Search Results (1,374)

Adverse childhood experiences (ACEs; e.g., physical abuse) can impact lifelong mental health both directly and intergenerationally, with effects transmitted from the parent to the child. Several physiological mechanisms have been proposed to explain the impacts of ACEs on mental health. The purpose of this narrative review was to synthesize and critique the peer-reviewed literature on physiological mechanisms proposed to underlie the impacts of ACEs on mental health, specifically: (1) hypothalamic–pituitary–adrenal axis functioning, (2) inflammation, (3) genetic inheritance and differential susceptibility, (4) epigenetics, (5) brain structure and function, (6) oxidative stress, and (7) metabolic profiles. We searched Google Scholar using variations of the terms “adverse childhood experiences”, “mechanisms”, and “mental health” to locate relevant peer-reviewed literature. We also mined citations of the identified literature to find additional important sources. The role of inflammation in the etiology of mental health conditions among those exposed to ACEs appeared promising, followed by hypothalamic–pituitary–adrenal axis functioning, brain structure and function, genetics, epigenetics, metabolism, and lastly, oxidative stress. Replication studies that examine the associations among ACEs, genetic inheritance and differential susceptibility, epigenetics, oxidative stress, and metabolism are required to better define links with mental health. Full article

Backfill mining is an important means of ensuring the high efficiency and safety of the coal mining under thin bedrock and loose aquifers. Based on the case study of Taiping Coalmine, the theoretical analysis of entropy and numerical modeling methods are adopted to establish the visualization model of temporal–spatial cube of stress and displacement induced by the multiple layers backfill mining. Moreover, the quantitative characterization and measurement framework of symmetric KL-divergence is established based on information entropy and mutual information. The results show that: (1) The non-uniformity of stress and displacement is enhanced due to the multiple layers backfill mining, showing certain fluctuation characteristics. (2) The KL-divergence of stress to displacement is slightly greater than that of displacement to stress, and the hotspot distribution law of stress–displacement related efficiency is consistent with KL-divergence. (3) The hotspots of stress entropy and the gap between stress entropy and displacement entropy in multiple layers backfill mining decrease obviously. (4) Stress plays a main role in displacement, and displacement is a linkage response to stress due to the coordinated deformation. Multiple layers backfill mining results in an enhanced correlation degree and more chaotic state between stress and displacement. The results will provide engineering geological basis for optimal design and safe production of backfill mining under loose aquifers. Full article

Time-shifted seismic research plays an important role in monitoring changes in the gas-water interface uplift, the weakening of amplitude attributes, and gas distribution due to mining. When time-shifted seismic research involves non-repeatable data with significant differences between data sets due to variations in seismic data acquisition parameters and seismic geometries, it necessitates consistent processing before time-shifted monitoring comparisons. In this paper, a study of time-shifted seismic monitoring using two non-repetitive data sets based on the ocean bottom cable (OBC) and towed streamer data is presented. First, amplitude, frequency, wavelet, and time difference are processed to achieve consistency for time-shifted comparisons. Secondly, three modes of seismic geometry normalization are compared to optimize the appropriate offset, azimuth, and signal-to-noise ratio (SNR). Finally, after eliminating the fault surface wave, the maximum trough amplitude attribute is extracted for the same position in the two data sets to analyze time-shifted differences under the three modes using the ratio method and difference method. The conclusions show the following: the OBC and towed streamer data can achieve consistency in terms of amplitude, frequency, wavelet, azimuth, SNR, and time difference; the data reconstruction method outperforms other methods in normalizing offset, azimuth, and SNR; and the time-shifted comparison method of the amplitude attribute ratio method proves more effective than the difference method. This study offers a reliable foundation for future time-shifted seismic research with non-repetitive data to monitor changes in subsurface oil and gas. It also provides a methodological basis for carbon capture and storage (CCS) monitoring technology. Full article

