Search Results (565)

Leaf nitrogen concentration (LNC) is an important indicator of Ginkgo nutritional status, but its hyperspectral estimation remains challenging because leaf spectra are high dimensional, strongly collinear, and affected by overlapping structural and biochemical signals. This study examined how spectral preprocessing, wavelength selection sequence, and regression model choice influence leaf scale Ginkgo LNC estimation, while separating simulation-assisted model development from measured sample-based prediction assessment. We assembled 717 field measured Ginkgo leaf spectra with corresponding laboratory measured LNC values and used PROSPECT-PRO simulated spectra only for wavelength screening or calibration augmentation, not as independent validation data. Three evaluation schemes were compared: measured-only analysis, simulated spectra-assisted wavelength selection followed by measured data calibration and testing, and simulated spectra-assisted wavelength selection and calibration followed by measured-only testing. The third scheme was used as the main inference framework because it retained an independent measured sample test boundary. Within this framework, multiple preprocessing methods, two wavelength selection sequences, and four regression models (PLSR, GPR, 1D-CNN, and DGP) were evaluated. MSC showed comparatively low error in the preprocessing comparison, and CARS-SPA identified a compact set of informative wavelengths concentrated mainly in the shortwave infrared region. Under the simulation-assisted calibration framework, the combination of MSC preprocessing, CARS-SPA wavelength selection, and DGP regression produced the lowest test error on the measured sample set (R² = 0.82; RMSE = 2.07 mg g⁻¹). These results indicate that Ginkgo LNC estimation depends on the combined choice of preprocessing method, wavelength selection strategy, and regression model, and provide a methodological reference for simulation-assisted hyperspectral modeling. Full article

This study quantified total biomass stocks in Carrasco (CAR, n = 12), a dense tropical deciduous vegetation type from the Brazilian semi-arid region for which biomass information remains scarce. We also evaluated differences in floristic composition, diversity, structure, and biomass allocation patterns relative to Cerrado sensu stricto (CSS, n = 40). Forest inventories were conducted in southeastern Brazil. Woody biomass was estimated using a regional allometric equation. Roots were sampled in a position adjacent to the plots, and litter was collected at the center of each plot using a frame. Necromass was assessed along a linear transect corresponding to the length of each plot using the line-intersect method. Biomass differences between vegetation types were assessed using generalized linear and mixed-effects models (GLMs and GLMMs). Total biomass reached 45.24 Mg ha⁻¹ in CSS and 59.01 Mg ha⁻¹ in CAR. In CSS, woody biomass predominated (20.47 Mg ha⁻¹; 45%), followed by roots (18.47 Mg ha⁻¹; 41%), litter (5.49 Mg ha⁻¹; 12%), and necromass (0.81 Mg ha⁻¹; 2%). In CAR, roots were the dominant component (32.37 Mg ha⁻¹; 55%), followed by woody biomass (16.57 Mg ha⁻¹; 28%), litter (8.39 Mg ha⁻¹; 14%), and necromass (1.68 Mg ha⁻¹; 3%). CSS and CAR shared only 10% of their species and showed significant differences in total biomass (TB) and belowground biomass (BGB), while aboveground biomass (AGB), aboveground woody biomass (AGWB), litter, and necromass did not differ significantly (α = 0.05). The BGB/AGWB ratio was <1 in CSS and >1 in CAR, resembling global patterns of savanna/shrubland and grassland formations, respectively. Considering the sampling design adopted, despite the higher stem density in CAR, larger individuals in CSS compensated for structural differences, resulting in similar aboveground biomass stocks. Our findings reinforce the floristic and structural distinctiveness of Carrasco and reveal contrasting biomass allocation strategies, with a strong dominance of belowground biomass in CAR. These results demonstrate that aboveground-based assessments can substantially underestimate total biomass in semi-arid transitional vegetation and highlight the need to incorporate non-forest ecosystems into biomass inventories, conservation planning, and climate change mitigation strategies. Full article

