Extensive Landscape Research Articles

Temporal and Spatial Variations in Landscape Pattern–Function Risk Coupling over 20 Years in the Dry–Hot Valley of the Jinsha River in China

Extensive and profound landscape alterations significantly contribute to ecological vulnerability in environmentally delicate regions. Existing research primarily emphasizes ecological risks caused by landscape alterations, while overlooking vulnerable characteristics of landscape functions; particularly lacking are studies on the driving mechanism of landscape ecological risk through the reciprocal relationship between landscape pattern risk and function risk. Based on these issues, this paper constructed a landscape pattern risk index (LPRI), a landscape function risk index (LFRI), and a landscape ecological risk index (LERI) in the counties of the dry–hot valley of the Jinsha River in southwest China. By employing a coupling degree and a coordination model, we analyzed temporal and spatial variations in the interaction between two types of ecological risk, thereby revealing the driving mechanisms of landscape ecological risk. The results indicated that the average LPRI values of the study area were 0.373, 0.327, and 0.427, respectively, while the average LFRI values were 0.451, 0.356, and 0.442 in 2000, 2010, and 2020, respectively. More than 90% of the study area exhibited a medium coupling relationship between the two types of ecological risks. The area proportion of the coupling coordination regions has increased from 25.58% to 31.07% from 2010 to 2020. The two types of risk exhibited a low level of constraint inhibition. Extremely evident expansion of high pattern–function risk areas and the area increase of coupling coordination region resulted in the acceleration of regional landscape ecological risk level. Increasing competition between market-driven land-use activities and ecological regulations from the government has rendered the diversification of landscape ecological risk sources and its underlying mechanisms intricate. This study serves as a model reference for assessing landscape ecological risk and a theoretical basis for sustainable landscape management and ecological regulation in the Yangtze River basin.

Failure to Drain: Expert Resistance and Environmental Thought in the Seventeenth-Century Dutch Republic

Abstract Historical scholarship has long highlighted the extensive landscape interventions initiated by state agents, early capitalists and experts in the early modern period, and pointed to the fierce, often violent resistance they evoked from local and rural communities. Such an approach risks narrowly aligning expertise with intervention in the service of states or capitalist elites and positioning experts in direct opposition to people. This article uses the history of land reclamation in the seventeenth-century Dutch Republic, usually told as a harmonious success story of premodern human intervention in nature, to explore the nature and politics of expertise and environmental thought as different elites clashed. Focusing on the proposed but not executed drainage of the Haarlemmermeer, it demonstrates how experts came to act as agents of resistance who argued for conservation and caution rather than intervention, and shows we can use expert exchanges to gain better insight into the divisive nature of environmental thought in the early modern period.

Molecular Spies in Action: Genetically Encoded Fluorescent Biosensors Light up Cellular Signals.

Cellular function is controlled through intricate networks of signals, which lead to the myriad pathways governing cell fate. Fluorescent biosensors have enabled the study of these signaling pathways in living systems across temporal and spatial scales. Over the years there has been an explosion in the number of fluorescent biosensors, as they have become available for numerous targets, utilized across spectral space, and suited for various imaging techniques. To guide users through this extensive biosensor landscape, we discuss critical aspects of fluorescent proteins for consideration in biosensor development, smart tagging strategies, and the historical and recent biosensors of various types, grouped by target, and with a focus on the design and recent applications of these sensors in living systems.