The intelligent transformation of coal mines is one of the current trends in developing China’s coal mining industry. To explore the impact of miners’ insecurity on their safety behavior under this trend, miners’ psychological resilience was introduced as the mediating variable, and team safety climate was used as the moderating variable to conduct a questionnaire survey of frontline miners. The data analysis was carried out using descriptive statistics, correlation analysis, structural equation modeling (SEM), and the fsQCA method to explore the impact of job insecurity on miners’ risk behavior through psychological resilience from the dimensions of job loss insecurity, job performance insecurity, and interpersonal insecurity. The results show that the sense of insecurity of the miners has a significant negative correlation with security behavior and a significant negative correlation with psychological toughness; miners’ psychological resilience plays an intermediary role in the correlation between job loss insecurity and miners’ risk behavior. Meanwhile, team safety climate has a significant moderating effect on the relationship between job insecurity and psychological resilience, as well as the relationship between psychological resilience and safety behavior; that is, a good team safety climate can effectively reduce the negative impact of job insecurity brought about by the transformation and upgrading of coal mines. Full article

Rising urban temperatures, noise and air pollution, and the loss of biodiversity are pressing problems in cities worldwide that call for action at different scales to improve the livability of urban areas. This study focuses on the role that buildings and façade design play in the urban environment, exploring how their informed design might help mitigating these environmental issues at a local scale. It explores the field by means of a systematic review aimed at identifying the impact of façade design choices focusing on three main design variables: material, geometry, and vegetation in façades. Scopus and Web of Science databases were explored between 17 April and 20 April 2023, ending up with 121 scientific articles, then categorized and data-mined to allow for descriptive statistical analysis to discuss scientific results obtained via digital simulation or empirical measurements. Risk of bias was addressed through double revision of the gathered sample. This study ends with the identification of desirable façade attributes based on their reported impacts, in terms of material properties, geometric operations, and main vegetation parameters, which we discuss by outlining compatibilities and clashes between them to guide conscious building design decisions to improve the urban ecosystem. Full article

The development of each nation is evaluated by its infrastructure, and each nation is competing with the others in infrastructure advancement, especially in the construction of roadways, since they play a vital role in the economic and social development of the nation. The conventional materials used for road construction are concrete and asphalt, which pose significant environmental challenges. This research gives insight into the potential of fly ash (FA) and corn stover (CS) in synthesizing geopolymer, as an alternative material for the construction of roads. This study examines the impact of three FA and CS mixture percentages and the particle size of CS on the compressive strength and porosity of geopolymer. The results indicate that incorporating larger amounts of CS in fly ash-based geopolymer may decrease the compressive strength of the geopolymer. Smaller CS particle sizes also tend to lead to lower compressive strength. Porosity of the geopolymer tended to increase with the incorporation of higher percentages of CS, particularly for smaller corn stover sizes. As a fine aggregate replacement for geopolymer, CS incorporation has the potential to reduce mined aggregate obtained from a process that harms the environment. Full article

