Search Results (25,242)

Cold stress (CS) severely limits the growth and productivity of rice (Oryza sativa L.), particularly in temperate regions where abrupt temperature declines frequently occur during early developmental stages. In recent years, nanoparticle (NP) application has emerged as a promising approach for alleviating CS; however, systematic comparisons of different NPs across multiple growth stages remain unclear. This study evaluated the effectiveness and physiological mechanisms of four NPs (Fe₂O₃, ZnO, TiO₂, and CeO₂) in enhancing CS tolerance of rice seedlings using four cultivars with contrasting cold tolerance: two conventional cultivars (ZJZ-17, cold-sensitive; XZX-6, cold-tolerant) and two hybrid cultivars (LLY-7108, cold-tolerant; LLY-32, cold-sensitive). Seedlings were subjected to CS (14 °C day/10 °C night) for 5 days at three developmental stages (14, 21, and 28 days after emergence), followed by a 7-day recovery period under optimal conditions. CS markedly reduced plant height (34.8%), fresh weight (57.2%), dry weight (50.0%), and chlorophyll a and b contents (48%) following recovery. Foliar application of NPs significantly mitigated the adverse effects of CS, with Fe₂O₃ and ZnO showing the highest effectiveness. Fe₂O₃ treatment increased plant height, fresh weight, and dry weight by 25.6%, 43.5%, and 40.6%, respectively, relative to cold-stressed plants, while chlorophyll a and b contents increased by 41.6% and 42.2%. NPs application alleviated oxidative damage by reducing reactive oxygen species (up to 67.4%), malondialdehyde (up to 51.2%), and proline accumulation (up to 60.4%). Enhanced antioxidant defense was evidenced by increased activities of superoxide dismutase (66.6%), peroxidase (59.6%), and catalase (34.3%) under Fe₂O₃ treatment. Yield-related traits also showed significant recovery, with Fe₂O₃ increasing tiller number, spikelets per panicle, and grain yield per plant. The hybrid cultivar LLY-7108 consistently exhibited greater CS tolerance than conventional cultivars, while the cold-sensitive cultivar ZJZ-17 showed the greatest susceptibility. CS imposed at later growth stages (28-day-old seedlings) caused less damage and allowed greater recovery than early-stage stress (14-day-old seedlings). Overall, NP-mediated enhancement of photosynthesis and antioxidant capacity significantly improves CS tolerance and yield performance in rice, with Fe₂O₃ NPs emerging as a promising strategy for mitigating CS. These findings provide practical insights for rice cultivation in regions prone to chilling events and contribute to the development of nanoparticle-based approaches for rice production under climate stress. Full article

Liquid-state ¹³C–¹³C 2D INADEQUATE NMR spectroscopy provides unparalleled direct carbon–carbon connectivity mapping via scalar couplings, delivering unambiguous structural evidence that conventional HSQC and HMBC methods cannot, particularly for quaternary carbons, fully substituted aromatics, and overlapping polymer resonances. Despite sensitivity challenges arising from low ¹³C natural abundance (~1.1%), this review examines the technique’s evolution from its 1981 introduction to contemporary innovations, compiling over 100 application entries across natural products, synthetic molecules, mixtures, fullerenes, metabolomics, oligomers and polymers. Significant methodological advancements—including cryogenic probe technology (up to 5.5-fold sensitivity gain), J-compensated sequences, composite refocusing (INADEQUATE CR), hybrid INEPT-INADEQUATE approaches, adiabatic pulses, and Overhauser DNP-INADEQUATE—are critically assessed alongside computerized analysis algorithms (CCBond, FRED) and network-based metabolomics tools (INETA, PyINETA). Practical guidance covering optimal J_CC coupling selection, relaxation management, and a decision tree for experiment selection is consolidated. Looking forward, the convergence of artificial intelligence, non-uniform sampling, and integration with density functional theory promises to transform 2D INADEQUATE from a specialist technique of last resort into a routinely accessible tool for structural elucidation. Full article

