Preformed porous media (PPM) technology has emerged as a transformative approach to enhance heat and mass transfer in vacuum freeze-drying (VFD) of agricultural and food products. This review systematically analyzes recent advances in PPM research, with particular focus on spray freeze-drying (SFD) as the dominant technique for precision pore architecture control. Empirical studies confirm PPM’s efficacy: drying time reductions of 20–50% versus conventional VFD while improving product quality (e.g., 15% higher ginsenoside retention in ginseng, 90% enzyme activity preservation). Key innovations include gradient porous structures and multi-technology coupling strategies that fundamentally alter transfer mechanisms through: resistance mitigation via interconnected macropores (50–500 μm, 40–90% porosity), pseudo-convection effects enabling 30% faster vapor removal, and radiation enhancement boosting absorption by 40–60% and penetration depth 2–3 times. While inherent VFD limitations (e.g., low thermal conductivity) persist, we identify PPM-specific bottlenecks: precision regulation of pore structures (<5% size deviation), scalable fabrication of gradient architectures, synergy mechanisms in multi-field coupling (e.g., microwave-PPM interactions). The most promising advancements include 3D-printed gradient pores for customized transfer paths, intelligent monitoring-feedback systems, and multiscale modeling bridging pore-scale physics to macroscale kinetics. This review provides both a critical assessment of current progress and a forward-looking perspective to guide future research and industrial adoption of PPM-enhanced VFD.

Accurately monitoring coupled water–nitrogen stress is critical for wheat (Triticum aestivum L.) productivity under climate change. This study developed a machine learning framework utilizing multimodal leaf electrophysiological signals––intrinsic resistance, impedance, capacitive reactance, inductive reactance, and capacitance––to decouple water and nitrogen stress signatures in wheat. A parallel modelling strategy was implemented employing Gradient Boosting, Random Forest, and Ridge Regression, selecting the optimal algorithm per feature based on predictive performance. Controlled pot experiments revealed IZ as the paramount biomarker across leaf positions, indicating its sensitivity to ion flux perturbations under abiotic stress. Crucially, algorithm-feature specificity was identified: Ridge Regression excelled in modeling linear responses due to its superior noise suppression, while GB effectively captured nonlinear dynamics. Flag leaves during reproductive stages provided significantly more stable predictions compared to vegetative third leaves, aligning with their physiological primacy as source organs. This framework offers a robust, non-invasive approach for real-time water and nitrogen stress diagnostics in precision agriculture.

Urban drainage systems rely on fixed surveillance cameras that operate continuously under daytime, nighttime-infrared, rainy, and non-rainy conditions, yet most vision-based water-monitoring methods are validated only on daytime imagery and require costly pixel-level annotation. This paper investigates whether a promptable segmentation foundation model can monitor water surfaces and waterlogging zero-shot—without any task-specific training or labeling—across the full illumination–weather spectrum of real surveillance operations. We construct a benchmark of 3651 frames from 25 fixed cameras of an urban drainage monitoring network in Zhenjiang, China, spanning four scene categories (river/canal, road ponding, underpass, pump-station apron), seven days in July–September 2026, and all four illumination–weather quadrants, explicitly including non-rainy negative days. Using SAM 3 with the text prompt “water”, we extract per-frame water-area ratio and normalized waterline level, and evaluate (i) quadrant-wise segmentation stability, (ii) rain-event detection, (iii) false-alarm behavior on negative days, and (iv) pixel-level agreement with expert reference masks. Zero-shot segmentation met the scene-specific validity criterion on 96.9% of the 162 diurnal-cycle frames at the canal reference site (100% in both no-rain quadrants, 88.9% in the night-rain quadrant); failures concentrate in heavy-rain-obscured and sediment-turbid dusk frames. On 10 August 2026—a day with two visually verified heavy-rain events—the waterline indicator detected both events (peaks 72.9% and 71.4% vs. a 43–62% control-day band), and the same afternoon event was independently corroborated by severe ponding at road and underpass sites city-wide. A level-only alarm rule produced an 11.8% false-alarm rate on 102 negative-day frames, traced to a camera re-aiming event that shifted the normalized indicator distribution; a compound rule combining area anomaly and rise rate produced no alarms on the 102 examined negative-day frames (vs. 12/102 for the level-only rule) while preserving both event detections—a retrospective, in-sample result that we do not treat as a validated false-alarm rate. We therefore froze the complete pipeline and rules and evaluated them prospectively on 14 additional days (7 days with event-triggered 5-min capture and 7 routine hourly capture days; 1072 frames) that had never been used for threshold or rule design: on the 13 visually verified dry days (784 frames) the frozen compound rule produced only six alarm frames (0.77%), all at illumination-mode switchover transients, and a two-frame persistence variant eliminated all of them (0/13 days; upper 95% confidence bound on the day-level false-alarm rate 20.6%) while detecting both the nighttime rise and the afternoon flood stage of a newly encountered heavy-rain day. Pixel-level verification on an expanded stratified subset of 79 frames from 24 sites with expert reference masks yields a mean IoU of 0.633 (0.652 excluding two failure frames) and near-zero false positives on 15 water-free frames (mean 0.15% of pixels); the reported metrics change by at most 0.003 mIoU under ±5-pixel annotation boundary tolerance. Against two same-protocol baselines—an open-vocabulary detector (YOLOE, mIoU 0.268) and a classic HSV color rule (mIoU 0.453, 68% false-positive area on negative frames)—zero-shot SAM 3 is the only approach that remains accurate in the nighttime-infrared quadrants, and its mIoU varies by less than 0.04 across confidence thresholds 0.15–0.35 at the reported prompt and stays within 0.48–0.66 across four prompt wordings. Reference-mask reliability was independently confirmed by a second annotator (inter-annotator IoU 0.71, Dice 0.80). Inference costs ~46 ms per frame on a consumer laptop GPU (not an edge platform), supporting further evaluation of multi-camera processing. These results establish text-prompted foundation models as a practical, training-free candidate for drainage surveillance across day/night and rain/no-rain conditions—promising for operational evaluation, though not yet operationally validated—and delineate the specific conditions under which they fail.