High-silica adakites exhibit specific compositions, as follows: SiO₂ ≥ 56 wt.%, Al₂O₃ ≥ 15 wt.%, Y ≤ 18 ppm, Yb ≤ 1.9 ppm, K₂O/Na₂O ≥ 1, MgO < 3 wt.%, high Sr/Y (≥10), and La/Yb (>10). Devonian I-type adakitic granitoids in the northern Appalachians of New Brunswick (NB, Canada) share geochemical signatures of adakites elsewhere, i.e., SiO₂ ≥ 66.46 wt.%, Al₂O₃ > 15.47 wt.%, Y ≤ 22 ppm, Yb ≤ 2 ppm, K₂O/Na₂O > 1, MgO < 3 wt.%, Sr/Y ≥ 33 to 50, and La/Yb > 10. Remarkably, adakitic intrusions in NB, including the Blue Mountain Granodiorite Suite, Nicholas Denys, Sugar Loaf, Squaw Cap, North Dungarvan River, Magaguadavic Granite, Hampstead Granite, Tower Hill, Watson Brook Granodiorite, Rivière-Verte Porphyry, Eagle Lake Granite, Evandale Granodiorite, North Pole Stream Suite, and the McKenzie Gulch porphyry dykes all have associated Cu mineralization, similar to the Middle Devonian Cu porphyry intrusions in Mines Gaspé, Québec. Trace element data support the connection between adakite formation and slab break-off, a mechanism influencing fertility and generation of porphyry Cu systems. These adakitic rocks in NB are oxidized, and are relatively enriched in large ion lithophile elements, like Cs, Rb, Ba, and Pb, and depleted in some high field strength elements, like Y, Nb, Ta, P, and Ti; they also have Sr/Y ≥ 33 to 50, Nb/Y > 0.4, Ta/Yb > 0.3, La/Yb > 10, Ta/Yb > 0.3, Sm/Yb > 2.5, Gd/Yb > 2.0, Nb + Y < 60 ppm, and Ta + Yb < 6 ppm. These geochemical indicators point to failure of a subducting oceanic slab (slab rollback to slab break-off) in the terminal stages of subduction, as the generator of post-collisional granitoid magmatism. The break-off and separation of a dense subducted oceanic plate segment leads to upwelling asthenosphere, heat advection, and selective partial melting of the descending oceanic slab (adakite) and (or) suprasubduction zone lithospheric mantle. The resulting silica-rich adakitic magmas ascend through thickened mantle lithosphere, with minimal affect from the asthenosphere. The critical roles of transpression and transtension are highlighted in facilitating the ascent and emplacement of these fertile adakitic magmas in postsubduction zone settings. Full article

Carajasia cangae (Rubiaceae) is a narrow endemic species from the canga ecosystems of the Carajás National Forest that is facing extinction due to a limited range and habitat disturbance from hydroclimatological changes and mining activities. This study examines the influence of rhizospheric and endophytic bacteria on C. cangae seed germination to support conservation efforts. Soil samples, both rhizospheric and non-rhizospheric, as well as plant root tissues, were collected. Bacteria from these samples were subsequently isolated, cultured, and identified. DNA sequencing revealed the presence of 16 isolates (9 rhizospheric and 7 endophytic), representing 19 genera and 6 phyla: Proteobacteria, Actinobacteria, Acidobacteria, Firmicutes, Bacteroidetes, and Chloroflexi. The endophytic isolates of Bacillus and the rhizospheric isolates of Planococcus and Lysinibacillus reduced the median germination time and initiation time, while the rhizospheric isolates Serratia and Comamonas increased the germination time and decreased the germination percentage in comparison to the control sample. These findings emphasize the crucial role of endophytic bacteria in the germination of C. cangae and highlight isolates that could have beneficial effects in the following stages of plant growth. Understanding the impact of endophytic and rhizospheric bacterial isolates on seed germination can enhance conservation efforts by shortening the germination period of this species and thereby improving seedling production. Additionally, this knowledge will pave the way for future research on the role of bacteria in the establishment of C. cangae. Full article

The advent of the Internet of Things (IoT) has revolutionized numerous sectors, with healthcare being particularly significant. Despite extensive studies addressing healthcare challenges, two persist: (1) the need for the swift detection of abnormalities in patients under medical care and timely notifications to patients or caregivers and (2) the accurate diagnosis of abnormalities tailored to the patient’s condition. Addressing these challenges, numerous studies have focused on developing healthcare systems, leveraging technologies like edge computing, which plays a pivotal role in enhancing system efficiency. Fog computing, situated at the edge of network hierarchies, leverages multiple nodes to expedite system processes. Furthermore, the wealth of data generated by sensors connected to patients presents invaluable insights for optimizing medical care. Data mining techniques, in this context, offer a means to enhance healthcare system performance by refining abnormality notifications and disease analysis. In this study, we present a system utilizing the K-Nearest Neighbor (KNN) algorithm and Raspberry Pi microcomputer within the fog layer for a diabetic patient data analysis. The KNN algorithm, trained on historical patient data, facilitates the real-time assessment of patient conditions based on past vital signs. A simulation using an IBM SPSS dataset and real-world testing on a diabetic patient demonstrate the system’s efficacy. The results manifest in prompt alerts or normal notifications, illustrating the system’s potential for enhancing patient care in healthcare settings. Full article

