Search Results (1,349)

Advanced Metering Infrastructure (AMI) over 5G New Radio (NR) massive machine-type communication (mMTC) networks require efficient and adaptive communication mechanisms to support reliable data delivery for large numbers of smart meters under dynamic traffic and channel conditions. In this work, we propose a framework in which each smart meter chooses, at runtime, whether to transmit directly to the base station (BS) or via a nearby Data Aggregation Point (DAP). The optimal choice is dynamic and depends on DAP buffer occupancy, periodic congestion, channel quality, and packet deadline pressure. Formulating this as a per-meter binary decision yields an action space of size $2^N$ for N meters, which is intractable for reinforcement learning (RL). We reformulate the problem as regional strategy composition: the RL agent selects one parameterized association strategy for each DAP region from a small library of interpretable rules, and a deterministic mapping expands the regional choice into per-meter modes. It reduces the policy action space from $2^N$ to $K^D$, where D is the number of DAPs and K the number of strategies, while preserving meter-level control granularity. We evaluate Proximal Policy Optimization (PPO) and Deep Q-Network (DQN) controllers against eight meter-level baselines on a 5G NR-calibrated simulator with 1500 m, six DAPs, deadline-bounded delivery, stale channel-state information, and phase-offset congestion cycles. Across three traffic regimes and five random seeds, PPO improves packet delivery ratio (PDR) over the strongest heuristic by +0.63, +2.41, and +2.66 percentage points under baseline, high-load, and bursty-cycle conditions, respectively; all gains are statistically significant (paired t-test, $p<0.001$; Cohen’s d up to 5.12), and the advantage grows with traffic stress. The results show that learned regional composition of classical heuristics outperforms any single fixed heuristic precisely when no individual rule is globally optimal. Full article

Background: Cell division plays a crucial role in plant growth and development. Cyclin-dependent kinase inhibitors (ICK/KRP) negatively regulate the cell cycle, thereby affecting cell elongation and organ development. This study aimed to systematically identify and characterize the ICK/KRP gene family in pepper, and to explore their roles in growth, development, and stress responses. Methods: Bioinformatics approaches were used for genome-wide identification, chromosomal localization, collinearity analysis, sequence characterization, promoter element prediction, and tissue-specific expression profiling of pepper ICK genes. Phylogenetic analysis was performed with homologs from Arabidopsis, tomato, maize, and rice. Quantitative real-time PCR and virus-induced gene silencing (VIGS) were applied to validate gene expression patterns and gene function, respectively. Subcellular localization assays were also conducted. Results: A total of six ICK genes were identified in pepper. They were classified into three subfamilies and distributed on different chromosomes, with one pair showing evidence of duplication. All ICK/KRPs contain the conserved Motif 1 (amino acid sequence: KIPTTREIEEFFATAEKQQQRRFIEKYNFDPVNEKPL) and were predicted to localize to the nucleus. Promoter analysis revealed cis-acting elements associated with plant development, stress responses, and hormone signaling. Expression pattern analysis indicated tissue-specific divergence and significant induction/repression under temperature stress. qRT-PCR results were consistent with transcriptome data, and expression differences were observed in materials with different stigma lengths. Subcellular localization confirmed that Caz03g38750.1 and Caz12g03790.1 proteins localize to both the nucleus and plasma membrane. Silencing of CazICK1 significantly repressed stigma elongation and altered stigma morphogenesis. Conclusions: The six pepper ICK/KRP genes display distinct diversity in distribution, structure and expression, and function in plant growth, development and stress adaptation. This work not only lays a solid basis for exploring the cell cycle regulatory network of pepper and contributes to relevant theoretical research, but it also identifies key gene resources for improving stigma traits. It has great potential for application in molecular breeding to promote high yield and efficient hybrid seed production in pepper. Full article

