Search Results (71)

This study systematically compares the environmental and economic performance of three wastewater treatment systems: constructed wetlands (CWs), microbial fuel cells (MFCs), and their integration (CW–MFC). Lab-scale units of each system were constructed using a multi-media matrix (gravel, zeolite, and granular activated carbon), composite native wetland species (Juncus effusus, Iris sp., and Typha angustifolia), carbon-based electrodes (graphite), and standard inoculum for CW and CW–MFC. The MFC system employed carbon-based electrodes and proton-exchange membrane. The experimental design included a parallel operation of all systems treating domestic wastewater under identical hydraulic and organic loading rates. Environmental impacts were quantified across construction and operational phases using life cycle assessment (LCA) with GaBi software 9.2, employing TRACI 2021 and ReCiPe 2016 methods, while techno-economic analysis (TEA) evaluated capital and operational costs. The key results indicate that CW demonstrates the lowest global warming potential (142.26 kg CO₂-eq) due to its reliance on natural biological processes. The integrated CW–MFC system achieved enhanced pollutant removal (82.8%, 87.13%, 78.13%, and 90.3% for COD, NO₃, TN, and TP) and bioenergy generation of 2.68 kWh, balancing environmental benefits with superior treatment efficiency. In contrast, the stand-alone MFC shows higher environmental burdens, primarily due to energy-intensive material requirements and fabrication processes. TEA results highlight CW as the most cost-effective solution (USD 627/m³), with CW–MFC emerging as a competitive alternative when considering environmental benefits and operational efficiencies (USD 718/m³). This study highlights the potential of hybrid systems, such as CW–MFC, to advance sustainable wastewater treatment technologies by minimizing environmental impacts and enhancing resource recovery, supporting their broader adoption in future water management strategies. Future research should focus on optimizing materials and energy use to improve scalability and feasibility. Full article

This study investigates the evolution of customer-centric innovations within modern business ecosystems through the lens of servitization, a concept gaining momentum in contemporary service delivery frameworks. Recognizing the limited exploration of servitization beyond manufacturing, particularly in the context of value-added services, this research employs a multiple case study methodology focused on the tea sector in India and Nepal. Drawing on seven diverse entrepreneurial cases and supported by a thematic analysis, the study identifies nine critical factors influencing successful servitization, including knowledge gaps, procurement strategies, market segmentation, and customer engagement. Central to this investigation is the transformative role of structured training interventions, exemplified by the Chaya School of Tea, which catalyzed innovation and performance improvements among participating businesses. The findings highlight how digital tools, customer education, and strategic planning contribute to product–service integration, yielding enhanced quality, operational efficiency, and sustainable growth. This research contributes to theory by refining the concept of “servitization of services” as a strategic approach for empowering ecosystems through complementary offerings that transcend traditional service delivery. This work provides both conceptual and empirical insights into how service firms, particularly in under-researched sectors, can leverage servitization to drive long-term competitiveness and ecosystem-wide value creation. Full article

Amino acids (AAs) and tea polyphenols (TPs) are essential quality indicators in tea, impacting sensory attributes and economic value. Hyperspectral technology enables efficient, real-time detection of these compounds on field-grown tea leaves. “The original spectra were preprocessed using fractional-order derivatives (0.1–1.0 orders) to enhance subtle spectral features. Compared to fixed integer-order derivatives (e.g., first or second order), fractional-order derivatives allow continuous tuning between 0 and 1, thereby amplifying minor absorption peaks while effectively suppressing noise amplification”. The Competitive Adaptive Reweighted Sampling (CARS) method selects optimal spectral bands, and Partial Least Squares Regression (PLSR) models were built with raw spectral reflectance as independent variables and AA and TP content as dependent variables. Results show that FOD had better prediction accuracy compared to classical integer-order derivatives, e.g., the optimal FOD order of 0.7 for AA prediction increased the R² from 0.73 to 0.80 and reduced the RMSE from 0.30% to 0.25%, while for TP prediction, a FOD order of 0.1 raised the R² from 0.40 to 0.42 and lowered the RMSE from 4.03% to 3.96%. In addition, CARS shows a better performance over the correlation coefficient (CC) method in model accuracy, contributing to more accurate selection of sensitive bands for the content prediction of tea ingredients. Our FOD–CARS–PLSR models achieved an R² of 0.80 and RMSE of 0.25% for AAs, and an R² of 0.42 and RMSE of 3.96% for TPs in fresh tea leaves. Beyond tea quality monitoring, this flexible preprocessing and modeling framework can be readily adapted to estimate biochemical or biophysical properties in other crops, soils, or vegetated ecosystems, offering a generalizable tool for precision agriculture and environmental sensing. Full article

