Search Results (775)

Peatlands store substantial amounts of carbon (C) in the form of peat, but are increasingly threatened by drought and shrub encroachment under climate warming. However, how peat decomposition and its temperature sensitivity (Q₁₀) vary with depth and plant litter input under these stressors remains poorly understood. We incubated peat from two depths with different degrees of decomposition, either alone or incubated with Sphagnum divinum shoots or Betula ovalifolia leaves, under five temperature levels and two moisture conditions in growth chambers. We found that drought and Betula addition increased CO₂ emissions in both peat layers, while Sphagnum affected only shallow peat. Deep peat alone or with Betula exhibited higher Q₁₀ than pure shallow peat. Drought increased the Q₁₀ of both depths’ peat, but this effect disappeared with fresh litter addition. The CO₂ production rate showed a positive but marginal correlation with microbial biomass carbon, and it displayed a rather similar responsive trend to warming as the microbial metabolism quotient. These results indicate that both deep and dry peat are more sensitive to warming, highlighting the importance of keeping deep peat buried and waterlogged to conserve existing carbon storage. Additionally, they further emphasize the necessity of Sphagnum moss recovery following vascular plant encroachment in restoring carbon sink function in peatlands. Full article

Infection control and bleeding management in deep wounds remain urgent and unmet clinical challenges that demand innovative, multifunctional, and sustainable solutions. Unlike previously reported sodium alginate and silk fibroin-based gel formulations, the present work introduces a dual-functional system combining antimicrobial and haemostatic activity in the form of conformable aerogel beads. This dual-functional formulation is designed to absorb exudate, promote clotting, and provide localized antimicrobial action, all essential for accelerating wound repair in high-risk scenarios within a single biocompatible system. Aerogel beads were obtained by supercritical drying of a silk fibroin–sodium alginate blend, resulting in highly porous, spherical structures measuring 3–4 mm in diameter. The formulations demonstrated efficient ciprofloxacin encapsulation (42.75–49.05%) and sustained drug release for up to 12 h. Fluid absorption reached up to four times their weight in simulated wound fluid and was accompanied by significantly enhanced blood clotting, outperforming a commercial haemostatic dressing. These findings highlight the potential of silk-based aerogel beads as a multifunctional wound healing platform that combines localized antimicrobial delivery, efficient fluid and exudate management, biodegradability, and superior haemostatic performance in a single formulation. This work also shows for the first time how the prilling encapsulation technique with supercritical drying is able to successfully produce silk fibroin and sodium alginate composite aerogel beads. Full article

In the irrigation districts of Northern China, the flood resources utilization for deep storage irrigation, which is essentially characterized by active excessive irrigation, aims to have the potential to mitigate freshwater shortages, and long-term groundwater overexploitation. It is crucial to detect the effects of irrigation amounts on agricultural yield and the mechanisms under deep storage irrigation. A three-year field experiment (2020–2023) was conducted in the Guanzhong Plain, according to five soil wetting layer depths (RF: 0 cm; W1: control, 120 cm; W2: 140 cm; W3: 160 cm; W4: 180 cm) with soil saturation water content as the irrigation upper limit. Results exhibited that, compared to W1, the W2, W3, and W4 treatments led to the increased plant height, leaf area index, and dry matter accumulation. Meanwhile, the W2, W3, and W4 treatments improved kernel weight increment achieving maximum grain-filling rate (W_max), maximum grain-filling rate (G_max), and average grain-filling rate (G_ave), thereby enhancing the effective spikes (ES) and grain number per spike (GS), and thus increased wheat grain yield (GY). In relative to W1, the W2, W3, and W4 treatments increased the ES, GS, and GY by 11.89–19.81%, 8.61–14.36%, and 8.17–13.62% across the three years. Notably, no significant difference was observed in GS and GY between W3 and W4 treatments, but W4 treatment displayed significant decreases in ES by 3.04%, 3.06%, and 2.98% in the respective years. The application of a structural equation modeling (SEM) revealed that deep storage irrigation improved ES and GS by positively regulating W_max, G_max, and G_ave, thus significantly increasing GY. Overall, this study identified the optimal threshold (W3 treatment) to maximize wheat yields by optimizing both the vegetative growth and grain-filling dynamics. This study provides essential support for the feasibility assessment of deep storage irrigation before flood seasons, which is vital for the balance and coordination of food security and water security. Full article

