Search Results (703)

Soluble sugars are the key photo-assimilates in higher plants, playing critical roles in growth, development, and stress regulation. The transport of sugars in plants involves the coordinated action between several sugar transporter families, including the SUT, STP, pGlcT, VGT, TMT, INT, PLT, SFP, and SWEET families. Over recent decades, numerous studies have elucidated the molecular functions of major sugar transporters. Phylogenetic and evolutionary analyses support the conservation of substrate specificity and transport direction, at least to some extent. Structural analyses have provided key insights into the structural–function relationships of important transporters (e.g., OsSWEET2b and AtSTP10), which can be effectively leveraged for artificial intelligence (AI)-enabled protein structure prediction and rational design. Advances in omics technologies now enable low-cost, routine transcriptome profiling and cutting-edge techniques (e.g., single-cell multi-omics and spatiotemporal RNA-seq), providing unprecedented ways to understand how sugar transporters function coordinately at multiple levels. Here, we describe the classification of major sugar transporters in plants and summarize established functional knowledge. We emphasize that recent groundbreaking advances in AI-enabled protein analyses and multi-omics will revolutionize molecular physiology in crops. Specifically, the integration of functional knowledge, AI-based protein analyses, and multi-omics will help unravel the orchestration of different sugar transporters, thereby enhancing our understanding of how sugar transportation and source–sink interactions contribute to crop development, yield formation, and beyond, ultimately boosting carbohydrate transport- related crop improvement. Full article

Organic light-emitting diodes (OLEDs) have emerged as a leading high-resolution display and lighting technology, as well as for photo-therapeutic applications, due to their light weight, flexibility, and excellent color rendering. However, achieving long-term thermal stability and high energy efficiency remains a principal issue for their widespread adoption. Strong thermal robustness in OLED emitter materials is a critical parameter for achieving long device lifetimes, stable film morphology, reliable high-temperature processing, and sustained interface integrity in high-performance hosts. Bipolar emitters RB14 (N-(9-ethylcarbazole-3-yl)-4-(diphenylamino)phenyl-9H-carbazole-9-yl-1,8-naphthalimide), RB18 (N-phenyl-4-(diphenylamino)phenyl-9H-carbazole-9-yl-1,8-naphthalimide), and RB22 (N-phenyl-3-(2-methoxypyridin-3-yl)-9H-carbazole-9-yl-1,8-naphthalimide) were newly synthesized. RB18 is a yellow bipolar OLED emitter that has a glass transition temperature (T_g) of 162 °C and thermal durability (T_d) of 431 °C, which is the highest reported value for naphthalimide-based bipolar emitter derivatives for yellow OLEDs. Meanwhile, RB14 and RB22 are green OLED emitters that have glass transition temperatures (T_g) of 133 °C and 167 °C, and thermal durabilities (T_d) of 336 °C and 400 °C, respectively. We have fabricated OLED devices using these bipolar emitters dispersed in CBP host matrix, and we have found that the maximum EQEs (%) for RB14, RB18, and RB22 emitter-based devices are 7.93%, 3.40%, and 4.02%, respectively. For confirmation of thermal stability, we also used UV-visible spectroscopy measurements at variable temperatures on annealed spin-coated glass films of these emitter materials and found that RB22 is the most thermally stable emitter among these materials. Full article

With the advancement of Internet of Things (IoT) technology, flexible sensors with dual optoelectronic sensing modes have emerged as a research hotspot for next-generation smart devices, further driving the urgent demand for environmentally friendly functional materials. Here, we innovatively integrated wastepaper recycling technology with a polyethyleneimine (PEI)-assisted pulping strategy to develop a novel cellulose-based sustainable photo-triboelectric hybrid nanogenerator (PT-HNG). Based on the working mechanism of a freestanding triboelectric nanogenerator (TENG), the PT-HNG can directly convert pressure stimuli into electrical energy and triboelectrification-induced electroluminescence (TIEL) signals. It achieves luminescence brightness of 0.06 mW cm⁻² (3.84 cd m⁻²) and simultaneously delivers excellent electrical output performance (172.4 V, 6.36 μA, 43.7 nC) under sliding motion. More importantly, compatible with existing industrial papermaking processes, the PT-HNG is scalable for large-scale production. By combining PT-HNG with deep learning algorithms, a handwritten e-book system based on trajectory recognition was constructed, with a recognition accuracy of up to 95.5%. In addition, real-time intelligent control of PowerPoint presentations via PT-HNG was demonstrated. This study provides a new pathway for converting wastepaper into intelligent products and presents a novel idea for the interdisciplinary integration of the circular economy and advanced electronic technology. Full article