Chlorophyll is an essential pigment for photosynthesis in tea plants, and fluctuations in its content directly impact the growth and developmental processes of tea trees, thereby influencing the final quality of the tea. Therefore, achieving rapid and non-destructive real-time monitoring of leaf chlorophyll content (LCC) is beneficial for precise management in tea plantations. In this study, derivative transformations were first applied to preprocess the tea hyperspectral data, followed by the use of the Stable Competitive Adaptive Reweighted Sampling (SCARS) algorithm for feature variable selection. Finally, multiple individual machine learning models and stacking models were constructed to estimate tea LCC based on hyperspectral data, with a particular emphasis on analyzing how the selection of base models and meta-models affects the predictive performance of the stacking models. The results indicate that derivative processing enhances the sensitivity of hyperspectral data to tea LCC; furthermore, compared with individual machine learning models, the stacking models demonstrate superior predictive accuracy and generalization ability. Among the 17 constructed stacking configurations, when the meta-model is fixed, the predictive performance of the stacking model improves continuously with an increase in the number and accuracy of the base models and with a decrease in the structural similarity among the selected base models. Therefore, when constructing stacking models, the base model combination should comprise various models with minimal structural similarity while ensuring robust predictive performance, and the meta-model should be chosen as a simple linear or nonlinear model. Full article

Straw application (SP) is a promising strategy for the improvement of soil fertility, but the biological effects and the mechanisms of its effects on microorganisms remain unclear. The investigation into the tea plantations (CK/S) in southern Henan, China, without/with straw amendment was carried out to assess the effects of SP on the soil bacterial communities using high-throughput sequencing. SP induced the community restructuring of the dominant phyla, e.g., Acidobacteriota, Pseudomonadota, Chloroflexota, with significantly increasing Nitrospirota, Vicinamibacterales and Anaerolineaceae (p < 0.05), while reducing Terriglobales (p < 0.05). These transitions correlated with significantly enhanced α-diversity and β-diversity divergence (p < 0.05). The linear discriminant analysis effect size (LEfSe) results confirmed the significant selective enrichment of nitrogen-cycling taxa (Nitrospira), copiotrophs (Chryseotalea), and anaerobic degraders (Anaerolineaceae), along with the suppression of the oligotrophic lineage (Ellin6067) by SP (p < 0.05). The co-occurrence networks of S had lower topological properties and negative cohesion (p < 0.05), which exhibited intensified simplified complexity and competition. The soil water content (WC) and pH were the main drivers of β-diversity variation and the keystone taxa assembly, as calculated out by distance-based redundancy analysis (dbRDA). This study demonstrates that SP can enhance bacterial network stability and functional redundancy by resource-driven niche partitioning between copiotrophic taxa and nitrogen-cycling guilds through a competition–cooperation equilibrium. Full article

This study leverages physical fitness assessment sensors to investigate the effects of a brief in-season break (detraining period) on the physical performance of female basketball players. Sixty-seven players (Senior (n = 19), U18 (n = 19), and U14 (n = 29)) were evaluated before and after a 3-week break using sensor-derived data from a countermovement jump (CMJ), an Abalakov jump (ABK), a linear speed test (20 m sprint), a seated medicine ball throw test (SMBT), and a Basketball-Specific Agility Test (TEA-Basket). The Total Score of Athleticism (TSA), computed as the mean Z-Score across tests, served as a composite indicator of physical fitness. Data obtained from performance sensors revealed significant interactions between time and category for the CMJ, ABK, 20 m sprint, and SMBT, while TEA-Basket measurements showed no significant changes. Time and baseline fitness level interactions were also significant for the CMJ, ABK, and SMBT but not for sprint time or the TEA-Basket. Despite observed declines in explosive strength, speed, and upper-body power across all groups, TSA scores remained stable. These findings underscore the utility of sensor-based evaluation methods in highlighting the adverse effects of short-term detraining and emphasize the necessity of tailored training strategies during competitive breaks. Full article

