Search Results (3,320)

The aim of this study was to optimize the pre-harvest application of seaweed-based biostimulants (BIOs) to improve potato yield, quality, and post-storage attributes. To achieve this, six potato cultivars differing in maturity periods were subjected to three BIO concentrations (2.4, 7.2, and 9.6 g/L), application timings (from planting to post-bloom), and frequencies (1–4 applications). The results indicated that BIO efficacy was strongly cultivar-specific. Responsive cultivars (such as ‘Minshu No. 1’, ‘Leshu No. 1’, ‘Xiazhai 65’, and ‘Qingshu No. 2’) showed significant yield increases when treated with 7.2 or 9.6 g/L BIO applied 3 to 4 times during key developmental stages (from seedling to 3 weeks after full bloom). In contrast, ‘Qingshu No. 10’ and ‘Qingshu No. 9’ exhibited no significant response. Post-storage analysis revealed that optimal BIO treatments in responsive cultivars (‘Minshu No. 1’ and ‘Xiazhai 65’) delayed the losses in dry matter content and starch content after 3 months of storage at 7.5 °C. Furthermore, BIO application significantly enhanced the uptake of potassium (K) in ‘Minshu No. 1’, ‘Xiazhai 65’, and ‘Qingshu No. 2’ potatoes. In conclusion, applying 7.2 g/L BIO three times during the seedling to post-bloom period is a potential strategy to enhance yield and quality for responsive early- and mid-maturing potato cultivars, offering a targeted solution for sustainable potato production. Full article

The field study was conducted between 2016 and 2018 by the Department of Agronomy at Poznań University of Life Sciences. The experiment took place at the fields of the Research and Education Centre in Gorzyń, Złotniki branch. It was a single-factor trial involving six different sowing dates of an ultra-early maize cultivar: A1—12 April, A2—26 April, A3—10 May, A4—24 May, A5—7 June, and A6—21 June. The cultivar ‘Pyroxenia’ was used in the study. It is characterized by very early maturity (FAO 130), rapid early growth, and intensive stem elongation. In the present study, the optimal sowing time for the maize variety ‘Pyroxenia’ was late April (A2) and early May (A3). Later sowing of this variety resulted in a reduction in fresh and dry matter yields, as well as a reduction in the quality of the feed. The difference between the first (A1) and the last sowing date (A6) resulted in a 47% reduction in fresh weight and a 49% reduction in dry weight yield. No effect of sowing date was observed on starch content or structural carbohydrates, including crude fiber and its fractions (NDF, ADF, and ADL), in maize forage intended for ensiling. Data analysis for the years 2016–2018 showed that air temperature and precipitation had a significant effect on fresh and dry straw weight yields. Partial factor productivity of nitrogen (PFPF_N) decreased with delayed sowing of maize. On average, this parameter for maize sown in June compared with April, was lower by 38.8% for straw dry yield, 54.5% for ear dry yield, and 46.3% for whole-plant dry yield. Full article

The production of “winter wines” in south-eastern Brazil, enabled by the double pruning technique, requires precise assessment of grape technological maturity to ensure high-quality outputs. However, conventional monitoring approaches are destructive, labor-intensive, and limited in their ability to capture vineyard spatial variability. This study aimed to develop and validate a non-destructive predictive framework for Soluble Solids (°Brix) and Titratable Acidity (TA) by integrating spatial remote sensing data with temporal agrometeorological information. Multispectral imagery was acquired via an unmanned aerial vehicle in a vineyard cultivated with Sauvignon Blanc and Syrah, from which vegetation indices were derived and combined with Growing Degree-Days to train machine learning models, including Random Forest, Multilayer Perceptron, and XGBoost. The incorporation of agrometeorological data significantly improved predictive performance compared to models based solely on vegetation indices. Among the tested algorithms, XGBoost achieved the highest accuracy, with coefficients of determination of 0.89 for °Brix and 0.77 for TA, achieved by XGBoost on an independent hold-out test set. Model interpretability analysis indicated that Growing Degree-Days and cultivar were the primary drivers of maturation dynamics, while vegetation indices refined predictions by accounting for spatial variability in plant vigor. Overall, the proposed approach represents a promising proof-of-concept framework for non-destructive maturity monitoring in precision viticulture, supporting improved monitoring of grape maturation. However, multi-season validation across diverse vineyard conditions is required to confirm its generalizability and support its application as a routine decision-support tool. Full article