A Modified Method for Reducing the Scale Effect in Land Surface Temperature Downscaling at 10 m Resolution

Satellite-derived Land Surface Temperature (LST) plays an important role in research on natural energy balance and water cycle. Considering the tradeoff between spatial and temporal resolutions, accurate fine-resolution LST must be obtained through the use of LST downscaling (DLST) technology. Various methods have been proposed for DLST at fine resolutions (e.g., 10 m) and small scales. However, the scale effect of these methods, which is inherent to DLST processes at different extents, has rarely been addressed, thus limiting their application. In this study, a modified daily 10 m resolution DLST method based on Google Earth Engine, called mDTSG, is proposed in order to reduce the scale effect at fine spatial resolutions. The proposed method introduces a convolution-based moving window into the DLST process for the fusion of different remote sensing data. The performance of the modified method is compared with the original method in six regions characterized by various extents and landscape heterogeneity. The results show that the scale effect is significant in the DLST process at fine resolutions across extents ranging from 100 km2 to 22,500 km2. Compared with the original method, mDTSG can effectively reduce the LST value differences between tile edges, especially when considering large extents (>22,500 km2) with an average R2 improvement of 33.75%. The average MAE is 1.63 °C, and the average RMSE is 2.3 °C in the mDTSG results, when compared with independent remote sensing products across the six regions. A comparison with in situ observations also shows promising results, with an MAE of 2.03 °C and an RMSE of 2.63 °C. These findings highlight the robustness and scalability of the mDTSG method, making it a valuable tool for fine-resolution LST applications in diverse and extensive landscapes.

Synthetic peptides as valuable and versatile tools for research: our 20 year journey in Chile

According to the International Union of Pure and Applied Chemistry (IUPAC), peptides are small proteins with a size between 2 and 50 amino acids residues. They are ubiquitous across the evolutionary scale, fulfilling a wide variety of functions, from immune system effectors in simple organisms to signaling or neuromodulating agents in high vertebrates. Following nature’s example, peptides have emerged as alternatives in various fields. One particularly relevant area is in drug discovery, offering alternatives to face the emergence of antibiotic-resistant microorganisms. Peptides are also prevalent in other sectors, such as the food industry, where they serve as food additives to enhance nutritional characteristics or aid in food preservation. Moreover, peptides are increasingly being utilized in cosmetics. Additionally, peptides serve as valuable tools in both basic and applied research, facilitating the exploration of specific activity mechanisms and the verification of particular activities, among various other applications. Despite certain limitations and disadvantages compared to other bioactive molecules, peptides remain a focal point of interest in research, as well as in applied and developmental fields, due to their versatility. In this report, we provide an overview of the extensive application landscape of synthetic peptides, presenting examples developed in-house across different areas which include a summary of the methodologies and results obtained.

Precipitation sensitivity of vegetation growth in southern China depends on geological settings

Southern China is in the only humid zone at its latitudes of Northern Hemisphere and has the most extensive karst landscapes. Although the region experienced significant greening, the fragile karst geological setting and reduced precipitation may affect the greening efforts. Here we analyze the sensitivity of vegetation to precipitation between karst and non-karst areas in the region. We find that in karst areas, vegetation growth is more affected by seasonal precipitation variations (Pre_season), with relative importance of 24.02 % for Leaf Area Index (LAI) and 25.27 % for kernel Normalized Difference Vegetation Index (kNDVI). In contrast, vegetation in non-karst areas is more temperature-dominated. After controlling temperature effects, mean annual precipitation (MAP) thresholds of 1337–1438 mm are established to identify precipitation-sensitive areas (below the thresholds), with slightly higher thresholds in karst areas. These findings imply the heightened vulnerability of southern China’s karst areas to ongoing drying trends, posing a threat to the region’s vegetation growth and the remarkable vegetation restoration achievements.

Accelerating Discovery of Water Stable Metal-Organic Frameworks by Machine Learning.