Aeration dominates energy use in biological wastewater treatment, but aggressive energy-saving control risks violating dissolved oxygen (DO) safety margins. We present a guardrail-enforced tabular Q-learning controller for a pilot biofilm anoxic/oxic (A/O) system (2 × 4.86 m³) treating piggery wastewater, compared online against always-on aeration and a hysteresis rule. A first-order-plus-deadtime DO model (R² = 0.973) identified from pseudo-random excitation was used to pre-train a 108-state policy balancing energy, DO-band keeping, and switching wear, deployed as a 5 min decision loop nested inside a 1 min hard-guardrail monitor with command readback verification. In 24 h online campaigns, the RL controller cut metered aeration energy by 44.3% versus always-on (26.05 vs. 46.8 kWh·d⁻¹; 95% CI 43.2–45.4%) with zero guardrail interventions and day-to-day reproducibility within 1%; the cheaper hysteresis rule (−55.2%) required 9–12 hard-guardrail interventions per day. Effluent COD and NH₄⁺-N remained far below the enterprise discharge requirement under all controllers throughout the 24 h campaign horizons, with no detectable short-term effluent penalty, and a manual actuator-override incident was contained autonomously by the guardrail layer. Because the hydraulic retention time (≈17 d) far exceeds the campaign length, longer-horizon effluent effects remain open and are stated as a limitation. A deliberately simple, auditable tabular policy thus captures most of the achievable aeration savings on small decentralised systems while its safety case rests on hard guardrails that bound the worst case independently of what the learned policy outputs.

Leaf-based sensors can characterize crop responses to nutrient supply, but their relative performance may differ across supply intervals. We compared five electrical descriptors extrapolated to zero clamping force with optical SPAD readings collected from the same winter-wheat vessels on the same dates. The experiment covered four compound mineral supply formulations (25%, 50%, 75%, and 100%) and three adjacent contrasts. The paired B2 dataset comprised 192 vessel–date observations from 12 vessels across 16 dates. We evaluated four feature sets using seven classifiers, with nested leave-one-date-out validation as the primary analysis and alternative leave-out schemes as sensitivity checks. In the reproductive stage, electrical resistance (IR) outperformed SPAD for the 25–50% contrast (date-averaged area under the receiver operating characteristic curve [ROC-AUC]: 0.870 versus 0.574; ΔAUC = +0.296; 95% date-cluster bootstrap interval: +0.092 to +0.491), whereas SPAD outperformed IR for the 75–100% contrast (0.981 versus 0.352; IR − SPAD ΔAUC = −0.630; interval: −0.871 to −0.352). Thus, the relative ranking reversed across the two endpoint intervals. Performance in the intermediate interval varied with feature representation and classifier, and fusion did not exceed the strongest non-fused feature set in the six reference-model tasks. Electrical-only results also differed between B1 and B2, although B1 could not test the paired optical–electrical ranking. These findings support reporting the target formulation interval, feature–classifier specification, validation design, and cross-batch evidence when comparing leaf sensors. The results concern assigned compound formulations, not calibrated plant nutrient diagnoses or management thresholds.