The primary objective of this research was to develop an efficient method for accurately identifying and localizing weeds in ecological tea garden environments, aiming to enhance the quality and yield of tea production. Weed competition poses a significant challenge to tea production, particularly due to the small size of weed plants, their color similarity to tea trees, and the complexity of their growth environment. A dataset comprising 5366 high-definition images of weeds in tea gardens has been compiled to address this challenge. An enhanced U-Net model, incorporating a Double Attention Mechanism and an Atrous Spatial Pyramid Pooling module, is proposed for weed recognition. The results of the ablation experiments show that the model significantly improves the recognition accuracy and the Mean Intersection over Union (MIoU), which are enhanced by 4.08% and 5.22%, respectively. In addition, to meet the demand for precise weed management, a method for determining the center of weed plants by integrating the center of mass and skeleton structure has been developed. The skeleton was extracted through a preprocessing step and a refinement algorithm, and the relative positional relationship between the intersection point of the skeleton and the center of mass was cleverly utilized to achieve up to 82% localization accuracy. These results provide technical support for the research and development of intelligent weeding equipment for tea gardens, which helps to maintain the ecology of tea gardens and improve production efficiency and also provides a reference for weed management in other natural ecological environments. Full article

The conventional hand-picking of tea buds is inefficient and leads to inconsistent quality. Innovations in tea bud identification and automated grading are essential for enhancing industry competitiveness. Key breakthroughs include detection accuracy and lightweight model deployment. Traditional image recognition struggles with variable weather conditions, while high-precision models are often too bulky for mobile applications. This study proposed a lightweight YOLOV5 model, which was tested on three tea types across different weather scenarios. It incorporated a lightweight convolutional network and a compact feature extraction layer, which significantly reduced parameter computation. The model achieved 92.43% precision and 87.25% mean average precision (mAP), weighing only 4.98 MB and improving accuracy by 6.73% and 2.11% while reducing parameters by 2 MB and 141.02 MB compared to YOLOV5n6 and YOLOV5l6. Unlike networks that detected single or dual tea grades, this model offered refined grading with advantages in both precision and size, making it suitable for embedded devices with limited resources. Thus, the YOLOV5n6_MobileNetV3 model enhanced tea bud recognition accuracy and supported intelligent harvesting research and technology. Full article

Anxi Tieguanyin belongs to the oolong tea category and is one of the top ten most famous teas in China. In this study, hyperspectral imaging (HSI) technology was combined with chemometric methods to achieve the rapid determination of free amino acid and tea polyphenol contents in Tieguanyin tea. Here, the spectral data of Tieguanyin tea samples of four quality grades were obtained via visible near-infrared hyperspectroscopy in the range of 400–1000 nm, and the free amino acid and tea polyphenol contents of the samples were detected. First derivative (1D), normalization (Nor), and Savitzky–Golay (SG) smoothing were utilized to preprocess the original spectrum. The characteristic wavelengths were extracted via principal component analysis (PCA), competitive adaptive reweighted sampling (CARS), and the successive projection algorithm (SPA). The contents of free amino acid and tea polyphenol in Tieguanyin tea were predicted by the back propagation (BP) neural network, partial least squares regression (PLSR), random forest (RF), and support vector machine (SVM). The results revealed that the free amino acid content of the clear-flavoured Tieguanyin was greater than that of the strong-flavoured type, that the tea polyphenol content of the strong-flavoured Tieguanyin was greater than that of the clear-flavoured type, and that the content of the first-grade product was greater than that of the second-grade product. The 1D preprocessing improved the resolution and sensitivity of the spectra. When using CARS, the number of wavelengths for free amino acids and tea polyphenols was reduced to 50 and 70, respectively. The combination of 1D and CARS is conducive to improving the accuracy of late modelling. The 1D-CARS-RF model had the highest accuracy in predicting the free amino acid (R_P² = 0.940, RMSEP = 0.032, and RPD = 4.446) and tea polyphenol contents (R_P² = 0.938, RMSEP = 0.334, and RPD = 4.474). The use of hyperspectral imaging combined with multiple algorithms can be used to achieve the fast and non-destructive prediction of free amino acid and tea polyphenol contents in Tieguanyin tea. Full article