Skin aging causes reduced hydration, elasticity, and increased wrinkles. Recent safety and compliance concerns over oral collagen supplements have increased interest in plant-based alternatives like Hibiscus sabdariffa with antioxidant and anti-aging properties. However, clinical evidence regarding its efficacy remains limited. We aimed to evaluate the effects of this plant-based collagen alternative (VC-H1, Hibiscus Enzyme Extract) supplement on skin hydration, transepidermal water loss (TEWL), desquamation, elasticity, and wrinkle reduction in photoaged individuals. A randomized, double-blind, placebo-controlled clinical trial was conducted with 98 participants (aged 35–60 years) presenting with dry skin and periorbital wrinkles. Participants randomly received 1.5 g/day of VC-H1 or placebo for 12 weeks. Skin hydration, TEWL, deep moisture, keratin index, elasticity, and wrinkle parameters were assessed at baseline, 6 weeks, and 12 weeks. VC-H1 supplementation significantly increased skin hydration, reduced the TEWL and keratin index, and improved deep moisture content for those receiving it compared with the controls. Wrinkle depth significantly decreased, and skin elasticity also improved. Those in the VC-H1 group showed greater overall improvement than those in the control group. Oral VC-H1 supplementation significantly improved skin hydration, elasticity, and wrinkle reduction, suggesting its potential as a plant-based alternative to traditional collagen supplements for skin rejuvenation. Full article

Introduction: Deep dry needling (DDN) is commonly applied in physiotherapy to treat musculoskeletal pain. The quadratus femoris (QF) muscle, located in the ischiofemoral space (IFS), represents a clinically relevant yet anatomically complex target. However, limited evidence exists on the safety, accuracy, and reliability of non-ultrasound-guided DDN in this region. Aims: To assess the safety and accuracy of a standardized, non-ultrasound-guided DDN approach to the QF muscle, and to evaluate the intra- and inter-rater reliability of key procedural outcomes. Additionally, to determine the agreement between ultrasound imaging and anatomical dissection as validation methods for needle placement. Methods: An experimental cross-sectional study was conducted on five fresh cadavers (n = 24 approaches) by two physiotherapists with different DN experience. A standardized dry needling protocol was executed without ultrasound guidance, and anatomical and procedural variables were documented. Reliability (intra/inter-rater) was assessed for needle size, sciatic nerve (SN) puncture, IFS targeting, and overall success. In a subset, needle placement was validated through ultrasound and subsequent dissection. Results: The IFS was reached in 70.8% of procedures, and the SN was punctured in 16.7%. Inter-rater reliability for needle size was poor (κ = 0.04). Agreement between ultrasound and dissection was excellent for the ischiofemoral location and success (100%) and moderate for non SN puncture (90%; κ = 0.62). Conclusions: The standardized protocol demonstrated moderate accuracy and revealed a relevant clinical risk when targeting the quadratus femoris muscle. While inter-rater reliability was limited, agreement between ultrasound and dissection methods was high, supporting their complementary use for validating needle placement in anatomically complex procedures. Full article