Metal-organic frameworks (MOFs) provide an extensive design landscape for nanoporous materials that drive innovation across energy and environmental fields. However, their practical applications are often hindered by water stability challenges. In this study, a machine learning (ML) approach is proposed to accelerate the discovery of water stable MOFs and validated through experimental test.First, the largest database currently available that contains water stability information of 1133 synthesized MOFs is constructed and categorized according to experimental stability. Then, structural and chemical descriptors are applied at various fragmental levels to develop ML classifiers for predicting the water stability of MOFs. The ML classifiers achieve high prediction accuracy and excellent transferability on out-of-sample validation. Next, two MOFs are experimentally synthesized with their water stability tested to validate ML predictions. Finally, the ML classifiers are applied to discover water stable MOFs in the ab initio REPEAT charge MOF (ARC-MOF) database. Among ≈280000 candidates, ≈130000 (47%) MOFs are predicted to be water stable; furthermore, through multi-stability analysis, 461 (0.16%) MOFs are identified as not only water stable but also thermal and activation stable. The ML approach is anticipated to serve as a prerequisite filtering tool to streamline the exploration of water stable MOFs for important practical applications.

Reinforcing Tunnel Network Exploration in Proteins Using Gaussian Accelerated Molecular Dynamics.

Tunnels are structural conduits in biomolecules responsible for transporting chemical compounds and solvent molecules from the active site. They have been shown to be present in a wide variety of enzymes across all functional and structural classes. However, the study of such pathways is experimentally challenging, because they are typically transient. Computational methods, such as molecular dynamics (MD) simulations, have been successfully proposed to explore tunnels. Conventional MD (cMD) provides structural details to characterize tunnels but suffers from sampling limitations to capture rare tunnel openings on longer time scales. Therefore, in this study, we explored the potential of Gaussian accelerated MD (GaMD) simulations to improve the exploration of complex tunnel networks in enzymes. We used the haloalkane dehalogenase LinB and its two variants with engineered transport pathways, which are not only well-known for their application potential but have also been extensively studied experimentally and computationally regarding their tunnel networks and their importance in multistep catalytic reactions. Our study demonstrates that GaMD efficiently improves tunnel sampling and allows the identification of all known tunnels for LinB and its two mutants. Furthermore, the improved sampling provided insight into a previously unknown transient side tunnel (ST). The extensive conformational landscape explored by GaMD simulations allowed us to investigate in detail the mechanism of ST opening. We determined variant-specific dynamic properties of ST opening, which were previously inaccessible due to limited sampling of cMD. Our comprehensive analysis supports multiple indicators of the functional relevance of the ST, emphasizing its potential significance beyond structural considerations. In conclusion, our research proves that the GaMD method can overcome the sampling limitations of cMD for the effective study of tunnels in enzymes, providing further means for identifying rare tunnels in enzymes with the potential for drug development, precision medicine, and rational protein engineering.

A Systematic Literature Review of Modalities, Trends, and Limitations in Emotion Recognition, Affective Computing, and Sentiment Analysis

This systematic literature review delves into the extensive landscape of emotion recognition, sentiment analysis, and affective computing, analyzing 609 articles. Exploring the intricate relationships among these research domains, and leveraging data from four well-established sources—IEEE, Science Direct, Springer, and MDPI—this systematic review classifies studies in four modalities based on the types of data analyzed. These modalities are unimodal, multi-physical, multi-physiological, and multi-physical–physiological. After the classification, key insights about applications, learning models, and data sources are extracted and analyzed. This review highlights the exponential growth in studies utilizing EEG signals for emotion recognition, and the potential of multimodal approaches combining physical and physiological signals to enhance the accuracy and practicality of emotion recognition systems. This comprehensive overview of research advances, emerging trends, and limitations from 2018 to 2023 underscores the importance of continued exploration and interdisciplinary collaboration in these rapidly evolving fields.

Against wastelanding: distributed design at the pace of soil in the Conca de Barberà

This paper proposes a methodology for designers to work in the extensive yet marginal landscapes that are produced as part of the process of agricultural intensification. The distributed design approach, which focuses on enriching the existing bioinfrastructure of the soil, emerged out an ongoing research project in the Conca de Barberà, Spain. The project involves managing an interconnected series of currently unproductive on unmaintained fields through the rotational grazing of horses, with the hypothesis that the systematic grazing regime will catalyse soil biological activity. By attending to the soil infrastructure, the project necessarily developed a unique design approach. Management decisions became design problems and design work took on something of the call and response of management. Though it was shaped by the specific constraints and opportunities of the research project, the methodology is applicable to a wide variety of design projects in extensive landscapes with limited resources.

