Search Results (427)

This study evaluates the effectiveness of natural zeolite (Shankhanai deposit, Kazakhstan) as a functional hydroponic substrate compared to a commercial foamed-glass control (GrowPlant). Using the Nutrient Film Technique (NFT), we assessed the growth and metabolic responses of Medicago sativa L. and three cultivars of Lactuca sativa L. Brunauer–Emmett–Teller (BET) analysis confirmed that zeolite (particle size 3.70 ± 1.20 mm) possesses a high specific surface area (21.80 m²/g), significantly exceeding the control (0.49 m²/g). This structure ensured superior moisture retention and cation exchange, even after a moderate decrease in surface area to 16.66 m²/g post-cultivation due to organic pore-filling. In M. sativa experiments, zeolite increased seedling viability and promoted a more branched root system compared to the artificial substrate. Gas chromatography–mass spectrometry (GC–MS) metabolic profiling of L. sativa revealed a significant substrate-driven reprogramming: zeolite increased the relative proportion of fatty acids and their derivatives (up to +51.27% in May King variety roots), suggesting membrane-protective adaptation. Genotype-specific responses were observed, with the Yeralash cultivar showing increased polyol synthesis (+2.93%) for osmoregulation. The results demonstrate that natural zeolite is an efficient, stable substrate for intensive hydroponics, optimizing root development and physiological stability through enhanced nutrient and water management. Full article

The rapid growth of cryptocurrencies and non-fungible tokens (NFTs) has expanded technological opportunities, but it has also increased the exposure surface to cyber threats, creating a need for a more precise understanding of the field’s scientific evolution. This study aims to systematically analyse academic output related to cybersecurity and cyber threats within cryptocurrency and NFT ecosystems, identifying central themes, the most influential authors, and emerging trends. A bibliometric methodology was employed, based on the PRISMA 2020 protocol and scientific mapping tools such as SciMAT (v1.1.06) and VOSviewer (v1.6.20), using a corpus of 337 articles published between 2014 and 2025. The findings indicate sustained growth in the literature, a marked geographical and editorial concentration, and the presence of motor themes such as blockchain, cybersecurity, emerging technologies and illegal mining, alongside emerging areas such as intrusion detection. The results also reveal a progressive integration of artificial intelligence techniques in the detection and prevention of attacks. In conclusion, this study provides a comprehensive overview of the state of the art, identifies critical gaps, and underscores the need for interdisciplinary approaches to strengthen security in decentralised environments. Full article

This study examines the pricing dynamics of Non-Fungible Tokens (NFTs) in the secondary market using advanced machine-learning techniques. We construct a large dataset of Ethereum-based NFT transactions initially comprising over 500,000 raw blockchain observations spanning multiple NFT segments, including art, collectibles, gaming, metaverse, and utility assets, over the period from November 2018 to March 2023. Following data preprocessing, synchronization across data sources, and the construction of history-dependent features, the analysis focuses on a final analytical sample of approximately 70,000 transactions. To address the challenges of non-fungibility, thin trading, and high price dispersion, we develop an interpretable predictive framework that integrates domain-informed manual feature engineering, automated Deep Feature Synthesis, and dimensionality reduction via Principal Component Analysis. Three non-linear models—Random Forest, XGBoost, and a Multilayer Perceptron—are trained and evaluated using both random and time-aware validation strategies. The results indicate that XGBoost consistently achieves the highest predictive accuracy, both overall and across individual NFT segments, while historical transaction prices emerge as the dominant predictor of future prices. Segment-level analysis reveals substantial heterogeneity in predictability, with art and collectible NFTs exhibiting more stable pricing patterns than gaming and metaverse assets. Overall, the findings highlight strong path dependence and reputation-driven valuation in NFT markets and demonstrate that carefully designed machine-learning models can deliver high predictive performance without sacrificing economic interpretability. Full article

