity. The findings challenge simplistic linear assumptions in bike-sharing management while providing quantitative evidence for spatially differentiated strategies that account for moderate threshold behaviors and factor synergies. Cross-validation results (5-fold, R² = 0.89 ± 0.018) confirm model robustness, while comprehensive performance metrics demonstrate substantial improvements over traditional approaches (35.1% RMSE reduction, 36.6% MAE improvement). The proposed framework offers urban planners a data-driven tool for evidence-based decision-making in sustainable mobility systems, with broader methodological applicability for similar urban contexts. Full article

Background/Objectives: 16S ribosomal RNA sequencing has, for several years, been the main means of identifying bacterial and archaeal species. Low-throughput Sanger sequencing is often used for the detection and identification of microbial species, but this technique has several limitations. The use of high-throughput sequencers may be a good alternative to improve patient identification, especially for polyclonal infections and management. Kraken 2 and KrakenUniq are free, high-throughput tools providing a very rapid and accurate classification for metagenomic analyses. However, Kraken 2 can present false-positive results relative to KrakenUniq, which can be limiting in hospital settings requiring high levels of accuracy. The aim of this study was to establish an alternative next-generation sequencing technique to replace Sanger sequencing and to confirm that KrakenUniq is an excellent analysis tool that does not present false results relative to Kraken 2. Methods: DNA was extracted from reference bacterial samples for Laboratory Quality Controls (QCMDs) and the V2-V3 and V3-V4 regions of the 16S ribosomal gene were amplified. Amplified products were sequenced with the Illumina 16S Metagenomic Sequencing protocol with minor modifications to adapt and sequence an Illumina 16S library with a small 500-cycle nano-flow cell. The raw files (Fastq) were analyzed on a commercial Smartgene platform for comparison with Kraken 2 and KrakenUniq results. KrakenUniq was used with a standard bacterial database and with the 16S-specific Silva138, RDP11.5, and Greengenes 13.5 databases. Results: Seven of the eight (87.5%) QCMDs were correctly sequenced and identified by Sanger sequencing. The remaining QCMD, QCMD6, could not be identified through Sanger sequencing. All QCMDs were correctly sequenced and identified by MiSeq with the commercial Smartgene analysis platform. QCMD6 contained two bacteria, Acinetobacter and Klebsiella. KrakenUniq identification results were identical to those of Smartgene, whereas Kraken 2 yielded 25% false-positive results. Conclusions: If Sanger identification fails, MiSeq with a small nano-flow cell is a very good alternative for the identification of bacterial species. KrakenUniq is a free, fast, and easy-to-use tool for identifying and classifying bacterial infections. Full article

Nicotinamide mononucleotide, known as NMN, is an important nicotinamide adenine dinucleotide (NAD⁺) precursor. It is integral in cellular metabolism, energy generation, and processes associated with aging. Since NMN provides healthy value, it becomes a major focus for the biotechnological industry. This study presents a new biosynthetic pathway for producing NMN without limits on intracellular PRPP (5′-phosphoribosyl pyrophosphate) metabolic flux. The route started by converting cytidine into 1-phosphoribose via pyrimidine-nucleoside phosphorylase (PyNP), after transforming into nicotinamide riboside (NR) through either purine-nucleoside phosphorylase (XapA) or nicotinate riboside kinase (NRK). NR was phosphorylated by NRK in the presence of nicotinamide (NAM) to produce NMN. We established an in vitro enzyme activity verification system for the feasibility check. The optimization of multienzyme cascade reactions was figured out for the NMN biosynthesis. Finally, the enzymes of PyNP and NRK were expressed in the cytidine-producing strain; we established a de novo biosynthesis pathway from glucose to NMN, achieving a production titer of 33.71 mg/L at a shake-flask scale. Full article

