Search Results (40)

The utilization of recycled concrete aggregate presents an effective solution for construction waste mitigation. However, concrete service in sulfate environments leads to sulfate ion retention in recycled aggregates, substantially impairing their quality and requiring modification approaches. A critical question remains whether traditional recycled aggregate modification techniques can effectively enhance the performance of these sulfate-containing recycled aggregates (SRA). Cement paste wrapping in various proportions was used in this investigation to enhance SRA. The performance of both SRA and modified aggregates was systematically assessed through measurements of apparent density, water absorption, crushing value, and microhardness. Microstructural analysis of the cement wrapping modification mechanism was conducted by scanning electron microscopy coupled with mercury intrusion porosimetry. Results revealed that internal sulfate addition decreased the crushing value and increased the water absorption of recycled aggregates, primarily due to micro-cracks formed by expansion. Additionally, the pores were occupied by erosion products, leading to a slight increase in the apparent density of aggregates. The performance of SRA was effectively enhanced by cement paste wrapping at a 0.6 water–binder ratio, whereas it was negatively impacted by a ratio of 1.0. The modifying effect became even more effective when 15% fly ash was added to the wrapping paste. Scanning electron microscopy observations revealed that the interface of SRA was predominantly composed of gypsum crystals. Cement paste wrapping greatly enhanced the original interface structure, despite a new dense interface formed in the modified aggregates. Full article

Background: Developing countries face significant challenges in accessing high-quality pharmaceutical products due to resource constraints, limited regulatory capacity, and market dynamics that often prioritize cost over quality. This review addresses the critical gap in regulatory frameworks that fail to ensure pharmaceutical quality equity between developed and developing nations. Objective: This comprehensive review examines a novel dual-pathway regulatory framework that leverages stringent regulatory authority (SRA) approvals, artificial intelligence-based evaluation systems, and harmonized pricing mechanisms to ensure pharmaceutical quality equity across global markets. Methods: A comprehensive systematic analysis of current regulatory challenges, proposed solutions, and implementation strategies was conducted through an extensive literature review (202 sources, 2019–2025), expert consultation on regulatory science, AI implementation in healthcare, and pharmaceutical policy development. The methodology included an analysis of regulatory precedents, an economic impact assessment, and a feasibility evaluation based on existing technological implementations. Results: The proposed framework addresses key regulatory capacity gaps through two complementary pathways: Pathway 1 enables same-batch distribution from SRA-approved products with pricing parity mechanisms. At the same time, Pathway 2 provides independent evaluation using AI-enhanced systems for differentiated products. Key components include indigenous AI development, which requires systematic implementation over 4–6 years across three distinct stages, outsourced auditing frameworks that reduce costs by 40–50%, and quality-first principles that categorically reject cost-based quality compromises. Implementation analysis demonstrates a potential for achieving a 90–95% quality standardization, accompanied by a 200–300% increase in regulatory evaluation capability. Conclusions: This framework has the potential to significantly improve pharmaceutical quality and access in developing countries while maintaining rigorous safety and efficacy standards through innovative regulatory approaches. The evidence demonstrates substantial public health benefits with projected improvements in population access (85–95% coverage), treatment success rates (90–95% efficacy), and economic benefits (USD 15–30 billion in system efficiencies), providing a compelling case for implementation that aligns with global scientific consensus and Sustainable Development Goal 3.8. Full article

Inaccurate cost and schedule estimations in road infrastructure projects continue to be a critical source of contractual disputes and financial inefficiencies, particularly in developing countries. While quantitative risk analysis (QRA) methods such as Monte Carlo simulation (MCS) and schedule risk analysis (SRA) are well-established in the literature, their practical adoption remains limited in contexts with low technical capacity and limited access to advanced modeling tools. This study addresses this gap by proposing a practical and accessible quantitative risk analysis framework tailored to the needs of professionals with limited expertise in probabilistic techniques. The framework combines MCS and SRA using probability distributions (PERT, triangular, and normal) and was empirically validated through three road projects in Peru. Results indicated substantial reductions in uncertainty, achieving cost contingency estimates between 1.34% and 11% which were significantly lower than documented overruns of up to 32.29%. Schedule contingencies ranged from 28.71% to 91.67%, markedly improving accuracy. The novelty of this research lies in its context-adapted implementation strategy, offering a robust and easily replicable approach for similar infrastructure environments in Latin America and beyond. This contribution bridges the gap between theoretical risk modeling and its practical adoption, thus enhancing the reliability of infrastructure planning under resource-constrained conditions. Full article

