Search Results (418)

Feed shortage and water scarcity critically constrain livestock production in semiarid regions, yet the interactive mechanism of growth by which planting density and mowing shape the trade-off between biomass yield and nutritional quality remain poorly quantified for rainfed sweet sorghum (Sorghum bicolor L. Moench). The Loess Plateau is located in the north-central part of China. It belongs to the semiarid continental monsoon climate and is mainly rainfed by agriculture. A two-year field experiment (2023–2024) was conducted in this region to investigate how four planting densities (50,000, 70,000, 90,000 and 110,000 plants·ha⁻¹) and two mowing regimes regulate plant morphology, root development and comprehensive nutritional traits. Compared with a mowing treatment, a non-mowing treatment significantly increased dry matter yield from 8.54 t·ha⁻¹ to 21.59 t·ha⁻¹ (an increase of 152.7%) in 2023 and from 10.32 t·ha⁻¹ to 20.91 t·ha⁻¹ (an increase of 102.6%) in 2024. Increasing planting density elevated population biomass, with the 90,000 plants·ha⁻¹ treatment optimally balancing individual growth and interplant resource competition. Mowing reduced stem diameter, leaf area index, and root biomass, thereby lowering fiber concentration but suppressing total dry matter accumulation. Structural equation modeling (GFI = 0.937) quantified this dual effect: mowing exerted a strong negative direct effect on dry matter yield (path coefficient = −4.211, explaining 94.9% of the yield variance) but indirectly improved nutritional quality by thinning stems to optimize leaf allocation. Integrated random forest and radar chart multi-index evaluation identified non-mowing at 90,000 plants·ha⁻¹ as the optimal cultivation regime (comprehensive score Y = 0.917), which coordinated population biomass and nutritional performance while delivering a 23.3% higher net profit than conventional mowing treatments. This study revealed the physiological trade-off path between biomass productivity of sorghum and its quality under drought stress under rainfed conditions on the Loess Plateau and provides a quantitative, replicable evaluation framework to optimize agronomic management for sweet sorghum and analogous C4 crops in global semiarid rainfed ecosystems, offering practical strategies for sustainable forage production. Full article

Predictive accuracy is often treated as a sufficient condition for profitable algorithmic trading, yet whether machine learning forecasts translate into trading performance that holds up across different market regimes remains contested. This study develops and stress-tests a prediction-driven, dynamically optimized trading framework across twelve instruments spanning equities, commodities, foreign exchange, and cryptocurrencies, and three structurally distinct regimes: a calm market (2018), the COVID-19 crisis (2020), and the Russian–Ukrainian geopolitical shock (2022). Daily price forecasts from RNN, LSTM, GRU, and hybrid architectures feed a rule-based framework that opens long or short positions from the divergence between predicted and observed prices, applies volatility-adjusted stop-loss and take-profit thresholds optimized by Sharpe-ratio grid search, and is extended with a rolling-MAPE confidence filter and an error-based dynamic position-sizing rule. Across the resulting 36 asset-period combinations, the prediction-based strategies outperformed the buy-and-hold benchmark in 86–92% of cases on cumulative return and 92% of cases on the Sharpe ratio; a one-sided binomial sign test rejects the null of no systematic advantage at p < 0.001 for every strategy variant, and the pattern is stable when each of the three regimes is examined separately (10–11 of 12 assets per period). Excluding crude oil—the only instrument where active strategies persistently underperformed, plausibly reflecting structural breaks such as the negative 2020 futures prices—raises the win rate to roughly 97%. Median outperformance reached about 15 percentage points in cumulative return and close to two Sharpe-ratio points, with maximum drawdowns falling in almost every case. The rolling-MAPE filter was the most effective mechanism for containing losses in turbulent regimes, while the position-sizing variant delivered the strongest average risk-adjusted returns. These results indicate that the value of machine learning forecasts in trading depends less on raw predictive accuracy than on the risk-management logic wrapped around it, and that this advantage is statistically robust across assets, regimes, and specification choices rather than an artifact of a single favorable sample. Full article