Litchi chinensis seeds are rich in flavonoids and exhibit potent antioxidant activity. This study constructed a D-galactose-induced oxidative stress model in mice and applied ultra-high performance liquid chromatography–mass spectrometry (UHPLC-MS), network pharmacology, and molecular docking to clarify the antioxidant activity and material basis of ethanolic litchi seed extract. Litchi seed extract was orally given by gavage at 100 and 200 mg/kg in antioxidant tests, whereas a dosage of 500 mg/kg was adopted for the detection of absorbed constituents in plasma. The results showed that the total flavonoid content of litchi seed extract reached 68.37%. The extract could markedly reduce malondialdehyde (MDA) levels and elevate superoxide dismutase (SOD) activity in the serum, liver and kidney tissues of model mice, thereby mitigating oxidative damage. Thirteen prototype compounds absorbed into blood were characterized by UHPLC-MS. Most of these substances were flavonoids, with isorhamnetin, quercetin and naringenin as the major representatives. Core targets including IGF1R, PIK3R1, EGFR, PIK3CA, ERBB2 and proto-oncogene tyrosine-protein kinase Src (SRC) were screened using network pharmacology, among which SRC was identified as the pivotal hub target. Molecular docking results revealed that isorhamnetin, quercetin, naringenin, and diosmetin were able to bind stably to the SRC protein. The present study demonstrated that litchi seed extract exhibits remarkable antioxidant activity, with isorhamnetin, quercetin, naringenin, and diosmetin as the main bioactive antioxidant components. Full article

Detecting and identifying faults in industrial chemical plants is essential for safe and stable operation, and modern monitoring systems increasingly rely on deep learning to classify faults from multivariate sensor data. A practical obstacle to adoption is trust: most deep-learning diagnosers reach their decisions through a classification layer that operators cannot inspect, making it hard to see how the model maps process signals to a particular fault. This study targets fault diagnosis on the Tennessee Eastman (TE) process, a standard benchmark of simulated chemical-plant sensor data, and asks whether this final decision stage can be made directly inspectable without sacrificing accuracy. We propose CNN–Transformer–KAN (CTKAN), a hybrid model that learns local temporal patterns with a one-dimensional convolutional encoder, captures global inter-time-step dependencies with a Transformer encoder, and classifies faults with a Kolmogorov–Arnold Network (KAN) head whose learnable B-spline activations can be plotted and examined individually, in place of a conventional multi-layer perceptron (MLP). On the TE benchmark, CTKAN attains a Macro-F1 of 91.38 ± 0.26% over ten independent runs, comparable to a CNN + Transformer + MLP ablation (91.21 ± 0.32%) and a capacity-matched MLP-head variant (91.43 ± 0.37%) within seed-to-seed variability. The main finding is therefore not a higher score: at matched capacity the KAN and MLP heads are statistically indistinguishable in accuracy, so the KAN head’s value is to add a directly inspectable view of the classification stage at no measurable accuracy cost, helping process engineers sanity-check how the diagnoser separates faults in safety-critical settings. Full article

Water scarcity in semi-arid regions threatens seed germination and early crop establishment, driving the development of biodegradable Nature-based Solutions to replace synthetic plastic mulches. Porous cellulose membranes were fabricated from rice husk (RH), banana pseudostem (BP), and sugarcane bagasse (SB) by thermo-chemical extraction and high-shear homogenization (n = 5 replicates per membrane type). Membranes were characterized by ATR-FTIR and scanning electron microscopy, confirming removal of non-cellulosic components and biogenic silica preservation in RH, and revealing biomass-dependent porous architectures linked to mechanical and transport behavior. RH produced the most compact fibrillar matrix (compressive strength: 8.16 ± 0.24 MPa; WVT: 170 ± 60 g m⁻² day⁻¹), BP an open interconnected network with superior deformability (9.83 ± 0.25% elongation) and moisture transport (WVT: 400 ± 100 g m⁻² day⁻¹), and SB the highest moisture-retention capacity (215.7 ± 15.8%). Germination assays with Brassica oleracea var. botrytis under water stress showed SB achieved the highest germination rate (90.5 ± 0.99%), confirming that sustained moisture availability governs germination more decisively than transport rate alone. Soil burial tests confirmed biodegradable behavior across all membranes (R² ≥ 0.995; k = 0.043–0.046 day⁻¹). These findings establish a hydrogen-bond-mediated structure–property–function framework for designing biomass-specific cellulose membranes as biodegradable solutions for water-limited agricultural systems. Full article

