Search Results (4,409)

Farmland in the hilly and mountainous regions of southern China is characterized by complex terrain and highly variable operating loads. Conventional diesel-powered tracked harvesters are constrained by high crop losses, excessive impurity rates, frequent blockages, and low overall energy-use efficiency. Distributed electric drive provides a promising solution; however, threshing cylinder blockage, high-frequency load transients, and slope operation make it difficult for conventional energy-management strategies to simultaneously ensure dynamic responses, fuel economy, and battery state of charge (SOC) stability. This study therefore proposes a deep deterministic policy gradient (DDPG)-based reinforcement learning energy-management strategy (RL-EMS) for a range-extended, distributed-drive hybrid tracked combine harvester. First, a full-vehicle dynamic model incorporating eight electric-drive units and strong electromechanical coupling is established. Second, power allocation is formulated as a Markov decision process (MDP), with a multi-objective reward function that accounts for fuel consumption, SOC tracking, and boundary violations; the load-rate-of-change is introduced as a feedforward state. Finally, a supervisory physical layer comprising feasible power projection, safety filtering, and rate limiting is inserted between the policy network output and the physical plant so that the executed command satisfies range extender power, battery SOC, current, and power-slew constraints. Under the standard 1000 s cycle, SOC-corrected energy-equivalent comparison shows that the RL-EMS reduces fuel consumption by 1.5% relative to the adaptive equivalent consumption minimization strategy (A-ECMS) and by 26.6% relative to the constant-torque energy-management strategy (CT-EMS). Under an unseen complex random cycle, the RL-EMS reduces fuel consumption by 5.1% relative to A-ECMS. It also suppresses DC-bus voltage sag during a threshing cylinder blockage transient, demonstrating favorable electromechanical transient response. The proposed method provides a modeling and control reference for the intelligent energy management of range-extended, distributed-drive agricultural machinery. Full article

Plant wearable sensors have emerged as a transformative technology for precision agriculture and plant phenotyping, enabling in situ, real-time, and continuous acquisition of physiological signals from plant surfaces or internal tissues. However, existing reviews have organized the literature by monitoring targets, sensing functions, or material platforms, without systematically comparing technologies from the fundamental dimension of the degree of intervention imposed on plants. Drawing on representative studies identified through a structured literature search, this review establishes a three-tier classification framework—invasive, minimally invasive, and non-invasive—and conducts a head-to-head comparison across six dimensions: signal characteristics, plant disturbance, long-term stability, manufacturing complexity, field deployability, and biosafety. The results reveal that invasive sensors (nanobionic probes, implantable microelectrodes, and organic electrochemical transistors) achieve nM–pM detection limits, yet wound responses generally limit their effective monitoring duration to the order of days; non-invasive sensors (flexible patches, strain sensors, and multimodal platforms) support weeks-to-months of continuous monitoring and are amenable to scaled deployment, but the indirectness of surface signals confines detection limits to the μM level; minimally invasive technologies (microneedle arrays and ultra-thin microelectrodes) offer a compromise between the two extremes. On this basis, a decision framework based on three-layer selection is proposed to guide technology selection across laboratory research, field deployment, and controlled environment agriculture. Future efforts should focus on standardized performance evaluation protocols, biodegradable self-powered systems, and the integration of invasive–non-invasive hybrid sensing networks with plant digital twins. Full article