Prolonged storage of pelleted feed causes arching above the outlet of conical silos, yet how far the wall material governs the discharge stability of non-spherical pellets remains unresolved. An experimentally calibrated Discrete Element Method (DEM) framework was established in Ansys Rocky for the gravity discharge of cylindrical feed pellets (2 mm × 5 mm, aspect ratio 2.5) from 1:10 scaled steel and fiberglass conical silos at an outlet-to-particle-size ratio of approximately 18, after 4 h of consolidation. Of the hopper half-angles tested (10°, 20°, 30° and 45°), only 10° sustained gravity discharge; the others blocked completely in both wall materials. Parameters calibrated from consolidated-load direct shear tests reproduced the cumulative discharged mass to a MAPE of 2.5% (R² = 0.99, steel) and 3.9% (R² = 0.963, fiberglass), whereas the instantaneous flow rate remained stochastic. Both silos discharged in pulses, at 0.94 and 0.79 kg/s, with coefficients of variation of 25% and 28%. Mass flow indices of 0.211 and 0.278 place both in the funnel-flow regime, and measured peak wall pressures near the outlet reached ≈2000 Pa (steel) against ≈1650 Pa (fiberglass). Reducing wall friction from 0.83 to 0.74, therefore, did not improve stability: for elongated pellets at a low outlet-to-particle-size ratio, surface smoothness alone is no remedy, and outlet sizing, interlocking and wall load management must be addressed together. Full article

Apostichopus japonicus is a temperate marine echinoderm whose growth and reproduction are strongly influenced by temperature. We compared adult sea cucumbers exposed to stepwise warming (10–12, 12–14, and 14–16 °C across three successive 20-day stages) with animals maintained at 15 °C. Growth indices, gonadosomatic index (GSI), gonadal histology, and energy-budget components were evaluated in females and males. The 60 d experiment used three replicate tanks for each treatment-by-sex combination. Compared with the constant 15 °C treatment, the stepwise-warming regime showed a higher female GSI at stage III; representative gonadal sections documented stage-related structural changes in both treatments. No significant treatment effects were detected for final wet weight, specific growth rate, daily growth rate, or feed conversion ratio. Excretory energy decreased during stages II and III, while the proportion allocated to gonadal growth increased. Stage-specific within-sex comparisons showed higher gonadal growth energy under stepwise warming in both sexes during stage II, while the clearest GSI difference occurred in females during stage III; treatment × sex interactions were not significant. The clearest differences between the two temperature regimes were observed during stages II and III (12–16 °C). The findings provide an integrative physiological basis for temperature management during broodstock conditioning of A. japonicus. Full article

This research examines the effect of a 9-week feeding intervention on laying hens with flavonoid-enriched rations to modulate the colour and lipid quality of egg yolks. Triglycerides and phosphatidylcholine were the principal neutral and polar classes, respectively, as determined by TLC-FID. Oleic, palmitic, and linoleic were the major fatty acids (FAs) as determined by GC-FID/MS, while essential DHA predominated omega-3 FAs. Flavonoid supplementation did not markedly modify the total fat, lipid classes, or FA profile of yolks, whilst FAs were mostly affected by basal diet ingredients. However, higher-dose hesperidin and naringin improved the MUFA/PUFA ratio of yolk fatty acids. Egg yolks were not differentiated by colour or total carotenoid content irrespective of the feeding intervention. Study time, on the other hand, contributed to observed differences. Remarkably, a 1-week adaptation to a standardised regime proved adequate for the production of yolks with a homogenous colour profile, while incremental changes in the total carotenoids boosted red/yellow colouration. By completing the intervention, significant nutritional improvements emerged for all groups: (i) phosphatidylcholine levels increased; (ii) n-6/n-3 shifted to lower values; and (iii) peroxidisability indices diminished. The aforementioned findings, along with low atherogenic/thrombogenic indices and combined with the oxidative stability of yolk lipophilic constituents, feature eggs with consistent yolk colour and enhance lipid-rich yolks’ contribution towards a healthy diet. Full article

