Search Results (7,406)

Background/Objectives: Human colostrum plays a crucial role in early microbial colonization, immune development, and gut health of newborns. Its microbiota is highly dynamic and influenced by numerous factors, yet the determinants remain poorly understood. This systematic review aims to investigate the composition of colostrum microbiota and the intrinsic and extrinsic factors that influence its diversity and abundance. Methods: PubMed and Scopus were systematically searched using a prespecified search phrase. Data on microbial composition, diversity, and influencing factors were extracted and analyzed. The systematic review is registered in PROSPERO (CRD42025644017). Results: A total of 44 eligible studies involving 1982 colostrum samples were identified. Colostrum microbiota consists predominantly of Firmicutes and Proteobacteria, with core genera including Staphylococcus, Streptococcus, Lactobacillus, and Bifidobacterium. Some studies reported higher diversity in colostrum compared to mature milk, while others noted elevated bacterial abundance in the former. Factors influencing colostrum microbiota include maternal BMI, delivery mode, gestational age, diet, gestational diabetes mellitus (GDM), maternal stress, maternal age, secretor status, perinatal antibiotic exposure, neonatal gender, geographic location, feeding type, milk collection method, and mastitis. Conclusions: Colostrum hosts a diverse and dynamic microbiota shaped by multiple maternal, neonatal, and environmental factors. Understanding these influences is crucial for optimizing infant health outcomes, emphasizing the need for further research on the functional roles of colostrum’s microbiota. Full article

Air quality is a vital factor for safeguarding public and environmental health. Particulate matter (i.e., PM2.5 and PM10) and nitrogen dioxide are among the most harmful air pollutants leading to severe health risks such as respiratory and cardiovascular diseases, while also affecting the environment negatively by contributing to the formation of acid rains and ground level ozone. The European Union has introduced new thresholds on those pollutants to be met by the year 2030, taking into consideration the guidelines set by the World Health Organization, aiming for a healthier environment for humans and living species. Cyprus is an island that is vulnerable to those pollutants mostly due to its geographic location, facilitating shipping activities and dust transport from Sahara Desert, and the methods used to produce electricity which primarily rely on petroleum products. Furthermore, the country suffers from heavy traffic conditions, making it susceptible to high levels of nitrogen dioxide. Thus, the projection of air pollutants according to different scenarios based on regulations and policies of the European Union are necessary towards clean air and better practices. The Screening for High Emission Reduction Potential on Air (SHERPA) is a tool developed by the European Commission which allows the simulation of emission reduction scenarios and their effect on the following key pollutants: NO, NO₂, O₃, PM2.5, PM10. This study aims to assess the potential of the SHERPA simulation tool to support air quality related decision and policy planning in Cyprus to ensure that the country will remain within the thresholds that will be applicable in 2030. Full article

Background: Communication difficulty restricts education, healthcare, and social participation, yet population-level data for Saudi Arabia have been scarce. This study analysed the 2017 Saudi National Disability Survey to estimate prevalence, describe severity and demographic patterns, and identify factors linked to these difficulties. Objectives: We aimed to estimate national and regional prevalence, assess severity, and gender differences, and identify socio-demographic and disability-related correlates. Methods: A cross-sectional, two-stage stratified cluster sample of 33,575 households (weighted N = 20,408,362 citizens) provided self-reported data on communication difficulty and socio-demographics. Weighted frequencies described prevalence and multivariable logistic regression identified independent correlates. Results: Among all Saudi citizens, 7.1% reported at least one functional difficulty, and of this group 15.7%—equivalent to 1.1% of the total population (n = 226,510)—had a communication difficulty; within that communication difficulty stratum, (n = 185,508) (0.9% of all citizens) experienced it alongside additional impairments, whereas (n = 41,002) (0.2% of all citizens) reported communication difficulty in isolation. The communication difficulties exhibit significant regional variation, ranging from 0.45% in Najran to 1.55% in Aseer. Most cases were classified as being associated with some difficulty (72%); females were over-represented in the extreme category despite a modest male excess overall (adjusted odds ratio [AOR] = 1.09). Higher education, married status, and bilateral first-cousin marriage (AOR = 1.22) were associated with greater risk. Chronic disease (44%) and perinatal causes (13%) predominated, and 84% of cases co-occurred with at least one other disability. Independent predictors included a long duration (AOR = 4.18), disease or delivery-related cause, and consanguinity. Conclusions: Findings highlight geographically clustered need, genetic risk factors, and substantial multimorbidity, indicating the importance of region-specific screening, premarital counselling, and integrated rehabilitation within chronic disease services. Full article