Immune checkpoint inhibitors (ICIs) have transformed the landscape of cancer therapy by reactivating immune surveillance mechanisms against tumor cells. In the context of oral squamous cell carcinoma (OSCC) and broader head and neck squamous cell carcinoma (HNSCC), agents such as pembrolizumab, durvalumab, and ipilimumab target PD-1, PD-L1, and CTLA-4, respectively. This review comprehensively examines their clinical efficacy, safety profiles, mechanisms of action, and therapeutic potential in OSCC management, with an emphasis on strategies to overcome therapeutic resistance. A systematic analysis of the literature was conducted, focusing on clinical outcomes, ongoing trials, and emerging combination therapies. Pembrolizumab has demonstrated significant improvements in overall survival (OS) and progression-free survival (PFS) in OSCC patients. Durvalumab, mainly utilized in locally advanced or recurrent disease, has shown survival benefit, particularly in combination or maintenance settings. Ipilimumab exhibits durable responses in advanced OSCC, with enhanced efficacy observed when used alongside nivolumab in dual checkpoint blockade regimens. Although both pembrolizumab and nivolumab target PD-1, they differ in clinical indications and regulatory approvals. Notably, ICIs are associated with immune-related adverse events (irAEs), requiring careful monitoring. Collectively, these agents represent promising therapeutic options in oral cancer, though future studies must prioritize the identification of predictive biomarkers and the development of optimized combination strategies to maximize therapeutic benefit while minimizing toxicity. Full article

Background/Objectives: Epilepsy is a neurological disorder that severely impacts patients’ quality of life. In clinical practice, specific pharmacological and surgical interventions are tailored to distinct seizure types. The identification of the epileptogenic zone enables the implementation of surgical procedures and neuromodulation therapies. Consequently, accurate classification of seizure types and precise determination of focal epileptic signals are critical to provide clinicians with essential diagnostic insights for optimizing therapeutic strategies. Traditional machine learning approaches are constrained in their efficacy due to limited capability in autonomously extracting features. Methods: This study proposes a novel deep learning framework integrating temporal and spatial information extraction to address this limitation. Multivariate variational mode decomposition (MVMD) is employed to maintain inter-channel mode alignment during the decomposition of multi-channel epileptic signals, ensuring the synchronization of time–frequency characteristics across channels and effectively mitigating mode mixing and mode mismatch issues. Results: The Bern–Barcelona database is employed to classify focal epileptic signals, with the proposed framework achieving an accuracy of 98.85%, a sensitivity of 98.75%, and a specificity of 98.95%. For multi-class seizure type classification, the TUSZ database is utilized. Subject-dependent experiments yield an accuracy of 96.17% with a weighted F1-score of 0.962. Meanwhile, subject-independent experiments attain an accuracy of 87.97% and a weighted F1-score of 0.884. Conclusions: The proposed framework effectively integrates temporal and spatial domain information derived from multi-channel epileptic signals, thereby significantly enhancing the algorithm’s classification performance. The performance on unseen patients demonstrates robust generalization capability, indicating the potential clinical applicability in assisting neurologists with epileptic signal classification. Full article

KRAS is the most frequently mutated oncogene in cancer. Its activating mutations are associated with aggressive tumor behavior and resistance to certain therapies, including anti-EGFR treatments in colorectal cancer. In particular, the KRAS G12C mutation, which accounts for approximately 3–4% of colorectal cancers (CRCs) and 12–14% of non-small cell lung cancers (NSCLCs), involves a cysteine substitution at codon 12. This has provided the opportunity to develop selective covalent inhibitors that trap the mutant protein in its inactive state. The first targeted therapies for KRAS G12C-mutant cancers comprise sotorasib and adagrasib, both of which have been authorized for use in patients with previously treated NSCLC and CRC. Nevertheless, despite the evidence of clinical activity for this class of agents, primary and acquired resistance, dose optimization, and toxicity management remain significant open challenges. In this review, we summarize recent advances in KRASG12C tumor biology and pharmacological targeting. We also provide additional insights to guide future efforts to overcome the limitations of the current approaches and implement the treatment of KRASG12C-mutant cancers. Full article

This work mainly focuses on the energy and exergy analysis of a single-effect absorption cooling system operating with the couple H₂O-LiBr, under different climatic conditions in Senegal and France. A simulation model was developed, using the Engineering Equation Solver V10 (EES) software. Results indicate that the system can achieve a maximum COP of 0.76 and an exergy efficiency of 56%, which decreases as the generator temperature increases. Increasing the generator temperature from 87 to 95 °C significantly improves COP, but gains become marginal beyond 100 °C. The highest exergy destruction occurs in the generator, followed by the absorber, condenser, and evaporator. A temperature difference above 44 °C between the generator and the absorber is required to maintain H2O-LiBr solution stability. Optimal temperatures for hot climates like Senegal are 90 °C (generator), 42 °C (absorber/condenser), and 7 °C (evaporator), while maximum exergy efficiency (56%) is reached at 81 °C, typical of moderate climates (France). Evaporator exergy efficiency increases from 16 to 52% with rising ambient temperature, while absorber and condenser efficiencies drop. Increasing the cooling water flow rate from 0.2 to 1.4 kg/s reduces exergy losses in the absorber and the condenser by up to 36%. The solution heat exchanger (SHE) optimal effectiveness of 0.75 reduces exergy consumption in the absorber and the generator. Full article

