Search Results (9,271)

This study investigated the chemical and autogenous shrinkage behaviour of limestone calcined clay cement (LC³) pastes incorporating calcined clays sourced from Australia, France, and India. Hydration development and microstructural evolution were examined using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), and pore-size distribution analysis. Results showed that LC³ mixes hydration accelerates during early phases, with the main silicate hydration peak appearing more prominently than that in the GP and FA reference pastes, indicating increased nucleation and growth of hydration products due to the limestone filler effect. LC³ pastes exhibited higher autogenous shrinkage overtime, strongly influenced by calcined clay reactivity and particle fineness. A clear correlation was observed between pore refinement and autogenous deformation during the early phases (7 days): pastes with a greater volume of fine pores showed higher early-age autogenous shrinkage during the first 7 days of hydration. In contrast, the chemical shrinkage of LC³ mixes was comparable to that of the GP and FA systems at early ages (≤7 days) but became lower after 28 days, attributed to both the matrix densification and additional nucleation sites provided by the limestone. Overall, LC³ reduces long-term chemical shrinkage and densifies the microstructure; however, the refined pore structure and increased internal water demand lead to higher autogenous shrinkage. These findings demonstrate a direct link between hydration-driven microstructural evolution (phase formation and pore refinement) and the resulting shrinkage behaviour. Full article

Facing the significant challenges posed by global population growth and urbanization, plant factories, as an efficient closed cultivation system capable of precise environmental control, have become a key direction in the development of modern agriculture. However, high energy consumption, particularly lighting (which accounts for over 50%), remains a major bottleneck limiting their large-scale application. This study systematically explored the effects of dynamic light regulation strategies on lettuce (Lactuca sativa L.) growth, physiological and biochemical indicators (such as chlorophyll, photosynthetic, and fluorescence parameters), nutritional quality, energy utilization efficiency, and post-harvest shelf life. Four different light treatments were designed: a stepwise increasing photosynthetic photon flux density (PPFD) from 160 to 340 μmol·m⁻²·s⁻¹ (T1), a constant light intensity of 250 μmol·m⁻²·s⁻¹ (T2), a three-stage strategy with high light intensity in the middle phase (T3), and a three-stage strategy with sequentially increasing light (T4). The results showed that the T4 treatment exhibited the best overall performance. Compared with the T2 treatment, the T4 treatment increased biomass by 23.4%, significantly improved the net photosynthetic rate by 50.32% at the final measurement, and increased ascorbic acid (AsA) and protein content by 33.36% and 33.19%, respectively. Additionally, this treatment showed the highest energy use efficiency. On the 30th day of treatment, the light energy use efficiency (LUE) and electrical energy use efficiency (EUE) of the T4 treatment were significantly increased, by 23.41% and 23.9%, respectively, compared with the T2 treatment. In summary, dynamic light regulation can synergistically improve crop yield, chlorophyll content, photosynthetic efficiency, nutritional quality, and energy utilization efficiency, providing a theoretical basis and solution for precise light regulation and energy consumption reduction in plant factories. Full article

The escalating threat of cyanobacterial blooms necessitates a deeper understanding of the environmental factors regulating their toxicity. While light intensity effects are well-documented, it remains unclear whether photoperiod regulates microcystin (MC) production. This study investigates the effects of five light–dark (LD) cycles on the growth and MC-LR production of two Microcystis strains in batch culture under a light intensity of 25 μmol m⁻² s⁻¹. Longer photoperiods enhanced early growth, although long-term biomass accumulation proved strain-dependent. Regarding toxin production, cellular MC-LR (total toxin per cell) during the initial 9-day phase was analyzed using a mixed-effects model. The analysis revealed significant main effects of photoperiod and cell density, supporting both direct and growth-mediated indirect effects of photoperiod. Moreover, a significant strain × photoperiod × day interaction (p < 0.001) was observed, indicating additional strain-specific and time-dependent regulation. Conversely, a general linear model of the strictly intracellular MC-LR at the 27-day endpoint showed significant independent effects of photoperiod and cell density, with no interaction. The photoperiod effect strengthened after controlling for cell density. These findings reveal a phase-dependent regulation of toxicity, suggesting that risk assessment based solely on biomass is inadequate. Sustainable bloom management should therefore incorporate photoperiod dynamics and adopt phase-specific strategies. Full article