Polyhydroxybutyrate (PHB), as a biodegradable and green polymer, holds significant potential for replacing traditional petroleum-based plastics. However, its production efficiency and cost remain bottlenecks limiting large-scale application. In recent years, hybrid systems constructed from photosensitive nanomaterials and microorganisms have provided a novel pathway for enhancing PHB synthesis efficiency. These systems augment the supply of intracellular reducing power through efficient photo-generated electron injection, thereby driving microbial carbon fixation and PHB anabolic metabolism. This review systematically summarizes the mechanisms and performance of various types of photosensitive materials (including g-C₃N₄, CdS, polymer dots, etc.) in regulating PHB synthesis in microorganisms, such as Cupriavidus necator H16. It focuses on the influence of material composition, structure, energy band characteristics, and their interfacial interactions with microorganisms on electron transfer efficiency and biocompatibility. Furthermore, the article outlines the current challenges faced by these hybrid systems in key energy and mass transfer processes, including light energy conversion, transmembrane electron transport, and NADPH regeneration. It also prospects the design principles of novel bio-inspired multi-level heterojunction materials and their application potential in constructing efficient “material microbe” collaborative synthesis systems. This review aims to provide a material-level theoretical foundation and design strategies for developing high-performance and sustainable light-driven biomanufacturing technologies for PHB. Full article

Direct Air Capture (DAC) technology plays a crucial role in reducing atmospheric CO₂, but large-scale deployment faces challenges such as high energy consumption, operational costs, and slow material development. This study provides a comprehensive review of DAC principles, including chemical and solid adsorption methods, with a focus on emerging technologies like Metal–Organic Frameworks (MOFs) and graphene aerogels. MOFs have achieved adsorption capacities up to 1.5 mmol/g, while modified graphene aerogels reach 1.3 mmol/g. Other advancing approaches include DAC with Methanation (DACM), variable-humidity adsorption, photo-induced swing adsorption, and biosorption. The study also examines global industrialization trends, noting a significant rise in DAC projects since 2020, particularly in the U.S., China, and Europe. The integration of DAC with renewable energy sources, such as photovoltaic/electrochemical regeneration, offers significant cost-reduction potential and can cut reliance on conventional heat by 30%. This study focuses on the integration of Artificial Intelligence (AI) for accelerating material design and system optimization. AI and Machine Learning (ML) are accelerating DAC R&D: high-throughput screening shortens material design cycles by 60%, while AI-driven control systems optimize temperature, humidity, and adsorption dynamics in real time, improving CO₂ capture efficiency by 15–20%. The study emphasizes DAC’s future role in achieving carbon neutrality through enhanced material efficiency, integration with renewable energy, and expanded CO₂ utilization pathways, providing a roadmap for scaling DAC technology in the coming years. Full article

This study explores the application of JAYA optimization algorithms to significantly enhance the performance of indoor optical wireless communication (OWC) systems. By strategically optimizing photo-signal parameters, the system was able to improve signal distribution and reception within a confined space using circular and randomly positioned diffuse spots. The primary objective was to maximize signal-to-noise ratio (SNR) and minimize delay spread (DS), two critical factors that affect transmission quality in OWC systems. Given the challenges posed by background noise and multipath dispersion, an effective optimization strategy was essential to ensure robust signal integrity at the receiver end. Key achievements of JAYA optimization include significant performance gains, such as a 29% improvement in SNR, enhancing signal clarity and reception, and a 23.3% reduction in delay spread, ensuring stable and efficient transmission. System stability also improved, with the standard deviation of SNR improving by up to 5%, leading to a more consistent performance, while the standard deviation of delay spread improved by up to 9.9%, minimizing variations across receivers. Resilience against environmental challenges: Optimization proved effective even in the presence of ambient light noise and complex multipath dispersion effects, reinforcing its adaptability in real-world applications. The findings of this study confirm that JAYA optimization algorithms offer a powerful solution for overcoming noise and dispersion issues in indoor OWC systems, leading to more reliable and high-quality optical wireless communications. These results underscore the importance of algorithmic precision in enhancing system performance, paving the way for further advancements in indoor optical networking technologies. Full article

