Search Results (10,974)

As the demand for sustainable construction materials grows, wood ash and nanosilica have emerged as promising components for eco-friendly mortars, whose optimization requires advanced analytical techniques capable of capturing their complex linear and nonlinear interactions, making frameworks such as response surface methodology and artificial neural networks essential for effective mix design. This study examines the mechanical performance of eco-friendly mortar incorporating wood ash (WA) as a partial cement replacement and nanosilica solution (NSS) as a strength-enhancing additive, with the aim of optimizing compressive and flexural behaviour. Wood ash was substituted at levels of 5–25%, while NS (0.265 moL⁻¹) was substituted at levels of 0–1.7%. Twenty-one mortar samples were produced and tested at multiple curing ages. Two modelling techniques, response surface methodology (RSM) and artificial neural networks (ANNs), were employed to evaluate the individual and interactive effects of WA and NSS on strength development at curing ages of 28 and 180 days. While RSM provided insight into factor significance and linear interactions, ANN more effectively captured nonlinear behaviour, achieving superior predictive accuracy (R² = 1.000 for 28-day strength). Experimental results revealed that nanosilica substantially enhanced strength up to an optimal dosage of approximately 2.5 g, beyond which performance declined due to particle agglomeration or matrix over-refinement. In contrast, higher WA contents produced strength reductions attributable to dilution effects. Optimization showed that mixtures containing low WA (≤30 g) combined with moderate NSS (2.0–2.5 g) exhibited the highest mechanical performance. Collectively, the findings confirm that ANN-based models outperform RSM and multilinear regression, underscoring their effectiveness for mix design optimization and performance forecasting in sustainable cementitious systems. Full article

Geovisualizations based on open data are increasingly used as public-facing interfaces for communicating geospatial information, yet their evaluation often remains limited to isolated design, usability, or technical aspects. This study addresses that gap by developing and applying an integrative evaluation framework that combines four analytical dimensions: cartographic representation, interaction and engagement affordances, openness, and accessibility, while treating contextual characteristics as conditioning factors. The framework is operationalized through a mixed-methods content analysis of 26 publicly available geovisualizations based on open data. The results show that most cases are produced by public-sector actors, focus on environmental and transport themes, and rely on conventional cartographic techniques combined with medium levels of interactivity that support structured exploration rather than deeper analytical reasoning. Although many geovisualizations cite data sources and provide some form of data access, licensing remains inconsistent, particularly for the visualization artefacts themselves, limiting reuse potential. Accessibility is implemented even less consistently across geovisualizations, with recurring shortcomings in color contrast, keyboard navigation, screen-reader compatibility, and multilingual support. Overall, the findings suggest that the broader societal potential of geovisualizations based on open data may not be determined by individual features, but by balanced cross-dimensional configurations. Strengthening the integration of openness and accessibility alongside interaction and design may enhance the potential of geovisualizations to support reuse, inclusiveness, and public engagement. Full article

The pore-throat structure is a key factor in the exploration and development of tight sandstone reservoirs. In the present study, 14 tight sandstone samples from the Chang 8 member of the Ordos Basin were analyzed using high-pressure mercury intrusion, cast thin section analysis, scanning electron microscopy and cathodoluminescence imaging techniques. Fractal dimensions, obtained from the slopes of log(S_W) versus log(P_c) double-logarithmic plots, were applied to quantitatively characterize pore-throat structures and classify reservoirs through multifractal analysis, and discuss the diagenetic controlling factors affecting the pore-throat structure of different reservoir types. The results showed that the Chang 14 tight sandstones are characterized as two segments fractal features, which indicated that these samples have complex pore-throat structure and consist of two types of spaces: mesopore-throat spaces and micropore-throat spaces. The mesopore-throat system shows a higher fractal dimension (D1: 2.74–2.99), indicating greater heterogeneity and irregularity, while the micropore-throat system exhibits a lower dimension (D2: 2.28–2.61). D1 exhibits a negative correlation with the porosity and permeability of mesopores, while D2 shows a weak positive correlation with the properties of micropores. The total fractal dimension (D) is weakly correlated with overall reservoir properties, confirming that reservoir storage and flow capacity are primarily governed by the mesopore system rather than the micropore system. By analyzing the contribution of pore throats to sample physical properties, the results indicate that the 14 samples can be classified into two types based on 35% porosity contribution and 60% permeability contribution thresholds. Type 1, reservoirs dominated by microporous throat space (D values ranging from 2.603 to 2.644); Type 2, reservoirs dominated by mesoporous throat space (D values ranging from 2.544 to 2.598). Type 1 is characterized by primary intergranular pores, residual intergranular pores and intergranular dissolution pores, which enhance connectivity and reduce network complexity, thereby improving fluid permeability. In contrast, Type 2 consists mainly of intragranular dissolution pores, intergranular gap pores and micro-dissolution pores in clay minerals, which significantly inhibit fluid mobility. Diagenesis, including compaction, dissolution and cementation, exerts a significant control on the fractal characteristics and pore-throat structure evolution. The fractal characteristics exhibited in the pore-throat structure could provide a desirable analytical method, distinguishing from classification based on scale or size, for the evaluation and classification of tight sandstone reservoirs. Full article