Uranium is a critical strategic resource. However, uranium mining can cause severe contamination of surrounding soils by potentially toxic elements (PTEs). Conventional approaches that separately perform source apportionment and risk assessment fail to establish a direct linkage between pollution sources and their associated environmental and health risks. Herein, we develop an integrated framework that combines the absolute principal component score–multiple linear regression (APCS-MLR) model, the potential ecological risk index, and Monte Carlo simulation. The established framework was used to try quantifying the potential sources of soil PTEs and their corresponding ecological and human risks in a uranium mining area. The results showed that the concentrations of U, Th, Cd, and Cr significantly exceeded local background levels, with Cd and U exhibiting higher spatial variability than the other PTEs. The APCS-MLR model identified three potential sources of soil PTE contamination: mining activities, mixed anthropogenic-natural, and natural sources. Mining and mixed sources were the dominant contributors to ecological risk, jointly accounting for 88.7% of the total ecological risk, with Cd identified as the primary ecological risk pollutant. The mixed sources also contributed the largest proportions of non-carcinogenic and carcinogenic health risks, accounting for 52.56% and 67.5%, respectively. Furthermore, children were found to face greater health risks than adults due to higher exposure levels. Priority factor analysis indicated that pollution management should continuously monitor Cd derived from mining activities based on statistical inference. Overall, the proposed integrated framework successfully established a quantitative linkage between pollution sources and associated risks, providing a scientific basis for source-specific and zone-specific soil pollution management in uranium mining areas. Full article

In situ and operando small-angle X-ray and neutron scattering (SAXS and SANS) have become powerful techniques for studying adsorption processes because they provide real-time information that traditional ex situ methods cannot capture. This review examines the use of these techniques in four major material groups: soft matter and polymers, carbon-based materials, biological systems and ceramics. Unlike conventional before-and-after characterization, in situ methods allow researchers to follow structural changes during adsorption as they happen, revealing adsorption kinetics, intermediate states and pore-filling mechanisms. Operando approaches further connect nanoscale structural evolution with overall adsorption performance, leading to deeper mechanistic understanding. Important developments include monitoring structural rearrangements in polymers during adsorption, studying sodium storage mechanisms in hard carbon anodes using combined small- and wide-angle neutron scattering, observing protein conformational changes at hydrated interfaces, and identifying organic functional groups in mesoporous ceramics in real time. SANS contrast variation, especially through hydrogen/deuterium substitution, allows selective visualization of different components in complex systems, while synchrotron SAXS enables kinetic studies with millisecond time resolution. Despite these advantages, several challenges remain, including model-dependent interpretation of scattering data, difficulties in separating real structural changes from contrast-related artifacts, limitations in accessible timescales and the technical complexity of isotopic labeling. Future research is expected to focus on improved contrast methods, machine learning-assisted data analysis, multimodal approaches combining scattering with spectroscopy or microscopy and the application of these techniques to more complex and disordered adsorbents such as activated carbons, coals and bio-based materials. Overall, this review summarizes current methodologies, compares adsorption-related structural changes across different materials and highlights both current limitations and future opportunities for in situ and operando SAS studies in adsorption research. Full article

Traditional target-oriented task allocation and path planning methods often struggle to balance real-time responsiveness to dynamic task alterations with multi-UAV cooperative operations in complex urban environments under meteorological disturbances. To address these challenges, this paper proposes a dynamic path planning method for low-altitude Unmanned Aerial Vehicles (UAVs) tailored for urban inspection missions. Integrating an improved Discrete Particle Swarm Optimization (DPSO) algorithm with a decoupled Soft Actor–Critic (SAC) and B-spline smoothing framework, the proposed approach optimizes upper-level task allocation and lower-level trajectory planning within a 3D joint meteorological-obstacle feasible region. For task scheduling, an improved DPSO algorithm embedded with a spatial topology guidance mechanism dynamically coordinates task flows governed by Poisson processes. effectively addressing the spatial blindness and fragmented route assignments typical of conventional discrete optimization. Concurrently, local trajectory replanning executes receding-horizon spatial exploration via SAC deep reinforcement learning, followed by B-spline refinement to strictly enforce UAV kinematic limits, systematically bridging continuous-space exploration with low-level flight compliance to overcome the kinematic infeasibility common in pure learning-based models. Validated through extensive Monte Carlo comparative simulations (N = 50) and further verified by a high-fidelity AirSim dynamic physics engine, the results demonstrate that: (1) The improved DPSO constrains the average response latency for high-priority emergency tasks to within 40 s even under 50 concurrent dynamic tasks. (2) The lower-level replanning achieves an average execution time of 3.60 ± 0.18 s and a path success rate of 95.8 ± 1.2%, in numerical tests, while maintaining a 96.2% kinematic feasibility rate under realistic rigid-body inertia and aerodynamic drag. While the current 3.60 s latency presents a potential bottleneck for millisecond-level dynamic emergency reactions, the developed framework offers a highly effective and safe closed-loop dynamic scheduling solution that lays a rigorous computational foundation for low-altitude urban inspections. Full article