Short-term mortality following the last dose of immune checkpoint inhibitors (ICIs) is an increasingly recognized real-world outcome measure, yet its clinical predictors remain poorly characterized. This multicenter retrospective study included 458 consecutive patients with advanced melanoma, non-small cell lung cancer, or renal cell carcinoma who received ICIs at four tertiary centers in Turkey between 2018 and 2023. The primary endpoint was 30-day mortality after the final ICI dose. Among 458 patients, 71 (15.5%) died within 30 days. Multivariable logistic regression identified ECOG performance status ≥ 2, number of metastatic sites ≥ 3, and log-transformed C-reactive protein-to-albumin ratio (log-CAR) as independent predictors of 30-day mortality in Model 1 (AUC 0.954), while ECOG PS ≥ 2, brain metastasis, metastatic sites ≥ 3, and log-NLR were independent predictors in Model 2 (AUC 0.912). In the lung cancer subgroup, log-CAR and NLR remained independent predictors while ECOG PS did not. Patients who died within 30 days had significantly shorter progression-free survival (1.18 vs. 4.63 months) and overall survival (2.30 vs. 14.39 months) compared with survivors. These findings suggest that routine assessment of inflammatory and nutritional biomarkers alongside tumor burden parameters may help identify patients at high risk of early mortality and inform the timing of supportive care in ICI-treated populations. Full article

This paper presents a novel approach to design an attitude motion control strategy of a vehicle to mitigate lateral or longitudinal inertial forces acting on the passenger during cornering, braking, and acceleration maneuvers. The collaboration of active suspension system and active airfoil substantially enhances the attitude motion of a vehicle. By incorporating integral control action for both the desired body attitude roll or pitch angle and zero dynamic tire deflection within the performance index, the optimal controller maintains the ideal roll or pitch angle while preserving the road holding capability. The computer simulations were conducted to evaluate the dynamic performance of the proposed system in comparison with various other suspension systems based on a 4-degree-of-freedom half-car model. Four scenarios for rolling and pitching motions were simulated as follows: the first case examines the rolling response to a one-sided bump input applied to a lateral half-car model during straight-line driving. The second case investigates the rolling performance during a cornering maneuver. The third and fourth cases analyze the pitching responses to braking and acceleration using a longitudinal half-car model. The simulation results demonstrate that the proposed system maintains the ideal body attitude, attenuates the effect of the lateral or longitudinal inertial forces and keeps an ideal road holding capability. As a result, the proposed control system substantially improves ride comfort while enhancing the dynamic safety of the vehicle. Full article

The authentication of fish maw is of considerable importance for preventing product substitution and protecting market confidence in high-value aquatic foods. This study developed a rapid and nondestructive authentication strategy by combining hyperspectral imaging (HSI) with wavelength selection and a Kolmogorov–Arnold Network (KAN) to discriminate 10 commercially representative fish maw varieties. Hyperspectral datasets were collected in the visible and near-infrared (VNIR, 400–1000 nm) and short-wave infrared (SWIR, 900–1700 nm) regions. To improve spectral quality and model robustness, four preprocessing methods (SG, SG−MeanNor, SG−DT, and SG−SNV) were evaluated, followed by the construction of PLS-DA, SVM, MLP, CNN, and KAN models. Feature wavelengths were subsequently selected separately from the VNIR and SWIR spectra using CARS, iVISSA, and SPA to establish reduced-variable authentication models. The results showed that SG-DT achieved the best overall preprocessing effect, confirming its ability to reduce spectral noise and baseline variation. In addition, SWIR-based models consistently outperformed VNIR-based models, suggesting that compositional information captured in the SWIR region played an important role in fish maw authentication. Among all tested models, the SWIR@SG-DT-SPA-KAN model exhibited the best performance, achieving 98.67% accuracy, 98.75% precision, 98.67% recall, and 98.64% F1-score using only 16 SPA-selected wavelengths from the SG-DT-preprocessed SWIR spectra. This study demonstrates that HSI coupled with feature wavelength and KAN modeling can provide an accurate and efficient tool for fish maw authentication. More importantly, the reduced-wavelength model offers practical potential for developing fast and cost-effective multispectral systems for authenticity screening in the aquatic food market. Full article