Precipitation, as a key input in the water cycle, directly influences the formation and change process of runoff. Meanwhile, the return runoff intuitively reflects the available quantity of water resources in a river basin. An in-depth analysis of the evolution laws and response relationships between precipitation and return runoff over a long time scale serves as an important support for exploring the evolution of hydrometeorological conditions and provides an accurate basis for the scientific planning and management of water resources. Taking Lanzhou Station on the upper Yellow River as a typical case, this study proposes the VSSL (LSTM Fusion Method Optimized by SSA with VMD Decomposition) deep learning precipitation element series extension method and the SSVR (SVR Fusion Method Optimized by SSA) machine learning runoff element series extension method. These methods achieve a reasonable extension of the missing data and construct 100-year precipitation and return runoff series from 1921 to 2020. The research results showed that the performance of machine learning and deep learning methods in the precipitation and return runoff test sets is better than that of traditional statistical methods, and the fitting effect of return runoff is better than that of precipitation. The 100-year precipitation and return runoff series of Lanzhou Station from 1921 to 2020 show a non-significant upward trend at a rate of 0.26 mm/a and 0.42 × 10⁸ m³/a, respectively. There is no significant mutation point in precipitation, while the mutation point of return runoff occurred in 1991. The 100-year precipitation series of Lanzhou Station has four time-scale alternations of dry and wet periods, with main periods of 60 years, 20 years, 12 years, and 6 years, respectively. The 100-year return runoff series has three time-scale alternations of dry and wet periods, with main periods of 60 years, 34 years, and 26 years, respectively. During the period from 1940 to 2000, an approximately 50-year cycle, precipitation and runoff not only have strong common-change energy and significant interaction, but also have a fixed phase difference. Precipitation changes precede runoff, and runoff responds after a fixed time interval. Full article

Xerostomia, the subjective complaint of a dry mouth, is frequently associated with salivary flow reduction and/or salivary gland hypofunction. This condition significantly impacts an individual’s quality of life and oral health, including difficulties in speaking, chewing, and swallowing. Xerostomia may be caused by autoimmune diseases, xerogenic medications, and radiation therapy. Our objective was to identify differentially expressed proteins in the saliva of patients with medication and autoimmune disease-associated xerostomia compared to non-xerostomic control subjects. Two groups of individuals (N = 45 total) were recruited: non-xerostomic subjects (NX-group; n = 18) and xerostomic patients (XP-group; n = 27). Dried saliva spot samples were collected from major salivary glands, i.e., parotid (left and right) and submandibular glands. Proteomic analysis was performed by deep nanoLC-MS/MS. Differential protein expression in the XP-group relative to the NX-group was determined by the Mann–Whitney U-test with FDR Benjamini–Hochberg correction (p_adj < 0.05). The Search Tool for Recurring Instances of Neighboring Genes (STRING_v12.0) was used to generate interaction networks and perform pathway analysis. A total of 1407 proteins were detected. Of these, 86 from the left parotid gland, 112 from the right parotid gland, and 73 from the submandibular gland were differentially expressed proteins (DEPs). Using STRING analysis, we identified, for the first time, several neurodegenerative disease-associated networks, primarily involving the downregulation of the 20S proteasome core complex and glyoxalase proteins across salivary glands. In this study, we determined neuronal dysregulation and impaired methylglyoxal (MGO) detoxification, possibly through reduced protein expression of glyoxalase Parkinson’s Disease (PD) Protein 7 (encoded by the PARK7 gene) in major salivary glands of xerostomic patients. Indeed, impaired MGO detoxification has been previously shown to cause salivary gland dysfunction in a mouse model of type 2 diabetes. Based on other DEPs associated with neurodegenerative disorders, our results also suggest a possible deficiency in the parasympathetic nervous system innervation of salivary glands, warranting further investigation. Full article

The rising demand for sustainable energy storage has fueled the development of green batteries as alternatives to conventional systems. However, a major research gap lies in the unified integration of environmentally friendly materials and processes across all battery components—electrodes, electrolytes, and separators—without compromising performance or scalability. This review addresses this gap by highlighting recent advances in eco-conscious battery technologies, focusing on green electrode fabrication using water-based methods, electrophoretic deposition, solvent-free dry-press coating, 3D printing, and biomass-derived materials. It also examines the shift toward safer electrolytes, including ionic liquids, deep eutectic solvents, water-based systems, and solid biopolymer matrices, which improve both environmental compatibility and safety. Additionally, biodegradable separators made from natural polymers such as cellulose and chitosan offer enhanced thermal stability and ecological benefits. The review emphasizes the importance of lifecycle considerations like recyclability and biodegradability, aligning battery design with circular economy principles. While significant progress has been made, challenges such as standardization, long-term stability, and industrial scalability remain. By identifying key strategies and future directions, this article contributes to the foundation for next-generation green batteries, promoting their adoption in environmentally sensitive applications ranging from wearable electronics to grid storage. Full article