Coal extraction in China is increasingly moving towards colder regions such as Xinjiang and Inner Mongolia. However, these mines face land restoration challenges due to a scarcity of fertile topsoil. This study explores the potential of coal gangue, a mining byproduct, as a viable substitute for topsoil. The study examines the effects of humidity fluctuations and freeze–thaw cycles, both individually and in combination, on the weathering disintegration of coal gangue. Coal gangue samples were subjected to controlled laboratory conditions simulating environmental factors. Fourteen interventions were analyzed, and the findings indicated that the combined application of humidity and freeze–thaw cycles significantly accelerated the disintegration process, outperforming the individual interventions. In addition, it was found that significant temperature variations caused the moisture and salts within the gangue to expand, which affected the rate of disintegration. The study showed that the rate of weathering disintegration was significantly higher in conditions of saturated humidity–freeze–thaw cycles compared to unsaturated humidity conditions. This highlights the essential role of ice crystals in accelerating the weathering process during temperature fluctuations. This study highlights the importance of humidity over temperature in the weathering and disintegration of coal gangue. It also suggests that freeze–thaw cycles can enhance this process. The study provides valuable insights for the management and utilization of coal gangue in cold and arid regions. Full article

Turbidity, as a key indicator of water quality linked to underwater light attenuation, is crucial for evaluating water quality. Control in high-turbidity water environments plays a critical role in navigable rivers. For this purpose, our study proposed a framework for analyzing the spatio-temporal variation of turbidity and its driving factors in a navigable and turbid river using in situ measurement data, satellite data, socioeconomic data, a power index function model, and correlation analysis. The results show that the proposed model is feasible for quantitative turbidity monitoring of the Xitiaoxi River. Its upstream turbidity is lower than downstream, with seasonal averages for spring, summer, autumn, and winter of 93.9, 111.3, 113.5, and 120.9 NTU, respectively. Furthermore, the turbidity in the middle and lower reaches of the Xitiaoxi River continuously increased before 2005 and began to decline after 2005 due to the policy of mining moratorium. This trend is especially noticeable at monitoring points along the main stream of the Xitiaoxi River, such as downstream of the Xitiaoxi River (S1), Gangkou station (S2), middle reaches of the Xitiaoxi River (S4), Hengtangcun station (S6), upper stream of the Xitiaoxi River (S7), and Huxi River (S8). Mining and shipping have significantly contributed to the turbidity of the target river. This framework offers a practical approach for assessing the environmental impacts of both natural and anthropogenic factors, thereby providing valuable insights for river management practices. Full article

A log data analysis plays an important role in the uranium mining process. Automating this analysis using machine learning methods improves the results and reduces the influence of the human factor. In particular, the identification of reservoir oxidation zones (ROZs) using machine learning allows a more accurate determination of ore reserves, and correct lithological classification allows the optimization of the mining process. However, training and tuning machine learning models requires labeled datasets, which are hardly available for uranium deposits. In addition, in problems of interpreting logging data using machine learning, data preprocessing is of great importance, in other words, a transformation of the original dataset that allows improving the classification or prediction result. This paper describes a uranium well log (UWL) dataset generated with the employment of floating data windows and designed to solve the problems of identifying ROZ and lithological classification (LC) on sandstone-type uranium deposits. Comparative results of the ways of solving these problems using classical machine learning methods and ensembles of machine learning algorithms are presented. It has been shown that an increase in the size of the floating data window can improve the quality of ROZ classification by 7–9% and LC by 6–12%. As a result, the best-quality indicators for solving these problems were obtained, f1_score_macro = 0.744 (ROZ) and accuracy = 0.694 (LC), using the light gradient boosting machine and extreme gradient boosting, respectively. Full article