Background: Electroencephalographic (EEG) functional connectivity analysis requires multiple signal-processing, source-modelling, and statistical steps that can limit its adoption in clinician-led randomised controlled trials (RCTs). NeuroStat was developed as a prototype research tool to integrate this workflow; formal usability validation with clinician end-users has not yet been conducted. Methods: NeuroStat is an open-source Python/PyQt6 desktop application that integrates automated artefact removal (a Generalised Eigenvalue Decomposition for Artefact Identification [GEDAI] pathway and a traditional Artefact Subspace Reconstruction (ASR)/Independent Component Analysis (ICA)/ICLabel pathway), boundary element model (BEM) source localisation using the Desikan–Killiany atlas (68 cortical regions), Phase Lag Index (PLI) connectivity estimation across five canonical frequency bands, and RCT-oriented statistical analysis. Evaluation separated sensor-space and source-space claims: a sensor-level simulation (repeated across five independent random seeds) tested preprocessing robustness, a repeated source-space simulation tested recovery of a known cortical parcel-pair contrast after forward projection and inverse reconstruction, a PhysioNet benchmark tested posterior Desikan–Killiany alpha PLI in 20 healthy adults, and an illustrative application to 20 sessions from a published chiropractic RCT demonstrated real-world workflow applicability. Results: In the sensor-level simulation benchmark, the Traditional pathway achieved a mean absolute error of $0.168\pm 0.017$ PLI units and root mean squared error of $0.219\pm 0.045$ (mean ± SD across five independent random seeds) across all artefact conditions. In the source-space simulation, reconstructed alpha PLI for the known bilateral lateral-occipital parcel pair exceeded anterior control edges across 60 repeated condition runs (mean known-control difference = 0.105 PLI units, 95% CI 0.096–0.114; $t\left(59\right)=22.61$, $p<0.001$). In the PhysioNet source-space benchmark, posterior Desikan–Killiany alpha PLI was higher during eyes-closed than eyes-open rest (Cohen’s $d=0.85$, $p=0.001$; 16/20 subjects showing the expected direction) after ICLabel-enabled preprocessing. In the pilot RCT application, all 20 sessions completed processing without manual intervention, with default-mode network alpha PLI showing a pre-to-post change of +0.071 in the intervention group versus +0.015 in the active control group. Conclusions: NeuroStat integrates preprocessing, source-space construction, connectivity estimation, and statistical reporting within a parameter-logged desktop workflow for EEG functional connectivity studies. Current evidence supports initial technical feasibility, sensor-level preprocessing robustness for one pathway in controlled simulations, source-space recovery of a known parcel-level contrast, source-space sensitivity to an expected posterior alpha resting-state contrast, and error-free processing across 20 real RCT sessions in a pilot workflow demonstration. Formal usability testing, test–retest reliability analysis, participant-specific source-model validation, and clinical-population validation remain necessary before clinician-facing or trial-deployment claims can be made. Full article

Intrusion detection in industrial cyber-physical systems is constrained by small labelled-attack corpora and by the subtler signal of physical-process attacks compared with classical IT-network intrusions, motivating renewed interest in foundation-model-based detectors; classical detectors are typically trained per dataset and degrade under the distribution shift that is common in operational technology, where attack repertoires evolve faster than retraining cycles. Two foundation-model families are now plausible candidates: open-source Large Language Models (LLMs) and recent tabular foundation models (TabPFN, TabICL) pre-trained for in-context tabular inference. We compare the two families head-to-head, alongside Random Forest and XGBoost classical anchors, across three established industrial security benchmarks (SWaT, HAI, WUSTL-IIoT-2021) under a controlled multi-seed full-holdout protocol with paired McNemar and cross-seed Mann–Whitney tests. The empirical picture is dataset-dependent rather than universal: tabular foundation models establish a strong, previously unreported baseline that is competitive with or superior to classical anchors on every dataset evaluated, while LLMs are complementary detectors with a specific advantage on schemas that carry process-engineering semantics (such as SWaT’s named sensor channels). A per-class analysis on the WUSTL five-class attack taxonomy shows that the two families have structurally different strengths: tabular methods dominate traffic-rich attacks (Denial-of-Service, Reconnaissance), whereas LLMs are competitive on rare attack types (Backdoor, Command Injection). A confidence-gated cascade that escalates only low-confidence tabular decisions to an LLM exceeds either detector alone at a small query budget, and a leave-one-attack-type-out analysis shows that foundation-model detectors generalise to unseen attack families substantially better than the classical anchors. The appropriate detector choice in industrial cyber-physical security is therefore informed by the dataset’s feature schema, the attack-type mix, and the operational cost envelope, rather than by a specific performance metric. Full article