(1) Background: A safe and effective nucleic acid sample transportation method was developed that is suitable for underdeveloped areas which lack advanced sequencing capabilities, specifically for virus genomic sequencing and infectious disease monitoring. (2) Methods: This study evaluated the use of Flinders Technology Associates (FTA) cards for transporting amplified whole-genome DNA from 120 SARS-CoV-2-positive nasopharyngeal swab samples in Sierra Leone. Nucleic acid extraction and whole-genome amplification were conducted at a local laboratory. Amplified products were applied to FTA Elute cards for room temperature shipment to China CDC for elution and sequencing. (3) Results: The FTA card method achieved a 9.6% recovery rate for amplicons, sufficient for viral genome sequencing. In total, 86 (71.7%) high-quality SRAS-CoV-2 genomic sequences were obtained, with the majority reaching depths exceeding 100X. Sequence analysis revealed co-circulation of Delta, Omicron, and B.1 lineages. Higher Ct values in the original sample significantly reduced coverage and depth, with Ct ≤ 27; 73.6% of samples yielded effective sequences. (4) Conclusions: Transportation of amplified nucleic acid samples using FTA cards enables virus genomic sequencing in resource-limited areas. This approach can potentially improve local virus surveillance and outbreak response capabilities. Further optimizations could improve sequence recovery rate. Implementing this method could significantly enhance sequencing accessibility in underdeveloped regions. Full article

Background: Machine learning (ML) techniques have been recently proposed as a solution for aiding in the prevention and diagnosis of microbiome-related diseases. Here, we applied auto-ML approaches on real-case metagenomic datasets from saliva and subgingival peri-implant biofilm microbiomes to explore a wide range of ML algorithms to benchmark best-performing algorithms for predicting peri-implantitis (PI). Methods: A total of 100 metagenomes from the NCBI SRA database (PRJNA1163384) were used in this study to construct biofilm and saliva metagenomes datasets. Two AI-driven auto-ML approaches were used on constructed datasets to generate 100 ML-based models for the prediction of PI. These were compared with statistically significant single-microorganism-based models. Results: Several ML algorithms were pinpointed as suitable bespoke predictive approaches to apply to metagenomic data, outperforming the single-microorganism-based classification. Auto-ML approaches rendered high-performing models with Receiver Operating Characteristic–Area Under the Curve, sensitivities and specificities between 80% and 100%. Among these, classifiers based on ML-driven scoring of combinations of 2–4 microorganisms presented top-ranked performances and can be suitable for clinical application. Moreover, models generated based on the saliva microbiome showed higher predictive performance than those from the biofilm microbiome. Conclusions: This feasibility study bridges complex AI research with practical dental applications by benchmarking ML algorithms and exploring oral microbiomes as foundations for developing intuitive, cost-effective, and clinically relevant diagnostic platforms. Full article

In differential MEMS resonant sensors, a pair of resonators are interconnected with other structural components while sharing a common substrate. This leads to mutual coupling of vibration energy between resonators, interfering with their frequency outputs and affecting the sensor’s static performance. This paper aims to model and analyze the vibration coupling phenomena in differential common-based MEMS resonators (DCMR). A mechanical model of the DCMR structure was established and refined through finite element simulation analysis. Theoretical calculations yielded vibration coupling curves for two typical silicon resonant accelerometer (SRA) structures containing DCMR: SRA-V1 and SRA-V2, with coupling stiffness values of 2.361 × 10⁻⁴ N/m and 1.370 × 10⁻² N/m, respectively. An experimental test system was constructed to characterize the vibration coupling behavior. The results provided coupling amplitude-frequency characteristic curves and coupling stiffness values (7.073 × 10⁻⁴ N/m and 1.068 × 10⁻² N/m for SRA-V1 and SRA-V2, respectively) that validated the theoretical analysis and computational model. This novel approach enables effective evaluation of coupling intensity between 5resonators and provides a theoretical foundation for optimizing device structural designs. Full article