The cold-chain supply of perishable fruits continues to face challenges such as fuel wastage, fragmented stakeholder coordination, and limited real-time adaptability. Traditional solutions, based on static routing and centralized control, fall short in addressing the dynamic, distributed, and secure demands of modern food supply chains. This study presents a novel end-to-end architecture that integrates multi-agent reinforcement learning (MARL), blockchain technology, and generative artificial intelligence. The system features large language model (LLM)-mediated negotiation for inter-enterprise coordination, Pareto-based reward optimization balancing spoilage, energy consumption, delivery time, and climate and emission impact. Smart contracts and Non-Fungible Token (NFT)-based traceability are deployed over a private Ethereum blockchain to ensure compliance, trust, and decentralized governance. Modular agents—trained using centralized training with decentralized execution (CTDE)—handle routing, temperature regulation, spoilage prediction, inventory, and delivery scheduling. Generative AI simulates demand variability and disruption scenarios to strengthen resilient infrastructure. Experiments demonstrate up to 50% reduction in spoilage, 35% energy savings, and 25% lower emissions. The system also cuts travel time by 30% and improves delivery reliability and fruit quality. This work offers a scalable, intelligent, and sustainable supply chain framework, especially suitable for resource-constrained or intermittently connected environments, laying the foundation for future-ready food logistics systems. Full article

To address the challenges of low diagnostic accuracy arising from the non-stationary and nonlinear time-varying characteristics of acoustic signals in rolling bearing fault diagnosis, as well as their susceptibility to noise interference, this paper proposes a fault diagnosis method based on a Weighted Average Algorithm–Feature Mode Decomposition (WAA-FMD) and a Local–Global Adaptive Multi-scale Attention Mechanism (LGAF)–Swin Transformer. First, the WAA is utilized to optimize the key parameters of FMD, thereby enhancing its signal decomposition performance while minimizing noise interference. Next, a bilateral expansion strategy is implemented to extend both the time window and frequency band of the signal, which improves the temporal locality and frequency globality of the time–frequency diagram, significantly enhancing the ability to capture signal features. Ultimately, the introduction of depthwise separable convolution optimizes the receptive field and improves the computational efficiency of shallow networks. When combined with the Swin Transformer, which incorporates LGAF and adaptive feature selection modules, the model further enhances its perceptual capabilities and feature extraction accuracy through dynamic kernel adjustment and deep feature aggregation strategies. The experimental results indicate that the signal denoising performance of WAA-FMD significantly outperforms traditional denoising techniques. In the KAIST dataset (NSK 6205: inner raceway fault and outer raceway fault) and the experimental dataset (FAG 30205: inner raceway fault, outer raceway fault, and rolling element fault), the accuracies of the proposed model reach 100% and 98.62%, respectively, both exceeding that of other deep learning models. In summary, the proposed method demonstrates substantial advantages in noise reduction performance and fault diagnosis accuracy, providing valuable theoretical insights for practical applications. Full article

This study proposes a comprehensive artificial intelligence (AI)-based framework to predict, disaggregate, and optimize energy consumption and associated CO₂ emissions across a multi-building university campus. Leveraging real-world data from 27 buildings at the University of Valladolid (Spain), six AI models—artificial neural networks (ANN), radial basis function (RBF), autoencoders, random forest (RF), XGBoost, and decision trees—were trained on heat exchanger performance metrics and contextual building parameters. The models were validated using an extensive set of key performance indicators (MAPE, RMSE, R², KGE, NSE) to ensure both predictive accuracy and generalizability. The ANN, RBF, and autoencoder models exhibited the highest correlation with actual data (R > 0.99) and lowest error rates, indicating strong suitability for operational deployment. A detailed analysis at building level revealed heterogeneity in energy demand patterns and model sensitivities, emphasizing the need for tailored forecasting approaches. Forecasts for a 5-year horizon further demonstrated that, without intervention, energy consumption and CO₂ emissions are projected to increase significantly, underscoring the relevance of predictive control strategies. This research establishes a robust and scalable methodology for campus-wide energy planning and offers a data-driven pathway for CO₂ mitigation aligned with European climate targets. Full article