Do linear clearings in boreal peatlands recover? Comparing taxonomic, phylogenetic, and functional plant diversity

Land-use changes and anthropogenic disturbances are major threats to biodiversity, affecting ecosystem function and recovery. In boreal Alberta, Canada, petroleum development has led to extensive landscape fragmentation, notably through linear clearings created for seismic exploration that remove surface vegetation and microtopography. Despite partial recovery on forested seismic lines, peatland recovery is often arrested, impacting wildlife and carbon dynamics. Restoration efforts employ silviculture techniques to create microtopography and foster tree growth, but the efficacy of restoration treatments in restoring peatland plant diversity remains uncertain. We compared taxonomic, phylogenetic, and functional diversity of understory plant communities across treated and untreated seismic lines, alongside reference sites in treed bogs and fens. Treated lines generally had a twofold increase in plant height, leaf dry matter content, and foliar nitrogen and phosphorus contents compared to reference sites. In bogs, treated lines differed from reference conditions driven by increases in herbaceous taxa, while fens displayed disparities mainly in taxonomic and phylogenetic diversity. Differential responses of bogs and fens underline the necessity for tailored management strategies. Changes in plant diversity away from restoration targets have implications for ecosystem recovery, emphasizing the importance of long-term monitoring to evaluate the effectiveness of silviculture techniques in restoring boreal peatland plant communities.

Bibliometric Analysis and Research Trends on Creative Accounting from 2000 to 2023

This study employs a comprehensive bibliometric analysis to explore the extensive landscape of creative accounting research from 1958 to 2024. By analyzing thematic clusters, research trends over specific years, potential research topics in less explored areas, and patterns of author collaboration, the study provides a multidimensional view of the field's evolution and current state. Thematic analysis reveals core areas such as policy, earnings management, and the role of external audits, illustrating their interconnectedness within the research community. Temporal trend analysis identifies shifts from regulatory and ethical discussions to more complex issues like technology’s impact on creative accounting practices. Emerging research topics suggest future areas of study, including the influence of cultural perceptions and technological advancements on creative accounting. Furthermore, the author collaboration network highlights influential researchers and suggests robust collaborative ties within the community, offering insights into the social structure of academic collaboration. This study maps the development of creative accounting research, providing a foundation for future academic inquiry and practical application in the field.

Insights of ecological resilience in Caatinga assemblages – landscape configuration drives Chrysomeloidea (Coleoptera) diversity in a seasonally dry tropical forest

Seasonally Dry Tropical Forests (SDTFs) have long suffered from chronic, intensive and extensive landscape transformations due to human activities, challenging biodiversity conservation in this biome. The Caatinga is the largest Neotropical SDTF, serving as an excellent model to understand how land use changes modulate ecological communities. In this study, we assessed the effects of landscape configuration and composition on the abundance and diversity of Chrysomeloidea in a region of Caatinga. We sampled beetles in 10 landscape units, in which we measured landscape configuration (patch size, edge density) and composition (landscape diversity, patch richness). Hill numbers (q0, q1, q2) were used to analyze the diversities of Chrysomeloidea. A total of 484 individuals belonging to 82 species were collected. Landscape configuration affected Chrysomeloidea assemblages with a positive correlation between edge density and Chrysomeloidea diversity. We provide a new perspective regarding the spatial distribution of Chrysomeloidea in Caatinga and conclude that landscape configuration, but not composition, directly affects Chrysomeloidea assemblages.