Autonomous robotic systems operating in residential environments must maintain safe, reliable, and adaptive performance under heterogeneous conditions characterized by environmental uncertainty. Achieving this performance is difficult because such systems must simultaneously cope with variable contact states and complex subsystem interactions. Conventional component-oriented engineering approaches often fail to capture how internal faults, external disturbances, and architectural decisions interact to generate system-level instabilities and hazards. This paper proposes a Systems Thinking-based Safety-by-Design framework to support the sustainable early-stage design of autonomous residential robotic systems. The methodology formalizes the relationships among internal faults (F), environmental variability (E), system behaviour (S), architecture (A), instabilities (I), hazards (H), and safety barriers (B) through the dependencies S = f(F,E), I = g(S,A), and H = h(I,B). Environmental variability is treated as an active design driver influencing system behaviour, architectural decisions, and safety requirements. The framework is evaluated as a proof-of-concept, semi-quantitative analytical demonstration, rather than an experimentally validated result, using an autonomous window-cleaning robot: its functional subsystems are mapped to the conceptual model and fault-dominated, environment-dominated, and coupled degradation scenarios are assessed through a semi-quantitative index. The results show that risk emerges from subsystem interactions rather than isolated component failures and that adaptive architectures reduce instability propagation under uncertain operating conditions. The proposed framework supports early engineering decisions that improve robustness, resilience, safety, maintainability, and sustainability. Full article

The complexity of voltage stability assessment in modern smart grids has increased significantly with the growing penetration of renewable energy sources and the dynamic nature of load variations. Although standard analytical methods are accurate, they are computationally expensive and unsuitable for real-time applications. This paper proposes a hybrid analytical–machine learning framework for efficient voltage stability assessment and classification. The proposed approach consists of two stages. First, a power flow analysis is performed to compute the Fast Voltage Stability Index (FVSI) and quantify the proximity of the system operating conditions to voltage instability. Then, the FVSI values are converted into binary stability labels to formulate a supervised classification problem. In the second stage, the Extreme Gradient Boosting (XGBoost) algorithm is employed to learn the relationship between system operating variables and the corresponding stability states. The performance of the proposed method is evaluated on the IEEE 30-bus system and compared with that of conventional machine learning and deep learning models, such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Deep Neural Networks (DNNs). The simulation results show that the XGBoost-based framework outperforms the benchmark models in terms of classification accuracy, robustness, and computational efficiency. The proposed method provides a fast, reliable, and interpretable solution for real-time voltage stability monitoring. Therefore, it is suitable for modern smart grid applications. Full article

Conventional diagnosis of chronic kidney disease (CKD) relies predominantly on invasive blood-based examinations, limiting the scalability of kidney health screening in resource-constrained environments. This study presents embedded engineering framework for non-invasive, breath-based CKD staging framework supported by machine learning and implemented on a low-cost embedded platform. To account for physiological sex differences in baseline creatinine production, estimated glomerular filtration rate (eGFR) values and breath ammonia concentrations were derived from two independent clinical cohorts using sex-specific MDRD equations (incorporating the standard male formula and the $0.742$ female correction factor, respectively) and creatinine–BUN conversion models, with male- and female-parameterized algorithms developed in parallel. The resulting feature space was analyzed using four unsupervised clustering approaches to stratify subjects into five clinically meaningful kidney function stages. Stage-specific ammonia thresholds were implemented within an Arduino Nano-based prototype equipped with an MQ-137 gas sensor and OLED display, enabling real-time point-of-care classification. Dataset-level classification accuracy reached 82% for the male algorithm and 92% for the female algorithm. Hospital-based validation on 29 patients (22 male, 7 female) yielded a real-world testing accuracy of 90.5% ($20/22$) for male patients and 71.4% ($5/7$) for female patients, a discrepancy largely attributable to the small female sample size. Because the current evaluation lacks healthy control subjects and is constrained by sample size, these empirical results serve primarily to demonstrate hardware-software functional integration and real-world deployment feasibility rather than definitive clinical efficacy. Despite these preliminary, sample-limited clinical datasets, results suggest this approach holds promise as an accessible, non-invasive screening complement to conventional diagnostic pathways, particularly in low-resource healthcare settings. Full article