Binary code similarity detection (BCSD) ranks candidates but does not quantify the reliability of an already selected Top-1 match. We study this post-retrieval problem in a known-source, closed-set protocol: the target Top-1 is frozen before same-source cross-compilation views are queried, so auxiliary evidence audits cannot replace it. A frozen 34-variable map feeds a low-capacity logistic model with project-grouped cross-fitting, Platt calibration, and training-side threshold selection. On 413 families from 16 projects, cross-view evidence improved discrimination over target score/margin features. GCC-O0 was a dominant-anchor regime: Full showed no statistically resolved ROC-AUC gain over Primary-anchor, whereas Clang-O0 benefited from complementary non-primary evidence. On 240 project-identity-disjoint families from 55 projects, the design-locked structural branch accepted 75/240 GCC and 99/240 Clang candidates (31.3%/41.3% coverage) with no observed family-level errors. Correspondence mismatch reduced discrimination toward chance. Corrected TF-IDF remained supportive because correction followed label access. The contribution of this paper is a versioned candidate-preserving audit interface with explicit evidence and deployment boundaries, but not a universal retrieval improvement or distribution-free guarantee. Full article

Global streamflow reanalyses such as GloFAS-ERA5 are available everywhere yet lose fidelity in small, snow- and glacier-fed headwaters that feed Central Asia’s transboundary rivers. We present an explainable, calibrated deep ensemble framework that corrects the GloFAS-ERA5 log-residual at gauges of the Syr Darya and Amu Darya systems using the CA-discharge archive. An entity-aware long short-term memory (LSTM) backbone drives a regime-gated mixture of experts trained under a closed-form mixture continuous ranked probability score (CRPS) and augmented with snow physics constraints and a regime-conditional (Mondrian) conformal layer; skill was assessed under temporal holdout, leave-one-basin-out and prediction in ungauged region protocols, with grouped Shapley value attribution. Correction rendered all 74 gauges skillful, raising the median modified Kling–Gupta efficiency (KGE′) from 0.386 (raw) to 0.825 (flagship); on temporal point skill the framework is statistically tied with gradient boosting (paired Wilcoxon p = 0.49). Under-dispersed raw intervals (90% coverage 0.68) were recalibrated to near-nominal coverage (~0.90), and high-flow exceedance decision skill was moderate (Q90 Brier skill score 0.34, ROC-AUC 0.92), while low-flow (Q10) exceedance showed no skill over climatology. Transfer to ungauged, more glacierized catchments was a measured limit that degraded with glacier fraction and basin area. The framework’s value is calibration, an inspectable (supervised) regime structure and regional physical insight, not point skill superiority. Full article

Monomer sequence strongly influences copolymer properties, but direct block formation from a monomer mixture requires a large reactivity contrast. Here, fluorination was used to regulate anhydride reactivity in the cesium pivalate-catalyzed ring-opening alternating copolymerization (ROAC) of tetrafluorophthalic anhydride (FPA), phthalic anhydride (PA), and 3-perfluorohexyl-1,2-epoxypropane (PFE). Time-resolved nuclear magnetic resonance (NMR) spectroscopy showed that FPA reached >99% conversion before detectable PA incorporation. With 1,4-benzenedimethanol as a bidirectional initiator, this sequential consumption generated a central poly(FPA-alt-PFE) segment followed by poly(PA-alt-PFE) growth from both chain ends. Molar mass evolution, end-group analysis, and diffusion-ordered spectroscopy (DOSY) NMR collectively supported covalent block formation. Sequential incorporation was retained across three different FPA:PA feed ratios, and Beckingham–Sanoja–Lynd analysis yielded large and reciprocal effective reactivity-ratio descriptors ($r_{\mathrm{F}\mathrm{P}\mathrm{A}}\approx 8.3–8.6\times {10}^2$ and $r_{\mathrm{P}\mathrm{A}}\approx 1.2\times {10}^{-3}$), consistent with the experimentally observed real-block regime. Matched model reactions further indicated faster FPA ring-opening and higher observed epoxide-opening reactivity in a fluorinated aromatic carboxylate model system. These results demonstrate that H-to-F substitution can provide the kinetic bias required to program block copolyester sequence within a single ROAC platform. Full article

