Search Results (4,254)

As an emerging threat to global food security, wheat blast necessitates the development of a rapid and field-deployable detection system to facilitate early diagnosis, enable effective management, and prevent its further spread to new regions. In this study, we aimed to validate and improve a Recombinase Polymerase Amplification coupled with PCRD lateral flow detection (RPA-PCRD strip assay) kit for the rapid and specific identification of Magnaporthe oryzae pathotype Triticum (MoT) in field samples. The assay demonstrated exceptional sensitivity, detecting as low as 10 pg/µL of target DNA, and exhibited no cross-reactivity with M. oryzae Oryzae (MoO) isolates and other major fungal phytopathogens under the genera of Fusarium, Bipolaris, Colletotrichum, and Botrydiplodia. The method successfully detected MoT in wheat leaves as early as 4 days post-infection (DPI), and in infected spikes, seeds, and alternate hosts. Furthermore, by combining a simplified polyethylene glycol-NaOH method for extracting DNA from plant samples, the entire RPA-PCRD strip assay enabled the detection of MoT within 30 min with no specialized equipment and high technical skills at ambient temperature (37–39 °C). When applied to field samples, it successfully detected MoT in naturally infected diseased wheat plants from seven different fields in a wheat blast hotspot district, Meherpur, Bangladesh. Training 52 diverse stakeholders validated the kit’s field readiness, with 88% of trainees endorsing its user-friendly design. This method offers a practical, low-cost, and portable point-of-care diagnostic tool suitable for on-site genomic surveillance, integrated management, seed health testing, and quarantine screening of wheat blast in resource-limited settings. Furthermore, the RPA-PCRD platform serves as an early warning modular diagnostic template that can be readily adapted to detect a wide array of phytopathogens by integrating target-specific genomic primers. Full article

The accurate short-term forecasting of global horizontal irradiance (GHI) is essential to optimizing the operation and integration of solar energy systems into the power grid. This study evaluates the performance of the Weather Research and Forecasting (WRF) model in predicting GHI over a 48 h forecast horizon at an Italian site: the ENEA Casaccia Research Center, near Rome (central Italy). The instantaneous GHI provided by WRF at model output frequency was post-processed to derive the mean GHI over the preceding hour, consistent with typical energy forecasting requirements. Furthermore, a decomposition model was applied to estimate direct normal irradiance (DNI) and diffuse horizontal irradiance (DHI) from the forecasted GHI. These derived components enable the estimation of solar energy yield for both concentrating solar power (CSP) and photovoltaic (PV) technologies (on tilted surfaces) by accounting for direct, diffuse, and reflected components of solar radiation. Model performance was evaluated against ground-based pyranometer and pyrheliometer measurements by using standard statistical indicators, including RMSE, MBE, and correlation coefficient (r). Results demonstrate that WRF-based forecasts, combined with suitable post-processing and decomposition techniques, can provide reliable 48 h predictions of GHI and DNI at the study site, highlighting the potential of the WRF framework for operational solar energy forecasting in the Mediterranean region. Full article

Background: Self-amplifying mRNA (sa-mRNA) represents a promising platform for vaccines and gene therapies, offering sustained protein expression at low doses through self-replication. For vaccines targeting respiratory pathogens, pulmonary delivery of sa-mRNA lipid nanoparticles (LNPs) is particularly advantageous, enabling direct delivery to the infection site and induction of mucosal immunity. Objective: In this study, we evaluated the stability of sa-mRNA–LNPs under refrigerated and frozen conditions and developed a dry powder formulation suitable for inhalation, produced by freeze-drying followed by cryomilling with leucine. Methods: sa-mRNA–LNPs formulated in HEPES buffer with 20% (w/v) sucrose were stored for up to 8 weeks as liquid or freeze-dried samples at various temperatures (−80 °C, −20 °C, 4 °C, and 20 °C). Biological stability was assessed by transfection efficiency in HeLa cells, while physical stability was characterized by encapsulation efficiency, zeta potential, particle size, and polydispersity index. Results: Liquid formulations remained stable for at least 8 weeks at −80 °C and −20 °C but rapidly lost stability at 4 °C and 20 °C. Freeze-drying effectively preserved sa-mRNA–LNP functionality and structural integrity for up to 8 weeks at 4 °C, with only minor structural changes. Subsequent cryomilling in the presence of 4 wt-% leucine produced a respirable dry powder while retaining approximately 60% of the original sa-mRNA–LNP functionality. Although cryomilling induced some structural alterations, the remaining functional fraction remained stable during storage. The resulting powders displayed favorable aerosol performance for deep lung delivery, as demonstrated by cascade impaction (MMAD = 4.13 ± 0.26 µm). Conclusions: In conclusion, freeze-drying effectively preserved sa-mRNA–LNP integrity at 4 °C, whereas cryomilling with leucine produced a respirable dry powder suitable for pulmonary delivery, providing a foundation for globally accessible, needle-free sa-mRNA vaccines against respiratory diseases. Full article