Mulberry (Morus spp.) leaves, fruits, branches, and root bark are rich in bioactive compounds, including 1-deoxynojirimycin (1-DNJ) and γ-aminobutyric acid (GABA), supporting their potential use in food, medicinal, and feed applications. Their high moisture content, however, makes fresh materials highly susceptible to postharvest quality deterioration, making drying essential for stabilization and high-value utilization. Drying technologies involve trade-offs among efficiency, energy consumption, sensory quality, rehydration, and bioactive-compound retention. This review provides a comprehensive overview of pretreatment and drying technologies for mulberry materials, with particular attention to differences in raw-material characteristics, processing conditions, analytical methods, and reporting bases that limit direct comparisons among studies. Current evidence suggests that low-temperature, low-oxygen, or short-duration technologies, including vacuum freeze-drying, microwave drying, and microwave-vacuum drying, may better preserve quality in certain thermosensitive products, although their benefits remain product- and process-dependent. Hot-air, solar, infrared, heat-pump, and hybrid drying remain practical options for bulk products but require optimization to balance quality, energy efficiency, and scalability. For juice and functional powders, carrier selection, powder properties, and bioaccessibility require further study. Overall, the available evidence is heterogeneous, and some conclusions rely on limited mulberry-specific data or extrapolation from related plant matrices. Future research should emphasize standardized quality evaluation, harmonized reporting, kinetic modeling, multi-objective optimization, online monitoring, energy and carbon-footprint assessment, and industrial-scale validation. Full article

Globba spp. is a perennial herbaceous plant of the Zingiberaceae family, characterized by its unique floral architecture and diverse coloration with high ornamental and medicinal value. However, research on the genetic basis of Globba is still insufficient, and only a limited number of molecular markers have been developed so far, which has greatly hampered the progress of its molecular breeding. Herein, we performed the first deep identification of genome-wide SSR markers based on the whole-genome data of G. sherwoodiana. A total of 276,809 SSR loci were identified with an average density of 189.24–297.49 SSRs/Mb within each chromosome. Mononucleotide repeat loci were most abundant, accounting for 58.95% of all SSRs, with dinucleotide and trinucleotide repeats accounting for 19.83% and 19.27%, respectively. Using G. sherwoodiana ‘MJ16’ and G. winitii C.H. Wright as parental lines, we constructed a hybrid F₁ population containing 173 individual plants. The coefficient of variation (CV) of the 10 phenotypic traits ranged from 13.09% to 27.43%, exhibiting a normal distribution. Phenotypic traits including terminal leaf width, inflorescence length, inflorescence width, secondary-inflorescence pedicel length, number of ornamental bracts, and basal inflorescence-bract length showed abundant variation, with all CV values exceeding 20%. Moreover, 27 polymorphic genomic SSRs (gSSR) were screened from the synthesized 192 primer pairs, amplifying a total of 203 alleles. On average, each marker detected 7.52 polymorphic loci, with a mean effective allele number of 3.63 and a mean polymorphism information content of 0.64, reflecting a relatively rich genetic diversity within the population. Through phenotype–marker association analysis, six gSSR loci were found to be significantly associated with six phenotypic traits. The highest interpretation ratio (16.86%) was observed for basal inflorescence-bract width. Three loci (gSSR48, gSSR105 and gSSR184) were simultaneously associated with more than two phenotypic traits, indicating a pattern consistent with pleiotropy or linkage. The informative gSSR markers and the association analysis results in this study provide an effective theoretical basis for germplasm identification, genetic diversity evaluation, and marker-assisted breeding of Globba. Full article

Leaf senescence is a pivotal developmental program in plants, precisely regulated by diverse signals including salicylic acid (SA). The Arabidopsis VQ protein AtVQ25 has been previously characterized as a positive regulator of SA-mediated leaf senescence, functioning through interaction with AtWRKY53 to relieve the transcriptional self-repression of AtWRKY53 at its own promoter. However, the upstream regulatory mechanisms governing AtVQ25 itself remain elusive. In this study, a yeast library screening was performed, and the mitogen-activated protein kinases AtMPK1 and AtMPK2 were identified as interacting partners of AtVQ25. The direct physical interaction was validated by yeast two-hybrid (Y2H), luciferase complementation imaging (LCI), pull-down, and co-immunoprecipitation assays (Co-IP). Furthermore, AtVQ25 was shown to be directly phosphorylated by AtMPK1/AtMPK2, which enhanced its protein stability and retarded its degradation, thereby positively modulating leaf senescence progression. Genetic analyses revealed that the function of AtVQ25 in SA-mediated leaf senescence depends on the functional integrity of AtMPK1/AtMPK2. Collectively, these findings establish AtMPK1/AtMPK2 as upstream interactors of AtVQ25 that coordinate SA-mediated leaf senescence through phosphorylation-dependent enhancement of AtVQ25 protein stability, providing novel insights into the upstream regulatory circuitry of VQ proteins. Full article