Indoor Air Quality (IAQ) monitoring, especially in multi-zone smart buildings, is typically limited by the high computational and energy requirements of continuous sensor processing, which makes event-driven methods desirable for efficiency. Energy proportionality, in this context, refers to a system whose computational cost scales with the significance of detected environmental changes rather than with the fixed sampling rate. This paper presents a spike-driven neuromorphic sensing framework for decentralized IAQ monitoring that combines adaptive Kalman filter preprocessing, dynamic threshold-based asynchronous spike encoding, and a Leaky Integrate-and-Fire neural network with Spike-Timing-Dependent Plasticity (STDP) learning. Multiple-parameter IAQ data including PM₁, PM_2.5, PM₁₀, CO₂, CO, TVOCs, and O₃ were sampled from nine functionally differing zones of an educational building in Metro Manila, Philippines. The neuromorphic model yielded a mean Sparse Firing Ratio of 10.94%, a Mean Response Time of 10.62 timesteps, and an energy efficiency proxy score of 9.28. Neuron population scaling and parameter robustness analyses revealed that the four neurons per parameter were enough to saturate the performance, and FLOP-based estimation indicated an 8.9-fold computational reduction (approximately 89% fewer FLOPs) compared to LSTM inference. In addition, the revised Performance Efficiency Index and composite efficiency score corroborated the stable and energy-proportional nature of behavior in all zones. These results illustrate that spike-based neuromorphic computation is an energy-efficient and scalable way for decentralized smart-building IAQ monitoring, though hardware-level validation on dedicated neuromorphic processors remains necessary for absolute power saving verification. Multi-seed validation (five seeds) with expanded baselines including GRU, Temporal CNN, XGBoost, and Logistic Regression confirmed the robustness and repeatability of reported metrics. Full article

Background/Objectives: Chest X-ray (CXR) images are a widely used first-line screening tool for pulmonary tuberculosis (TB) detection but are difficult to interpret, which has increased demand for an automated screening tool. Deep-learning-based computer-aided diagnosis systems have demonstrated a classification performance comparable to that of trained radiologists, but they rely on dense annotations such as lesion-level or pixel-level labels, which are costly and difficult to obtain in routine clinical workflows. We developed CPM-XNet, an annotation-efficient framework for lesion-annotation-free downstream TB classification in CXR images. Methods: CPM-XNet incorporates a compressing–projecting mask (CPM) to provide soft lung-aware modulation while preserving global contextual information. The CPM-modulated images are then used for downstream classification with multiple convolutional neural network backbones and a vision transformer baseline. Results: Experiments were conducted using an internal hospital dataset and public TB datasets, and CPM-XNet showed improved performance compared with baseline models trained on unmodulated images. In a repeated-seed evaluation of the main ResNet-101 configuration on the Tung cohort, CPM-ResNet101 showed higher and more stable performance than the non-CPM counterpart and demonstrated significant paired improvement using McNemar’s exact test. An ablation analysis indicated that CPM modulation was the main contributor to performance improvement while data augmentation and the classifier architecture further influenced the overall robustness. Conclusions: CPM-XNet provides an annotation-efficient strategy for lesion-annotation-free downstream TB classification in CXR images. The findings support preliminary technical feasibility, although larger, naturally imbalanced, cross-institutional validation is required before clinical deployment can be inferred. Full article

Edge-side acoustic monitoring enables animal sound recognition in remote environments, but microcontroller deployment remains constrained by feature extraction, numerical consistency, memory, latency, and energy consumption. This study presents a sensor-based tiny machine learning (TinyML) acoustic monitoring system on an Arduino Nano 33 BLE Sense Rev2 platform, integrating onboard pulse-density modulation (PDM) microphone acquisition, Mel-frequency cepstral coefficient (MFCC) feature extraction, deployment-side standardization, 8-bit integer (INT8) neural-network inference, and edge-side decision output. To reduce training-to-deployment feature drift, consistent frame parameters, mirrored C++ feature operators, and exported standardization parameters are used to align personal-computer-side and microcontroller-side feature representations. A source-isolated seven-class protocol was constructed for six target animal classes and one compound background-noise class. In the single-run baseline comparison, the proposed multilayer perceptron achieved 98.28% test accuracy and 97.21% test macro-F1, while the ten-seed stability analysis yielded 98.64% ± 0.26% test accuracy and 97.87% ± 0.38% test macro-F1. The deployed INT8 model occupied approximately 26.9 KB, with a post-window latency of about 303 ms. System-level input power was 0.783–0.825 W, corresponding to an estimated autonomy of 7.63–8.03 h under the reference battery setting. Full article