Background/Objectives: Although information and communication technology (ICT) offers opportunities to address challenges, evidence among frail populations is limited. We aimed to evaluate the effectiveness and feasibility of an ICT-based intervention incorporating an artificial intelligence (AI)-assisted smartphone dietary application and group communication tools to improve dietary quality and social connection among community-dwelling older adults with frailty. Methods: A non-randomized, quasi-experimental study was conducted among 29 older adults (≥65 years) in Tokyo, Japan. Participants were assigned to the intervention (n = 11) or control (n = 18) group. The 3-month intervention included weekly photo uploads of meals via an AI-based dietary application providing automated image analysis and personalized feedback, supervised by registered dietitians, along with peer communication through a group chat. The primary outcome was dietary quality. The secondary outcomes included body weight, body mass index (BMI), skin carotenoid score, and loneliness. Results: The adjusted Japanese Food Guide Spinning Top Score at 3-month follow-up was 49.0 (standard error [SE] = 2.6) and 39.5 (SE = 2.0) in the intervention and control groups, respectively. The adjusted mean difference between groups was +9.5 (95% confidence interval: 2.3 to 16.7, p = 0.01). After using analysis of covariance for adjusting for respective baseline values, age, education status, and antihypertension drug use, no statistically significant between-group differences were observed at 3-month follow-up for any secondary outcomes. Conclusions: AI-based dietary intervention and peer communication effectively improved dietary quality among older adults, highlighting the potential of such an intervention to promote healthier eating habits in this population. Full article

The textile industry releases effluents containing toxic contaminants such as azo dyes, which severely affect water quality and aquatic ecosystems. This study optimized the Fe⁰/H₂O₂/UV photo-Fenton process through Response Surface Methodology (RSM) using a Box–Behnken design applied to real textile wastewater. The process relies on in situ hydroxyl radicals (•OH) generation, which degrades refractory organic compounds. Under optimal conditions (pH 3.5, 0.5 g Fe⁰, and 0.55 mL H₂O₂), the system achieved complete color removal, 91% aromatic structures degradation, and an 80% COD reduction within 3 h. Statistical validation indicated an excellent model fit (R² = 1.0; Q² = 1.0), with strong correlation between experimental and predicted results. Spectroscopic analyses (UV–Vis and FTIR) further confirmed the cleavage of chromophoric and aromatic structures, indicating efficient pollutant degradation. Overall, the findings indicate that the Fe⁰/H₂O₂/UV system is an effective and sustainable technology for treating textile wastewater, offering strong potential for industrial-scale application. Full article

Rapid progress on the fabrication of lead halide perovskite has led to the development of high performance optoelectronic devices, particularly in the field of solar cell technologies. This initial success has subsequently inspired investigations into layered 2D-halide perovskite structures, motivated in part by their good environmental stability, but more significantly by their intriguing fundamental photo-physics. They have recently been used to improve the photoresponsivity of monolayer transition metal dichalcogenides in hybrid heterostructures. In this paper, we report on the synthesis of the (PEA)₂(MA)_n−1Pb_nI_3n+1 series (with n = 1, 2, 3) of 2D-halide perovskites, in order to develop a platform that provides ultra-thin layers for the fabrication of hybrid heterostructures. The crystal synthesis method and its basic structural and optical characterization are shown, highlighting the differences in the crystal synthesis processes. Furthermore, we explore the preparation of 2D halide perovskite ultra-thin flakes using the mechanical exfoliation method, and few-layer-areas of n = 1 member of the series are identified using atomic force microscopy. Finally, we study the deposition of thin and ultra-thin films using the spin coating technique to provide an alternative process to the exfoliation. Full article