The proliferation of Internet of Things (IoT) devices due to remarkable developments in mobile connectivity has caused a tremendous increase in the consumption of broadband spectrums in fifth generation (5G) mobile access. In order to secure the continued growth of IoT, there is a need for efficient management of communication resources in the 5G wireless access. Cognitive radio (CR) is advanced to maximally utilize bandwidth spectrums in the radio communication network. The integration of CR into IoT networks is a promising technology that is aimed at productive utilization of the spectrum, with a view to making more spectral bands available to IoT devices for communication. An important function of CR is spectrum sensing (SS), which enables maximum utilization of the spectrum in the radio networks. Existing SS techniques demonstrate poor performance in noisy channel states and are not immune from the dynamic effects of wireless channels. This article presents a comprehensive review of various approaches commonly used for SS. Furthermore, multi-agent deep reinforcement learning (MADRL) is proposed for enhancing the accuracy of spectrum detection in erratic wireless channels. Finally, we highlight challenges that currently exist in SS in CRIoT networks and further state future research directions in this regard. Full article

This study examines how tourism development in Tunisia responds to environmental degradation, renewable energy consumption, technological innovation, and foreign direct investment. Using annual data for 1990–2023, we apply the Autoregressive Distributed Lag (ARDL) bounds approach to identify long-run equilibria and short-run dynamics and validate the results with Fully Modified Ordinary Least Squares (FMOLS). The bounds tests confirm stable long-run relationships among tourism development and its structural determinants—environmental degradation, renewable energy, technological innovation, and foreign direct investment. The empirical results show that environmental degradation depresses tourism development in the long run, whereas renewable energy and technological innovation promote it. Foreign direct investment provides the strongest positive contribution. Complimentary Granger causality tests confirm unidirectional causality from environmental degradation, renewable energy, and technological innovation to tourism development, and bidirectional causality between tourism and foreign direct investment, validating the robustness and direction of influences among variables. Short-run effects appear weaker and occasionally mixed; however, the negative and highly significant error-correction term indicates convergence toward equilibrium. The FMOLS estimates closely match the ARDL results, providing further confidence in the results. Accordingly, policymakers should bolster environmental management, increase renewable energy as part of tourism infrastructure, advance digital and eco-innovation, and attract FDI in cleaner technologies and higher standards of services. This study fills conceptual and regional evidence gaps by integrating environmental, technological, and financial dimensions within a unified framework. It offers practical guidance consistent with the Sustainable Development Goals; specifically, Goals 7 (clean energy), 8 (sustainable growth and jobs), and 13 (climate action). Full article

Background: Frailty, a multisystem decline in physiological reserves, is a key indicator of aging health. Nutrition is a major modifiable factor associated with its development and progression. This study provides a systematic scientometric analysis of global research trends in nutrition and frailty, thereby addressing a significant gap in the literature. Methods: We systematically retrieved relevant publications from the Web of Science Core Collection (WoSCC) database for the period 2000–2024. After rigorous screening, a total of 754 publications were included for bibliometric analysis. Using VOSviewer, CiteSpace, and the R package bibliometrix, we analyzed publication trends, collaboration networks (countries, institutions, authors), journal co-citations, reference bursts, and keyword co-occurrence. Additionally, the structured literature review of 257 studies was conducted to synthesize key findings on nutrition-frailty associations. Results: Analysis of 754 global publications revealed consistent growth. The United States and China led contributions. Harvard T.H. Chan School of Public Health was the leading institution. Nutrients (n = 89, 11.8%) published most frequently, while Journals of Gerontology Series A was the most co-cited journal (n = 2058). Fernando Rodríguez–Artalejo had the highest publication count; Linda P. Fried was the most co-cited author. Keyword analysis identified frailty prevention and treatment as the predominant focus. The integrated the literature review specifically highlighted significant gaps, particularly in mechanistic insights and personalized nutrition interventions for frailty. Conclusions: This bibliometric analysis maps the intellectual landscape of nutrition and frailty research. Through quantitative assessment of publication patterns, leading contributors, knowledge domains, and thematic evolution, we characterize the current paradigm and identify emerging directions. Crucially, the synthesis explicitly defines critical research voids, particularly the overreliance on observational evidence, the scarcity of interventional trials, and the lack of global diversity in study populations, thereby providing a clear direction for future interdisciplinary investigations. Full article