Enhancing selenium (Se) content of aromatic plants addresses micronutrient deficiencies affecting billions. Plants are the primary dietary Se source, so biofortification can enhance Se intake. This study examined the effects of Se biofortification with sodium selenate (5 μM Na₂SeO₄) and moderate salinity stress (10 mM sodium chloride NaCl) on dill (Anethum graveolens L.) grown in a Plant Factory with Artificial Lighting using Nutrient Film Technique (NFT-PFAL) or Floating System (FS-PFAL), and in a Greenhouse with FS (FS-GH). Se biofortification and moderate salinity stress did not affect dill yield in any hydroponic system. Plants under combined Se biofortification and salinity stress (Se + NaCl) showed increased Se concentration in leaves of 31.78 mg kg⁻¹, 33.12 mg kg⁻¹, and 23.32 mg kg⁻¹ in NFT-PFAL, FS-PFAL, and FS-GH, respectively, compared to Se alone. Total phenolics content in leaves increased under Se biofortification with salinity stress across all systems, showing 159.57%, 223.13%, and 82.64% increases over control in NFT-PFAL, FS-PFAL, and FS-GH. Oxidative stress enzymes increased in response to Se, NaCl, and combined treatments across systems. FS-GH showed highest ascorbate peroxidase and guaiacol peroxidase activities, while PFAL systems showed lower but comparable activities. This study demonstrates that combining Se biofortification with moderate salinity stress in hydroponic systems can enhance plant functionality and human nutrition. Full article

Root-zone temperature is a critical environmental parameter affecting the development, physiology, and nutritional status of leafy vegetables in soilless systems such as the nutrient film technique (NFT) and aeroponics. In the present article, we report on responses of mini Romaine lettuce (Lactuca sativa L.) upon cultivation using heated nutrient solution targeting minimum temperatures of 14, 18, and 22 °C versus ambient (control; 11–12 °C), both in the NFT and in the aeroponics system. In both systems, the higher temperatures (i.e., 18 and 22 °C) led to considerably higher leaf mass per system area (127–232% in NFT; 54–75% in aeroponics) and leaf length (more than 21% in all cases). Root dry weight and total soluble solids were positively affected by increasing temperatures only in the NFT. Performance indices of the photosynthetic mechanism (PI_abs and PI_tot) were increased in the lower temperatures in the NFT. Antioxidant activity and total phenolics were not affected in either soilless system. Total chlorophylls and carotenoids were enhanced by 18 and 22 °C in the NFT and aeroponics, respectively, while anthocyanins were also variably affected. Finally, nitrate content was significantly reduced (−42%) in 18 °C in the NFT. Sub-optimal root-zone temperatures constrained root development and biomass accumulation, indicating that growth limitation was mainly driven by sink-related processes rather than carbon assimilation. Overall, heating the nutrient solution to a minimum of 22 °C in low- and mid-tech greenhouses during cool months can increase the production efficiency of mini Romaine lettuce in the NFT and aeroponics. Full article

The age, provenance, and evolution of some terranes in the Klamath Mountains are poorly constrained because of low detrital zircon yields. We present petrography and 947 new detrital and igneous zircon U-Pb ages from the North Fork (NFT), Eastern Hayfork (EHT), and Western Hayfork (WHT) terranes in the central and southern Klamath Mountains. Chert and argillite are abundant in the NFT and EHT, but matrix sandstones with abundant Proterozoic-to-Archean zircon ages indicate that the EHT received more sediment from North America. Detrital zircon ages from the WHT are ~171 Ma with scattered pre-Mesozoic ages, consistent with previous ages and continental input. A younger population of three grains at 145 Ma is interpreted as Pb loss during metamorphism. In the southernmost EHT, a 143 Ma dike correlates with plutons in the northern Sierra Nevada, which were offset from the Klamath Mountains 140–130 Ma. A 158 Ma metavolcanic/metavolcaniclastic rock in the EHT is a possible extrusive equivalent of the Wooley Creek intrusive suite. The metamorphosed EHT matrix has a young population of six ages at 69 Ma, which we tentatively interpret may represent Pb loss during metamorphism. This study documents an exposure of Late Jurassic arc cover sequence and suggests there may be previously unrecognized local metamorphism/magmatism ≤69 Ma. Full article