Three-dimensional (3D) endothelium–stromal co-cultures were established using human endometrial cells from biopsy of healthy women (n = 13) and serum samples from both healthy and endometriotic women (n = 5). For 3D construction, stromal cells were mixed with extracellular matrix components, followed by endothelial cell seeding. Morphological analysis confirmed the organization of tissue-like structures. Immunofluorescence and flow cytometry verified the expression of specific stromal and endothelial markers (Cytokeratin, Vimentin, Insulin-like growth factor-binding protein 1, and von Willebrand factor). Cell viability and proliferation increased over time, with minimal cell death. To test functional responsiveness, these co-cultures were exposed to inflammatory serum from endometriotic patients. After 48 h, cytometric bead array showed elevated levels of IL-1β, IL-6, and IL-8 in cultures treated with inflammatory serum, indicating preserved functional activity and responsiveness. By allowing detailed investigation of functional endometrial states within a physiologically relevant cellular network, this approach provides a valuable organoid-like tool to explore conditions such as implantation failure and infertility and to study the cellular interactions underlying reproductive pathologies. Full article

The Q-Learning Relational Game (QLRG) framework provides a theoretically rigorous method for identifying minimal winning coalitions in online social networks (OSNs) under the restrictive assumption of global agent symmetry or uniform matroid structure. Real-world OSNs, however, exhibit significant asymmetry. This paper introduces the Cluster-Based Q-Learning Relational Game (C-QLRG), a practical extension that relaxes the global symmetry requirement by leveraging community structure. We partition the agent set into communities with bounded internal variation and represent the state solely by community membership counts of the seed set. Because the closure operator already captures all eventual influence spread, the problem reduces to a sequential seed selection task where the agent decides, at each step, from which community to add the next seed. We prove that the optimal Q-function of a suitably regularized reach-efficiency objective is Lipschitz continuous and derive a performance bound for the learned policy. The full algorithm is presented, and its complexity is analyzed. Empirical evaluations on a synthetic asymmetric network and Zachary’s Karate Club demonstrate that C-QLRG is highly sensitive to reward parameters, where default settings lead to premature stopping, but parameter tuning combined with a corrected minimality verification recovers high-efficiency coalitions by removing non-contributing agents. With tuned parameters, C-QLRG produces a near-winning coalition of size 11 and 99% reach on the synthetic network, surpassing the greedy baseline’s efficiency (size 12) despite a one-node coverage gap, while identifying the optimal winning coalition of size 1 on the Karate Club dataset, matching all baselines. The framework thus offers a principled trade-off between model fidelity and scalability, with the reward design choice being critical for practical deployment. Full article

Background/Objectives: Spontaneous intracranial hypotension (SIH) is caused by spinal cerebrospinal fluid (CSF) leakage and is typically diagnosed by clinical presentation and characteristic MRI signs; however, objective tools for monitoring physiological changes and treatment response remain limited. Cine phase-contrast MRI (PC-MRI) enables noninvasive quantification of aqueductal CSF dynamics, yet reliable analysis is challenging since the cerebral aqueduct is extremely small and susceptible to low contrast, partial volume effects, and ROI-dependent measurement variability—particularly in SIH where CSF pulsatility is often reduced. Methods: We propose an end-to-end automated framework that integrates (1) a cascade localization–segmentation strategy, consisting of Tiny YOLOv4 detection followed by MultiResUNet segmentation on a YOLOv4-derived cropped ROI; (2) physiology-informed pulsatility-based segmentation (PUBS) to refine anatomical masks into functional flow ROIs; and (3) one-dimensional convolutional neural networks (1D-CNNs) to extract exploratory waveform morphology features from 32-phase cardiac-cycle velocity waveforms. The study includes 39 participants, yielding 59 cine PC-MRI examinations: 11 controls, 28 Pre-treatment SIH scans and 20 Post-treatment Recovery scans. Results: The cascade model significantly improves segmentation robustness compared with a full-image baseline, achieving higher Dice scores and markedly lower boundary errors across cohorts (e.g., Pre-treatment SIH HD95: 1.66 ± 0.74 px vs. 15.37 ± 44.98 px). PUBS refinement reduces quantification deviation from expert manual references in SIH (mean relative error: 7.4% to 5.6%) and improves diagnostic performance for multiple hemodynamic parameters (e.g., downward mean flow AUC: 0.747 to 0.792). For waveform morphology analysis, the end-to-end 1D-CNN classifier was evaluated using repeated-seed participant-level grouped LOOCV. The repeated-seed ensemble prediction showed modest out-of-sample discrimination between Normal controls and Pre-treatment SIH scans, with an AUC of 0.646, a bootstrap 95% confidence interval of 0.455–0.826, and a permutation-test p-value of 0.072. Separately, exploratory analysis of the final baseline-trained 1D-CNN latent space showed marked, apparent Normal-versus-SIH separability and an intermediate recovery distribution in PCA space, suggesting that aqueductal waveform morphology may encode SIH-related physiological information. Conclusions: These findings suggest that SIH-related information may be reflected not only in flow magnitude but also in aqueductal CSF waveform morphology. However, the modest and statistically non-significant out-of-sample performance of the end-to-end 1D-CNN classifier indicates that morphology-based AI features should currently be regarded as exploratory biomarker candidates rather than validated stand-alone diagnostic tools. Larger independent cohorts are required to confirm their reproducibility, physiological meaning, and clinical utility. Full article