Electro-Fenton (EF) technology holds significant promise for degrading recalcitrant organic pollutants. Still, it faces distinct challenges in high-pollutant-load wastewater, including insufficient radical generation, electrode passivation, and mass transfer limitations. This review systematically organizes recent advances in material design and operational strategies to address these issues. We highlight innovative cathode materials (e.g., graphene-based structures, carbon nanotubes, and metal–organic frameworks), stable anodes such as boron-doped diamond, and catalysts tailored for harsh conditions. Key operational improvements are discussed, including pH adaptability, current density optimization, and oxygen supply enhancement. The integration of hybrid systems, such as bio-electro-Fenton and photo-electro-Fenton, is also examined. Looking forward, future research for treating high-pollutant load wastewater should focus on: (1) Developing electrodes and catalysts with superior antifouling properties and long-term stability in high-strength, complex wastewaters; (2) Constructing intelligent control systems capable of real-time response to water quality fluctuations for adaptive parameter optimization; (3) Exploring energy-efficient, self-sustaining EF systems coupled with renewable energy sources or incorporating energy recovery units. This review aims to provide a comprehensive reference for subsequent research endeavors and practical applications related to the treatment technology of EF systems in high-pollutant-load wastewater contexts. Full article

This work investigates smartphone-based image processing for monitoring reservoir water quality, driven by the necessity for accessible and economical environmental evaluation techniques. The objective is to examine the correlation between water color and essential water quality parameters, including turbidity, total phosphorus, and chlorophyll-a, utilizing basic, readily accessible technology. The concept entails taking water photos from three principal reservoirs in Taiwan—Shimen, Liyutan, and Hushan—utilizing a smartphone camera, succeeded by sophisticated image processing algorithms, encompassing RGB color space analysis and ripple filtering. The findings indicate strong correlations between the G/R ratio obtained from the photos and conventional water quality parameters, particularly turbidity and chlorophyll-a. The correlation analysis yielded R² = 0.72 (p < 0.01) for turbidity and R² = 0.68 (p < 0.05) for chlorophyll-a, confirming the statistical significance of the results. Full article

Background: Adolescence represents a critical period for growth and athletic development, yet young athletes frequently demonstrate significant gaps in nutritional knowledge that can impair performance and long-term health outcomes. Limited research exists on comprehensive nutrition education interventions for adolescent soccer players in North African populations. Objective: To evaluate both general and sports-specific nutritional knowledge among adolescent soccer players from an elite Tunisian club and assess the feasibility of a digital nutrition intervention using mobile application technology. Methods: A cross-sectional survey was conducted between June and August 2024 among 50 male soccer players aged 11–18 years from Étoile du Sahel club in Sousse, Tunisia. Data were collected via a structured questionnaire comprising sections on basic nutrition knowledge, influences on food choices, sports nutrition knowledge and practices, and demographic information. A pilot digital intervention using the FatSecret app was implemented with 8 participants over 4 weeks, involving meal photo uploads and nutritionist feedback. Results: Participants had a mean age of 15.16 ± 1.55 years, with 92% reporting no formal nutrition education. While 90% correctly identified carbohydrates as the primary energy source, only 2% recognized that fat provides the highest energy density. Significant misconceptions existed regarding sports nutrition: 74% incorrectly believed that consuming protein 2–4 h before an event enhances performance, and only 17% knew the recommended pre-event carbohydrate intake. Food choices were primarily influenced by cravings (80%) and sensory appeal rather than health considerations (20%). The digital intervention demonstrated extremely low engagement, with minimal participation in meal photo uploads. Conclusions: This study reveals critical gaps in both general and sports-specific nutritional knowledge among adolescent soccer players in Tunisia, providing important descriptive information about knowledge distribution in this population. While knowledge deficits are substantial, it is important to acknowledge that this cross-sectional assessment documents only knowledge patterns, without measures of actual dietary intake or athletic performance. The persistent misconceptions and the low feasibility of the digital intervention provide important lessons regarding technology-based approaches to nutrition education in this age group, highlighting challenges in sustained engagement that must be addressed in future intervention design. Full article