Heavy metal (HM) contamination near mining operations in Kazakhstan poses a serious threat to the environment. However, data on the state of chernozem soils in this region is limited. This study assessed the bioaccumulation of HMs and translocation within the soil–sunflower (Helianthus annuus L.) system in a southern Calcic Chernozem in the Kostanay region (Northern Kazakhstan), which is located 50 km from the nearest mining facility. The content of seven HMs (Cd, Co, Cr, Cu, Ni, Pb, and Zn) and arsenic (As), as well as five macroelements (K, Ca, S, Mg, and P), was determined in 18 soil samples from the complete soil pedon (0–150 cm) and in eight anatomical parts of six sunflower plants at physiological maturity. Most metals exhibited a deficiency relative to upper continental crustal Clarke values (Clarke of Concentration (CC) < 1 for Cr, Cu, Ni, Pb, and Zn), with a moderate lithogenic anomaly for Cd (CC = 1.65–3.57) and a localized Co anomaly in the Bk horizon (56.26 mg kg⁻¹), indicating no pronounced HM contamination at the investigated agricultural site. Metal distribution exhibited strong organ specificity in sunflower plants. Cd, Cu, and Zn accumulated preferentially in the leaves, whereas Ni and Co were more concentrated in the seeds and stems, respectively. Only cadmium exceeded the threshold values for both BCF > 1 (1.01) and TF > 1 (1.47), confirming the status of sunflower as a cadmium accumulator. These results provide a preliminary reference dataset of the organ-specific distribution of heavy metals in H. annuus L. plants, which can serve as a local baseline for sunflower growth in uncontaminated southern Chernozems. This information can contribute to future environmental monitoring purposes in the region, acting as an exploratory benchmark. Full article

Selective harvesting of perilla (Perilla frutescens) leaves requires the repeated removal of mature outer leaves while preserving the central growing region, including the apical meristem and immature inner leaves, on the same plant. Conventional harvesting end-effectors developed for fruits or whole-head leafy vegetables are not directly applicable to this task because of the dense leaf arrangement, thin and flexible leaf blades, and the need to protect the central growing region. This study proposes and evaluates a dual-module end-effector that integrates a central growing region protection and stem-support (CPS) module with a petiole grasping (PG) module using pneumatic soft pads and a scissor-lift mechanism for petiole tensile separation. FSR-based pneumatic-pressure calibration and photoelectric-sensor-based position-adaptive stopping control were implemented to reduce grasping damage and accommodate plant-to-plant variation in petiole height. The developed end-effector was evaluated using mock perilla plants under aligned, rotated, and overlapped leaf conditions and cultivated perilla plants over two harvesting sessions separated by two weeks. In the mock-plant experiment, the system achieved an attempt success rate of 96.7% and a leaf harvest rate of 98.3%. In the real perilla experiment, it achieved an attempt success rate of 88.5% and a leaf harvest rate of 90.4%. The target-leaf damage rate was 2.1%, and no damage was observed on the main stem. In the second harvesting session, the system maintained an attempt success rate of 91.7% for newly developed leaves on the same plants. These results indicate that the developed end-effector can selectively harvest mature perilla leaves with low damage while preserving plant structures required for continued growth. Full article

At present, photovoltaic (PV) systems are becoming the core of low-carbon power systems, but their large-scale integration is still limited by weather-driven intermittency, heterogeneous data, equipment failures, operational uncertainty, and life-cycle sustainability requirements. Unlike specific task reviews that only focus on photovoltaic forecasting, fault diagnosis, or general artificial intelligence applications in renewable energy, this review develops an integrated data-driven perspective for machine learning and artificial intelligence in photovoltaic power generation systems. It links data governance, feature engineering, prediction, and uncertainty quantification, fault diagnosis and predictive maintenance, energy management, market participation, and carbon-aware optimization within a framework for photovoltaic systems. This review indicates that traditional machine learning, deep learning, graph learning, reinforcement learning, generative artificial intelligence, and physics-based artificial intelligence are suitable for different photovoltaic tasks based on data structure, time range, operational constraints, and deployment maturity. The main contribution is cross-task integration, which links the output of artificial intelligence models, including scheduling, storage scheduling, maintenance planning, virtual power plant operation, and low-carbon management, with actual decision-making. The review further identified the most critical deployment barriers, such as incomplete benchmarks, weak cross-site generalization, insufficient uncertainty calibration, limited interpretability, network security risks, and computational costs. The resulting methodological approach emphasizes data management, uncertainty awareness, physical constraints, decision orientation, and sustainability-driven photovoltaic intelligence. Full article