There is an important debate about the appropriate policy measures for reducing greenhouse gas (GHG) emissions in the transport sector. Strong expansion of battery electric vehicles (BEVs) following a ban on the registration of new vehicles with internal combustion engines (ICEs) by 2035 is a prominent but controversial proposal. To evaluate achievable GHG emission reductions, it is essential to understand the temporal dynamics of such a fleet transition. This study provides a time-resolved, policy-oriented quantification of annual and cumulative lifecycle GHG emissions during this process. Therefore, it uses an annual simulation model to assess GHG emissions from vehicle production and use during the transition of Germany’s passenger car fleet between 2019 and 2060. The analysis compares an ICE registration ban by 2035 with alternative scenarios and evaluates the effects of electricity decarbonization, greener BEV production, and the supply of additional Zero Emission Fuels (ZEFs). This study reveals a substantial time lag of 10–20 years between changes in new vehicle registrations and effective emission reductions. Even with a complete ICE ban by 2035, annual GHG emissions decline by only 3.7% by 2030 relative to 2025, while cumulative emissions over this period fall by just 1.6%. Larger reductions occur later, reaching 39% in 2040, 77% in 2050, and 82% in 2060 compared with 2025; cumulative emissions until 2060 decrease by 45%. Without an ICE ban and with a 75% BEV share from 2035 onward, cumulative reductions fall to 34%. Introducing additional ZEFs equivalent to 10% of 2030 fuel demand increases this value to 41%, compensating for much of the lower BEV uptake. Full article

Background: For almost two decades now, chimeric antigen receptor-natural killer cells (CAR-NK) have been investigated in pre-clinical breast cancer models, yet clinical evidence on efficacy remains scarce. This meta-analysis provides pooled evidence of pre-clinical CAR-NK effectiveness and safety in breast cancer and an overview of current clinical trials to support clinical translation. Methods: Following PRISMA guidelines and a registered protocol (PROSPERO, CRD420251131530), PubMed, Web of Science, and Scopus were searched up to 30 June 2025 for pre-clinical CAR-NK studies in breast cancer. Clinical studies were retrieved from clinicaltrials.gov and the International Clinical Trial Registry Platform (ICTRP) up to 1 March 2026. Pre-clinical studies without in vivo data or non-human CAR-NK cells were excluded. Primary outcomes were tumor burden (ratio of means, ROMs) and survival (median survival ratio, MSR). Data were analyzed in JASP™ and risk of bias (RoB) was assessed using SYRCLE’s tool. Results: Fourteen pre-clinical studies (38 CAR-NK treatment groups targeting EGFR, HER2, tissue factor, CD70, mesothelin, or folate receptor, with peripheral blood as the primary NK source, and a 5–10 million cell dose) and 11 early-phase clinical studies (targeting HER2, TROP2, PD-L1, MUC1, or NKG2D ligands under ongoing investigation) were included. In pooled pre-clinical analysis, CAR-NK significantly reduced tumor burden against untreated and unmodified/mock controls (ROM 0.311 [0.22–0.44] and 0.42 [0.33–0.53], p < 0.001, respectively). Survival was also prolonged significantly (MSR 1.47 [1.15–1.87], p = 0.010 vs. untreated; 1.30 [1.09–1.60], p = 0.007 vs. unmodified/mock NK cells). Subgroup analyses indicated improved efficacy with peripheral blood source and 5–10 M dosing. No treatment-related toxicities were reported. CAR-NK persistence was generally higher than unmodified/mock NK cells. Discussion and Conclusions: Significant heterogeneity was observed in ROM analysis which the multi-level meta-analysis configured as intra-study interventional variability. There was moderate RoB in pre-clinical studies. Published results from clinical trials remain limited, highlighting early stages of investigation. Overall, CAR-NK therapy demonstrated consistent pre-clinical efficacy and safety, supporting further translational and clinical evaluation in breast cancer. Full article