Cryptic biodiversity presents an ongoing challenge to the accuracy and effectiveness of biodiversity assessment and conservation. To address this, we developed a non-lethal genetic monitoring framework for the subterranean rodent, European lesser blind mole rat (BMR), Nannospalax leucodon species complex, which encompasses multiple chromosomally diversified and reproductively isolated cryptic-species and subspecies. Our workflow combines species and sex detection through: karyotyping, Inter-Simple Sequence Repeat (ISSR) PCR profiling, and Sry-based sex determination of 33 individual samples from five BMR cryptic-species collected at 25 localities in Serbia. As conventional karyotyping protocols for BMR required animal sacrifice, we developed the first non-lethal fibroblast culture-based karyotyping approach from BMR skin biopsy, producing high-quality metaphase chromosomes across five cryptic-species. Among twelve tested ISSR primers, three yielded reproducible, species-specific DNA profiles that resolved four of the five cryptic-species. The Sry assay accurately determined the sex of all examined individuals. Our findings demonstrate that integrating fibroblast culture-based karyotyping, with fast, cost-effective ISSR-PCR species identification, and Sry-based sex determination, provides a reliable approach for identifying and monitoring cryptic BMR species without sacrificing individuals. This framework has potential applications in conservation programmes and contributes to an integrative taxonomy approach essential for the study and protection of other cryptic taxa. Full article

Current distribution in serial–parallel battery packs is commonly assumed to be uniform in the absence of cell-to-cell variations. However, in practical systems, the electrical topology and finite resistance of current-collecting busbars can introduce significant inhomogeneities even when all cells are identical. In this work, a matrix-based modeling framework is developed to analyze the current and voltage distribution in large battery packs with arbitrary serial–parallel configurations. The results reveal that the resistance of current-supplying busbars plays a dominant role in shaping current distribution, leading to pronounced current imbalance that increases with both resistance and operating C-rate. To quantify this effect, a current non-uniformity factor is introduced and used to define an illustrative criterion for acceptable operation. Based on this metric, together with a maximum-cell-voltage constraint, design maps are constructed to identify operating regions that are acceptable or critical with respect to current overload and localized overvoltage as a function of busbar resistance and charging rate. The analysis further demonstrates that topology-induced current inhomogeneity can lead to cell-level voltage divergence and localized overcharge under high-current operation. Such local effects may remain hidden when only the pack voltage or the voltage of a series-connected cell group is monitored, because conventional battery management systems (BMSs) typically do not resolve individual cell currents or local voltage drops within parallel-connected cell groups. The proposed approach enables the derivation of design-oriented constraints linking electrical performance to physical parameters such as busbar resistance and cell spacing. The resulting design maps provide a practical tool for battery pack engineering, enabling the determination of the maximum allowable busbar resistance or operating current to ensure safe, homogeneous pack operation. Full article

Severe pulmonary diseases, including atelectasis, pneumonia, and meconium aspiration syndrome, are major causes of neonatal respiratory distress, weaning difficulties, ventilator or oxygen dependence, prolonged oxygen requirements, extended hospitalization, and poor prognosis. The lack of simple and effective treatment strategies seriously endangers the survival and health of newborns, particularly premature infants. Recent advances in lung ultrasound (LUS) technology have made LUS-guided non-fiberobronchoscopic bronchoalveolar lavage (NFB-BAL) a promising solution. This approach addresses the limitations of conventional BAL in neonates by enabling accurate diagnosis, precise lesion localization, and dynamic procedural monitoring, hence reducing complications. Based on international expert consensus, these guidelines were developed to improve knowledge and promote the application of this technology and enhance operational standardization to ensure its effectiveness and safety. These guidelines contain 17 recommendations on 13 key clinical issues for reference and implementation in clinical practice. The wide application of these guidelines is expected to help significantly improve the prognosis of critically ill newborns with atelectatic pulmonary diseases. Full article

The sustainable production of leafy vegetables in Mediterranean and East Asian regions is increasingly constrained by water scarcity and nutrient imbalance in soil–plant systems, making timely and accurate nutrient diagnosis essential for precision fertilization. Conventional tissue analysis of nitrogen (N), phosphorus (P), and potassium (K) in lettuce is destructive, costly, and time-consuming, while existing non-destructive approaches based on traditional machine learning or deep learning still suffer from limited accuracy and poor generalization. To address these limitations, this study proposes ResNet-SA, a residual convolutional network assisted by a multi-head self-attention (MSA) mechanism, for the rapid and non-destructive estimation of leaf N, P, and K contents from top-view RGB images of lettuce trays under soilless cultivation. Two fusion strategies between the MSA mechanism and the ResNet50 trunk were evaluated, namely lateral side-connection of the attention block (ResNet50_SA_R series) and replacement of a trunk stage (ResNet50_SA_E series), each with three insertion depths. In the fixed-split evaluation, ResNet50_SA_RV1 achieved the best performance, with a test-set R² of 0.92 versus 0.81 for the ResNet50 baseline (absolute R² gains of 0.11 and 0.08 for RV1 and RV3, respectively). Grouped five-fold cross-validation by sampling batch tentatively verified the stability of this improvement (ResNet50_SA_RV1: R² = 0.92 ± 0.03; RMSE = 7.16 ± 1.77 mg/g; MAE = 4.06 ± 1.58 mg/g, macro-averaged across N, P, and K), with significantly lower prediction error than both the ResNet50 baseline (ΔMAE = −2.25 mg/g, 95% CI: −2.88 to −1.62, Holm-adjusted p < 0.001) and four conventional CNN architectures (R² range: 0.49–0.80). These results demonstrate that integrating the MSA mechanism into ResNet provides a reliable, non-destructive tool for lettuce nutrient diagnosis, offering practical support for precision fertilization and sustainable greenhouse production. Full article