Understanding evapotranspiration (ET) partitioning into soil evaporation (E) and plant transpiration (T) is crucial for improving agricultural water use efficiency in water-scarce regions. The isotope mass balance (IMB) method and AquaCrop model are two widely used approaches for ET partitioning, yet their comparative performance across different crop growth stages remains poorly characterized. This study systematically compared these two methods using two consecutive years (2012–2013) of field isotopic observations in a summer maize field on the North China Plain, a core maize production area facing severe agricultural water scarcity. Stable isotope analysis showed that the local meteoric water line (LMWL) had a slope lower than the global meteoric water line. The 0–5 cm surface soil water evaporation lines had slopes of 5.84 (2012) and 8.06 (2013), confirming significant evaporative enrichment in the topsoil. Plant water isotopic composition closely resembled that of 40–100 cm deep soil water, indicating limited root uptake from the surface layer. IMB-estimated transpiration ratio (T/ET) exhibited distinct phenological patterns, increasing from 37 to 44% at jointing to a peak of 94–96% at filling, then declining to 84–85% at maturity. The two methods agreed well during filling to maturity (differences of 2–10%), but compared with the IMB method, AquaCrop substantially underestimated T/ET at jointing (0.9% vs. 43.8% in 2013) due to its canopy-cover-based transpiration algorithm. These findings identify the filling stage as the critical water demand period, providing a quantitative reference for precision irrigation management under similar climate and soil conditions. Full article

Neuroinflammation, driven by microglial activation and oxidative stress, is a key pathological feature of various neurodegenerative diseases. Dendropanax morbifera Léveille (DM) is a medicinal plant known for its diverse pharmacological activities; however, the influence of leaf developmental stage on its neuroprotective potential remains poorly understood. In this study, we compared the phytochemical profiles of young DM (YDM) and mature DM leaves and evaluated their effects on neuronal metabolism and microglia-mediated neuroinflammation. HPLC analysis revealed that YDM contained approximately 2.4-fold higher levels of chlorogenic acid than DM, while DM exhibited higher quercetin content. In differentiated N2A neuronal cells, YDM treatment significantly upregulated the expression of key metabolic and mitochondrial regulators, including PGC-1α, PPARγ, and CPT2, suggesting enhanced mitochondrial and metabolic regulatory signaling related to biogenesis and fatty acid β-oxidation. Under inflammatory conditions, YDM more potently suppressed the secretion of pro-inflammatory cytokines (IL-6 and TNF-α) in LPS-stimulated BV2 microglia compared to DM. Furthermore, in N2A cells treated with BV2-conditioned medium, both extracts effectively mitigated reactive oxygen species production and restored brain-derived neurotrophic factor expression. These findings demonstrate that leaf age is a critical determinant of the phytochemical composition and biological activity of DM. Our results suggest that chlorogenic acid-rich YDM preparations may offer superior therapeutic advantages in targeting neuroinflammatory and metabolic dysregulation in the central nervous system. Full article

Vegetative propagation is widely used in forest plantations to propagate elite genotypes with traits of economic or ecological interest. However, the loss of the ability to form adventitious roots is a dramatic effect of tree age and maturation and represents a major limitation for the clonal propagation of high-quality genotypes. This review describes the evolution of our understanding—from the traditional role of plant growth regulators to current findings on cellular signaling—regarding the maturation-related decline of adventitious rooting in forest species. Evidence suggests that interactions between the cell wall, plasma membrane, and cytoskeleton play a key role in this process. Specific and dynamic modifications of the interactions between the cell wall and cytoskeleton could be possible targets for hormonal, developmental, environmental, and epigenetic regulation associated with the maturation-related decline in adventitious root formation. Full article