Post-infectious bronchiolitis obliterans (PIBO) is a severe chronic airway disease in children following lower respiratory tract infections. Human adenovirus (HAdV) and Mycoplasma pneumoniae (MP) are the major associated pathogens, with geographic variations in their relative importance. This review analytically compares the mechanistic divergence and convergence between HAdV and MP. Both pathogens converge on MyD88/NF-κB/MAPK signaling and neutrophil-driven inflammation, but diverge in initial host engagement (CAR/integrins vs. TLR2/6 and CARDS toxin) and inflammasome activation (TLR9-related vs. NLRP3-related). This review aims to propose an integrative model linking acute immune activation to fibrotic bronchiolar narrowing and to evaluate the risk factors for PIBO. Genetic susceptibility and epigenetic regulation help explain population differences in PIBO risk and geographic distribution. Despite progress, significant knowledge gaps remain, including the lack of single-cell resolution studies, the absence of co-infection animal models, and uncertainty regarding the long-term efficacy of targeted immunomodulatory therapies. Addressing these gaps is essential for improving early diagnosis and clinical outcomes. Full article

This study develops a comprehensive carbon footprint assessment model that integrates forward and reverse logistics to evaluate and compare greenhouse gas emissions from online and offline apparel sales channels in China, with a particular focus on high return rates. The model quantifies emissions from transportation, packaging, storage, and operations, incorporating return and exchange logistics. The system boundary is limited to enterprise-controllable sales-phase activities and excludes consumer travel. Three sales models are compared: factory-to-consumer (F2C), traditional business-to-consumer (B2C) e-commerce, and brick-and-mortar retail (BMR). Within this defined boundary, BMR exhibits the lowest carbon footprint (0.296 kg CO₂e/item), followed by F2C (0.408 kg CO₂e/item) and B2C (0.602 kg CO₂e/item). Packaging dominates online emissions (55–57%), whereas store operations are the main contributor to offline emissions (43%). Return rates are identified as a decisive factor, accounting for over 31% of e-commerce emissions and potentially increasing them by 171.3% under extreme scenarios. Sensitivity analysis reveals that trunk line distance (factory to warehouse) has a greater impact on emissions than last-mile return route optimization. Relocating the factory closer to consumers reduces B2C transport emissions by 72.3%, whereas replacing conventional packaging with recycled plastic reduces total B2C emissions by 46.0%. These findings provide channel-specific sustainability strategies: return reduction and packaging innovation for online channels, and energy efficiency improvements for physical stores. These results are conditional on the defined system boundary. If consumer travel by private car were included, the relative advantage of offline channels would diminish or could reverse. Full article

This paper presents a hierarchical control architecture for high-precision trajectory tracking of a car-like mobile robot (CLMB) operating under external disturbances arising from normal and tangential wheel forces. The proposed solution addresses the critical challenge of simultaneously rejecting disturbances and accurately following a predefined path at a determined cruise velocity. Since the vehicle is equipped with an electronic differential at the low level, a nonlinear dynamic control (NDC) scheme is implemented to regulate the speed in each wheel. This controller actively estimates and compensates for differential traction losses and other lumped disturbances in real time, ensuring robust wheel velocity tracking across varying terrain conditions. The compensated system is then governed by a high-level frugal model predictive controller (FMPC) that leverages a dynamic vehicle model to compute optimal steering and velocity commands, thereby minimizing future trajectory-tracking errors. To achieve a precise and reliable state estimation necessary for feedback control, an Extended Kalman Filter (EKF) is designed to fuse high-frequency data from wheel encoders with absolute pose measurements from a motion capture system, mitigating the drift inherent in odometry alone. Experimental results on a physical robotic platform demonstrate tracking accuracy and robust disturbance rejection under different operating conditions. Full article