NFT prices are shaped by heterogeneous signals including visual appearance, textual narratives, transaction trajectories, and on-chain interactions, yet existing studies often model these factors in isolation and rarely unify multimodal alignment, temporal non-stationarity, and heterogeneous relational dependencies in a leakage-safe forecasting setting. We propose MM-Temporal-Graph, a cross-modal temporal graph transformer framework for explainable NFT valuation and information-centric risk forecasting. The model encodes image, text, transaction time series, and blockchain behavioral features, constructs a heterogeneous NFT interaction graph (co-transaction, shared creator, wallet relation, and price co-movement), and jointly performs relation-aware graph attention and global temporal–structural transformer reasoning with an adaptive fusion gate. A contrastive multimodal alignment objective improves robustness under market drift, while a risk-aware regularizer and a multi-source risk index enable early warning and interpretable attribution across modalities, time segments, and relational neighborhoods. On MultiNFT-T, MM-Temporal-Graph improves MAE from 0.162 to 0.153 and R² from 0.823 to 0.841 over the strongest multimodal graph baseline, and achieves 87.4% early risk detection accuracy. These results support accurate, robust, and explainable NFT valuation and proactive risk monitoring in Web3 markets. Full article

Cadmium (Cd) pollution is a global environmental issue that severely impacts crop growth and food safety. This study systematically investigates the accumulation characteristics and physiological responses of different varieties of non-heading Chinese cabbage under Cd stress. A Cd stress experiment was conducted using 79 non-heading Chinese cabbage varieties under nutrient film technique (NFT) cultivation, leading to the identification of 11 high-Cd accumulation varieties, 32 medium-Cd accumulation varieties, and 36 low-Cd accumulation varieties. The results showed that all varieties primarily accumulated Cd in the roots, with weak translocation of Cd to the aerial parts. To thoroughly analyze the physiological mechanisms of Cd accumulation, two extreme phenotypes, low accumulation (GX-61) and high accumulation (GX-05), were selected for subsequent comprehensive analysis. The low-accumulation variety (GX-61) exhibited higher sensitivity to Cd stress, with significant inhibition of leaf area, canopy area, and photosynthesis. In contrast, the high-accumulation variety (GX-05) maintained a more stable physiological state by enhancing photoprotective capacity and activating peroxidase (POD) to compensate for the functional loss of catalase (CAT). Cd stress inhibition of photosynthesis was initially limited by stomatal factors, later transitioning to non-stomatal limitations, and low concentrations of Cd induced a protective response that slightly promoted plant growth. This study, through high temporal resolution analysis at key growth stages, reveals the differential responses in growth, photosynthesis, and physiological metabolism between low- and high-Cd-accumulating non-heading Chinese cabbages, providing a theoretical basis for the selection of efficient phytoremediation materials and the safe production of non-heading Chinese cabbage. Full article

Trust (or distrust) in artificial intelligence (AI) is a critical research topic, given AI’s pervasive integration across societal domains. Despite its significance, scholarly attention to process-based learned trust in AI remains limited. To address this gap, this study designed a virtual non-fungible token (NFT) investment task, featuring seven rounds of risk decision-making scenarios, to simulate an investment/trust game to explore participants’ multifaceted trust under the influence of different chatbots’ social role. The findings suggested the chatbot’s social role had a significant impact on participants’ trust behaviors and perceptions over time. Trust in the two chatbot types diverged until the system-induced failures occurred. The friend-like chatbot elicited a higher level of behavioral trust than the servant-like counterpart. During those trust-damaging moments, the friend-like chatbot proved more effective in mitigating trust erosion and facilitating trust repair, as evidenced by relatively stable investment behaviors. The findings reinforce the notion that friendship with AI can function as a relational buffer, softening the impact of trust violations and facilitating smoother trust recovery. Full article