The authentication of Algerian honey represents a critical challenge for the valuation of national biological patrimony. The present review provides a comprehensive synthesis of existing literature regarding Algerian honeys, emphasizing their diverse botanical origins and complex chemical profiles across seven distinct biogeographical regions, while proposing an innovative Foodomics and AI-driven roadmap to secure geographic authenticity and sustainable rural development. Such evidence underscores the necessity of transitioning from this classical analytical framework toward the emerging ‘Foodomics’ paradigm. By integrating advanced technologies like DNA metabarcoding and molecular fingerprinting, the establishment of a proposed ‘digital passport’ is proposed as a strategic solution to secure Protected Geographical Indications (PGI). Beyond technical innovation, this evolution is presented as a vital socio-economic necessity to ensure the sustainability of rural beekeeping and the international competitiveness of the industry. Ultimately, bridging established data with a molecular roadmap ensures that the biological prestige of this natural heritage is preserved for future generations. Beyond chemical and botanical analyses, this roadmap also incorporates Chemometric Modeling as a cognitive system. By applying techniques such as self-organizing maps (SOMs) and principal component analysis (PCA). This combination ensures highly accurate classification and supports the implementation of a sustainable digital passport system for the local honey industry. Full article

Volcanic ash preserves critical information on eruption dynamics, magma evolution, and fragmentation processes, yet its small size and fragile structure pose challenges for conventional analytical methods. Advances in SEM-based automated mineralogy combined with X-ray mapping (GXMAP) provide high-resolution characterization of ash textures, particle morphology, and mineral assemblages, offering a more robust basis for interpreting pyroclastic deposits. This study applies an integrated GXMAP workflow alongside sieve-based granulometry to the Early Pleistocene trachyandesite to rhyolitic pyroclastic sequences at the Kurtan quarry (Kechut Volcanic Province, Armenia), a key regional stratigraphic marker associated with early human occupation. GXMAP-based granulometry minimizes preparation-induced fragmentation and yields more consistent and reliable grain-size and morphological data for fine ash deposits than dry sieving. The three stratigraphic units at Kurtan display distinct combinations of grain size, mineral assemblages, and particle morphologies, reflecting contrasting magma evolution, fragmentation conditions, and depositional regimes. Shape-parameter fields derived from BSE images reveal clear differences between the highly irregular, concave compound fragments dominating TP-13-1 and the smoother, more compact particles characteristic of TP-13-2 and TP-13-3. Most particles fall within the ductile domain of established shape-morphology diagrams, indicating that ductile deformation of bubble walls was a major component of fragmentation, accompanied by heterogeneous brittle breakage. These results demonstrate the effectiveness of the combined SEM-based automated mineralogy and GXMAP approach for resolving primary fragmentation, sorting characteristics, and depositional processes in fragile pyroclastic deposits. The Kurtan sequence provides new constraints on explosive volcanism in the Lesser Caucasus Mts. region. At the same time, the methodological framework offers broad applicability to tephra studies worldwide and underscores the potential of imaging-based techniques in volcanology. Full article