Post-Burn, Post-Flood Effects In A Degraded Grassland, Lake Texoma, Bryan County, Oklahoma

Plant communities change over time, sometimes leading to an increase or decrease in biological diversity. Often, absence of active management of a site leads to its degradation including loss of native species and invasion by non-native weeds. Lake Texoma, Texas and Oklahoma, represents an area where extensive landscape change has happened over the course of almost a century. The Denison Dam was completed in 1938, forming the lake, which over time has altered conditions in the forested and formerly-grazed locations surrounding it. The location studied in this paper is a 186-ha tract of land situated between Johnson Creek and the Roosevelt Bridge in Bryan County, Oklahoma. In summer 2000, a species list was compiled for a grassland located at the lake site as part of a larger study. This grassland comprised ~10% of the total site area. Following two major floods and an extended drought, the site was resampled in 2018. Results indicated it had suffered a serious decline in species richness and an increase in abundance of invasive or encroaching species. Species richness was reduced by approximately 50% between 2000 and 2018. Fewer transects were sampled in 2018 because of woody encroachment on the original site. In spring 2021, following an extensive prescribed burn, the site was resampled to see if burning led to any reduction in undesirable species. The most frequent species in 2000 included Panicum philadelphicum, Lespedeza virginica, Rudbeckia hirta and Ambrosia psilostachya and in 2018 they were Lespedeza cuneata, Ambrosia psilostachya, and Dichanthelium oligosanthes. It is possible that the invasive Lespedeza cuneata (sericea lespedeza) spread after a 2007 flood because of some combination of reduced competition and transport of seed in floodwater. In 2021, the most frequent species were the same as in 2018, showing little effect of the burn. However, the Shannon diversity and evenness in both early and late summer sampling periods after the burn were higher than those for the 2018 data, suggesting that the burn may have had some effect. To attempt to restore the site to more “native” conditions would probably require some combination of regular burning, flash grazing, and possibly herbicide use. Once sericea lespedeza establishes, it is very difficult to eradicate from a location.

Fast Pure Shift NMR Spectroscopy Using Attention-Assisted Deep Neural Network.

Pure shift NMR spectroscopy enables the robust probing on molecular structure and dynamics, benefiting from great resolution enhancements. Despite extensive application landscapes in various branches of chemistry, the long experimental times induced by the additional time dimension generally hinder its further developments and practical deployments, especially for multi-dimensional pure shift NMR. Herein, this study proposes and implements the fast, reliable, and robust reconstruction for accelerated pure shift NMR spectroscopy with lightweight attention-assisted deep neural network. This deep learning protocol allows one to regain high-resolution signals and suppress undersampling artifacts, as well as furnish high-fidelity signal intensities along with the accelerated pure shift acquisition, benefitting from the introduction of the attention mechanism to highlight the spectral feature and information of interest. Extensive results of simulated and experimental NMR data demonstrate that this attention-assisted deep learning protocol enables the effective recovery of weak signals that are almost drown in the serious undersampling artifacts, and the distinction and recognition of close chemical shifts even though using merely 5.4% data, highlighting its huge potentials on fast pure shift NMR spectroscopy. As a result, this study affords a promising paradigm for the AI-assisted NMR protocols toward broader applications in chemistry, biology, materials, and life sciences, and among others.

Characterization of diverse targetable ERBB2 alterations in 512,993 patients with solid tumors.