Guava (Psidium guajava L.), a perennial species native to tropical regions of the Americas, holds significant economic value and plays an important role in the global fruit industry. Although several reference genomes have been published, population-level genomic studies remain limited, hindering genetic improvement efforts. In this study, we conducted whole genome re-sequencing of 62 guava accessions, primarily from Southern China and Brazil. A total of 4,887,006 high-quality SNPs and 731,469 InDels were identified for population genomic analyses. Phylogenetic and population structure analyses revealed subgroupings that largely corresponded to geographic origins. The data indicated that extensive hybridization between accessions from Brazil and or within China has contributed to the development of many dominant commercial varieties. Genetic diversity analyses showed that Brazilian accessions exhibited higher nucleotide diversity and more rapid linkage disequilibrium decay than those from China. Environmental factors and artificial selection likely imposed selective pressures that shaped guava’s adaptability and agronomic traits. A preliminary genome-wide association study (GWAS) identified PgMYB4 as a candidate gene potentially associated with fruit flesh color. These findings provide novel insights into the genetic diversity, population history, and domestication of guava, and lay a valuable foundation for future breeding and improvement strategies. Full article

Red bricks serve as an important material for load-bearing or enclosing structures in traditional architecture and are widely used in construction projects both domestically and internationally. Fujian red bricks, due to geographical, trade, and immigration-related factors, have spread to Taiwan and various regions in Southeast Asia, giving rise to distinctive red brick architectural complexes. To further investigate the types of damage, such as cracking and missing bricks, that occur in traditional red brick buildings due to multiple factors, including climate and human activities, this study takes Fujian red brick buildings as its research subject. It employs the YOLOv12 rapid detection method to conduct technical support research on structural assessment, type detection, and damage localization of surface damage in red brick building materials. The experimental model was conducted through the following procedures: on-site photo collection, slice marking, creation of an image training set, establishment of an iterative model training, accuracy analysis, and experimental result verification. Based on this, the causes of damage types and corresponding countermeasures were analyzed. The objective of this study is to attempt to utilize computer vision image recognition technology to provide practical, automated detection and efficient identification methods for damage types in red brick building brick structures, particularly those involving physical and mechanical structural damage that severely threaten the overall structural safety of the building. This research model will reduce the complex manual processes typically involved, thereby improving work efficiency. This enables the development of customized intervention strategies with minimal impact and enhanced timeliness for the maintenance, repair, and preservation of red brick buildings, further advancing the practical application of intelligent protection for architectural heritage. Full article

Gastronomic tourism is a strategic tool for territorial development, as it promotes cultural heritage, supports local economies and encourages environmentally responsible consumption. This study attempts to analyse the evolution of key gastronomic products through digital marketing tools, reflecting on the need to know this real data in order to carry out sustainable territorial and tourism planning. To do so, it uses a methodology based on the analysis of data obtained through Google Trends, taking as a reference a set of terms related to seafood, traditional meats and wines with designation of origin. The study examines the seasonal patterns and geographical distribution of interest in these terms, evaluating their impact both inside and outside Galicia as a replicable methodological case. The results show significant differences between categories. In addition, there is a generalised decrease in the search for gastronomic terms, which may indicate a reduction in the relative weight of this element as a factor in the creation of the image of the territories. In conclusion, the article demonstrates the capacity of this methodology to propose more sustainable tourism, territorial and economic planning strategies based on the transformation of qualitative imaginaries into quantitative data and trends. Full article

This study evaluates digital object identifier (DOI) hallucination in large language model (LLM)-generated scholarly citations, with a focus on systematic geographic disparities. To conduct this study, we systematically evaluated four LLMs (GPT-4o-mini, Claude-3-haiku, Gemini-2.0-flash-lite, and DeepSeek V3) using standardized information behavior prompts across ten countries with diverse income levels. The models generated 3451 citations, which we validated using the CrossRef API. The results showed that DOI hallucination follows systematic patterns influenced by model choice, geographic context, and publication recency. Hallucination rates exceeded 80% in lower-income countries and increased sharply for publications from the 2020s across all regions. Fabricated citations—citations that appear structurally complete but contain invalid DOIs—were especially prevalent in countries such as India and Bangladesh. Model-specific factors showed the strongest association with hallucination, followed by income level and publication period. These findings raise concerns about the epistemic reliability of LLM-generated scholarly references and underscore the need for region-aware training, real-time DOI validation, and robust verification protocols in academic contexts. Full article