The quality and flavor of tea leaves are significantly influenced by chemical composition, with the content of free amino acids serving as a key indicator for assessing the quality of Tencha. Accurately and quickly measuring free amino acids during tea processing is crucial for monitoring and optimizing production processes. However, traditional chemical analysis methods are often time-consuming and costly, limiting their application in real-time quality control. Hyperspectral imaging (HSI) has shown significant effectiveness as a component detection tool in various agricultural applications. This study employs VNIR-HSI combined with machine learning algorithms to develop a model for visualizing the total free amino acid content in Tencha samples that have undergone different processing steps on the production line. Four pretreating methods were employed to preprocess the spectra, and partial least squares regression (PLSR) and least squares support vector machine regression (LS–SVR) models were established from the perspectives of individual processes and the entire process. Combining competitive adaptive reweighted sampling (CARS) and variable iterative space shrinkage approach (VISSA) methods for characteristic band selection, specific bands were chosen to predict the amino acid content. By comparing modeling evaluation indicators for each model, the optimal model was identified: the overall model CT+CARS+PLSR, with predictive indicators Rc² = 0.9885, Rp² = 0.9566, RMSEC = 0.0956, RMSEP = 0.1749, RPD = 4.8021, enabling the visualization of total free amino acid content in processed Tencha leaves. Here, we establish a benchmark for machine learning-based HSI, integrating this technology into the tea processing workflow to provide a real-time decision support tool for quality control, offering a novel method for the rapid and accurate prediction of free amino acids during tea processing. This achievement not only provides a scientific basis for the tea processing sector but also opens new avenues for the application of hyperspectral imaging technology in food science. Full article

In recent years, the rapid development of the Taishan tea industry has become a business card of local specialty agriculture. However, as consumers’ demands for Taishan tea product quality and safety continue to improve, the centralized database traceability system that the traditional Taishan tea industry relies on shows insufficient information credibility and core data security risks, making it difficult to match the diversified expectations of the market and consumers. In order to solve this problem, this paper proposes a trusted traceability model for Taishan tea based on blockchain technology, which utilizes blockchain technology and data hierarchical uploading mechanism to ensure data accuracy and transparency, and, at the same time, improves data uploading efficiency. The optimized SM2 encryption algorithm is introduced, and the execution efficiency of the encryption algorithm is improved by the concurrent processing framework, which guarantees the security and transmission speed of the data. The experimental results show that the blockchain-based trusted traceability model for Taishan tea significantly improves the data security, query, and writing speed, and greatly optimizes the problems of traditional traceability methods. With this research, the results in this paper not only help to improve the quality and safety of Taishan tea products but also provide technical support for the production enterprises to enhance their brand competitiveness. Full article

Tea waste contains various substances with phenolic hydroxyl groups, including lignin, tannins, tea polyphenols, etc., which are rarely utilized. In this study, tea waste was directly dispersed with graphene oxide to prepare tea waste/reduced graphene oxide (TW/rGO) hydrogel through a one-step hydrothermal method. The prepared hydrogel presented a continuous three-dimensional porous structure and exhibited good mechanical properties with a compressive strength of 53.4 ± 4.0 kPa. It also showed excellent electrochemical performance as an electrode material. Its specific capacitance reached 434.7 F g⁻¹ at a current density of 1 A g⁻¹, and its capacitance retention was 55.8% when the current density was increased to 100 A g⁻¹. In addition, an TW/rGO assembled all-solid-state supercapacitor demonstrated a superior specific capacitance of 372.8 F g⁻¹ and a competitive energy density of 12.9 Wh kg⁻¹ at 1 A g⁻¹. Full article