This study aimed to isolate, characterize, and evaluate bacterial consortia capable of degrading the diamide insecticide cyantraniliprole in aqueous systems and to assess their bioremediation potential under environmentally relevant conditions. Four bacterial consortia, each comprising six isolates, demonstrated significant growth in mineral media containing cyantraniliprole as the sole carbon source, and the isolates were identified using conventional microbiological techniques in combination with MALDI-TOF-MS analysis. The bacterial consortia were enriched from pesticide-contaminated environments and systematically evaluated using microbiological, physiological, and analytical approaches to determine their degradation potential and environmental adaptability. The degradation performance of the consortia was systematically assessed under varying environmental parameters, including temperature, pH, salinity, and incubation time, with optimal degradation observed at 30–35 °C, pH 7.0–8.0, 0.5–5.0% NaCl, and 11 days of incubation at 150 rpm using an initial cyantraniliprole concentration of 50 mg/L. Biodegradation efficiency was further evaluated using DCPIP reduction assays, alongside measurements of biofilm formation and biomass production, indicating enhanced metabolic activity and adaptive responses under pesticide-induced stress. The consortia also exhibited the capacity to degrade structurally related diamide pesticides, including flubendiamide, chlorantraniliprole, cyclaniliprole, and fluchlordiniliprole, suggesting broad-spectrum biodegradation potential. Their performance was further validated in a simulated water microcosm system designed to mimic environmentally relevant contamination scenarios. In simulated contaminated water (60 mg/L cyantraniliprole), bacterial inoculants standardized to 10⁷ CFU/mL achieved substantial degradation after 20 days of incubation at 30 °C, as confirmed by HPLC analysis, with the six-strain consortium (T4), comprising Bacillus subtilis subsp. subtilis AZFS3, Bacillus pumilus AZFS5, Bacillus mojavensis AZFS15, Bacillus paramycoides AZFS18, Pseudomonas aeruginosa KZFS4, and Alcaligenes aquatilis KZFS11, demonstrating the highest removal efficiency (98.27%) and reducing the pesticide concentration to 1.00 mg/L, followed by consortium T3 (96.72%), which consisted of Bacillus subtilis Ht1, Bacillus subtilis Ht2, Bacillus mojavensis Ht3, Pseudomonas aeruginosa Ht4, Pseudomonas aeruginosa Ht5, and Pseudomonas aeruginosa Ht6. Residue analysis and predictive bioinformatic assessment further supported the biodegradation capacity of the selected bacterial communities and suggested the formation of simpler transformation products. Overall, the investigated bacterial consortia exhibited high degradation efficiency and environmental adaptability, highlighting their potential as effective and eco-friendly agents for the bioremediation of cyantraniliprole-contaminated water systems Full article

Within the South-Western food industry of Nigeria, the overall impact, associated challenges, and implementation of Big Data Analytics (BDA) have remained underexplored. Thus, this study aimed to investigate the extent of BDA adoption, identify key barriers and enablers, assess the operational impacts of BDA adoption, and propose a structured framework to guide effective integration. The study adopted a deductive, mono-quantitative method. Data were collected from 151 participants through a stratified sampling technique using an online survey questionnaire and analysed using descriptive and inferential statistical methods, including Chi-Square, Likelihood Ratio, and Fisher-Freeman-Halton Exact tests, using SPSS version 26 and Excel as analytical tools. While awareness and appreciation of BDA’s strategic benefits are growing, significant challenges such as high implementation costs, a shortage of skilled personnel, regulatory uncertainties, and technological limitations persist. Nevertheless, organisations that have embraced BDA report notable improvements in operational efficiency, strategic decision-making, customer satisfaction, and competitive advantage. This study proposes a practical BDA adoption framework designed to address the identified barriers and enhance successful implementation and offers several recommendations. The research helps bridge the knowledge gap on BDA adoption in emerging economies and offers actionable insights for business leaders, policymakers, and practitioners seeking to drive innovation and sustainability in Nigeria’s food industry. Full article

Deep tight sandstone reservoirs are characterized by strong microscopic pore structure heterogeneity and commonly exhibit a high-porosity, low-permeability profile, posing significant challenges for effective reservoir evaluation and “sweet spot” prediction. The microscopic pore structure of 209 tight sandstone samples from the deeply buried Huagang Formation in the Xihu Depression, East China Sea Basin, was systematically characterized by integrating multiple analytical techniques, including casting thin sections, scanning electron microscopy (SEM), X-ray diffraction (XRD), nuclear magnetic resonance (NMR), and high-pressure mercury injection (HPMI). The results indicate that the reservoir space is dominated by mesopores (55.48%) and transition pores (32.39%), with macropores (2.09%) and micropores (10.04%) being relatively underdeveloped. A significant vertical heterogeneity in reservoir quality is observed. The H4 member exhibits superior properties, characterized by a higher average movable fluid saturation (averaging 46%) and better pore connectivity. In contrast, the H5 member is more compact, with a notably higher proportion of bound fluid (averaging 47%). The differences in reservoir quality are controlled by a sedimentary–diagenetic coupling mechanism. High-energy, coarse-grained facies underwent a constructive pathway involving chlorite coating protection and dissolution enhancement, forming high-quality pore networks. In contrast, low-energy, fine-grained facies experienced a destructive pathway dominated by intense compaction and cementation, leading to the deterioration of pore structure. The petrophysical properties of the deep reservoirs are primarily governed by the three-dimensional connectivity and spatial distribution of effective “pore-throat assemblages” composed of dominant throats. Accordingly, a “binary” pore structure development pattern is established for the deep tight sandstone reservoirs in the study area. This pattern posits that the reservoir space is heterogeneously composed of a minority of connected “effective percolation assemblages” and a majority of isolated “ineffective assemblages”. Full article