Cloud Data Centers (CDCs) are the foundation of the digital economy, enabling data storage, processing, and connectivity for different academia/industry/commerce activities and digital services worldwide. As a result, their consistent power supply and reliable performance are critical factors; however, few works have considered power consumption optimization based on intelligent workload allocation. To this end, the proposed paper presents a Federated Learning (FL)-based server workload allocation model for optimal power optimization. In this strategy, the servers are modeled based on their Central Processing Unit (CPU), memory, storage, and network usage. A global server is considered as the global model responsible for final workload allocation decisions. Each server acts as a client in the federated learning framework, sharing its derived parameters with the global model securely and federatedly. Finally, after ten epochs of the system running, the model could optimize the system, decrease the overall power consumption, and reduce the workload pressure in each server by distributing it to other servers. The model is evaluated using different Key Performance Indicators (KPIs), and an appendix is provided, including the full performance results, workload shifting logs, and server resource status. Overall, the suggested FL allocator model shows promise in significantly lowering power consumption and alleviating server workload efficiently. Full article

Speed breeding technology has been used as a promising approach to accelerate plant breeding cycles and enhance agricultural productivity. However, systematic research on optimizing speed breeding conditions for alfalfa (Medicago sativa L.) in controlled plant factory environments remains limited. This study aimed to optimize light intensity, nutrient solution formulations, and photoperiod conditions for alfalfa speed breeding in plant factories equipped with full-spectrum LEDs, and to validate the applicability of these conditions across cultivars with different fall dormancy levels. Results demonstrated that a light intensity of 250 μmol·m⁻²·s⁻¹ significantly enhanced photosynthetic parameters, antioxidant enzyme activities, and biomass accumulation while minimizing malondialdehyde (MDA). The 75% concentration of the Japanese garden-test formula (JGTF) outperformed the Hoagland solution in promoting growth and photosynthetic pigment synthesis. An extended photoperiod (22 h/d) substantially accelerated growth and shortened flowering time. Under optimized conditions (250 μmol·m⁻²·s⁻¹ light intensity, 22 h/d photoperiod, and 75% Japanese Garden Test Formula), alfalfa cultivars reached initial flowering in approximately 37 days, regardless of fall dormancy level. This study establishes an effective speed breeding protocol for alfalfa, and the optimized conditions demonstrate broad applicability across cultivars with varying fall dormancy characteristics, providing a valuable foundation for accelerated alfalfa breeding programs and contributing to enhanced forage crop development efficiency. Full article

Background: Serum cystatin C is a promising biomarker for vascular risk, yet its nonlinear dose–response relationships and prognostic value in general populations remain unclear, particularly for stroke-specific outcomes. Methods: This study utilized data from the National Health and Nutrition Examination Survey (NHANES) conducted in 1999–2002 cycles. A total of 11,610 participants were included in the primary analysis examining the cross-sectional association between cystatin C and stroke morbidity, using multivariate logistic regression models and odds ratios (ORs). Analyses utilized complete-case data (n = 11,610 for morbidity; n = 11,598 for mortality). Subsequently, 11,598 adults were retained for mortality endpoint analyses, which focused on the longitudinal association between cystatin C and stroke mortality, using cause-specific weighted multivariable Cox models and ratios (HRs). Restricted cubic splines identified nonlinear thresholds, and piecewise regression quantified risk gradients. Models were adjusted for sociodemographic/clinical/behavioral confounders. Results: Serum cystatin C exhibited a nonlinear dose–response relationship with stroke morbidity (p for nonlinear < 0.001), with an inflection point at 1.24 mg/L; below this threshold, each 0.1 mg/L increase conferred 13.84-fold higher odds (95% CI: 7.11–27.03, p < 0.001). For mortality, nonlinear thresholds were identified at 1.24 mg/L for all-cause/cause-specific mortality (HR = 6.73–10.60 per 0.1 mg/L increase, p < 0.001) and 1.81 mg/L for stroke-specific mortality. Conversely, cerebrovascular mortality demonstrated a linear association (HR = 1.43 per 1 mg/L increase, p = 0.008), though cystatin C independently predicted risk (HR = 1.38/continuous, p = 0.034 in fully adjusted models). Conclusions: This study identifies serum cystatin C as an independent predictor after full adjustment of stroke morbidity and all-cause and cardio-cerebrovascular mortality. Consequently, cystatin C emerges as a dual-purpose biomarker for early vascular injury detection in subclinical populations and integrated mortality risk stratification. Future research should validate these thresholds in prospective neuroimaging-confirmed cohorts and investigate interventions targeting cystatin C pathways to optimize preventive strategies. Full article