A detailed understanding of the molecular mechanisms governing the flowering time of Amomum villosum Lour., a medicinal plant within the Zingiberaceae family, is currently lacking. In modern plants, the florigen activation complex (FAC), which includes PEBP, FD/bZIP, and GRF proteins, is known to regulate flowering. In this study, we identified 13 PEBP, 5 FD, and 19 GRF genes within the A. villosum genome and conducted phylogenetic, structural and promoter analysis. Notably, cross-species protein–protein interaction predictions and yeast two-hybrid assays uncovered an unexpected interaction pattern: an AREB3-like FD protein (AvFD5) and a GRF protein (AvGRF13) directly interact with specific PEBP members, whereas canonical FD-like proteins (AvFD1 and AvFD4) did not, which contrasts with the classical rice FAC model (Hd3a-14-3-3-OsFD1). These results imply that FAC assembly in A. villosum may involve alternative components or regulatory mechanisms, potentially indicating lineage-specific divergence within monocots. This research represents the first systematic characterization of FAC core gene families in A. villosum and Zingiberaceae, laying the groundwork for understanding flowering time regulation and facilitating future molecular breeding efforts in this economically significant plant. Full article

Groundcover chrysanthemums exhibit inherently poor low-temperature adaptability, making them highly vulnerable to cold stress and thereby constraining their large-scale regional cultivation. Interspecific hybridization was conducted between diploid Chrysanthemum lavandulifolium (Fisch. ex Trautv.) Makino (pollen donor) and hexaploid cultivar Chrysanthemum × morifolium Ramat., cv. ‘Yannong Qiujin’ (seed parent) to generate cold-tolerant groundcover chrysanthemum germplasm. Comprehensive physiological profiling and integrated multi-omics analyses were performed on both parental plants and their hybrid progeny to identify candidate molecular pathways associated with cold adaptation in ploidy-divergent Chrysanthemum hybrids. Hybrid progeny exhibited significantly lower semi-lethal temperature (LT₅₀), malondialdehyde (MDA) content, and relative electrical conductivity compared with the female parent, indicating heterosis-mediated enhancement of cellular membrane integrity. Integrated transcriptomic and metabolomic profiling identified significant enrichment of glycerophospholipid metabolism in the hybrid progeny. Upregulated expression of glycerol-3-phosphate acyltransferase (GPAT) and lysophosphatidic acid acyltransferase (LPAAT) genes promoted substantial accumulation of lysophosphatidic acid (LPA) and phosphatidic acid (PA). We found that higher expression levels of GPAT and LPAAT were positively associated with increased accumulation of LPA and PA. PA further contributes to phosphatidylcholine (PC) synthesis, potentially improving plasma-membrane permeability and sustaining plasma membrane integrity under chilling stress. Meanwhile, transcript abundance of the inducer of CBF expression (ICE), cold-regulated (COR) genes were markedly elevated. This study identifies candidate molecular pathways underlying cold adaptation in hybrid progeny derived from ploidy-divergent Chrysanthemum, thereby providing a robust theoretical foundation for breeding novel cold-tolerant chrysanthemum cultivars. Full article