Deep tight sandstone reservoirs are characterized by low porosity and permeability, complex pore structure, and strong heterogeneity. Conducting research on the heterogeneity characteristics of reservoirs could lay a foundation for evaluating their effectiveness and accurately identifying advantageous reservoirs, which is of great significance for searching for “sweet spot” oil and gas reservoirs in tight reservoirs. In this study, the deep tight sandstone reservoir in the Dibei area, northern Kuqa depression, Tarim Basin, China, is taken as the research object. Firstly, statistical methods are used to calculate the coefficient of variation (CV) and coefficient of heterogeneity (TK) of core permeability, and the heterogeneity within the reservoir is evaluated by analyzing the variations in the reservoir permeability. Then, based on fractal theory, the fractal and multifractal parameters of the GR and acoustic logs are calculated using the box dimension, correlation dimension, and the wavelet leader methods. The results show that the heterogeneity revealed by the box dimension, correlation dimension, and multifractal singular spectrum calculated based on well logs is consistent and in good agreement with the parameters calculated based on core permeability. The heterogeneity of gas layers is comparatively weaker, while that of dry layers is stronger. In addition, the fractal parameters of GR and the acoustic logs of three wells with the oil test in the study area were analyzed, and the relationship between reservoir heterogeneity and production was determined: As reservoir heterogeneity decreases, production increases. Therefore, reservoir heterogeneity can be used as an indicator of production; specifically, reservoirs with weak heterogeneity have high production. Full article

Arsenic (As) contamination in groundwater represents a major global public health threat, particularly in alluvial aquifer systems where redox-sensitive geochemical processes facilitate the mobilization of naturally occurring trace elements. This study investigates groundwater quality, particularly focusing on the origin of arsenic contamination in shallow and deep alluvial aquifers of the Lower Sakarya River Basin, which are crucial for drinking, domestic, and agricultural uses. Groundwater samples were collected from 34 wells—7 tapping the shallow aquifer (<60 m) and 27 tapping the deep aquifer (>60 m)—during wet and dry seasons for the hydrogeochemical characterization of groundwater. Environmental isotope analysis (δ¹⁸O, δ²H, ³H) was conducted to characterize origin and groundwater residence times, and the possible hydraulic connection between shallow and deep alluvial aquifers. Mineralogical and geochemical characterization of the sediment core samples were carried out using X-ray diffraction and acid digestion analyses to identify mineralogical sources of As and other metals. Pearson correlation coefficient analyses were also applied to the results of the chemical analyses to determine the origin of metal enrichments observed in the groundwater, as well as related geochemical processes. The results reveal that 33–41% of deep groundwater samples contain arsenic concentrations exceeding the WHO and Turkish drinking water standard of 10 µg/L, with maximum values reaching 373 µg/L. Manganese concentrations exceeded the 50 µg/L limit in up to 44% of deep aquifer samples, reaching 1230 µg/L. On the other hand, iron concentrations were consistently low, remaining below the detection limit in nearly all samples. The co-occurrence of As and Mn above their maximum contaminant levels was observed in 30–33% of the wells, exhibiting extremely low sulfate concentrations (0.2–2 mg/L), notably low dissolved oxygen concentration (1.45–3.3 mg/L) alongside high bicarbonate concentrations (450–1429 mg/L), indicating localized varying reducing conditions in the deep alluvial aquifer. The correlations between molybdenum and As (rdry = 0.46, rwet = 0.64) also indicate reducing conditions, where Mo typically mobilizes with As. Arsenic concentrations also showed significant correlations with bicarbonate (HCO₃⁻) (rdry = 0.66, rwet = 0.80), indicating that alkaline or reducing conditions are promoting arsenic mobilization from aquifer materials. All these correlations between elements indicate that coexistence of As with Mn above their MCLs in deep alluvial aquifer groundwater result from reductive dissolution of Mn/Fe(?) oxides, which are primary arsenic hosts, thereby releasing arsenic into groundwater under reducing conditions. In contrast, the shallow aquifer system—although affected by elevated nitrate, sulfate, and chloride levels from agricultural and domestic sources—exhibited consistently low arsenic concentrations below the maximum contaminant level. Seasonal redox fluctuations in the shallow zone influence manganese concentrations, but the aquifer’s more dynamic recharge regime and oxic conditions suppress widespread As mobilization. Mineralogical analysis identified that serpentinite, schist, and other ophiolitic/metamorphic detritus transported by river processes into basin sediments were identified as the main natural sources of arsenic and manganese in groundwater of deep alluvium aquifer. Full article