Accurate channel state information (CSI) is essential for reliable orthogonal frequency division multiplexing (OFDM) transmissions, especially when training resources are limited and iterative receiver processing is employed. This paper revisits least squares (LS) and minimum mean square error (MMSE) channel estimation based on training sequences and analyzes their impact on an iterative turbo receiver framework. The initial channel estimate is obtained from an OFDM training transmission, while subsequent refinement is performed using soft information generated by a soft-input soft-output (SISO) equalizer and decoder. Unlike conventional approaches that keep the channel estimate fixed after the training phase, the proposed architecture enables decision-directed channel refinement using reconstructed transmit symbols. The OFDM stage is employed for channel estimation, whereas BER performance is evaluated using independently generated turbo-coded BPSK sequences transmitted through the analyzed channel. The performance analysis investigates the influence of training sequence length and signal-to-noise ratio (SNR) on iterative estimation gains. Simulation results show that, for short training sequences, the proposed iterative strategies can significantly improve BER performance compared with conventional non-iterative LS and MMSE estimators. These results provide practical insights for the design of training-efficient OFDM-based communication systems. Full article

This paper presents a data-driven run-to-run (R2R) controller for manufacturing processes subject to process drift. The proposed approach combines the exponentially weighted moving average (EWMA) method with the K-nearest neighbors (KNN) algorithm to determine process recipe adjustments. Control actions are derived entirely from historical process output data. In the hybrid controller, the EWMA estimator recursively updates the accumulated process drift using historical process errors and generates the corresponding recipe compensation. The KNN-based controller, in turn, identifies the K nearest neighbors in the historical feature database based on the current process error and determines the compensation action from the associated error–compensation relationships. The proposed controller was evaluated through simulations of a chemical mechanical planarization (CMP) process, with removal rate as the control objective. Under linear process drift with random white-noise disturbances, the proposed controller maintained the removal rate close to the target value, with a maximum overshoot of 4.40%, and satisfied the settling criterion from the beginning of the control process. Its performance was superior to that of the conventional EWMA controller and the standalone KNN controller. The EWMA controller exhibited several oscillations during the initial runs, with a maximum overshoot of 15.17%. Although the KNN controller satisfied the settling criterion from the beginning of the control process and produced a relatively small maximum overshoot of 3.10%, it did not consistently maintain the removal rate near the target value. Under nonlinear process drift with random disturbances, the proposed controller also maintained the process output near the target value with satisfactory stability, provided that the process drift remained within a bounded range. The controller also has a simple and intuitive implementation, which may facilitate practical industrial application. Full article

Kneeosteoarthritis (KOA) is a common degenerative joint disease that causes pain, stiffness, and impaired mobility. Automated Kellgren–Lawrence (KL) grading from knee X-ray images facilitates efficient screening and follow-up assessment. However, models trained on a single-source dataset frequently suffer performance degradation when applied to external cohorts, due to heterogeneities in image quality, acquisition protocols, class distributions, and annotation patterns. Furthermore, conventional domain adaptation approaches typically treat all source samples uniformly, making them vulnerable to negative transfer induced by ambiguous or distributionally divergent instances. To overcome these limitations, the present study develops a supervised contrastive domain adaptation framework designed for robust KOA severity grading under domain shift. The framework incorporates two task-specific modules: (1) a source-domain sample screening module that dynamically allocates class-wise quotas based on transferability and identifies high-value source samples by evaluating target intra-class affinity, inter-class separability, and source-class compactness; and (2) a target-balanced ordinal contrastive learning module that aligns the screened source samples with target features and imposes stronger constraints on negative pairs with larger KL-grade distances. The framework was evaluated bidirectionally on KneeKL (8260 images) and MedicalExpert-I (1650 images), two public knee radiograph datasets for KOA grading. With ResNet-18, it achieved a Quadratic Weighted Kappa (QWK) of 0.8557 for KneeKL-to-MedicalExpert-I transfer, exceeding source-only training and direct source–target merging by 0.2652 and 0.0468, respectively. Comparisons with representative existing methods and multiple experimental analyses further validate the competitiveness of the proposed framework. Full article