3129 Background: Trastuzumab deruxtecan (T-DXd) is an antibody-drug conjugate (ADC) that has demonstrated activity against a range of HER2-expressing and HER2-mutant (mut) solid tumors. We present an extensive landscape of ERBB2 alterations (alt) that may predict sensitivity to HER2-targeted therapies in development with a focus on mutations and cancer types without approved indications. Methods: We queried institutional databases (MSKCC and Foundation Medicine, Inc.) of comprehensive genomic profiling (CGP) performed on solid tumor tissue samples using either MSK-IMPACT (N=83,332; MSK) or FoundationOne/FoundationOne CDx (N=429,661; FMI) and on patient-matched liquid biopsies using FoundationOne Liquid CDx (N=1,922). ctDNA tumor fraction (ctDNA TF) on FoundationOne Liquid CDx was estimated using a composite algorithm. Clinicopathological characteristics and treatment outcomes were annotated for MSK-IMPACT samples. Results: ERBB2 activating alt were detected in 6.6% (n=28,508) and 5.0% (n=4,133) of tumors in the FMI and MSK cohorts, respectively. In FMI tissue samples, ERBB2 alt were most prevalent in gastroesophageal (GEC; 18.1%), bladder (18.0%), salivary gland (14.1%), breast (12.7%), and uterine (12.1%) cancers. The cancer types with the largest number of ERBB2mut tumors, specifically, included NSCLC (n=1,618, 1.9%), breast (n=1,565, 3.5%), colorectal (CRC; n=1,221, 2.3%), bladder (n=855, 8.8%), and GEC (n=660, 3.4%). Across these 5 cancers, ERBB2 mut were most commonly located in the kinase domain (KD; 58%), followed by the extracellular domain (ECD; 28%) and transmembrane domain (TMD; 2%). Rare splice site mut and in-frame deletions resulting in exon 16 loss (Ex16Alt) were also observed (n=76, <0.1%). ERBB2 mut were found to be mutually exclusive with other RTK/MAPK driver oncogenes including: EGFR, KRAS, ALK, MET, BRAF, and RET in NSCLC; FGFR1 in breast cancer; BRAF in CRC; and FGFR3 in bladder cancer. In patient-matched liquid biopsy samples, sensitivity for detecting ERBB2 amplifications (amp; 33.3%, 43/129) was lower than for detecting ERBB2 mut (72.3%, 47/65) across select cancers. However, sensitivity for detection of both alts was improved in liquid samples with elevated ctDNA TF (≥1%), increasing to 52.6% (40/76) for amp and 97.3% (36/37) for mut. In the MSK cohort, 110 of 986 (11.2%) patients with ERBB2 mut non-NSCLC tumors received one or more HER2-targeted therapies, including ADCs. Clinical responses to HER2-targeted therapies were observed in patients harboring KD mut (n=10), ECD mut (n=6), TMD mut (V659E, n=1), and in 1 patient with a V697L mut. Conclusions: CGP detects diverse ERBB2activating alt at a high incidence in solid tumor types beyond NSCLC. These represent patient populations who may benefit from HER2-directed therapies. Trials of novel HER2-targeted agents are necessary to refine our understanding of the biological dependency of HER2 alterations.

Disinformation and political propaganda: An exploration of the risks of artificial intelligence

A significant shift is currently underway in the disinformation industry. We are transitioning from the era of disinformation fuelled by fake news and social media to disinformation on a larger scale generated through artificial intelligence (AI). Therefore, the objective of this text is to analyse this disinformation phenomenon, catalysed by social media and AI, from the media ecology perspective. This work is divided into two parts. In the first part of the text, we analyse the disinformation phenomenon, highlighting the involvement of certain governments. In the second part of the text, we focus on recognizing the effects that can arise from the use of AI within the extensive landscape of the disinformation industry.

Conservation Management Practices for Biodiversity Preservation in Urban Informal Green Spaces: Lessons from Central European City