Smart Agriculture (SA) combines cutting edge technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and real-time sensing systems with traditional farming practices to enhance productivity, optimize resource use, and support environmental sustainability. A key aspect of SA is the continuous monitoring of field conditions, particularly Soil Moisture (SM), which plays a crucial role in crop growth and water management. Accurate forecasting of SM allows farmers to make timely irrigation decisions, improve field management, and conserve water. To support this, recent studies have increasingly adopted soil sensors, local weather data, and AI-based data-driven models for SM forecasting. In the literature, most existing review articles lack a structured framework and often overlook recent advancements, including privacy-preserving Federated Learning (FL), Transfer Learning (TL), and the integration of Large Language Models (LLMs). To address this gap, this paper proposes a novel taxonomy for SM forecasting and presents a comprehensive review of existing approaches, including traditional machine learning, deep learning, and hybrid models. Using the PRISMA methodology, we reviewed over 189 papers and selected 68 peer-reviewed studies published between 2017 and 2025. These studies are analyzed based on sensor types, input features, AI techniques, data durations, and evaluation metrics. Six guiding research questions were developed to shape the review and inform the taxonomy. Finally, this work identifies promising research directions, such as the application of TinyML for edge deployment, explainable AI for improved transparency, and privacy-aware model training. This review aims to provide researchers and practitioners with valuable insights for building accurate, scalable, and trustworthy SM forecasting systems to advance SA. Full article

Cancer cells can sustain survival independently of exogenous growth factors. To investigate their adaptation to serum deprivation, we analyzed transcriptomic responses in two cancer cell lines. Transcriptome analysis revealed upregulation of mRNAs encoding cholesterol biosynthesis enzymes. This was a critical adaptive response, as a pharmacological inhibition of the pathway with statin triggered a robust apoptotic cell death accompanied by generation of a mitochondrial reactive oxygen species. The mechanistic target of rapamycin complex 1 (mTORC1), a master regulator of cell growth, is known to be engaged in controlling lipid biosynthesis. We detected the high polysomal and preribosomal peaks not only in serum-containing medium but also under serum deprivation, indicating a high rate of protein synthesis and ribosomal biogenesis independent of serum. In addition, the inhibition of mTOR kinase activity substantially reduced polysome abundance, with a more pronounced effect in serum-deprived cancer cells. Notably, the mTOR kinase inhibition also prevented the upregulation of the cholesterol synthesis enzyme that established a direct link between mTOR activity, protein synthesis, and cholesterol biosynthesis. Together, our results show that cancer cells adapt to serum withdrawal by activating the cholesterol synthesis pathway through mTOR-dependent regulation of gene expression and protein synthesis, underscoring a critical mechanism of survival under serum withdrawal. Full article

RNA methylation, particularly N6-methyladenosine (m⁶A) and 5-methylcytosine (m⁵C), functions as a pivotal post-transcriptional regulatory mechanism and plays a central role in plant growth, development, and stress responses. This review provides a systematic summary of recent advances in RNA methylation research in cucurbit crops. To date, high-throughput technologies such as MeRIP-seq and nanopore direct RNA sequencing have enabled the preliminary construction of RNA methylation landscapes in cucurbit species, revealing their potential regulatory roles in key agronomic traits, including fruit development, responses to biotic and abiotic stresses, and disease resistance. Nevertheless, this field remains in its early stages for cucurbit crops and faces several major challenges: First, mechanistic understanding is still limited, with insufficient knowledge regarding the composition and biological functions of the core protein families involved in methylation dynamics—namely, “writers,” “erasers,” and “readers.” Second, functional validation remains inadequate, as direct evidence linking specific RNA methylation events to downstream gene regulation and phenotypic outcomes is largely lacking. Third, resources are scarce; compared to model species such as Arabidopsis thaliana and rice, cucurbit crops possess limited species-specific genetic data and genetic engineering tools (e.g., CRISPR/Cas9-based gene editing systems), which significantly hampers comprehensive functional studies. To overcome these limitations, future research should prioritize the development and application of more sensitive detection methods, integrate multi-omics datasets—including transcriptomic and methylomic profiles—to reconstruct regulatory networks, and conduct rigorous functional assays to establish causal relationships between RNA methylation modifications and phenotypic variation. The ultimate objective is to fully elucidate the biological significance of RNA methylation in cucurbit plants and harness its potential for crop improvement through genetic and biotechnological approaches. Full article