Cycling is widely recognized as a sustainable urban mobility solution, and many municipalities focus on cycling infrastructure expansion to promote improved environmental sustainability. However, the current literature on cycling has predominantly focused on safety and health benefits, while the environmental benefits including GHG mitigation benefits remain less explored. To summarize findings from the current literature that explore the GHG emissions-related benefits (or costs) of cycling infrastructure, we conducted a literature review using five major scientific databases, following the PRISMA guidelines. Out of 824 screened records, 17 studies met the inclusion criteria. Most studies were published in the last decade, reflecting a limited but growing interest in this topic. The current analytical approaches include mode shift analysis, life cycle assessment, and scenario modelling. Among these, mode shift analysis (i.e., assessing the potential benefits related to replacement of car trips with cycling) remains a commonly used method. We found that cycling offers significant operational benefits by reducing GHG emissions, especially in the context of large-scale expansions of cycling infrastructure. Existing research indicates that even when embodied emissions are considered, bicycle is a more sustainable mode of transportation compared to cars or even public transit. However, emissions associated with installation and maintenance of cycling infrastructure may sometimes negate the GHG benefits associated with additional cycling. We discussed gaps in the current literature and directions for future research. Full article

Vehicle trajectories are time series, and entropy is a powerful tool for testing or quantifying the complexity of a given series. Entropy tools are often applied to variables such as velocity, acceleration, space headway, and time headway, but the local position data have not been addressed previously. The novelty of this study is that it uses the Improved Permutation Entropy (IPE) for the first time to analyze vehicle position data and convert those data into a limited range (0–0.3317), aiming to understand individual vehicle behavior during car-following and introduce a new prediction method for developing artificial intelligence-based car-following models. The study uses the IPE analysis results as a new input variable, in addition to existing input variables, to improve the prediction accuracy of these models. Three types of neural networks were adopted according to the development of artificial intelligence models: artificial neural networks (ANNs), long short-term memory networks (LSTMs), and Transformer models. The results indicate that all models using the proposed prediction method, which includes the IPE analysis result, outperformed those using the traditional prediction method. The Transformer & IPE model shows the best performance in prediction accuracy of the follower acceleration output; the statistically significant percentage improvements were 2.04%, 1.42%, 1.22%, and 2.62% for RMSE, MAE, MASE, and R², in that order. Furthermore, the results indicate that all models using the proposed prediction method outperformed the benchmarking Intelligent Driver Model (IDM) for the follower acceleration output. Full article

Background: Major level escalation following distal amputation for diabetic foot—defined as subsequent below-knee amputation (BKA)—represents a clinically meaningful endpoint with substantial functional implications. Identifying reliable and readily available preoperative biomarkers capable of predicting major level escalation remains a clinical challenge. This study aimed to evaluate the independent prognostic value of the C-reactive protein-to-albumin ratio (CAR) and blood urea nitrogen-to-albumin ratio (BAR) in predicting postoperative major level escalation. Methods: We retrospectively analyzed 151 consecutive patients who underwent distal lower extremity amputation for diabetic foot between January 2020 and October 2025. The primary outcome was ipsilateral below-knee amputation within the follow-up period. Preoperative CAR and BAR were calculated using laboratory parameters obtained within 24 h prior to surgery. Given the shared albumin component, two separate multivariable logistic regression models were constructed to evaluate independent associations, adjusting for age, peripheral arterial disease (PAD), and index amputation level. Results: During follow-up, 46 patients (30.5%) required major level escalation (BKA). Both CAR and BAR were significantly higher in patients who developed BKA (p < 0.001 and p = 0.006, respectively). In receiver operating characteristic (ROC) analysis, CAR demonstrated acceptable discriminative ability (AUC = 0.745; 95% CI 0.653–0.827), whereas BAR showed modest performance (AUC = 0.640; 95% CI 0.536–0.738). The optimal cutoff values were 1.50 for CAR (sensitivity 76.1%, specificity 61.9%) and 0.61 for BAR (sensitivity 73.9%, specificity 44.8%), although these thresholds were considered exploratory. In the primary multivariable analysis, both CAR (OR 1.16; 95% CI 1.02–1.32; p = 0.024) and BAR (OR 4.02; 95% CI 1.85–8.73; p = 0.005) were independently associated with major level escalation. In sensitivity analyses, BAR retained independent significance, whereas CAR did not (p = 0.100). Conclusions: Preoperative BAR demonstrated robust independent association with major level escalation across both primary and sensitivity analyses, while CAR showed association in the primary model only. These composite biomarkers may provide hypothesis-generating insights into systemic risk profiling in diabetic foot surgery, pending external validation. Full article