Chronological age tells us how long a person has lived—but not how well. Two individuals of the same age can differ dramatically in their cellular health, disease risk, and functional capacity. This gap between calendar age and biological age has driven growing interest in biomarkers that reflect true cellular aging rather than years lived. Mitochondria sit at the heart of this problem. Far more than cellular power plants, these organelles govern energy production, oxidative stress, immune signaling, and programmed cell death. As the body ages, mitochondria deteriorate in consistent and measurable ways—and crucially, these changes can be detected in circulating blood cells, offering a minimally invasive window into the body’s biological age. This narrative review synthesizes two decades of research (2005–2025) on three blood-based mitochondrial markers: mitochondrial DNA copy number (mtDNA-CN) in peripheral blood mononuclear cells, mitochondrial membrane potential (MMP), and cell-free mitochondrial DNA (cf-mtDNA) in plasma. Across 68 carefully selected studies, we evaluate the strength, consistency, and clinical relevance of each marker, alongside their associations with cardiovascular disease, metabolic dysfunction, cognitive decline, and mortality. The evidence is promising but still maturing. Significant methodological variation across studies limits direct comparisons, and robust prospective outcome data remain limited. We propose a four-phase framework for responsible clinical translation and identify specific research investments needed—from measurement standardization to large cohort studies and intervention trials—before these markers can responsibly inform patient care. Full article

The increasing demand for cleaner coloration of synthetic textiles has renewed interest in plant-based dyes; however, limited dye–fiber affinity and shade durability remain major challenges in nylon dyeing. This study developed a mordant-free dyeing process for nylon fabric using Terminalia catappa leaf extract by optimizing extraction pH, dyebath pH, dyeing time, and temperature. Mature T. catappa leaves were aqueously extracted under acidic, neutral, and alkaline conditions, followed by systematic dyeing optimization. The dyed fabrics were evaluated using color strength (K/S), CIELAB color coordinates, standard fastness tests, FTIR spectroscopy, and FE-SEM analysis. Alkaline extraction followed by acidic dyeing produced the highest dye uptake. The optimum condition was extraction pH 11, dyebath pH 3, 120 °C, and 60 min, yielding a maximum K/S value of 21.94 with ΔL, Δa, and Δb values of −54.25, 8.03, and 24.92, respectively. FTIR and SEM results supported possible dye–fiber interaction through hydrogen bonding, electrostatic attraction, and surface/inter-filament dye deposition without obvious fiber damage. The optimized fabric showed very good washing, dry rubbing, perspiration, and saliva fastness, good light fastness, excellent ultraviolet protection, and slightly lower wet rubbing fastness. Overall, T. catappa leaf extract offers a promising mordant-free route for durable nylon coloration. Full article

Maintenance plays an increasingly strategic role across industrial sectors, influencing not only asset availability and operational efficiency but also environmental and social performance. However, assessing the sustainability of maintenance practices remains a cross-disciplinary challenge due to the absence of integrative, broadly applicable evaluation frameworks. This study introduces the Tripartite Business Model Canvas (T-BMC), a novel diagnostic instrument that reconceptualises maintenance as a business model structured around the Triple Bottom Line. By embedding each of the nine Business Model Canvas blocks within the economic, environmental, and social dimensions, the T-BMC yields 27 analytical elements, operationalised through a 24-item assessment instrument after three design consolidations to avoid construct redundancy. The instrument supports maturity assessment, benchmarking, and continuous improvement across diverse industrial contexts. An exploratory pilot study with 17 maintenance experts from the Chilean seawater desalination sector assessed its applicability, internal coherence, and contextual relevance. The overall perceived maturity level was 3.96 out of 5, with the Social dimension scoring highest (4.10) and the Economic dimension lowest (3.88); at the block level, Key Activities (4.27) and Value Propositions (4.10) were the strongest areas, while Channels (3.74) and Revenue Streams (3.78) revealed the main sustainability gaps. Internal consistency was strong (Cronbach’s α = 0.94 overall; 0.85, 0.82, and 0.89 for the economic, environmental, and social subscales), although, given the sample size (n = 17), these findings constitute preliminary evidence rather than confirmatory validation. A Maintenance Sustainability Dashboard further translates diagnostic outputs into actionable visual insights for decision-making and cross-plant benchmarking. These contributions offer a structured, transferable pathway for embedding sustainability within maintenance strategy and a basis for future quantitative indicator development and large-scale validation. Full article