Colorectal cancer (CRC) is a heterogeneous disease and classified into various subtypes, among which transcriptional alterations result in CRC progression, metastasis, and drug resistance. Forkhead-box M1 (FOXM1) is a proliferation-associated transcription factor which is overexpressed in CRC and the mechanisms of FOXM1 regulation have been under investigation. Previously, we showed that FOXM1 binds to promoters of certain microRNAs. Database mining led to several microRNAs that might interact with FOXM1 3’UTR. The interactions between shortlisted microRNAs and FOXM1 3’UTR were quantitated by a dual-luciferase reporter assay. MicroRNA-532-3p interacted with the 3’UTR of the FOXM1 mRNA transcript most efficiently. MicroRNA-532-3p was ectopically overexpressed in colorectal cancer (CRC) cell lines, leading to reduced transcript and protein levels of FOXM1 and cyclin B1, a direct transcriptional target of FOXM1. Further, a clonogenic assay was conducted in overexpressed miR-532-3p CRC cells that revealed a decline in the ability of cells to form colonies and a reduction in migratory and invading potential. These alterations were reinforced at molecular levels by the altered transcript and protein levels of the conventional EMT markers E-cadherin and vimentin. Overall, this study identifies the regulation of FOXM1 by microRNA-532-3p via its interaction with FOXM1 3’UTR, resulting in the suppression of proliferation, migration, and invasion, suggesting its role as a tumor suppressor in CRC. Full article

Incorporating knowledge graphs as auxiliary information to enhance recommendation systems can improve the representations learning of users and items. Recommendation methods based on knowledge graphs can introduce user–item interaction learning into the item graph, focusing only on learning the node vector representations within a single graph; alternatively, they can treat user–item interactions and item graphs as two separate graphs and learn from each graph individually. Learning from two graphs has natural advantages in exploring original information and interaction information, but faces two main challenges: (1) in complex graph connection scenarios, how to adequately mine the self-information of each graph, and (2) how to merge interaction information from the two graphs while ensuring that user–item interaction information predominates. Existing methods do not thoroughly explore the simultaneous mining of self-information from both graphs and effective interaction information, leading to the loss of valuable insights. Considering the success of contrastive learning in mining self-information and auxiliary information, this paper proposes a dual-graph contrastive learning recommendation method based on knowledge graphs (KGDC) to explore a more accurate representations of users and items in recommendation systems based on external knowledge graphs. In the learning process within the self-graph, KGDC strengthens and represents the information of different connecting edges in both graphs, and extracts the existing information more fully. In interactive information learning, KGDC reinforces the interaction relationship between users and items in the external knowledge graph, realizing the leading role of the main task. We conducted a series of experiments on three standard datasets, and the results show that the proposed method can achieve better results. Full article

Antimony (Sb) is a toxic trace element for plants and animals. With the development of industrial applications and mining, Sb pollution is becoming more serious. Phytoremediation is regarded as an eco-friendly technique to reduce the threat of Sb to the environment and human health, and tall fescue that is highly adaptable to heavy metal stress can be a candidate species for Sb-contaminated soil phytoremediation. However, the mechanism of the Sb stress response in tall fescue is not clear. Therefore, transcriptomic analysis was used in this study to reveal the molecular mechanisms of Sb stress response regulation in tall fescue. The results suggested that the roots and leaves of tall fescue responded to Sb stress in different ways. In roots, the lignin and flavonoids might reduce the toxicity of Sb by anti-oxidation and Sb chelation. At the same time, the DEGs in leaves were mainly enriched in the pathways of glutathione metabolism, β-alanine metabolism, and glycine, serine, and threonine metabolism. Additionally, genes related to the pathways, such as 4CL, GST, AGXT2, and ALDH7A1, especially cytochrome P450 family genes (e.g., CYP73A, CYP75A, and CYP98A), might play key roles in the regulation of the Sb stress response in tall fescue. These findings provided a theoretical reference for the efficient use of tall fescue to control Sb-contaminated soil in the future. Full article