Cement production generates approximately 7% of global CO₂ emissions; Jordan’s kilns run on petroleum coke, coal, and olive residue with no demonstrated solar thermal integration for pre-heating. This paper addresses that gap and presents the first plant-data-backed techno-economic and environmental assessment of a hybrid dual-stage concentrating solar thermal system integrated into the pre-heater tower of a cement plant in Ma’an, Jordan (30.2° N, direct normal irradiance (DNI) = 2749 kWh/m²/yr). The study objectives are to (i) characterize Stage 5 and Stage 6 thermal loads from real plant operating data; (ii) size and simulate a 6000 m² parabolic trough collector (PTC) field with molten-salt thermal energy storage (TES) targeting Stage 5 (380 °C, 3200 kW) and an 8000 m² Linear Fresnel Reflector (LFR) field with rock/PCM TES targeting Stage 6 (300 °C, 2200 kW); (iii) quantify the combined solar fraction, fuel savings, and CO₂ avoidance; (iv) conduct a Jordan-specific cost analysis benchmarked against NREL 2015 solar heat for industrial process (SHIP) data under three CAPEX scenarios; and (v) evaluate net present value (NPV) at two discount rates with and without carbon credits. The PTC field achieves a Stage 5 solar fraction of 35.5%, and the LFR field achieves 50.0% at Stage 6, yielding a combined annual solar fraction of 41.4% and displacing 59,384 GJ/yr (4163 t CO₂/yr). Under Jordan base-case CAPEX of $3.89 M, 6% concessional finance, and a $50/t CO₂ carbon credit, NPV reaches +$1.11 M with a breakeven carbon credit of $29/t CO₂. These results confirm commercial viability under accessible green finance and carbon pricing, providing a replicable model for cement kiln pre-heater decarbonization in high-DNI Middle East and North Africa (MENA) countries. Full article

Nanomaterials have emerged as key platforms for environmental remediation owing to their tunable surface chemistry, high specific surface area, and multifunctional physicochemical properties. This review provides a critical comparison between conventional nanomaterials (CNMs) and phyto-mediated nanomaterials (PMNs), with particular emphasis on material design, surface chemistry, pollutant removal mechanisms, environmental performance, and sustainability. CNMs, including metal and metal oxide nanoparticles, carbon-based nanomaterials, and hybrid nanocomposites, offer excellent adsorption, photocatalytic, redox, and antimicrobial performance but remain constrained by concerns regarding toxicity, environmental persistence, and energy-intensive synthesis. PMNs provide a greener alternative by integrating plant-derived surface chemistry with nanomaterial functionality, potentially reducing reliance on hazardous synthesis reagents while modifying interfacial interactions relevant to environmental remediation. The review critically discusses the mechanistic roles of adsorption, surface complexation, photocatalytic degradation, electron-transfer processes, and reactive oxygen species (ROS) generation in pollutant removal. Recent advances in water purification, soil and groundwater remediation, carbon sequestration, and climate-related environmental applications are comprehensively summarized. Finally, current challenges associated with reproducibility, scalability, environmental safety, life-cycle assessment, and regulatory considerations are critically analyzed, together with future perspectives toward the rational design of sustainable nanomaterials for next-generation environmental remediation technologies. Full article

Seawater reverse osmosis (SWRO) desalination is electricity-intensive, making its environmental performance highly dependent on the power supply. This study couples site-specific energy-system modelling with life cycle assessment to examine how renewable integration changes both total impacts and life-cycle hotspots. A modelled SWRO plant with a specific electricity consumption of 3.4 kWh m⁻³, derived from a process model for Mediterranean feedwater at 48% recovery with energy recovery devices, was assessed in Gela and Trapani, two grid-connected coastal sites with contrasting wind resources, and Lipari, a non-interconnected island with carbon-intensive backup generation. Grid-only, photovoltaic (PV)-grid, wind-grid, and PV-wind-grid configurations were modelled in HOMER Pro without storage and with excess electricity limited to 13%, then evaluated in SimaPro using Environmental Footprint 3.1. Hybrid configurations supplied 46.7%, 64.4%, and 53.3% renewable electricity in Gela, Trapani, and Lipari, reducing climate-change impacts by 30%, 42%, and 45%, respectively. Renewable integration also lowered fossil resource use, whereas PV-containing scenarios increased land and mineral/metal resource use. As electricity-related impacts declined, chemical consumption became the main non-energy hotspot, particularly for ecotoxicity, freshwater and eutrophication. Environmental performance therefore depends not only on renewable penetration, but also on technology choice and the residual electricity supply. Comprehensive system boundaries are essential when planning lower-carbon desalination for coastal and island water security. Full article