Decentralized renewable energy (DRE) has emerged as a key tool for global energy transition and emissions reduction. While DRE has the potential to democratize energy production, evidence suggests it may cause unequal benefit distribution across population groups. This study provides the first comprehensive empirical analysis of DRE distribution patterns across all Israeli municipalities, examining policy implications for equitable energy transitions. We analyzed 16,998 rooftop solar installations across 232 municipalities between 2017 and 2022, categorized as residential and commercial installations. Using regression analysis, we examined how geographic, socioeconomic, and demographic factors associate with installation adoption rates. Findings reveal divergent patterns between installation types. For residential installations, socioeconomic status emerged as the primary determinant, with adoption rates increasing linearly with municipal wealth. These disparities widened significantly over time, contradicting expectations that declining costs would democratize access. For commercial installations, the urban–rural divide proved dominant, with rural areas showing substantially higher adoption rates. Our analysis reveals important policy implications and recommendations for global DRE deployment, emphasizing the need to integrate equity considerations into renewable energy policy design to accelerate the transition to renewable energy while minimizing socioeconomic disparities. Full article

Assessing the quality of groundwater, a primary source of water in many sectors, is of paramount importance. To this end, modeling the geospatial distribution of chemical contaminants in groundwater can be of great utility. Machine learning (ML) models are being increasingly used to overcome the shortcomings of conventional predictive techniques. We report here a systematic review of the nature and utility of various supervised and unsupervised ML models during the past two decades of machine learning groundwater hazard mapping (MLGHM). We identified and reviewed 284 relevant MLGHM journal articles that met our inclusion criteria. Firstly, trend analysis showed (i) an exponential increase in the number of MLGHM studies published between 2004 and 2025, with geographical distribution outlining Iran, India, the US, and China as the countries with the most extensively studied areas; (ii) nitrate as the most studied target, and groundwater chemicals as the most frequently considered category of predictive variables; (iii) that tree-based ML was the most popular model for feature selection; (iv) that supervised ML was far more favored than unsupervised ML (94% vs. 6% of models) with tree-based category—mostly random forest (RF)—as the most popular supervised ML. Secondly, compiling accuracy-based comparisons of ML models from the explored literature revealed that RF, deep learning, and ensembles (mostly meta-model ensembles and boosting ensembles) were frequently reported as the most accurate models. Thirdly, a critical evaluation of MLGHM models in terms of predictive accuracy, along with several other factors such as models’ computational efficiency and predictive power—which have often been overlooked in earlier review studies—resulted in considering the relative merits of commonly used MLGHM models. Accordingly, a flowchart was designed by integrating several MLGHM key criteria (i.e., accuracy, transparency, training speed, number of hyperparameters, intended scale of modeling, and required user’s expertise) to assist in informed model selection, recognising that the weighting of criteria for model selection may vary from problem to problem. Lastly, potential challenges that may arise during different stages of MLGHM efforts are discussed along with ideas for optimizing MLGHM models. Full article

This research presents Geographic Information System (GIS) mapping and development of biomass for combined heat and power (CHP) generation in Nigeria. It includes crop and forest classification, thermodynamic, and exergo-economic analyses using ArcGIS, Engineering Equation Solver, and Microsoft Excel. Syngas generated from biomass residues powered an integrated CHP system combining a gas turbine (GT), dual steam turbine (DST), and a cascade organic Rankine cycle (CORC) plant. The net power output of the integrated system stood at 2911 MW, with a major contribution from the gas turbine cycle (GTC) unit. The system had a total exergy destruction of 6480 MW, mainly in the combustion chamber (2143 MW) and HP-HRSG (1660 MW), and produced 3370.41 MW of heat, with a flue gas exit temperature of 74 °C. The plant’s energy and exergy efficiencies were 87.16% and 50.30%, respectively. The BCHP system showed good economic and environmental performance, with an annualized life cycle cost of USD 93.4 million, unit cost of energy of 0.0076 USD/kWh, and a 7.5-year break-even. The emissions and impact factors align with those of similar existing plants. It demonstrates that biomass residue can significantly support Nigeria’s energy needs and contribute to clean energy goals under the Paris Agreement and UN-SDGs. This work suggests a pathway to tackle energy insecurity, inform policymakers on biomass-to-energy, and serve as a foundation for future techno-economic–environmental assessment of biomass residues across suitable locations in Nigeria. Full article