The purpose of this study is to determine the optimal location for siting an onshore wind farm on the island of Skyros, thereby maximizing performance and minimizing the project’s environmental impacts. Seven evaluation criteria are defined across various sectors, including environmental and economic sectors, and six criteria weighting methods are applied in combination with four multicriteria decision-making (MCDM) ranking methods for suitable areas, resulting in twenty-four ranking models. The alternatives considered in the analysis were defined through the application of constraints imposed by the Specific Framework for Spatial Planning and Sustainable Development for Renewable Energy Sources (SFSPSD RES), complemented by exclusion criteria documented in the international literature, as well as a minimum area requirement ensuring the feasibility of installing at least four wind turbines within the study area. The correlations between their results are then assessed using the Spearman coefficient. Geographic information systems (GISs) are utilized as a mapping tool. Through the application of the methodology, it emerges that area A9, located in the central to northern part of Skyros, is consistently assessed as the most suitable site for the installation of a wind farm based on nine models combining criteria weighting and MCDM methods, which should be prioritized as an option for early-stage wind farm siting planning. The results demonstrate an absolute correlation among the subjective weighting methods, whereas the objective methods do not appear to be significantly correlated with each other or with the subjective methods. The ranking methods with the highest correlation are PROMETHEE II and ELECTRE III, while those with the lowest are TOPSIS and VIKOR. Additionally, the hierarchy shows consistency across results using weights from AHP, BWM, ROC, and SIMOS. After applying multiple methods to investigate correlations and mitigate their disadvantages, it is concluded that when experts in the field are involved, it is preferable to incorporate subjective multicriteria analysis methods into decision-making problems. Finally, it is recommended to use more than one MCDM method in order to reach sound decisions. Full article

The green transformation of the shipping industry urgently requires zero-carbon power, and hydrogen-powered ships such as hydrogen fuel cell ships face bottlenecks in in situ hydrogen production and storage and transportation. Methanol steam reforming (MSR) online hydrogen production is suitable for ship scenarios, reducing costs and increasing efficiency while helping achieve zero carbon throughout the entire lifecycle, which has important practical significance. The key technology for MSR technology is the performance of the catalyst. A series of Cu_1−xMn_xAl₂O₄ catalysts were successfully synthesized and applied for hydrogen production in this study. The catalyst structure was characterized using physicochemical techniques including XRD, SEM, and EDS. Hydrogen production performance was evaluated in a fixed-bed reactor under the following conditions: a liquid hourly space velocity (LHSV) of 20 h⁻¹, a water-to-methanol molar ratio of 3:1, and a reaction temperature range of 275 °C–350 °C. The results demonstrate that A-site Mn substitution significantly enhanced the catalytic performance. In addition, XRD analysis revealed that Mn incorporation effectively suppressed the formation of segregated CuO phases. However, excessive substitution (x is 0.9) led to the generation of an MnAl₂O₄ impurity phase. Finally, the Cu_0.7Mn_0.3Al₂O₄ catalyst achieved a methanol conversion of 68.336% at 325 °C, with a hydrogen production rate of 5.611 mmol/min/g_cat, and maintained CO selectivity below 1%. The results demonstrate that the hydrogen production catalyst developed in this study is a promising material for meeting the requirements of online hydrogen sources for ships. Full article

Natural recruitment of Manila clam (Ruditapes philippinarum) often persists in degraded estuaries, yet the environmental thresholds enabling this resilience remain quantitatively undefined. We employed binomial generalized additive model (GAM) coupled with field surveys (n = 168) in the Xiaoqing River estuary (Laizhou Bay, China) to identify critical limits for adult occurrence, which served as a field-based proxy for recruitment potential. Sediment median grain size (D₅₀), salinity (Sal) and dissolved inorganic nitrogen (DIN) were identified as the key factors, collectively explaining 79.30% of the deviance (AUC = 0.98). The probability of occurrence decreased sharply beyond two distinct thresholds: D₅₀ > 95 μm and salinity < 17.50‰. While DIN had a positive effect, it did not offset the strong negative associations with coarse sediment or low salinity. These field-validated thresholds provide quantifiable criteria to guide habitat suitability mapping, activation of early-warning systems against salinity-driven mortality, and site prioritization for ecological restoration in the Xiaoqing River estuary. Our findings offer a framework for developing management strategies to support clam resilience under environmental stress. Full article