The global energy landscape is undergoing a low-carbon, diversified, and high-efficiency transition, creating an urgent need to develop intelligent operation and maintenance (O&M) technologies for critical nuclear power equipment to boost plant economic efficiency. As the core “heart” component of the primary loop, the reactor coolant pump (main pump) must meet extremely stringent reliability criteria to ensure safe and stable operation of nuclear facilities. Existing remaining useful life (RUL) prognostics for main pumps mostly output deterministic point estimates; they fail to quantify predictive uncertainties and cannot provide credible risk intervals to support maintenance decision-making. To fill this research gap, this study first performs coupled thermomechanical failure simulations for three vulnerable main pump components: the rotor shaft assembly, double-cone sealing structure, and motor shielding sleeve. Simulation results are validated via tests on a full-scale main pump prototype bench to extract sensitive degradation characteristic parameters. Accordingly, a hybrid Bayesian Transformer-LSTM prognostic framework is proposed for main pump RUL prediction with built-in uncertainty quantification. Data augmentation is utilized to expand multi-source degradation datasets of main pumps. The Mahalanobis distance is employed to build component-level health indicators (HIs), and a cloud barycenter weighted evaluation method fuses these sub-component HIs into a unified system-level comprehensive health index (CHI). Using the fused CHI as model input, the Bayesian Transformer-LSTM architecture incorporates probabilistic fully connected layers to simultaneously capture local time-series fluctuations and long-term global degradation trends, enabling joint RUL regression and uncertainty quantification. A full-scale main pump prototype from an in-service nuclear power plant is used to validate the multi-source data fusion strategy. Quantitative evaluation results show that the proposed method achieves a coefficient of determination R² = 0.997, root mean square error (RMSE) = 0.018, and prediction interval coverage probability (PICP) = 0.839. Comparative ablation experiments further confirm that the proposed model delivers outstanding fitting precision and reliable uncertainty quantification, enabling long-timescale full-lifecycle health characterization of main pumps. Full article

Hybrid renewable energy systems (HRESs) have emerged as a promising solution for improving the sustainability and energy independence of critical infrastructure with continuous electricity demand. This study presents a techno-economic optimization framework for a hybrid photovoltaic (PV), wind turbine (WT), battery energy storage system (BESS), and pumped storage hydropower (PSH) configuration designed to supply a wastewater treatment plant (WWTP). The optimization simultaneously minimizes the levelized cost of energy (LCOE) and renewable energy surplus while satisfying a predefined Maximum Allowed Deficiency (MaxDef) reliability constraint. The framework is validated using two years (17,554 hourly records) of measured meteorological and operational data collected from the Ariel University wastewater treatment plant. The results demonstrate that the coordinated operation of BESS and PSH significantly improves renewable energy utilization and system reliability while reducing excess energy generation. For the investigated case study and the adopted technical and economic assumptions, an energy storage of approximately 25 kWh provides the most favorable techno-economic balance between investment cost, renewable energy utilization, and reliability. However, the optimal battery capacity is site-specific and may vary depending on local renewable resources, load characteristics, economic conditions, and reliability requirements. The proposed optimization framework provides a practical methodology that can be adapted to the design of reliable hybrid renewable energy systems for wastewater treatment plants and other critical infrastructure by incorporating site-specific operational and environmental data. Full article