Nickel’s durability and catalytic properties make it essential in the aerospace, automotive, electronics, and fuel cell technology industries. Wastewater analysis typically relies on sensitive but costly techniques such as ICP-MS, AAS, and ICP-AES, which require complex equipment and are unsuitable for on-site testing. This study introduces a novel screen-printed electrode array with 16 chemically and, optionally, electrochemically coated Au electrodes. Its electrochemical response to Ni²⁺ was tested using Na₂SO₃ and ChCl-EG deep eutectic solvents as electrolytes. Ni²⁺ solutions were prepared from NiCl₂·6H₂O, NiSO₄·6H₂O, and dry NiCl₂. In Na₂SO₃, the linear detection ranges were 20–196 mM for NiCl₂·6H₂O and 89–329 mM for NiSO₄·6H₂O. High Ni²⁺ concentrations (10–500 mM) were used to simulate industrial conditions. Two linear ranges were observed, likely due to differences in electrochemical behaviour between NiCl₂·6H₂O and NiSO₄·6H₂O, despite the identical Na₂SO₃ electrolyte. Anion effects (Cl⁻ vs. SO₄²⁻) may influence response via complexation or ion pairing. In ChCl-EG, a linear range of 0.5–10 mM (R² = 0.9995) and a detection limit of 1.6 µM were achieved. With a small electrolyte volume (100–200 µL), nickel detection in the nanomole range is possible. A key advantage is the array’s ability to analyze multiple analytes simultaneously via customizable electrode configurations. Future research will focus on nickel detection in industrial wastewater and its potential in the multiplexed analysis of toxic metals. The array also holds promise for medical diagnostics and food safety applications using thiol/Au-based capture molecules. Full article

In the natural field environment, the high planting density of sorghum and severe occlusion among spikes substantially increases the difficulty of sorghum spike recognition, resulting in frequent false positives and false negatives. The target detection model suitable for this environment requires high computational power, and it is difficult to realize real-time detection of sorghum spikes on mobile devices. This study proposes a detection-tracking scheme based on improved YOLOv8s-GOLD-LSKA with optimized DeepSort, aiming to enhance yield estimation accuracy in complex agricultural field scenarios. By integrating the GOLD module’s dual-branch multi-scale feature fusion and the LSKA attention mechanism, a lightweight detection model is developed. The improved DeepSort algorithm enhances tracking robustness in occlusion scenarios by optimizing the confidence threshold filtering (0.46), frame-skipping count, and cascading matching strategy (n = 3, max_age = 40). Combined with the five-point sampling method, the average dry weight of sorghum spikes (0.12 kg) was used to enable rapid yield estimation. The results demonstrate that the improved model achieved a mAP of 85.86% (a 6.63% increase over the original YOLOv8), an F1 score of 81.19%, and a model size reduced to 7.48 MB, with a detection speed of 0.0168 s per frame. The optimized tracking system attained a MOTA of 67.96% and ran at 42 FPS. Image- and video-based yield estimation accuracies reached 89–96% and 75–93%, respectively, with single-frame latency as low as 0.047 s. By optimizing the full detection–tracking–yield pipeline, this solution overcomes challenges in small object missed detections, ID switches under occlusion, and real-time processing in complex scenarios. Its lightweight, high-efficiency design is well suited for deployment on UAVs and mobile terminals, providing robust technical support for intelligent sorghum monitoring and precision agriculture management, and thereby playing a crucial role in driving agricultural digital transformation. Full article