Many bioactive constituents of medicinal plants depend on microbial biotransformation for their pharmacological activity, positioning postbiotics from plant substrates as microbially matured phytomedicines. An emerging framework integrates prebiotic phytochemicals with probiotic strains to modulate gut microbiota and host health. In this study, we explored the functional properties of heat-inactivated Lactobacillus strains following the fermentation of oligosaccharides obtained from sesame hulls (Sesamum indicum L.), underutilised agro-industrial residues. Cell-wall oligosaccharides were obtained by alkaline or enzymatic (Celluclast^® 1.5 L (Novonesis, Copenhagen, Denmark)) extraction with Ultraflo^® L (Novonesis, Copenhagen, Denmark) hydrolysis and fermented with Lactobacillus acidophilus, L. casei, or L. paracasei. Heat-inactivated pre-postbiotic preparations were profiled for antioxidant capacity, inhibition of metabolic enzymes implicated in obesity and type 2 diabetes, and anti-Helicobacter pylori urease activity. Moreover, these preparations were incorporated into a barley malt (Hordeum vulgare L.) beverage. Bioactivity was strain- and substrate-dependent: L. casei-derived postbiotics most strongly inhibited pancreatic lipase (47.82%) and α-glucosidase (52.14%); L. acidophilus most strongly inhibited α-amylase (43.67%); and L. paracasei exhibited the strongest urease inhibition (20.66%). All strains displayed enhanced antioxidant activity, with ABTS scavenging reaching 87.02%. The supplemented beverages improved antioxidant activity by ~20%. The fermentation of these oligosaccharides thus yields a microbially matured phytomedicine with multi-target activity, supporting postbiotics as active mediators of plant-based therapeutics. Full article

Sorghum is an important multi-purpose crop used for food, feed, brewing, and bioenergy. However, mechanised harvesting is hindered by its tall stature, complex panicle morphology, small and fragile grains, high-moisture stems and leaves, and susceptibility to lodging at maturity. This review provides a PRISMA-guided systematic literature search and narrative synthesis of mechanised sorghum harvesting from crop adaptability to intelligent equipment. The main literature search covered publications from January 1990 to May 2026 and included Web of Science Core Collection, Scopus, ScienceDirect, SpringerLink, Google Scholar, and other relevant sources. A total of 1928 records were identified, and 190 studies were finally included in the qualitative synthesis after duplicate removal, title-and-abstract screening, and full-text assessment. The review analyses how plant morphology, panicle exsertion, physical and mechanical properties, maturity stage, moisture content, varietal differences, and lodging affect harvesting suitability. The applicable conditions for segmented harvesting, panicle harvesting, direct grain harvesting, and multi-purpose coordinated harvesting are compared under different crop, regional, and machinery conditions. Key technological advances are synthesised in relation to header feeding, threshing and cleaning, straw management, and operating-parameter optimisation. Recent developments in machine vision, condition monitoring, adaptive control, DEM and CFD-DEM simulation, and digital twins are also assessed. The analysis shows that high-quality mechanised sorghum harvesting requires the coordinated optimisation of crop traits, harvesting methods, machine structures, operating parameters, and digital sensing–control feedback. The main contribution of this review is to establish an integrated crop–machine–operation framework for identifying technical constraints, comparing harvesting routes, and guiding the development of specialised, low-loss, low-breakage, and intelligent sorghum harvesting systems. Full article

Chemical absorption is currently the most mature technology for carbon capture from flue gas in coal-fired power plants. The selection of the amine solution system and process optimization directly determine the energy consumption of carbon capture and are critical to the large-scale implementation of the amine process. In this study, a composite amine solution of N-methyl-diethanolamine-piperazine-water (MDEA-PZ-H₂O) was selected as the CO₂ absorbent. Aspen HYSYS (14.0) software was used to establish a typical process model for CO₂ capture from flue gas in coal-fired power plants. Using single-factor sensitivity analysis, key process parameters in the typical carbon capture process—including amine solution composition, flue gas inlet temperature, lean liquid temperature, and gas-to-liquid ratio—were optimized. Based on the process optimization, this study conducted integrated energy-saving optimization by optimizing the temperature distribution in the absorption tower (achieved through the integration of inter-stage cooling in the absorption tower) and regeneration energy savings (achieved through the coupling of the Mechanical Vapor Recompression (MVR) process). The results indicate that the carbon capture system integrating the inter-stage cooling process with the MVR energy-saving process reduces the energy consumption per unit of carbon captured by 15.15% compared to a typical process system. This demonstrates that the integration of multiple energy-saving processes with the recovery of flue gas and CO₂ waste heat recovery within the system is an effective approach to reducing the energy consumption per unit of carbon capture. Full article