Glioblastoma (GBM), formerly referred to as glioblastoma multiforme, represents the most prevalent and aggressive form of glioma, predominantly affecting the aging population. Despite considerable advances in recent years in elucidating its pathogenesis and developing novel immunotherapeutic approaches, the overall survival rate for patients with this central nervous system (CNS) neoplasm remains dismally low. Consequently, there is an urgent and unmet need to identify and characterize additional therapeutic targets that could be employed synergistically with existing treatment modalities to enhance both survival outcomes and quality of life. Among the emerging areas of investigation, substantial interest has been directed toward aging-associated molecular signaling mechanisms that also constitute key oncogenic pathways in GBM. These include aberrant growth factor signaling, hyperactivation of the PI3K/AKT/mTOR axis, and inactivation of critical tumor suppressor pathways such as p53 and retinoblastoma (RB). The dysregulation of these signaling cascades results in profound disturbances of essential cellular homeostatic processes, notably autophagy and cellular senescence, which are intimately involved in both tumor initiation and progression. This review aims to delineate the complex interplay between autophagy and cellular senescence within the context of aging-related GBM pathogenesis. Furthermore, it explores the relevant intracellular signaling transduction mechanisms that govern these processes and discusses prospective therapeutic strategies. Full article

Owing to the high altitude and short frost-free period of the Tibetan Plateau, potato plants are frequently exposed to cold stress (CS), which severely restricts their growth and productivity. Thus, understanding the mechanisms underlying cold tolerance in potato varieties is crucial for breeding improvement. This study aims to investigate the role of caffeic acid in potato cold tolerance and to elucidate the molecular mechanisms underlying the CS response. To achieve this, we conducted comprehensive metabolomic and transcriptomic analyses of KY130 (cold-tolerant) and KY140 (cold-sensitive) potato cultivars under CS at the seedling stage. ELISA results showed that caffeic acid levels were higher in KY130 than in KY140, while CS-KY130 exhibited higher levels than those of CS-KY140. Across all treatments, KY130 showed significantly higher activities of antioxidant enzymes (CAT and SOD) and higher contents of osmolytes (proline, soluble protein, and soluble sugar) than those of KY140. Caffeic acid and naringenin* were identified as candidate metabolites potentially involved in the direct and indirect cold resistance of potatoes. StPAL(Soltu.Atl.03_2G004060, Soltu.Atl.03_2G004070, Soltu.Atl.03_2G008130) and StCSE(Soltu.Atl.04_1G006370 and Soltu.Atl.04_3G005440), identified as upstream regulators of caffeic acid, were associated with the direct and indirect cold resistance of potatoes. KEGG pathway analysis of differentially accumulated metabolites and differentially expressed genes revealed several key metabolic pathways, including “flavonoid-related metabolism,” “lipid metabolism,” and “amino acid metabolism.” This research enhances our understanding of caffeic acid and the molecular mechanisms involved in the response of potatoes to CS, and supports future efforts in potato screening and breeding programs. Full article

The biophysical characteristics of cellular membranes, particularly their electrical properties in the $\alpha$-dispersion frequency domain, offer valuable insights into cellular states and are increasingly important for cancer diagnostics through epidermal growth factor receptor (EGFR) expression analysis. However, a critical limitation in these electrical measurements is the confounding effect of morphological changes that inevitably occur during prolonged observation periods. These shape alterations significantly impact measured capacitance values, potentially masking true biological responses to epidermal growth factor (EGF) stimulation that are essential for cancer detection. In this study, we attempted to address this fundamental challenge by developing a deep learning method that establishes a direct computational relationship between cellular morphology and electrical properties. We combined optical trapping technology and capacitance measurements to generate a comprehensive dataset of HeLa cells under two different experimental conditions: (i) DPBS treatment and (ii) EGF stimulation. Our convolutional neural network (CNN) architecture accurately predicts 401-point capacitance spectra (0.1–2 kHz) from binary morphological images at low frequencies (0.1–0.8 kHz, < 10% error rate). This capability allows for the identification and subtraction of morphology-dependent components from measured capacitance changes, effectively isolating true biological responses from morphological artefacts. The model demonstrates remarkable prediction performance across diverse cell morphologies in both experimental conditions, validating the robust relationship between cellular shape and electrical characteristics. Our method significantly improves the precision and reliability of EGFR-based cancer diagnostics by providing a computational framework for a morphology-induced measurement error correction. Full article