The massive volumes of data and the intensification of digital transformation are reshaping recruitment practices within organizations, particularly for specialized information technology (IT) profiles. However, existing studies have often remained conceptual, focused on developed economies, or limited to a narrow set of factors, thereby leaving important gaps in emerging contexts. Moreover, there are few studies that critically assess how Data Mining is impacting the IT recruitment process, and none that assess this in the context of Morocco. This study employs an extensive literature review to explore the role of Data Mining in facilitating the recruitment of top IT candidates, focusing on its ability to improve selection quality, reduce costs, and optimize decision-making procedures. The study provides empirical evidence from the Moroccan aeronautical and digital services sectors, an underexplored context where IT talent scarcity and rapid technological change pose critical challenges. Primary data comes from a survey of 200 IT recruitment professionals operating in these sectors in Morocco, allowing an assessment of the impact of Data Mining on IT talent acquisition initiatives. The findings reveal that a range of capabilities resulting from the application of Data Mining significantly and positively influences the success of IT recruitment processes. The novelty of the article lies in integrating six key determinants of algorithmic recruitment into a unified framework and demonstrating their empirical significance through binary logistic regression. The focus on the Moroccan context adds value to the international discussion and extends the literature on HR analytics beyond its conventional geographical and theoretical boundaries. The article thus contributes to the emerging literature on the role of digital technologies in IT recruitment that will be of interest to industry practitioners and other researchers in this field. Full article

During the last two decades, the Macaronesian archipelagos have been the focus of multiple studies targeting the abundant and diversified fossil record from late Neogene and Quaternary deposits. This record of past biota, ecosystems and climates is crucial for understanding the impact of glacial–interglacial cycles on Atlantic littoral marine organisms. Coupled with ongoing studies on the factors responsible for global climate change and associated sea-level variations, they contributed decisively towards the development of the modern marine island biogeography theory. Our current knowledge of the evolutionary and biogeographic history of the past and extant, shallow-water marine organisms from the Macaronesian geographic region relies on detailed analysis of many individual fossiliferous outcrops by means of quantitative and qualitative methodologies. Here, we focus on the fossil record of a newly studied MIS 5e outcrop at Pedra-que-pica (PQP), on Santa Maria Island (Azores Archipelago, Portugal). This multidisciplinary work integrates geology, paleontology and biology, providing the first detailed description of the sedimentary facies and stratigraphic framework of the PQP MIS 5e sequence that, coupled with the documentation of the biodiversity and ecological composition of PQP molluscan assemblages, allows us to produce a paleoecological reconstruction and to compare PQP with other last interglacial outcrops from Santa Maria Island. Our results increase the number of the Azorean MIS 5e marine molluscs to 140 taxa (116 Gastropoda and 24 Bivalvia). Ervilia castanea (Montagu, 1803) is the most abundant bivalve, while Bittium nanum (Mayer, 1864) and Melarhaphe neritoides (Linnaeus, 1758) are the most abundant gastropod species. In addition, this work emphasizes the crucial importance of complementing quantitative collecting with qualitative surveys of the fossiliferous outcrops, because nearly 42% of the bivalve species and 28% of the gastropod taxa would be missed if only quantitative samples were used. Derivation of Hill numbers and rarefaction curves both indicate that the sampling effort should be increased at PQP. Thus, although Santa Maria Island is recognized by the scientific community as one of the best-studied islands regarding the last interglacial fossil record, this study emphasizes the need to continue with similar efforts in less known outcrops on the island. Full article