A single user-invariant tensor, kinematically impossible for any human finger to produce, bypasses bidirectional long short-term memory (BiLSTM) continuous-authentication defenders with numerically identical structure across four independently trained generators. We arrive at this finding by training generative adversarial networks against BiLSTM defenders on 51 users across three independent random seeds, with the data partition held fixed, to test the prevailing assumption that successful generative attacks must reproduce the victim’s kinematic behavior. Aggregate attack success rate varies from 31.4% to 45.1% across seeds, a 13.7 percentage-point spread arising purely from optimization stochasticity, demonstrating how unreliable single-seed reporting is as an estimator of the true attack surface. A four-group descriptive stratification shows that 8% of users are attacked across all three seeds, 31% are consistently safe, and 61% exhibit seed-dependent outcomes. Classifier accuracy on zero-effort impostors does not predict adversarial vulnerability (Spearman $\rho =-0.058$, permutation $p=0.688$), whereas intra-user behavioral variance does ($\rho =+0.351$, permutation $p=0.012$, Bonferroni-corrected). The mechanism is not behavioral emulation but convergence to an Adversarial Skeleton Key, a tensor located in an unregularized region of the BiLSTM’s decision surface that the network reliably maps to acceptance, despite lying many standard deviations outside any genuine human distribution. The mimicry-centric evaluation paradigm underestimates the real threat surface. Input-space plausibility must be treated as a defensive layer rather than a preprocessing concern. Full article

Defective seed filling, which manifests as seed wrinkling, severely impairs the yield and commercial quality of soybean crops. Soybean varieties independently developed in Heilongjiang Province exhibit distinct phenotypic variations in seed wrinkling across diverse ecological planting regions, whereas the molecular and physiological mechanisms driving such differences remain largely uncharacterized. In this study, two soybean genotypes with divergent heat resistance, namely, the heat-sensitive cultivar HH43 and the heat-tolerant cultivar HN76, were planted in three distinct ecological sites for comparative analysis. Statistical results indicated that ecological conditions serve as the predominant factor regulating seed-wrinkling variation, with high temperatures occurring during the seed-filling stage identified as the key abiotic stress trigger. Excessively high ambient temperatures triggered abnormal sucrose accumulation in the pod husks of heat-vulnerable HH43, disrupting the coupling relationship between sucrose metabolism and energy supply and thereby restricting starch biosynthesis in developing seeds. Transcriptome profiling combined with weighted gene co-expression network analysis (WGCNA) further demonstrated that heat stress significantly suppressed the expression of energy transport-related genes and induced the dysregulated expression of starch synthesis-associated genes in susceptible soybean plants, and these transcriptional alterations were further verified via qRT-PCR assays. Collectively, short-term extreme high temperatures interrupt the carbon transport and allocation process from pod husks to seeds in heat-sensitive soybean cultivars. By contrast, heat-tolerant genotypes can sustain a stable physiological metabolism and molecular regulatory networks to effectively cope with high-temperature stress during the seed-filling period. Full article