Feeds rich in protein are the dominant nitrogen input in intensive aquaculture, yet only part of dietary nitrogen is retained as animal biomass; the remainder enters water and sediment through uneaten feed, feces, dissolved wastes, mucus, sloughed tissue, and branchial ammonia excretion. This review aims to integrate nutritional, physiological, microbial, and disease-related evidence into an evidence-graded framework that positions ammonia nitrogen as a reactive node linking feed, host, water, sediment, and pathogen risk. To assemble this evidence, we conducted a structured narrative search of Web of Science and PubMed for records in English or Chinese published from 2006 to July 2026, with no restriction on publication type, and classified evidence as direct, indirect, or conceptual. The strongest evidence shows that dietary protein supply, amino acid balance, digestibility, and feeding regime regulate nitrogen retention and ammonia output, while microbial ammonification, nitrification, denitrification, dissimilatory nitrate reduction to ammonium, anammox, and assimilation determine whether reactive nitrogen is regenerated, retained, or removed. Experimental studies further show that ammonia impairs oxidative balance, mucosal barriers, immunity, and disease resistance. In contrast, evidence that pathogen infection quantitatively alters nitrogen retention, ammonia excretion, organic nitrogen release, and sedimentary ammonium regeneration remains limited and largely indirect. Accordingly, ammonia nitrogen is framed as a measurable reactive node rather than a unique source or a universally validated causal loop. Practical management should combine precision nutrition with water, biofloc, sediment, and disease surveillance, while future factorial studies and isotope tracer studies should quantify the complete nitrogen budget under pathogen challenge. Full article

Despite the potential of Eragrostis nigra in alleviating forage shortages and controlling soil erosion, there is little information about how nitrogen fertilization and row spacing affect its forage and seed production. Thus, a two-year field experiment was conducted to explore the effects of nitrogen (N) fertilization (0, 45, 90, 135 kg hectare⁻¹ (ha⁻¹)) and row spacing (20 cm, 40 cm) on its forage yield, nutritive value, seed yield and quality. Results showed that forage dry matter yield (DMY) and harvested seed yield (HSY) increased with higher N rates; however, no further enhancement was observed at 135 kg per ha⁻¹, making 90 kg ha⁻¹ optimum. This may be because excessive nitrogen disrupts the carbon–nitrogen metabolic balance. N fertilization increased the relative feed value by enhancing crude protein content and decreasing acid detergent fiber. Relative to the 20 cm spacing, the 40 cm spacing averagely raised PSY and HSY by 34.7% and 21.3%, respectively. The quadratic regression model yielded maximum predicted HSY values of 27.6 and 26.7 kg ha⁻¹, achieved at optimal N rates of 135 and 98.6 kg ha⁻¹ for the 20 cm and 40 cm spacings, respectively. Thousand-seed weight (TSW), germination index, and vigor index were significantly improved by row spacing and N application (p < 0.05). Seed yield enhancement from N was positively correlated with increases in number of fertile tillers (FT), number of spikelets per tiller and seed set (p < 0.05). Furthermore, FT and TSW had the strongest direct effects on seed yield, with standardized path coefficients of 0.362 and 0.390. Our findings highlight that an application rate of 90 kg ha⁻¹ N combined with 40 cm row spacing could be recommended as the efficient agronomic practice for maximizing forage yield, nutritive value, and seed yield in E. nigra. Importantly, this regime also enhances economic returns while reducing N leaching risks, thereby supporting more sustainable forage production systems. Full article

