Sustainable digital marketing under big data: an AI random forest model approach

Digital marketing refers to the process of promoting, selling, and delivering products or services through online platforms and channels using the internet and electronic devices in a digital environment. Its aim is to attract and engage target audiences through various strategies and methods, driving brand promotion and sales growth. The primary objective of this scholarly study is to seamlessly integrate advanced big data analytics and artificial intelligence (AI) technology into the realm of digital marketing, thereby fostering the progression and optimization of sustainable digital marketing practices. First, the characteristics and applications of big data involving vast, diverse, and complex datasets are analyzed. Understanding their attributes and scope of application is essential. Subsequently, a comprehensive investigation into AI-driven learning mechanisms is conducted, culminating in the development of an AI random forest model (RFM) tailored for sustainable digital marketing. Subsequent to this, leveraging a real-world case study involving enterprise X, fundamental customer data is collected and subjected to meticulous analysis. The RFM model, ingeniously crafted in this study, is then deployed to prognosticate the anticipated count of prospective customers for said enterprise. The empirical findings spotlight a pronounced prevalence of university-affiliated individuals across diverse age cohorts. In terms of occupational distribution within the customer base, the categories of workers and educators emerge as dominant, constituting 41% and 31% of the demographic, respectively. Furthermore, the price distribution of patrons exhibits a skewed pattern, whereby the price bracket of 0–150 encompasses 17% of the population, whereas the range of 150–300 captures a notable 52%. These delineated price bands collectively constitute a substantial proportion, whereas the range exceeding 450 embodies a minority, accounting for less than 20%. Notably, the RFM model devised in this scholarly endeavor demonstrates a remarkable proficiency in accurately projecting forthcoming passenger volumes over a seven-day horizon, significantly surpassing the predictive capability of logistic regression. Evidently, the AI-driven RFM model proffered herein excels in the precise anticipation of target customer counts, thereby furnishing a pragmatic foundation for the intelligent evolution of sustainable digital marketing strategies

Synchronous multi-decadal climate variability of the whole Pacific areas revealed in tree rings since 1567

Oceanic and atmospheric patterns play a crucial role in modulating climate variability from interannual to multi-decadal timescales by causing large-scale co-varying climate changes. The brevity of the existing instrumental records hinders the ability to recognize climate patterns before the industrial era, which can be alleviated using proxies. Unfortunately, proxy based reconstructions of oceanic and atmospheric modes of the past millennia often have modest agreements with each other before the instrumental period, raising questions about the robustness of the reconstructions. To ensure the stability of climate signals in proxy data through time, we first identified tree-ring datasets from distant regions containing coherent variations in Asia and North America, and then interpreted their climate information. We found that the multi-decadal covarying climate patterns of the middle and high latitudinal regions around the northern Pacific Ocean agreed quite well with the climate reconstructions of the tropical and southern Pacific areas. This indicates a synchronous variability at the multi-decadal timescale of the past 430 years for the entire Pacific Ocean. This pattern is closely linked to the dominant mode of the Pacific sea surface temperature (SST) after removing the warming trend. This Pacific multi-decadal SST variability resembles the Interdecadal Pacific Oscillation