With the growing interest in the microbiome, short-chain fatty acids (SCFAs) have emerged as key metabolites due to their critical roles in host physiology, including immune regulation, energy homeostasis, and inflammatory control. As a result, the accurate quantification of SCFAs in various biological samples has become increasingly important. However, reliable and standardized methods for measuring SCFAs across different sample types remain underdeveloped, highlighting the need for methodological refinement. To address this need, we optimized two analytical methods, headspace GC-MS and GC-MS/MS, for SCFA quantification. These techniques were applied to a range of biological matrices, including pure microbial cultures, low-abundance animal liver, animal feces, and standardized simulated human fecal samples. The headspace GC-MS approach enables direct analysis with minimal sample preparation, thereby enhancing throughput and ease of use. In contrast, the GC-MS/MS method, involving methanol extraction, alkaline treatment, and derivatization with MTBSTFA, offers superior sensitivity and precision, making it particularly suitable for small-volume and low-abundance samples. Together, these optimized protocols provide robust, sensitive platforms for profiling SCFAs across diverse biological matrices, facilitating a deeper understanding of microbiome–host interactions and supporting future translational applications. Full article

Background/Objectives: This study aimed to comparatively analyze online information regarding dental implants and dentures utilizing text-mining techniques. Methods: An automated text-mining program was employed to collect and process data using the Korean keywords for “implant” and “denture.” Data sources included major search engines, social networking services, and YouTube (Google LLC, Mountain View, CA, USA). A total of 9941 data points for dental implants and 9783 for dentures were retrieved. The analytical approach included word cloud generation, term frequency-inverse document frequency (TF-IDF) analysis, semantic network analysis, and sentiment analysis. Results: For implants, “dental clinic,” “treatment,” “surgery,” and “insurance” emerged as highly relevant keywords. In contrast, queries regarding dentures frequently included the term “implant,” alongside top-ranking, age-related terms such as “abnormality” and “discomfort.” TF-IDF analysis revealed that “surgery” and “procedure” ranked higher for implants, whereas “insurance” ranked higher for dentures. Sentiment analysis indicated a predominantly positive public perception of implants (63.09% positive, 36.91% negative), whereas dentures elicited a largely negative sentiment (40.70% positive, 59.30% negative). Conclusions: The text-mining analysis revealed distinct public perceptions regarding the two treatments. Implants were primarily associated with surgical procedures and positive sentiments, whereas dentures were more closely linked to insurance considerations and negative experiences. Full article

Accurate measurement and control of impurities are critical for ensuring the quality and therapeutic performance of solid-state pharmaceutical formulations. This study introduces a rapid, minimal sample preparation analytical approach for quantifying low-level dalmelitinib impurities in dalmelitinib mesylate, employing near-infrared (NIR) spectroscopy combined with partial least squares regression (PLSR). To mimic actual manufacturing conditions, a mixture system was designed comprising dalmelitinib mesylate, dalmelitinib impurity, and formulation excipients. Various spectral preprocessing strategies were systematically evaluated, including Savitzky–Golay first derivative (SG1st), Savitzky–Golay second derivative (SG2nd), multiplicative scatter correction (MSC), standard normal variate (SNV), wavelet denoising, wavelet compression, and their combinations. The optimal model was obtained using SG1st combined with wavelet denoising. The resulting PLSR model (7 latent variables) showed good predictive performance, with an R² of 0.99569 and an RMSECV of 0.00315. The limit of detection (LOD) and limit of quantification (LOQ) were 0.234% and 0.708%, respectively, demonstrating applicability for monitoring low-level impurities in pharmaceutical formulations. Method validation demonstrated satisfactory precision (RSD < 3%), accuracy (100.77–102.01%), and stability over 24 h (RSD ≤ 4.75%). Compared with conventional solid-state analytical techniques, the proposed NIR–PLSR framework enables rapid, non-destructive analysis with minimal sample preparation. The combination of derivative preprocessing and wavelet denoising improved extraction of impurity-related spectral information in complex pharmaceutical systems, highlighting the potential of this approach for process analytical technology (PAT) and pharmaceutical quality monitoring. Full article