The northern coast of Mauritania presents a strategic opportunity for clean energy investment due to its remarkable potential for green hydrogen production through wind energy. To determine the best location for wind-based green hydrogen production, this paper established a Multi-Criteria Decision-Making framework (MCDM) that combines the Analytic Hierarchy Process (AHP) and Evidential Reasoning (ER) to assess five coastal sites: Nouakchott, Nouamghar, Tasiast, Boulanoir, and Nouadhibou. Four main criteria (i.e., economic, technical, environmental, and social) and twelve sub-criteria were taken into account in the assessment. To ensure reliability and contextual accuracy, the data used in this study were obtained from geographic databases, peer-reviewed literature, and structured expert questionnaires. The results indicate that site 5 (Nouadhibou) is the most suitable location for green hydrogen generation using wind energy. Sensitivity analysis confirms the robustness of the ranking results, validating the reliability of the proposed hybrid framework. The findings of this study provide critical, data-driven decision-support insights for investors and policymakers, guiding the strategic development of sustainable wind-based green hydrogen projects along Mauritania’s coastline. Full article

Globally, more than 60,000 abandoned open-pit mines have been identified. Most of these sites lack effective management or ecological restoration measures. As a result, they pose substantial environmental and socioeconomic challenges. Against this backdrop, the reuse of quarry wastelands has emerged as a critical strategy for improving resource efficiency and promoting sustainable development in mining regions. Current domestic research mainly concentrates on ecological restoration techniques for abandoned quarry sites. However, systematic methods for prioritizing and ranking alternative reuse models remain limited. This study investigated four quarry reuse models: agricultural production, ecological protection, recreation-based education, and new energy development. The analysis integrated site suitability (${\mathrm{U}}_1$) with residents’ demands (${\mathrm{U}}_2$). Four representative quarry sites in Jiawang District, Xuzhou City, were selected as case studies. Based on coupled matching analysis, a priority identification method for quarry site reuse models was developed. Results indicated divergent prioritization between site suitability and resident demand. Site suitability composite values ranged from 3.9548 to 6.3094. Qishan and Kanshan sites demonstrated high suitability for recreation-based education and agricultural production, while the Dongshan site showed the highest ecological protection suitability. Suitability for emerging energy applications was generally low across all sites. Resident demand composite values showed significant variation across the four models. Recreation-based education demand (${\mathrm{U}}_2$ ranging from 0.3273 to 0.3778) substantially exceeded the other three land use types, with residents generally harbouring a degree of reluctance towards new energy development models. After coupling these factors, the original site suitability rankings were restructured: Qishan and Dongshan were selected for the recreation-based education model; Kanshan for agricultural production; and Changshan for ecological protection. This study offers insights for the diversified utilization of abandoned quarries in rural areas and provides a reference for ecological restoration and transformative development in mining regions. Full article

Norway spruce (Picea abies [L.] Karst.) plays a key role in European forestry as well as in the wood-processing industry. Identifying suitable alternative species has become increasingly important. In this study, we compared several spruce species originating from two sites in the Ore Mountains (Krušné hory, 483–883 m a.s.l.), an area severely affected by an extensive air-pollution disaster (high SO₂ concentrations) during the 1970s and 1980s. Norway spruce, Serbian spruce (Picea omorika [Panč.] Purk.) and blue spruce (Picea pungens Engelm.) were evaluated in terms of production potential, carbon sequestration relevant to climate-change mitigation, and selected physical wood properties (wood density and shrinkage). The greatest stem volume and corresponding carbon sequestration were recorded for P. omorika (0.191 m³; 75.5 kg), followed by P. abies (0.142 m³; 49.0 kg), while P. pungens showed significantly (p < 0,05) lower values (0.069 m³; 30.6 kg). In terms of wood properties, the highest wood-density values were obtained for P. omorika, together with P. abies, at both sites. P. pungens exhibited lower wood densities. In terms of shrinkage, the species displayed similar values. Overall, our results indicate that P. omorika is comparable to P. abies, and its wood could therefore serve as a suitable substitute for certain applications. Full article