Time headway is a key parameter for describing car-following behavior and microscopic traffic flow characteristics, and it is important for traffic safety analysis, road design, and optimizing intelligent-driving strategies. Existing research offers limited insight into the heterogeneity of time headway under different vehicle types and lane conditions. It is particularly important to investigate how time headway distributions differ across lane–vehicle-type combinations on highways, as these differences can affect safety evaluation and operational performance. This study is based on drone-captured vehicle trajectories from the publicly available HighD dataset. We select 378,751 vehicle–frame trajectory records; these records are used to construct valid follower–leader pairs and derive time headway (THW) samples for distribution fitting. Eight subsets are formed by combining two lane positions (inner vs. outer) and four follower–leader vehicle-type pairs (car–car, car–truck, truck–car, truck–truck). Six candidate distributions (Lognormal, Log-logistic, Burr, Weibull, Gamma, and Logistic) are fitted using maximum likelihood estimation, and their fit is evaluated using Kolmogorov–Smirnov, Anderson–Darling, and Chi-square tests, which are fused via an entropy-weighted composite score for model ranking. Results show pronounced heterogeneity across lane–vehicle-type subsets: Inner-lane samples exhibit smaller and more concentrated time gaps, whereas outer-lane samples show larger mean gaps, stronger dispersion, and heavier upper tails. Overall, Lognormal(3P) is selected as the top-ranked model in 5 of 8 subsets (62.5%), while Burr(4P) (car–truck, outer lane), Gamma(3P) (truck–car, outer lane), and Weibull(3P) (truck–truck, inner lane) are optimal in the remaining subsets. These findings indicate that lane position and vehicle-type pairing materially affect THW distributional characteristics, providing quantitative guidance for lane- and vehicle-aware traffic modeling, safety-oriented assessment, and intelligent-driving strategy design. Full article

The increasing deployment of autonomous vehicles in port logistics requires safety assessment methods that remain valid in mixed traffic environments. This study evaluates the safety of mixed automated guided vehicle (AGV) and human-driven vehicle (HDV) traffic in a seaport terminal connected to an external urban road network. A microscopic traffic model was developed in AIMSUN Next to represent gate areas, internal roads, storage-yard access, berth interfaces, and external container-truck traffic. HDVs were modeled using a Gipps-based car-following model, whereas AGVs were represented through an Adaptive Cruise Control framework. Vehicle trajectories were exported to the Surrogate Safety Assessment Model (SSAM), where Time-to-Collision (TTC) and Post-Encroachment Time (PET) were used to detect and classify conflicts. Six staged fleet-composition scenarios were evaluated in 36 simulation runs, ranging from fully human-driven operation to full automation. Total conflicts decreased from 89 in the fully human-driven scenario to 43 in the fully automated scenario (−51.7%), while rear-end conflicts decreased from 70 to 30 (−57.1%). Crossing conflicts remained relatively stable across scenarios. At the same time, mean TTC decreased from 0.80 to 0.24 s and mean PET from 1.57 to 0.38 s, indicating tighter but more coordinated interactions under automated control. These results show that automation improves longitudinal safety performance in port traffic, but also that conventional TTC and PET thresholds calibrated for human-driven traffic may not be directly applicable to automated port operations. Automation-sensitive surrogate safety criteria are therefore needed for seaport mixed-traffic evaluation. Full article