Grinding is the final precision machining step for bearing rings, which induces subsurface gradients in microstructure and mechanical properties. Rolling contact fatigue life and service reliability are directly determined by the gradients. Current research of the grinding metamorphic layer in bearing steels is synthesized in this review. The formation mechanisms, characterization approaches, and the influence of grinding parameters on metamorphic layers is covered. The coupled thermal–mechanical–phase transformation framework encompasses heat-driven phase transformation, high-strain-rate gradient plastic deformation, and their interactions, which collectively govern the formation of the three-layer gradient structure. When the surface temperature exceeds the austenitization threshold, the governing regime shifts from mechanically dominated to thermally dominated, producing an abrupt increase in white layer thickness and concurrent dark layer softening. The capabilities and limitations of characterization techniques for probing the gradient microstructure and residual stress profile are evaluated. The influence of grinding depth, wheel speed, feed rate, wheel characteristics, and cooling conditions on the metamorphic layer is analyzed. The areas requiring deeper investigation are identified. These insights aim to establish correlations between the grinding process and the surface integrity and service performance of bearing components, and to provide directions for future research on the grinding metamorphic layer. Full article

Exposure to stressful low temperatures during development can cause chilling injury, leading to impaired physiological performance. In insects, chilling injury is often associated with metabolic imbalance, oxidative stress and disruption of energy homeostasis, which can collectively compromise survival and growth. Because Tuta absoluta (Meyrick, 1917) is a tomato pest adapted to warm environments, we hypothesized that low-temperature exposure would induce chilling injury by disrupting metabolism and cellular function. We investigated the responses of T. absoluta larvae to three thermal regimes (25, 15 and 5 °C) over a 7-day period, using integrated physiological, metabolomic and transcriptomic analysis. Low-temperature stress reduced survival and feeding performance, accompanied by suppressed digestive enzyme activities (α-amylase, lipase and trypsin) and depletion of glycogen reserves, indicating impaired energy acquisition. In contrast, increased trehalose and proline accumulation suggested a shift toward protective metabolism. Importantly, low temperature induced a pronounced decoupling of energy metabolism and redox homeostasis, characterized by reduced antioxidant capacity (peroxidase; POD and superoxide dismutase; SOD) and elevated levels of reactive oxygen species (ROS). Metabolomic and transcriptomic analysis of stressed larvae revealed alterations in amino acid and carbohydrate metabolism, showing differential regulation of the genes involved in energy production, oxidative stress responses and growth. Integrative analysis demonstrated that metabolic reprogramming and transcriptional regulation are tightly linked under low-temperature conditions, revealing a resource allocation trade-off between growth and stress defense. Together, these findings identify metabolic and redox imbalances as mechanisms underlying cold-induced physiological decline, providing new insight into how low temperature constrains insect performance. Full article

Field vibration monitoring of EMU traction motor bearings is commonly constrained by weak or relative labels, fluctuations in operating conditions, and limited abnormal samples. Under these conditions, learning a normal boundary from a single bearing position may be unstable, and isolated score spikes may lead to unreliable alarms. To address these issues, this study proposes a multi-axle reference and temporal-consistency-enhanced Deep SVDD framework, termed MA-TC-Deep SVDD, for field anomaly detection of EMU traction motor bearings. Unlike closed-set fault diagnosis that requires known fault labels, the proposed framework focuses on identifying deviations from the stable operating regime. First, a compact 10-dimensional time-frequency representation is constructed from valid vibration segments. Second, stable samples from the target bearing position and screened stable samples from other monitored positions on the same EMU are organized as a multi-axle reference set for one-class normal-boundary learning. Third, feature recalibration, temporal-consistency regularization, reference-score standardization, causal smoothing, and consecutive-alarm judgment are incorporated to improve robustness against field disturbances. The anomaly-prior-guided health-state interpretation module is retained only as post hoc evidence for describing severity evolution and does not feed back into the anomaly detection threshold. Field data collected from an in-service EMU over D1–D5 are used for validation. The results show that bearing position 1 has low anomaly scores on D1–D2, exhibits transitional deviation on D3, and shows persistent state deviation on D4–D5, while the other monitored positions remain comparatively stable. Under the current weak-label evaluation protocol, MA-TC-Deep SVDD achieves higher average anomaly detection performance than the compared baseline methods, with AUC = 0.909, AP = 0.872, Precision = 0.887, Recall = 0.802, F1 = 0.843, and FAR = 0.047. These results indicate that the proposed framework can provide field anomaly-warning and severity-oriented interpretation under weak-label monitoring conditions. However, it should not be interpreted as a replacement for disassembly-confirmed fault-type diagnosis or remaining useful life prediction. Full article