This paper presents the design and implementation of an IoT-based automation system for indoor hydroponic plant cultivation using the Nutrient Film Technique. The system employs an ESP32-based controller with multiple sensors and actuators. These enable real-time monitoring and control of pH, TDS, temperature, humidity, light, tank level, and flow conditions. A modular five-layer architecture was developed. It combines the MING stack, which includes MQTT communication, InfluxDB time-series storage, Node-RED flow processing, and Grafana visualization. The system also includes a Flutter-based mobile app for remote access. Key features include temperature-compensated calibration, hysteresis-based control algorithms, dual-mode operation, TLS/ACL security, and automated alarm mechanisms. These features enhance reliability and safety. Experimental results showed stable pH/TDS regulation, dependable actuator and alarm responses, and secure long-term data logging. The proposed open-source and low-cost platform is scalable. It provides a solution for small-scale producers and urban farming, bridging the gap between academic prototypes and production-grade smart agriculture systems. In comparison to related works that mainly focus on monitoring, this study advances the state of the art. It combines continuous time-series logging, secure communication, flow verification, and integrated safety mechanisms to provide a reproducible testbed for future smart agriculture research. Full article

The rise in Blockchain-based digital assets has transformed the financial ecosystems, which has also created complex governance and taxation challenges. The pseudonymous and cross-border nature of crypto transactions undermines traditional tax enforcement, leaving regulators such as the South African Revenue Service (SARS) reliant on voluntary disclosures with limited verification mechanisms, while existing Blockchain forensic tools and regulatory technologies (RegTechs) have advanced in anti-money laundering and institutional compliance, their integration into issues related to taxpayer compliance and locally adapted solutions remains underdeveloped. Therefore, this study conducts a state-of-the-art review of Blockchain forensics, RegTech innovations, and crypto tax frameworks to identify gaps in the crypto tax compliance space. Then, this study builds on these insights and proposes a conceptual model that integrates digital forensics, cost basis automation aligned with SARS rules, wallet interaction mapping, and non-fungible tokens (NFTs) as verifiable audit anchors. The contributions of this study are threefold: theoretically, which reconceptualise the adoption of Blockchain forensics as a proactive compliance mechanism; practically, it conceptualises a locally adapted proof-of-concept for diverse transaction types, including DeFi and NFTs; and lastly, innovatively, which introduces NFTs to enhance auditability, trust, and transparency in digital tax compliance. Full article

As an alternative to confronting the actual challenges in the conventional agriculture, Controlled Environment Agriculture (CEA) and hydroponics arise as sustainable alternatives, allowing for an efficient use of resources during green-leaf crop production. Considering a Nutrient Film Technique (NFT) hydroponic wall-system and two different ranges of light spectra provided via LED technology, this study evaluated their impact on the yield and efficiency during the cultivation of Swiss Chard crops (Beta vulgaris var. cicla) based on an analysis of morphological and nutritional characteristics. The experiment was developed in a growth room chamber, which was monitored and controlled under an IoT scheme, mainly focusing on the lighting treatments; treatments T1 and T2 were defined with the LED Barina 42W and LED MURFURN 70W, correspondingly. The results show no significant differences between both treatments in most morphological cases; however, the foliar area was significantly larger in treatment T1 than in treatment T2. Additionally, the nutritional quality of treatment T1 showed higher concentrations of carbohydrates, proteins, minerals, Ca, and K. Conclusively, the light spectrum composition provided by the lighting system influences the chard physiology, favoring the nutrient concentration under red–blue spectrum combinations. Full article