Because of their high spatial resolution over extended lengths, distributed fiber optic sensors (DFOS) enable us to monitor a wide range of structural effects and offer great potential for diverse structural health monitoring (SHM) applications. However, even under controlled conditions, the useful signal in distributed strain sensing (DSS) data can be concealed by different types of measurement principle-related disturbances: strain reading anomalies (SRAs), dropouts, and noise. These disturbances can render the extraction of information for SHM difficult or even impossible. Hence, cleaning the raw measurement data in a pre-processing stage is key for successful subsequent data evaluation and damage detection on engineering structures. To improve the capabilities of pre-processing procedures tailored to DSS data, characteristics and common remediation approaches for SRAs, dropouts, and noise are discussed. Four advanced pre-processing algorithms (geometric threshold method (GTM), outlier-specific correction procedure (OSCP), sliding modified z-score (SMZS), and the cluster filter) are presented. An artificial but realistic benchmark data set simulating different measurement scenarios is used to discuss the features of these algorithms. A flexible and modular pre-processing workflow is implemented and made available with the algorithms. Dedicated algorithms should be used to detect and remove SRAs. GTM, OSCP, and SMZS show promising results, and the sliding average is inappropriate for this purpose. The preservation of crack-induced strain peaks’ tips is imperative for reliable crack monitoring. Full article

Hydraulic fracturing is the predominant technology for the development of unconventional resources, and understanding multi-fracture propagation behavior is the foundation for hydraulic fracturing optimization. To deeply understand multi-fracture propagation behavior in natural formations, this paper proposes a numerical simulation which considers the fluid-solid coupling process based on the displacement discontinuity method and the finite volume method. The simulation indicates that high stress difference and low approach angle are the main factors limiting the stimulated volume, while low stress difference and high approach angle are beneficial for creating modification zones. In addition, the natural fracture density also has a great effect on fracture propagation. With increasing natural fracture density, the stimulated volume also increases greatly, which plays a significant role in enhancing the SRA. These findings are critical in comprehending the impact of geological parameters on deep fractured shale reservoirs. Full article

This paper compares various approaches to the scalability and replicability analysis (SRA) of smart grid pilot projects, highlighting the need for a comprehensive SRA methodology as called for by the European Commission and International Energy Agency. This study addresses the need for a standardized SRA methodology and explores how three EU-funded projects—Platone, EUniversal, and IElectrix—adapted the general guidelines developed by the BRIDGE initiative. These guidelines provide recommendations for developing a comprehensive large-scale deployment analysis. The results show that while the guidelines are usable and flexible, project-specific conditions and data availability limitations—particularly in regulatory and technical analysis—can pose challenges. Some key recommendations to overcome these and facilitate future applications are identified. These include defining SRA methodologies and securing data-sharing agreements early. The lack of standardized approaches for presenting SRA results hampers cross-project comparison. Thus, creating an open-use case repository and updating the BRIDGE guidelines with more detailed examples, benchmarks, and reference networks is recommended. Additionally, linking SRA with cost–benefit analysis (CBA) is suggested in order to evaluate the commercial viability of smart grid solutions. The paper concludes that while the BRIDGE guidelines have proven to be fit for purpose, further developments are needed to facilitate their practical application in real-world projects. Full article