Aquaponic systems couple fish and plant production in recirculating loops, yet quantitatively assessed dynamic models for engineering analysis, scale-up, and operation under realistic conditions remain limited. Here, a modular process-based MATLAB R2026a framework is developed for the recirculating aquaponic system operated at the ASTREDHOR facility (France). The model links hydraulic transport with fish metabolism, nitrification, solids removal, and plant nitrate uptake, using monitoring-derived boundary conditions for temperature, dissolved oxygen, pH, and electrical conductivity. The fish-growth component was calibrated and evaluated against archived, temporally reconstructed biomass trajectories derived from campaign-based biometrics in three production cycles with different fish compositions and environmental regimes. Tank-wise $R^2$ values were 0.865–0.952 in the calibration windows and 0.700–0.921 in the fixed-parameter prediction windows, with prediction-period NRMSE values of 0.64–3.91%. These descriptive metrics quantify agreement on the reconstructed evaluation grid rather than performance over independently retained biometric sampling occasions. Complete corresponding time series were unavailable for TAN, ${\mathrm{NO}}_2^{-}$, ${\mathrm{NO}}_3^{-}$, total suspended solids, and plant uptake; these simulated outputs were therefore used only for mechanistic consistency assessment and exploratory scenario analysis, rather than independent validation. Local sensitivity analysis showed limited effects of temperature sensitivity (${\alpha }_T$), optimal temperature ($T_{opt}$), and minimum dissolved oxygen ($D{O}_{min}$) under observed conditions, whereas the feeding ratio ($TR$) and metabolic scaling exponent (n) strongly influenced simulated fish growth and nitrogen loading. Parametric sweeps provided preliminary, model-derived indications of feeding and biofilter-sizing limits under intensified loading; these thresholds require confirmation against independent water-quality measurements. The resulting framework is positioned as an off-line digital shadow with a fish-growth component assessed against reconstructed biomass trajectories and exploratory water-quality simulations. Full article

This article presents an Artificial Intelligence and Internet of Things (AI–IoT/AIoT) decision-making framework for smart water management and sustainable aquaponic systems. The framework connects sensors, IoT telemetry, machine learning algorithms, and real-time monitoring of key water quality parameters, with the aim of early detection of deviations, operational decision support, and risk reduction in system management. A special contribution of the paper is that water is viewed simultaneously as a limiting resource, a biological factor and an operational cost. The proposed framework defines the structure of a decision support system, including monitoring of temperature, pH value, dissolved oxygen, ammonia/ammonium, EC/TDS value, water flow, feeding regime, and basic biological indicators. In the methodological sense, the paper presents a conceptual-methodological framework based on publicly available data, scenario estimates, and a clearly defined protocol for future pilot validation of high-frequency operational data. In addition to the technical architecture, the framework includes elements of responsible application of AIoT systems: data quality control, sensor deviation and drift detection, model explainability through XAI/SHAP, data transfer security, and the possibility of human confirmation before risky interventions. The economic part of the paper shows ROI/NPV as a scenario estimate, based on explicit assumptions about costs, resource consumption and possible operational savings, and not as a confirmed financial result. The framework is aligned with the principles of the circular bioeconomy, as it links the monitoring of water quality, the reduction in nutrient losses, the reuse of resources, and better planning of interventions in aquaculture and aquaponics. The results indicate the potential of AIoT approaches to improve monitoring, transparency and operational decision-making, while the actual effects on productivity, water consumption, food consumption, energy, and economic sustainability must be confirmed in a pilot phase. Full article