Crop root development, and in turn crop growth, is strongly influenced by soil strength and the mechanical impedance of compacted layers, which restrict root elongation and exploration. Because the depth and thickness of compacted layers vary across a field, their identification is essential for site-specific tillage and sustainable root-zone management. A sensing approach that can support future real-time identification of compacted layers after soil-specific calibration, which would enable variable-depth tillage, reducing mechanical impedance and improving energy-use efficiency while maintaining crop yields. This study aimed to develop and evaluate prediction models that can support future real-time identification of compacted soil layers using soil electrical conductivity (EC) and moisture content as non-destructive indicators. A sandy clay soil (48.6% sand, 29.3% clay, 22.1% silt) was tested in a soil-bin laboratory under controlled conditions at three moisture levels (13, 18, and 22% db.) and six depth layers (C1–C6, 0–30 cm) identified from the penetration-resistance profile to measure penetration resistance, shear resistance, and EC. Penetration and shear resistance increased toward the most resistant depth layer and decreased with increasing moisture content, whereas EC generally increased with both depth layer and moisture content. Linear regression models relating penetration resistance ($R^2=0.893$) and shear resistance ($R^2=0.782$) to EC and moisture content were developed and evaluated. Field validation in a paddy field of similar texture showed that predicted penetration resistance differed from measured values by 3–6% across the three compaction treatments evaluated. Root length density and root volume decreased with increasing machine-induced compaction, confirming the agronomic relevance of the modeled patterns and supporting the suitability of the proposed indicators. Together, these results demonstrate that EC and moisture content can potentially be used as non-destructive proxies for compacted-layer identification and provide a calibration basis for future on-the-go sensing systems to support site-specific, variable-depth tillage in agricultural fields. Full article

Airborne micro- and nanoplastic particles (MNPs) are increasingly recognized as a potential occupational exposure hazard, yet substance-specific workplace data remain limited. This study quantified airborne MNP concentrations during polyester microfiber production using a correlative SEM–Raman approach that enabled chemical identification and size-resolved particle characterization. The aerosol mixture at the workplace was dominated by sub-micrometer particles, with PET—handled onsite—representing the main process-related MNP type, and black tire rubber (BTR) forming a substantial background contribution. Across both sampling periods, total MNP particle number concentrations ranged between 6.2 × 10⁵ and 1.2 × 10⁶ particles/m³, indicating consistently high particle counts. In contrast, estimated MNP-related mass concentrations were much lower, with PM₁₀ levels of 12–15 µg/m³ and PM_2.5 levels of 1.3–1.6 µg/m³, remaining well below applicable occupational exposure limits and near or below 8 h-equivalent WHO guideline values. Comparison with earlier workplace and indoor studies suggests that previously reported concentrations were likely underestimated due to sampling strategies with low efficiency for small particles. Moreover, real-time optical measurements substantially underestimated particle number and mass in this study, reflecting their limited suitability for aerosols dominated by small or dark particles. Overall, the data show that workplace MNP exposure at the investigated site is driven primarily by very small particles present in high numbers but low mass. The findings underscore the need for substance-specific, size-resolved analytical approaches to adequately assess airborne MNP exposure and to support future development of MNP-relevant occupational health guidelines. Full article

In intelligent construction and BIM–Reality integration applications, high-quality, large-scale construction scene point cloud data with component-level semantic annotations constitute a fundamental basis for three-dimensional semantic understanding and automated analysis. However, point clouds acquired from real construction sites commonly suffer from high labeling costs, severe occlusion, and unstable data distributions. Existing public datasets remain insufficient in terms of scale, component coverage, and annotation consistency, limiting their suitability for data-driven approaches. To address these challenges, this paper constructs and releases a BIM-derived synthetic construction scene point cloud dataset, termed the Synthetic Point Cloud (SPC), targeting component-level point cloud semantic segmentation and related research tasks.The dataset is generated from publicly available BIM models through physics-based virtual LiDAR scanning, producing multi-view and multi-density three-dimensional point clouds while automatically inheriting component-level semantic labels from BIM without any manual intervention. The SPC dataset comprises 132 virtual scanning scenes, with an overall scale of approximately $8.75\times {10}^9$ points, covering typical construction components such as walls, columns, beams, and slabs. By systematically configuring scanning viewpoints, sampling densities, and occlusion conditions, the dataset introduces rich geometric and spatial distribution diversity. This paper presents a comprehensive description of the SPC data generation pipeline, semantic mapping strategy, virtual scanning configurations, and data organization scheme, followed by statistical analysis and technical validation in terms of point cloud scale evolution, spatial coverage characteristics, and component-wise semantic distributions. Furthermore, baseline experiments on component-level point cloud semantic segmentation are provided. The results demonstrate that models trained solely on the SPC dataset can achieve stable and engineering-meaningful component-level predictions on real construction point clouds, validating the dataset’s usability in virtual-to-real research scenarios. As a scalable and reproducible BIM-derived point cloud resource, the SPC dataset offers a unified data foundation and experimental support for research on construction scene point cloud semantic segmentation, virtual-to-real transfer learning, scan-to-BIM updating, and intelligent construction monitoring. Full article