Urban informal green spaces (IGS) represent valuable reservoirs of biodiversity within urban areas and are increasingly recognized as integral components of green infrastructure. They are perceived as temporary ecosystems, and the management of their vegetation is relatively understudied. The development time of spontaneous vegetation on transformed lands is considered to be in the range of decades, which makes it even more necessary to provide managers with better guidelines for such a long period. Two suggested management approaches for these areas involve: (1) retaining vegetation at various stages of succession (non-forest IGS) and (2) protecting advanced developmental stages (forest IGS), with options for balanced intervention or complete non-intervention. However, the differences in biodiversity between these two types in cities across Central Europe remain unknown, as well as whether the predictors of biodiversity at both local and landscape scales are consistent for non-forest and forest IGS. We examined factors such as habitat continuity, landscape structure, soil quality, and human impact to shed light on pathways for enhancing urban floristic diversity. Conducting extensive botanical surveys in existing informal green spaces (IGS) in Warsaw, we derived various parameters, including the total number of species, Shannon-Wiener biodiversity index, hemeroby, urbanity, share of species from distinct ecological groups, and the number of rare and ancient forest plant species. Tracing habitat continuity from the early 20th century using digitized aerial imagery provided a unique long-term perspective on IGS development. We revealed that no management is pivotal for the conservation of select rare and ancient forest species. On the other hand, partial abandonment with occasional maintenance may enrich species diversity across different successional phases. We uncovered the significant influence of landscape structure and human activity on vegetation species composition within IGS. Notably, IGS proximate to extensive forest landscapes displayed a marked abundance of forest species, alongside a greater prevalence of rare species. However, the presence of other vegetation types in the vicinity did not yield similar effects. Our findings indicate that IGS, when left untouched for decades near forested areas, are valuable for urban biodiversity. As cities across the globe seek sustainable paths, this research underscores the importance of properly understanding and integrating IGS into urban ecological planning.

Exploration of Scientific Documents through Unsupervised Learning-Based Segmentation Techniques

Navigating the extensive landscape of scientific literature presents a significant challenge, prompting the development of innovative methodologies for efficient exploration. Our study introduces a pioneering approach for unsupervised segmentation, aimed at revealing thematic trends within articles and enhancing the accessibility of scientific knowledge. Leveraging three prominent clustering algorithms—K-Means, Hierarchical Agglomerative, and DBSCAN—we demonstrate their proficiency in generating meaningful clusters, validated through assessment metrics including Silhouette Score, Calinski-Harabasz Index, and Davies-Bouldin Index. Methodologically, comprehensive web scraping of scientific databases, coupled with thorough data cleaning and preprocessing, forms the foundation of our approach. The efficacy of our methodology in accurately identifying scientific domains and uncovering interdisciplinary connections underscores its potential to revolutionize the exploration of scientific publications. Future endeavors will further explore alternative unsupervised algorithms and extend the methodology to diverse data sources, fostering continuous innovation in scientific knowledge organization.

Real-time data analytics in retail: A review of USA and global practices

Real-time data analytics has emerged as a pivotal tool in the retail sector, revolutionizing decision-making processes and operational strategies. This study delves into the extensive landscape of real-time data analytics in retail, offering a comprehensive review of practices both in the USA and on a global scale. In the United States, retail giants have harnessed real-time data analytics to gain unprecedented insights into consumer behavior, preferences, and market trends. From personalized marketing campaigns to dynamic pricing strategies, retailers leverage real-time analytics to optimize inventory management and enhance the overall customer experience. The integration of technologies like RFID, IoT devices, and advanced analytics platforms has facilitated the seamless flow of data, enabling retailers to respond swiftly to changing market dynamics. On a global scale, diverse retail markets have embraced real-time data analytics to stay competitive in an ever-evolving landscape. Cross-border e-commerce and the proliferation of online marketplaces have intensified the need for real-time insights into global consumer trends. Retailers worldwide are adopting predictive analytics to forecast demand, reduce stockouts, and streamline supply chain operations. Moreover, the integration of artificial intelligence and machine learning algorithms enhances the ability to analyze vast datasets, uncovering hidden patterns and predicting future market trends. Challenges such as data privacy concerns, integration complexities, and the need for skilled professionals are acknowledged in this review. Despite these challenges, the transformative impact of real-time data analytics on the retail sector is undeniable. As technology continues to advance, the synergy between real-time data analytics and retail practices is expected to deepen, fostering innovation and reshaping the industry's landscape. This study provides a glimpse into the dynamic and evolving realm of real-time data analytics in retail, offering insights into the strategies employed by both the USA and global players to thrive in an era of rapid technological change.