The increasing penetration of variable renewable energy sources into electric power systems requires advanced optimization tools to address the complexity of hybrid hydrothermal scheduling while minimizing generation costs and carbon emissions. This study investigates the application of four deep learning architectures—Kolmogorov–Arnold networks (KANs), long short-term memory (LSTM), gated recurrent unit (GRU), and deep feedforward (DFF)—to solve the hydrothermal scheduling problem in hybrid power systems that incorporate wind power generation and pumped-storage hydropower (PSH) plants. The methods were evaluated on a 10-generator test system over a 24-h planning horizon in three objective-weighting scenarios, considering economic dispatch only, pure emission minimization only, and balanced objectives. All architectures successfully solved the integrated problem and satisfied the system constraints. This study reports the first application of the KAN to the hydrothermal scheduling problem, demonstrating its viability and interpretability potential for future applications in electric power systems. Full article

Proline dehydrogenase (ProDH) is a rate-limiting enzyme in the proline metabolism cycle that plays an important role in plant stress response and disease resistance. However, the function and mechanism of ProDH1 in the wheat–Puccinia striiformis f. sp. tritici (Pst) interaction remain poorly understood. In this study, a wheat proline dehydrogenase gene TaProDH1 was induced by stripe rust. qRT-PCR analyses showed that the transcript levels of TaProDH1 were highly increased during early infection. The transient expression of TaProDH1 in Nicotiana benthamiana leaves revealed that TaProDH1 increases the content of proline in leaves and induces the accumulation of reactive oxygen species. Silencing TaProDH1 via the barley stripe mosaic virus (BSMV)-induced gene silencing (VIGS) system led to compromised wheat resistance to the Pst avirulent pathotype CYR23, significantly increased proline content in the plant, and increased mycelial growth and sporulation of the pathogen. In addition, it was confirmed by the yeast one-hybrid (Y₁H) and dual-luciferase reporter systems that the transcription factor TaMYB30 can activate its transcriptional activity by binding to the TaProDH1 promoter region. In summary, the TaProDH1 gene is a positive regulator of stripe rust resistance in wheat. It is activated by the upstream transcription factor TaMYB30 and accelerates the process of proline metabolism, which is conducive to enhancing the resistance of wheat to stripe rust. Full article

Rind color is an important quality trait in zucchini (Cucurbita pepo). As a core transcription factor in plant pigment biosynthesis, APRR2 plays a conserved yet mechanistically diverse regulatory role in the formation of rind color in various vegetables. However, the APRR2 transcription factor regulates rind color but has not been systematically identified in C. pepo. In this study, 50 APRR2 genes were defined by the presence of the conserved REC domain verified. These genes were identified and found to be unevenly distributed across the 20 chromosomes, primarily expanded through tandem duplication events. Phylogenetic and structural analysis classified these genes into three distinct subgroups, all featuring the conserved REC domain essential for pigment regulation but exhibiting variations in motifs and intron–exon structures. Promoter analysis revealed abundant light-responsive, hormone-responsive and stress-responsive elements that may contribute to environmental adaptation and photomorphogenesis. Crucially, transcriptome analysis during rind development (0 and 10 days after pollination) in green (GR) and white (WR) rind lines demonstrated profound functional divergence. Expression clusters indicated temporal shifts in metabolism and enriched “circadian rhythm-plant” and “photosynthesis” pathways in WR at 0 DAP. Specific APRR2 genes were tightly correlated with rind color. qPCR validation of the 24 selected APRR2 genes classified them into four trend groups based on the direction of expression change at 10 DAP. In total, 14 genes were upregulated in both GR and WR, 6 were downregulated in both lines, 1 was upregulated in GR but downregulated in WR, and 3 were downregulated in GR but upregulated in WR. Furthermore, protein–protein interaction prediction and yeast two-hybrid (Y2H) assays detected a physical interaction in yeast between a core APRR2 protein and a bHLH62 transcription factor, suggesting a potential interaction that may be involved in rind color regulation, pending in planta validation. The study first identified the members of the APRR2 gene family in C. pepo and conducted a bioinformatics analysis on them. The study establishes the molecular basis of APRR2 function and offers valuable resources for breeding improved C. pepo varieties. Full article