Agricultural pests can cause direct damage to crops, including chlorosis, loss of vigor, defoliation, and wilting. In addition, they can also indirectly damage plants, such as by transmitting pathogenic micro-organisms while feeding on plant tissues, affecting the productivity and quality of crops and interfering with agricultural production. Among the known arthropod pests, mites are highly prevalent in global agriculture, particularly those from the Tetranychidae family. The two-spotted spider mite, Tetranychus urticae, is especially notorious, infesting about 1600 plant species and causing significant agricultural losses. Despite its impact on agriculture, the virome of T. urticae is poorly characterized in the literature. This lack of knowledge is concerning, as these mites could potentially transmit plant-infecting viral pathogens, compromising food security and complicating integrated pest management efforts. Our study aimed to characterize the virome of the mite T. urticae by taking advantage of publicly available RNA deep sequencing libraries. A total of 30 libraries were selected, covering a wide range of geographic and sampling conditions. The library selection step included selecting 1 control library from each project in the NCBI SRA database (16 in total), in addition to the 14 unique libraries from a project containing field-collected mites. The analysis was conducted using an integrated de novo virus discovery bioinformatics pipeline developed by our group. This approach revealed 20 viral sequences, including 11 related to new viruses. Through phylogenetic analysis, eight of these were classified into the Nodaviridae, Kitaviridae, Phenuiviridae, Rhabdoviridae, Birnaviridae, and Qinviridae viral families, while three were characterized only at the order level within Picornavirales and Reovirales. The remaining nine viral sequences showed high similarity at the nucleotide level with known viral species, likely representing new strains of previously characterized viruses. Notably, these include the known Bean common mosaic virus (BCMV) and Phaseolus vulgaris alphaendornavirus 1, both of which have significant impacts on bean agriculture. Altogether, our results expand the virome associated with the ubiquitous mite pest T. urticae and highlight its potential role as a transmitter of important plant pathogens. Our data emphasize the importance of continuous virus surveillance for help in the preparedness of future emerging threats. Full article

Introduction. Clival chordomas represent a rare but clinically significant subset of skull base tumors, characterized by a locally aggressive nature and a location in proximity to vital neurovascular structures. Surgical resection, often combined with adjuvant therapies, remains the cornerstone of clival chordoma treatment, and various approaches and techniques have evolved to maximize tumor removal while preserving neurological function. Recent advancements in skull base surgery, imaging, and adjuvant therapies have improved outcomes by reducing morbidity and thus enhancing long-term survival. Methods and Results. We have conducted a systematic review on PubMed/Medline following PRISMA guidelines regarding indications, the extent of resection (EOR), and complication rates. Then, we present three illustrative cases from our personal experience, which started 25 years ago with CVJ instrumentation procedures and 15 years ago with anterior decompressive transmucosal procedures performed with the aid of an operative microscope, an endoscope, and neuroradiological monitoring. Conclusions. Traditionally, the transoral approach (TOA) is the most frequently used corridor for accessing the lower clivus and the anterior craniovertebral junction (CVJ), without the need to mobilize or retract neural structures; however, it is associated with a high rate of complications. The endonasal approach (EEA) provides access to the anterior CVJ as well as to the lower, middle, and superior clivus, decreasing airway and swallowing morbidity, preserving palatal function, decreasing postoperative pain, and reducing the incidence of tracheostomy. The submandibular retropharyngeal approach (SRA) allows unique access to certain cervical chordomas, which is better suited when the lesion is located below the clivus and in the midline. Full article