Post-harvest processing (PHP) is a key determinant of coffee quality, flavor profile, and market classification, yet verifying PHP claims remains a significant challenge in the specialty coffee industry. This study introduces near-infrared spectroscopy (NIRS) coupled with chemometrics as a rapid, non-destructive approach to classify green coffee beans based on PHP. For the first time, seven distinct PHP categories—Alchemy, Anaerobic Processing (Deep Fermentation), Dry-Hulled, Honey, Natural, Washed, and Wet-Hulled—were discriminated using NIRS, encompassing 20 different processing protocols under varying environmental and fermentation conditions. The NIR spectra (350–2500 nm) of 524 green Arabica coffee samples were analyzed using PCA-LDA models (750–2450 nm), achieving classification accuracies up to 100% for underrepresented categories and strong performance (91–95%) for dominant PHP groups in an independent test set. These results demonstrate that NIRS can detect subtle chemical signatures associated with diverse PHP techniques, offering a scalable tool for quality assurance, fraud prevention, and traceability in global coffee supply chains. While limited sample sizes for some PHP categories may influence model generalization, this study lays the foundation for future work involving broader datasets and integration with digital traceability systems. The approach has direct implications for producers, traders, and certifying bodies seeking reliable, real-time PHP verification. Full article

The date palm industry is a vital pillar of agricultural economies in arid and semi-arid regions; however, it remains vulnerable to challenges such as pest infestations, post-harvest diseases, and limited access to real-time monitoring tools. This study applied the baseline YOLOv11 model and its optimized variant, YOLOv11-Opt, to automate the detection, classification, and monitoring of date fruit varieties and disease-related defects. The models were trained on a curated dataset of real-world images collected in Saudi Arabia and enhanced through advanced data augmentation techniques, dynamic label assignment (SimOTA++), and extensive hyperparameter optimization. The experimental results demonstrated that YOLOv11-Opt significantly outperformed the baseline YOLOv11, achieving an overall classification accuracy of 99.04% for date types and 99.69% for disease detection, with ROC-AUC scores exceeding 99% in most cases. The optimized model effectively distinguished visually complex diseases, such as scale insert and dry date skin, across multiple date types, enabling high-resolution, real-time inference. Furthermore, a visual analytics dashboard was developed to support strategic decision-making by providing insights into production trends, disease prevalence, and varietal distribution. These findings underscore the value of integrating optimized deep learning architectures and visual analytics for intelligent, scalable, and sustainable precision agriculture. Full article

Appropriate deep sowing holds significant potential in enhancing wheat production, particularly in dry and low-rainfall regions. Henan Province is a major winter wheat-producing area in China; evaluating the adaptability of wheat varieties to deep sowing through scientific methods is crucial to improve wheat production. This study investigates 26 wheat cultivars in Henan. By assessing key traits of seeds and seedlings at various sowing depths, we analyzed the effects of sowing depth on seed germination and seedlings. A comprehensive index for deep sowing tolerance was established using principal component analysis (PCA) and the membership function method, followed by the classification of the varieties according to their tolerance to deep sowing. The results indicated that, with increased sowing depth, seedling emergence time, coleoptile length, and coleoptile internode length increased, while seedling emergence rate, seedling height, leaf area, and shoot dry weight per unit area decreased. Based on PCA and membership function values, the 26 wheat varieties were classified into three categories: deep sowing tolerant, moderately tolerant, and intolerant, comprising 3, 19, and 4 varieties. This study provides valuable insights for optimizing wheat variety selection and improving sowing practices in Henan Province, offering both theoretical and practical contributions to local wheat production. Full article