Escherichia coli (E. coli) is a well-established indicator of faecal pollution and a potent pathogen linked to numerous gastrointestinal and systemic illnesses. Ensuring public safety requires rapid and sensitive detection methods capable of real-time, on-site deployment. Many conventional techniques are either laborious, time-intensive, costly, or require complex infrastructure, limiting their applicability in field settings. Raman spectroscopy offers label-free molecular fingerprinting; however, its inherently weak scattering signals restrict its effectiveness as a standalone technique. Surface-Enhanced Raman Spectroscopy (SERS) overcomes this limitation by exploiting plasmonic enhancement from nanostructured metallic substrates—most commonly gold, silver, copper, and aluminium. Despite the commercial availability of SERS-active substrates, challenges remain in achieving high reproducibility, long-term stability, and true field applicability, necessitating the development of integrated lab-on-chip platforms and portable, handheld Raman devices. This review critically examines recent advances in SERS-based E. coli detection across water and perishable food products with particular emphasis on the evolution of SERS substrate design, the incorporation of biosensing elements, and the integration of electrochemical and microfluidic systems. By contrasting conventional SERS approaches with next-generation biosensing strategies, this paper outlines pathways toward robust, real-time pathogen detection technologies suitable for both laboratory and field applications. Full article

Background/objectives: Involving parents in the care of hospitalized children can improve outcomes for both patients and families. Our team previously developed a unit-level model of family integrated care that supports families as key members of the neonatal intensive care team. However, the model’s suitability for general inpatient pediatric settings has not yet been explored. To proactively plan for adapting and implementing a feasibility and pilot study of this model in these settings, we examined healthcare professionals’ perspectives on optimizing family integrated care by identifying potential barriers to implementation. Methods: We conducted one-on-one semi-structured interviews with ten healthcare professionals along with observational site visits in three general inpatient pediatric units at a large tertiary pediatric hospital in Western Canada. We analyzed data using thematic analysis. Results: On average, participants in our study were 35.9 years old, reported 12.2 years of experience in healthcare, were predominantly female, and came from diverse disciplines, and reported substantial healthcare and unit experience. Several themes emerged from the interviews and site observations: resource constraints, workforce challenges, siloed team members, challenges to integrating families in care teams, diverse populations of patients and families, communication barriers, and workflow constraints. Participants indicated these themes may influence integration of families in care in general inpatient pediatric units. Conclusions: Our identification of key barriers to integrating families in care offers practical guidance for adapting and implementing family-integrated care in general inpatient pediatric settings. Full article

Syringic acid (SA) is a natural derivative of syringaldehyde (SD), derived from lignin depolymerization. Its application in the food industry focuses on the properties of natural functional ingredients; it is mainly used as a food antioxidant and food preservative, but can also be used as an ingredient to enhance food flavor and functional foods. This compound exhibits a remarkable spectrum of biological activities, including potent antioxidant, anti-inflammatory, neuroprotective, hypoglycemic, detoxifying, and anti-cancer effects, positioning it as a highly promising candidate for pharmaceutical and nutraceutical applications. In this study, suitable sites were first screened through homologous sequence alignment, and a variant of aryl-alcohol oxidase (CgAAO) with high efficiency in catalyzing the conversion of SD to SA was obtained via site-directed mutagenesis. A deep eutectic solvent (DES) system based on choline chloride/urea (ChCl/UR) in water was developed to enhance SA production. Additionally, key parameters of the biological reaction were optimized, including temperature, pH, metal ions, as well as the type and dosage of DES. The optimal performance was achieved using recombinant E. coli pRSFDuet-CgAAO-Y335F whole-cell biocatalysts, yielding 75% and producing 0.75 g/L SA in 100 mM KPB buffer (pH 7.0) containing 5 wt% ChCl/UR and 1 mM Fe³⁺. This study established a novel biosynthetic pathway for SA that was efficient, mild, green, and environmentally friendly. Full article