Climate change has emerged as one of the most significant challenges in modern agriculture, with potential implications for global food security. The impact of changing climatic conditions on crop yield, particularly for staple crops like wheat, has raised concerns about future food production. By integrating historical climate data, GCM (CMIP3) projections, and wheat-yield records, our analysis aims to provide significant insights into how climate change may affect wheat output. This research uses advanced machine learning models to explore the intricate relationship between climate change and wheat-yield prediction. Machine learning models used include multiple linear regression (MLR), boosted tree, random forest, ensemble models, and several types of ANNs: ANN (multi-layer perceptron), ANN (probabilistic neural network), ANN (generalized feed-forward), and ANN (linear regression). The model was evaluated and validated against yield and weather data from three Punjab, Pakistan, regions (1991–2021). The calibrated yield response model used downscaled global climate model (GCM) outputs for the SRA2, B1, and A1B average collective CO₂ emissions scenarios to anticipate yield changes through 2052. Results showed that maximum temperature (R = 0.116) was the primary climate factor affecting wheat yield in Punjab, preceding the $T_{min}$ (R = 0.114), while rainfall had a negligible impact (R = 0.000). The ensemble model (R = 0.988, nRMSE= 8.0%, MAE = 0.090) demonstrated outstanding yield performance, outperforming Random Forest Regression (R = 0.909, nRMSE = 18%, MAE = 0.182), ANN(MLP) (R = 0.902, MAE = 0.238, nRMSE = 17.0%), and boosting tree (R = 0.902, nRMSE = 20%, MAE = 0.198). ANN(PNN) performed inadequately. The ensemble model and RF showed better yield results with $R^2$ = 0.953, 0.791. The expected yield is 5.5% lower than the greatest average yield reported at the site in 2052. The study predicts that site-specific wheat output will experience a significant loss due to climate change. This decrease, which is anticipated to be 5.5% lower than the highest yield ever recorded, points to a potential future loss in wheat output that might worsen food insecurity. Additionally, our findings highlighted that ensemble approaches leveraging multiple model strengths could offer more accurate and reliable predictions under varying climate scenarios. This suggests a significant potential for integrating machine learning in developing climate-resilient agricultural practices, paving the way for future sustainable food security solutions. Full article

Lung cancer presents a global health challenge, demanding exploration of its molecular intricacies for treatment targets. The goal is to delay progression and intervene early, reducing patient burden. Novel biomarkers are urgently needed for early diagnosis. We analysed RNA sequencing on lung cancer samples from NCBI’s SRA database. Using Bioconductor in R, we identified key genes, including hub genes TOP2A and TMEM100, crucial for cellular processes. Additionally, FDA-approved drugs are repurposed as multitargeted inhibitors against upregulated genes, validated through simulations. This approach aims to inhibit the function of crucial genes, potentially offering effective treatment for lung cancer within a comprehensive strategy. Full article

Numerous studies have examined land surface temperature (LST) changes in Thailand using remote sensing, but there has been little research on LST variations within urban land use zones. This study addressed this gap by analyzing summer LST changes in land use zoning (LUZ) blocks in the 2012 Chiang Mai Comprehensive Plan and their relationship with surface biophysical parameters (NDVI, NDBI, MNDWI). The approach integrated detailed zoning data with remote sensing for granular LST analysis. Correlation and stepwise regression analyses (SRA) revealed that NDBI significantly impacted LST in most block types, while NDVI and MNDWI also influenced LST, particularly in 2023. The findings demonstrated the complexity of LST dynamics across various LUZs in Chiang Mai, with SRA results explaining 45.7% to 53.2% of summer LST variations over three years. To enhance the urban environment, adaptive planning strategies for different block categories were developed and will be considered in the upcoming revision of the Chiang Mai Comprehensive Plan. This research offers a new method to monitor the urban heat island phenomenon at the block level, providing valuable insights for adaptive urban planning. Full article

To study the life-cycle seismic reliability analysis (SRA) of cable-stayed bridges (CSBs) taking into account chloride-induced corrosion and degradation of components, an actual railway CSB with uncertainties in structural geometry and material corrosion coefficients was employed in this investigation, and time-dependent models of CSB components at different service times were studied. Based on the OpenSees batch program, we adapted a mass numerical computation to obtain time-dependent non-linear seismic response and probability density function (PDF) of response via the multiplier dimensional-reduction method (MDRM) and the maximum entropy method with fractional moments (FM-MEM). Next, the time-dependent failure possibility of every component and the association coefficient between the failure modes of different parts were acquired. In the end, the product of the conditional marginal (PCM) approach was employed to obtain the life-cycle failure possibility of the CSB system. The results showed that the system failure possibility of the CSB in a corrosive environment increases significantly with increasing servicing time. Full article