Hormones regulate numerous physiological processes and are essential for maintaining metabolic homeostasis. Accurate hormone quantification is crucial for the diagnosis and monitoring of endocrine and metabolic disorders. Electrochemical biosensors have recently emerged as promising platforms for hormone detection, offering simplicity, rapid response, cost-effectiveness, and high sensitivity compared to conventional techniques such as chromatography and mass spectrometry. This review summarizes the advances in electrochemical biosensors for detecting clinically relevant hormones, including cortisol, estrogen, progesterone, thyroid-stimulating hormone, parathyroid hormone, prolactin, and insulin, since 2010. Particular attention has been paid to developments in electrode modification strategies, including nanomaterials, redox enzymes, and novel recognition elements, which significantly improve the sensitivity and selectivity. These advances enable hormone detection at lower concentrations in various biological and environmental matrices. Despite these promising developments, challenges related to sensor stability, fabrication costs, and regeneration procedures limit their large-scale commercialization. Future research should focus on improving robustness, optimizing immobilization strategies, and integrating innovative materials to enhance the analytical performance. Continued collaboration among researchers, engineers, and healthcare professionals is essential. With ongoing technological progress, electrochemical biosensors are expected to play an important role in clinical diagnosis, point-of-care testing, and personalized medicine. Full article

The rapid growth of organic waste volumes in urban areas and increasing environmental pressures necessitate the transition toward sustainable and risk-informed municipal waste management systems. This study aims to develop a data-driven decision support framework for the risk-informed management of municipal organic waste within the context of sustainable urban development. The proposed approach integrates multi-source municipal data, advanced preprocessing techniques, entropy-based feature weighting, and an ensemble of machine learning models, including Random Forest, Gradient Boosting, and XGBoost. An integrated environmental risk index is formulated to quantify the state of the waste management system and to support predictive analytics. The results demonstrate high predictive performance and reveal that key risk drivers include demographic pressure, transport accessibility, infrastructure characteristics, and seasonal variability of waste generation. The developed framework enables the integration of predictive risk analytics into municipal decision support systems, facilitating optimized waste collection logistics, infrastructure planning, and early identification of critical conditions. The findings confirm that data-driven approaches can significantly enhance the efficiency and adaptability of urban waste management systems. The proposed framework contributes to sustainable urban development by supporting circular economy principles and enabling proactive, risk-aware governance of municipal organic waste systems. Full article

Imaging-based spatial transcriptomics has advanced from low-plex single-molecule fluorescence in situ hybridization to a diverse set of highly multiplexed platforms, with recent multimodal and pathology-compatible capabilities. Despite major differences in chemistry, coding, and imaging strategies across different platforms, their biological interpretation often converges on a few notable computational biology problems. This review examines imaging-based spatial transcriptomics through the lens of data interpretation and applications, focusing on the analytical framework that converts raw fluorescence signals or accompanying in situ sequencing data into molecule-, cell-, and tissue-level representations. We discuss the key challenges in preprocessing, registration, restoration, feature detection, barcode decoding, molecule calling, cell segmentation, transcript assignment, probabilistic cell typing, spatial-domain inference, and atlas integration. We also highlight how optical crowding, tissue thickness, panel bias, and multimodal complexity increase computational difficulty. Finally, we summarize applications of imaging-based spatial transcriptomics techniques, ranging from subcellular RNA localization to atlas-scale and pathology-aware spatial analysis. Full article

Delays are among air transport’s main operational challenges, with significant economic, societal and environmental consequences, and many methodological alternatives have been used in their study. Here we explore the use of symbolic regression, a data-driven technique that searches a space of analytic expressions to identify compact and interpretable models explaining a given set of data. We specifically use symbolic regression to characterise delays at the busiest European airports, how they evolve in time and depend on their own past, up to how they propagate across airports. This is done with the aim of evaluating the feasibility of using this approach, and the added value when compared to standard statistical and causal models. Results of this proof of concept point to a nuanced picture: while symbolic regression demonstrates clear potential for uncovering interpretable functional relationships in delay dynamics, its applicability is hindered by the significant computational cost and its stochastic nature. Full article