Color is a key indicator for evaluating the quality of tea during processing; various processing procedures can significantly affect the content of fat-soluble pigments of tea, which in turn affects the color and quality of finished tea. Therefore, there is an urgent demand for the fast, non-destructive detection of pigments of stacked tea during processing. This paper presents the use of hyperspectral imaging technology (HSI), combined with machine learning algorithms, to detect chlorophyll a, chlorophyll b, and carotenoids in stacked matcha tea during processing. Firstly, a quantitative relationship between HSI data of tea and their pigment contents was developed based on regression analysis, and the results showed that exceptional prediction performance was achieved by the partial least squares regression (PLSR) algorithm combined with the feature band algorithm of competitive adaptive reweighting (CARS), and the R_p² values of detection models of chlorophyll a, chlorophyll b and carotenoids were 0.90465, 0.92068 and 0.62666, respectively. Then, these quantitative detection models were extended to each pixel in hyperspectral images, achieving point-by-point prediction of pigment components, so the distribution of pigments of stacked tea leaves during processing procedures was successfully visualized on the processing line in situ. By integrating a hyperspectral imaging system into the real-world environment, operators can monitor pigment levels in real time and thus dynamically adjust processing parameters based on real-time data. This study enhances pigment detection efficiency in tea processing, supports process optimization, and aids in quality control. Full article

Background Soccer is linked to an acute inflammatory response and the release of reactive oxygen species (ROS). Antioxidant supplements have shown promising effects in reducing muscle damage and oxidative stress and enhancing the recovery process after eccentric exercise. This critical review highlights the influence of antioxidant supplements on performance and recovery following soccer-related activity, training, or competition. Methods: English-language publications from the main databases that examine how antioxidant-based nutrition and supplements affect the recovery process before, during, and after soccer practice or competition were used. Results: Coenzyme Q10 (CoQ10), astaxanthin (Asx), red orange juice (ROJS), L-carnitine (LC), N-acetyl cysteine (NAC), beetroot (BET), turmeric root, and tangeretin reduce muscle damage (creatine kinase, myoglobin, cortisol, lactate dehudrogenase, muscle soreness). Tangeretin, docosahexaenoic acid (DHA), turmeric root, and aronia melanocarpa restrict inflammation (leukocytes, prostalagdin E2, C-reactive protein, IL-6 and 10). Q10, DHA, Asx, tangeretin, lippia citriodora, quercetin, allopurinol, turmeric root, ROJS, aronia melanocarpa, vitamins C-E, green tea (GTE), and sour tea (STE) reduce oxidative stress (malondialdehude, glutathione, total antioxidant capacity, superoxide dismutases, protein carbonyls, ascorbate, glutathione peroxidase, and paraoxonase 1). BET and NAC reinforce performance (endurance, jump, speed, strength). Conclusions: Further research is needed to determine the main mechanism and the acute and long-term impacts of antioxidant supplements in soccer. Full article

This paper investigates the potential of Micro Gas Turbines (MGTs) in the global shift towards low-carbon energy systems, particularly focusing on their integration within microgrids and distributed energy generation systems. MGTs, recognized for their fuel flexibility and efficiency, have yet to achieve the commercialization success of rival technologies such as Internal Combustion Engines (ICEs), wind turbines, and solar power (PV) installations. Through a comprehensive review of recent techno-economic assessment (TEA) studies, we highlight the challenges and opportunities for MGTs, emphasizing the critical role of TEA in driving market penetration and technological advancement. Comparative analysis with ICE and RES technologies reveals significant gaps in TEA activities for MGTs, which have hindered their broader adoption. This paper also explores the learning and experience effects associated with TEA, demonstrating how increased research activities have propelled the success of ICE and RES technologies. The analysis reveals a broad range of learning and experience effects, with learning rates (α) varying from 0.1 to 0.25 and experience rates (β) from 0.05 to 0.15, highlighting the significant role these effects play in reducing the levelized cost of energy (LCOE) and improving the net present value (NPV) of MGT systems. Hybrid systems integrating MGTs with renewable energy sources (RESs) and ICE technologies demonstrate the most substantial cost reductions and efficiency improvements, with systems like the hybrid renewable energy CCHP with ICE achieving a learning rate of α = 0.25 and significant LCOE reductions from USD 0.02/kWh to USD 0.017/kWh. These findings emphasize the need for targeted TEA studies and strategic investments to unlock the full potential of MGTs in a decarbonized energy landscape. By leveraging learning and experience effects, stakeholders can predict cost trajectories more accurately and make informed investment decisions, positioning MGTs as a competitive and sustainable energy solution in the global energy transition. Full article