The rising usage of IoT devices in everyday life has formed smart cities that require the adoption of decentralized systems for a secure and transparent mechanism to manage asset exchange across automotive supply chains. Several existing Blockchain-based models built on public chains focus on traceability while overlooking scalability limits, transaction fees, conditional payment trust, or real-time delivery validation. We introduce DeFiTrustChain, a DeFi-enabled framework that combines free NFTs, escrow-based automation, and IoT verification within a Hyperledger Fabric network. It represents each vehicle using a unique NFT to capture the details of manufacturing and ownership, along with immutable asset verification. The payment release between stakeholders is governed by a dedicated escrow contract responsible for IoT-based delivery confirmation. The proposed framework ensures authenticated access and prevents identity misuse through integration of the Fabric Certificate Authority. The experimental results demonstrate the coherent and dependable execution of NFT creation, escrow enforcement, and IoT-triggered validation, with low local transaction processing time and consistent behavior across peers. Full article

The article presents a blockchain-based architecture for decentralized electricity trading that tokenizes energy delivery rights and cash-flows. Energy Attribute Certificates (EACs) are implemented as NFTs, while buy/sell orders are encoded as ERC-1155 tokens whose tokenId packs a time slot and price, enabling precise matching across hours. A clearing smart contract (Matcher) burns filled orders, mints an NFT option, and issues two ERC-20 assets: PT, the right to consume kWh within a specified interval, and YT, the producer’s claim on revenue. We propose a simple, linearly increasing discounted buyback for YT within the slot and introduce an aggregating token, IndexYT, which accumulates YTs across slots, redeems them at par at maturity, and gradually builds on-chain reserves—turning IndexYT into a liquid, yield-bearing instrument. We outline the PT/YY lifecycle, oracle-driven policy controls for DSO (e.g., transfer/splitting constraints), and discuss transparency, resilience, and capital efficiency. The contribution is a Pendle-inspired split of electricity into Principal/Yield tokens combined with a time-stamped on-chain order book and IndexYT, forming a programmable market for short-term delivery rights and yield derivatives with deterministic settlement. Full article

Background: Neurofeedback training (NFT) enhances athletic performance through alpha modulation, but optimal inter-session intervals and individual response variability remain poorly understood. Objective: This is the first randomized controlled trial to systematically compare neurofeedback periodization (2-day vs. 3-day inter-session intervals) on neurophysiological adaptations, strength performance, and retention in elite judo athletes. Methods: Thirty-one national-level judokas completed 15 alpha enhancement sessions in 2-day (n = 12), 3-day (n = 12), or control (n = 7) groups, receiving pseudo-neurofeedback with randomized, non-contingent feedback. Primary outcomes included Frontal Alpha Index changes (ΔFAI; frontal alpha power modulation ratio) and squat performance (35–100% 1RM), with secondary assessment of 48/72 h retention and response phenotypes. Results: Mean ΔFAI was modest (E15G-2d: 0.005 ± 0.205; E15G-3d: 0.052 ± 0.202), with early peak responses followed by stabilization. E15G-3d demonstrated superior retention (90.2 ± 3.4% at 72 h vs. 76.8 ± 4.1% at 48 h; p < 0.001) despite similar peaks. Both training groups showed significant strength improvements versus controls (E15G-2d: 2.37 ± 0.66 reps; E15G-3d: 2.00 ± 0.53 reps), yet neurophysiological-performance correlations were non-significant (p > 0.072), indicating strength adaptations via mechanisms independent of alpha modulation. Three response phenotypes emerged (high: 29.0%, moderate: 51.6%, low: 19.4%), representing the first empirical classification of neurofeedback responsiveness in athletes. Conclusions: Three-day intervals uniquely optimize retention through enhanced consolidation, establishing evidence-based periodization guidelines for elite athletes. The dissociation between neural and performance adaptations challenges traditional neurofeedback theory, while individual heterogeneity necessitates personalized protocols for optimal NFT periodization. Full article