Transcriptome landscape of the human placenta

<p>Abstract</p> <p>Background</p> <p>The placenta is a key component in understanding the physiological processes involved in pregnancy. Characterizing genes critical for placental function can serve as a basis for identifying mechanisms underlying both normal and pathologic pregnancies. Detailing the placental tissue transcriptome could provide a valuable resource for genomic studies related to placental disease.</p> <p>Results</p> <p>We have conducted a deep RNA sequencing (RNA-Seq) study on three tissue components (amnion, chorion, and decidua) of 5 human placentas from normal term pregnancies. We compared the placental RNA-Seq data to that of 16 other human tissues and observed a wide spectrum of transcriptome differences both between placenta and other human tissues and between distinct compartments of the placenta. Exon-level analysis of the RNA-Seq data revealed a large number of exons with differential splicing activities between placenta and other tissues, and 79% (27 out of 34) of the events selected for RT-PCR test were validated. The master splicing regulator <it>ESRP1 </it>is expressed at a proportionately higher level in amnion compared to all other analyzed human tissues, and there is a significant enrichment of ESRP1-regulated exons with tissue-specific splicing activities in amnion. This suggests an important role of alternative splicing in regulating gene function and activity in specific placental compartments. Importantly, genes with differential expression or splicing in the placenta are significantly enriched for genes implicated in placental abnormalities and preterm birth. In addition, we identified 604-1007 novel transcripts and 494-585 novel exons expressed in each of the three placental compartments.</p> <p>Conclusions</p> <p>Our data demonstrate unique aspects of gene expression and splicing in placental tissues that provide a basis for disease investigation related to disruption of these mechanisms. These data are publicly available providing the community with a rich resource for placental physiology and disease-related studies.</p

Moisture stress of a hydrological year on tree growth in the Tibetan Plateau and surroundings

Investigations of climate-growth interactions can shed light on the response of forest growth to climate change and the dendroclimatic reconstructions. However, most existing studies in the climatically important Tibetan Plateau (TP) and surrouding regions focus on linear growth responses to environmental variation. Herein we investigated both the linear and the nonlinear climate-growth interactions for 152 tree-ring chronologies in the TP and vicinity. Weintroduced the boosted regression tree (BRT) technique to study the nonlinear climate-growth relationships by pooling several sites with similar climate-growth relationships to mitigate potential biases due to the shortness of the instrumental records. Across most of the TP and surroundings, tree growth is stressed by drought. The warming induced drought has been evidenced by the strong interactions between temperature and precipitation in the BRT analyses. The drought stress on forest growth is particularly conspicuous for a hydrological year over much of the Northern TP and surroundings. The BRT analyses indicate the compensation effect of moisture prior to the growing season for the moisture deficit in the early growing season in May to July, when most of the ring-width formation occurs.Peer reviewe

An increase in the biogenic aerosol concentration as a contributing factor to the recent wetting trend in Tibetan Plateau

A significant wetting trend since the early 1980s in Tibetan Plateau (TP) is most conspicuous in central and eastern Asia as shown in the instrumental data and the long-term moisture sensitive tree rings. We found that anomalies in the large-scale oceanic and atmospheric circulations do not play a significant role on the wetting trend in TP. Meanwhile, the weak correlation between local temperature and precipitation suggests that the temperature-induced enhancement of the local water cycle cannot fully explain the wetting trend either. This may indicate the presence of nonlinear processes between local temperature and precipitation. We hypothesize that the current warming may enhance the emissions of the biogenic volatile organic compounds (BVOC) that can increase the secondary organic aerosols (SOA), contributing to the precipitation increase. The wetting trend can increase the vegetation cover and cause a positive feedback on the BVOC emissions. Our simulations indicate a significant contribution of increased BVOC emissions to the regional organic aerosol mass and the simulated increase in BVOC emissions is significantly correlated with the wetting trend in TP.Peer reviewe

GLiMMPS: Robust statistical model for regulatory variation of alternative splicing using RNA-seq data

Abstract To characterize the genetic variation of alternative splicing, we develop GLiMMPS, a robust statistical method for detecting splicing quantitative trait loci (sQTLs) from RNA-seq data. GLiMMPS takes into account the individual variation in sequencing coverage and the noise prevalent in RNA-seq data. Analyses of simulated and real RNA-seq datasets demonstrate that GLiMMPS outperforms competing statistical models. Quantitative RT-PCR tests of 26 randomly selected GLiMMPS sQTLs yielded a validation rate of 100%. As population-scale RNA-seq studies become increasingly affordable and popular, GLiMMPS provides a useful tool for elucidating the genetic variation of alternative splicing in humans and model organisms