In the Red List of Threatened Species, released by International Union for Conservation of Nature (IUCN), Paphiopedilum helenae has been classified as an endangered species. It exhibits exceptional decorative value and germplasm resource potential. To elucidate the ecological adaptation of this species and the characteristics of its rhizosphere microbiome, bacterial 16S rRNA and fungal ITS sequences of three wild populations of P. helenae were investigated using Illumina high-throughput sequencing technology and the microbial community structures and diversities were systematically compared. These three populations were spanned across distinct geographical locations in Longzhou County, Guangxi. The results showed that the bacterial community in the rhizosphere soil of P. helenae comprised 31 phyla, primarily including Actinobacteriota, Proteobacteria, Chloroflexi and Acidobacteriota. On the other hand, the fungal community consisted of 10 phyla, dominated by Ascomycota and Basidiomycota. There were significant differences in the diversity of rhizosphere microbes across different populations of P. helenae. The LG population had the highest bacterial richness (Chao index: 2912.71 ± 131.73; p < 0.05) and diversity (Shannon index: 6.40 ± 0.06; p < 0.01), while the MQ population had the lowest diversity (Shannon index: 3.47 ± 0.24; p < 0.01) of fungi. The degree of variation in fungal β-diversity was significantly higher than that of bacteria. Soil organic matter (SOM) and available nitrogen (AN) contents were the core factors shaping the microbial communities in the rhizosphere soil of P. helenae, which jointly explained 49.87% and 16.39% of variations in the bacterial and fungal communities. Furthermore, population-specific enrichment of functionally significant microorganisms was evident. Population MQ was enriched with plant growth-promoting and stress-resistant fungi, such as Geminibasidium, Trichoderma, etc. Population LG was enriched with oligotrophic bacteria (e.g., Patescibacteria), while population SL exhibited an overwhelming dominance of Ascomycota (93.25%) and enrichment of pathogenic fungal genus Nigrospora. This research revealed the variations in the functional adaptation strategy of P. helenae and the microbial communities in the rhizosphere soils across different geographical locations. This suggests that microbial community imbalance in rhizosphere soil may be one of the factors leading to the endangerment of this plant species. The study proposed a differentiated protection strategy for endangered plant species based on microbial resources. The results provide a theoretical basis for development of a “microorganism-assisted protection” strategy for ecological restoration and sustainable utilization of endangered orchid plants. Full article

Mountain settlements represent culturally rich but environmentally fragile landscapes, shaped by enduring processes of ecological adaptation and human resilience. In western Sichuan, Jiarong Tibetan villages, with their distinctive integration of defensive stone towers and settlements, embody this coupling of culture and the environment. We hypothesize that settlement cores in these villages were shaped by natural environmental factors, with subsequent expansion reinforced by the cultural significance of towers. To test this, we applied a micro-scale spatial–environmental framework to four sample villages in Suopo Township, Danba County. High-resolution World Imagery (Esri, 0.5–1 m, 2022–2023) was classified via a Random Forest algorithm to generate detailed land-use maps, and a 100 × 100 m fishnet grid extracted topographic metrics (elevation, slope, aspect) and accessibility measures (distances to streams, roads, towers). Geographically weighted regression (GWR) was then used to examine how slope, elevation, aspect, proximity to water and roads, and tower distribution affect settlement patterns. The results show built-up density peaks on southeast-facing slopes of 15–30°, at altitudes of 2600–2800 m, and within 50–500 m of streams, co-locating with historic watchtower sites. Based on these findings, we propose four zoning strategies—a Core Protected Zone, a Construction And Development Zone, an Ecological Conservation Zone, and an Industry Development Zone—to balance preservation with growth. The resulting policy recommendations offer actionable guidance for sustaining traditional settlements in complex mountain environments. Full article

In addition to air temperature and personal factors, other weather quantities govern the outdoor human thermal perception. This study provides a new targeted approach for the evaluation of extreme events based on a specific multivariable bioclimate index. Heat waves (HWs) and outdoor human thermal stress (OHTS) events that occurred in downtown Rome (Italy) over the years 2018–2023 are identified, characterized, and compared through appropriate indices based on the air temperature for HWs and the Mediterranean Outdoor Comfort Index (MOCI) for OHTS events. The overlap between the two types of events is evaluated for each year through the hit (HR) and false alarm rates. The outcomes reveal severe traits for HWs and OHTS events and higher values of HR (minimum of 66%) with OHTS as a predictor of extreme conditions. This pilot investigation confirms that the use of air temperature threshold underestimates human physiological stress, revealing the importance of including multiple parameters, such as weather variables (temperature, wind speed, humidity, and solar radiation) and personal factors, in the assessment of hazards for the population living in a specific geographical region. This type of approach reveals increasingly critical facets and can provide key strategies to establish safe outdoor conditions for occupational and leisure activities. Full article