Drought and salt stresses are critical environmental constraints affecting plant growth and development, and microorganisms can enhance plant tolerance to these abiotic stresses through complex mechanisms. This review systematically synthesizes the core mechanisms by which microorganisms regulate plant physiological and biochemical processes under such stresses, specifically including the following: (1) regulating the perception and transduction of abiotic stress signals to enhance plant adaptive responses; (2) boosting gene expression and protein synthesis for overall plant metabolic regulation; (3) activating the antioxidant system to strengthen plant tolerance; (4) modulating plant hormone levels to stimulate growth in response to adversity; (5) enhancing plant nutrition and absorption to improve resilience; (6) optimizing the photosynthesis system to promote the synthesis of essential substances, safeguarding plant growth and development amidst adversity. Finally, the application of microbial inoculants in saline–alkali soil improvement and crop cultivation in arid areas and prospective research directions are discussed. Full article

As global urbanization accelerates, cities increasingly shape economic growth and environmental outcomes, making sustainable urban and transport planning critical. Sustainable city branding (SCB) is emerging as a strategic tool that not only enhances a city’s global competitiveness but actively drives urban sustainability by integrating environmental, social, and economic dimensions aligned with the UN Sustainable Development Goals (SDGs). However, the direct link between SCB and transport planning remains largely unexplored, limiting actionable policy. This study introduces a novel conceptual framework connecting SCB with transport planning, positioning public transportation as a key lever for sustainable urban development. It identifies core interactions between city branding and sustainable mobility, proposes methodologies to evaluate SCB effectiveness, and addresses potential risks, challenges, and research gaps. A policy roadmap for decision-makers based on the framework is outlined. This roadmap is structured into three phases spanning a five-year program. In Phase 1, cities should lay the foundation by integrating SCB into municipal transport and sustainability plans and establishing measurable indicators aligned with the SDGs. Phase 2 focuses on engagement and experimentation, encouraging the creation of participatory branding platforms and the implementation of pilot projects, such as green mobility corridors or climate-resilient transit hubs. Finally, Phase 3 emphasizes monitoring and scaling, utilizing digital technologies for real-time tracking, evaluating pilot outcomes, and expanding successful initiatives based on key performance indicators, including ridership growth, carbon reduction, and citizen engagement. By linking SCB explicitly to transport planning and providing a concrete roadmap, this study offers a unique contribution to both urban sustainability research and practical policy-making, enabling cities to simultaneously strengthen their brand, enhance mobility, and achieve measurable sustainability outcomes. Full article

Although purple non-sulfur bacteria (PNSB) have been studied as good biofertilizers, their direct effects on maize seed vigor remain unclear. Additionally, the seedling stage is a vital factor for the later growth of maize. This study was conducted to evaluate the effectiveness of potassium-solubilizing PNSB (K-PNSB) in enhancing the vigor of hybrid maize seeds. A completely randomized design was employed, incorporating single strains, Luteovulum sphaeroides M-Sl-09, Rhodopseudomonas thermotolerans M-So-11, and Rhodopseudomonas palustris M-So-14, as well as a mixture of all three strains. Each was tested at bacterial suspension dilution ratios with sterile distilled water of 1:2000; 1:2250; 1:2500; 1:2750; and 1:3000 (v/v), with three replications per treatment. Each replicate consisted of a Petri dish containing 10 hybrid maize seeds of each hybrid of LVN 10, C.P. 511, and NK7328 Gt/BT, and was incubated for five days. The results showed that K-PNSB significantly enhanced root and shoot development compared to the control (p < 0.05). The 1:2500 dilution of the individual strains and the mixture notably improved germination rate, root length, shoot length, and seedling vigor index compared to the control. At the 1:2500 dilution, the improved vigor index increased by 73.5% for L. sphaeroides, 48.7% for R. thermotolerans, 47.4% for R. palustris, and 78.5% for the mixed inoculum in the LVN 10 hybrid. Similar trends were observed for C.P. 511 and NK7328 hybrids, confirming strain- and hybrid-specific responses. The findings highlight that K-PNSB can serve as effective bio-priming agents to enhance maize seed vigor through mechanisms related to potassium solubilization and phytohormone production. Field-scale validation is recommended to assess their long-term agronomic potential. Full article