Mg-doped gallium oxide films were prepared on single crystal sapphire substrates through radio frequency magnetron sputtering technology, and then AlN films of different thicknesses were deposited on them as passivation layers. Finally, Pt interdigitated electrodes were prepared through mask plate and ion sputtering technology to make metal–insulator–semiconductor–insulator–metal (MISIM) photodetectors. The influence of the AlN passivation layer on the optical properties and photodetection performance of the device was investigated using UV-Vis (ultraviolet-visible absorption spectroscopy) spectrophotometer and a Keith 4200 semiconductor tester. The device’s performance was significantly enhanced. Among them, the MISIM-structured device achieves a responsivity of 2.17 A/W, an external quantum efficiency (EQE) of 1100%, a specific detectivity (D*) of 1.09 × 10¹² Jones, and a photo-to-dark current ratio (PDCR) of 2200. The results show that different thicknesses of AlN passivation layers have an effect on the detection performance of Mg-doped β-Ga₂O₃ films in the UV detection of the solar-blind UV region. The AlN’s thickness has little effect on the bandgap when it is 3 nm and 5 nm, and the bandgap increases at 10 nm. The transmittance of the film increases with the increase in AlN thickness and decreases when the AlN’s thickness increases to 10 nm. The photocurrent exhibits a non-monotonic dependence on AlN thickness at 10 V, and the dark current gradually decreases. The thickness of the AlN passivation layer also has a significant impact on the response characteristics of the detector, and the response characteristics of the device are best when the thickness of the AlN passivation layer is 5 nm. The responsiveness, detection rate, and external quantum efficiency of the device first increase and then decrease with the thickness of the AlN layer, and comprehensive performance is best when the thickness of the AlN passivation layer is 5 nm. The reason is that the AlN layer plays a passivating role on the surface of Ga₂O₃ films, reducing surface defects and inhibiting its capture of photogenerated carriers, while the appropriate thickness of the AlN layer increases the barrier height at the semiconductor interface, forming a built-in electric field and improving the response speed. Finally, the AlN layer inhibits the adsorption and desorption processes between the photogenerated electron–hole pair and O₂, thereby retaining more photogenerated non-equilibrium carriers, which also helps enhance photoelectric detection performance. Full article

The present article focuses on the assessment of the potential advantages and disadvantages of the utilisation of modern building inventory technologies in crisis situations, using a case study of Ukraine, currently engulfed in armed conflict. The following methods are described in detail: laser scanning, 360-degree camera images, and photo series. The authors conducted an in-depth SWOT/TOWS analysis, adapted to the specifics of the post-conflict environment, with a view to the future reconstruction of damaged buildings. The originality of the study lies in the use of a modified, quantitative version of the conventional SWOT analysis, supplemented with a weighting and rating system, which allowed for a more accurate assessment of the effectiveness of various technologies, including laser scanning. While the study focuses on the Ukrainian context, the authors emphasise that the developed methodology is universal and can be successfully applied to other critical areas, such as regions affected by earthquakes, floods, fires, or technological disasters. A modified SWOT/TOWS analysis can serve as a valuable tool in crisis management and infrastructure reconstruction during emergencies, providing the data necessary for making rational and effective decisions regarding the use of modern technologies in construction. The analysis revealed that, of the analysed inventory strategies, only laser scanning technology fits the so-called “maxi-maxi” strategy, a scenario in which both internal resources and external capabilities are maximised. The remaining two strategies were designated as “maxi-mini,” signifying that their implementation is associated with elevated levels of risk despite their inherent advantages. It is imperative to acknowledge the existence of substantial external threats that persist. Nevertheless, this does not constitute a complete rejection of the concept. This study examines armed conflict as a research context for a selection of buildings in Ukraine. The analysis was constrained to the three most prevalent methods: The use of TLS, SfM, and 360-degree cameras is also a key component of the methodology. Full article

The proposed forest monitoring photo trap ecosystem integrates a cost-effective architecture for observation and transmission using Internet of Things (IoT) technologies and long-range digital radio systems such as LoRa (Chirp Spread Spectrum—CSS) and nRF24L01 (Gaussian Frequency Shift Keying—GFSK). To address low-bandwidth links, a novel approach based on the Monte Carlo sampling algorithm enables progressive, bandwidth-aware image transfer and its thumbnail’s reconstruction on edge devices. The system transmits only essential data, supports remote image deletion/retrieval, and minimizes site visits, promoting environmentally friendly practices. A key innovation is the integration of no-reference image quality assessment (NR IQA) to determine when thumbnails are ready for operator review. Due to the computational limitations of the Raspberry Pi 3, the PIQE indicator was adopted as the operational metric in the quality stabilization module, whereas deep learning-based metrics (e.g., HyperIQA, ARNIQA) are retained as offline benchmarks only. Although single-pass inference may meet initial timing thresholds, the cumulative time–energy cost in an online pipeline on Raspberry Pi 3 is too high; hence these metrics remain offline. The system was validated through real-world field tests, confirming its practical applicability and robustness in remote forest environments. Full article