Digital Analysis Research Articles

Digital Marketing Analysis of Brand Loyalty Among Generation Z on Local Skincare Brand Products

This research aims to explore the factors that influence brand loyalty among Generation Z in the context of skincare products. Using qualitative methods, the study involved in-depth interviews with ten participants representing the 18-25 age group, who actively use skincare products from various brands. Findings revealed that the main factors influencing brand loyalty among Generation Z include product quality, active social media presence, commitment to sustainability and positive user experience. This research provides important insights for marketers in developing more effective strategies to attract and retain young customers.

Clinical Decision Support Using Speech Signal Analysis: Systematic Scoping Review of Neurological Disorders.

Digital biomarkers are increasingly used in clinical decision support for various health conditions. Speech features as digital biomarkers can offer insights into underlying physiological processes due to the complexity of speech production. This process involves respiration, phonation, articulation, and resonance, all of which rely on specific motor systems for the preparation and execution of speech. Deficits in any of these systems can cause changes in speech signal patterns. Increasing efforts are being made to develop speech-based clinical decision support systems. This systematic scoping review investigated the technological revolution and recent digital clinical speech signal analysis trends to understand the key concepts and research processes from clinical and technical perspectives. A systematic scoping review was undertaken in 6 databases guided by a set of research questions. Articles that focused on speech signal analysis for clinical decision-making were identified, and the included studies were analyzed quantitatively. A narrower scope of studies investigating neurological diseases were analyzed using qualitative content analysis. A total of 389 articles met the initial eligibility criteria, of which 72 (18.5%) that focused on neurological diseases were included in the qualitative analysis. In the included studies, Parkinson disease, Alzheimer disease, and cognitive disorders were the most frequently investigated conditions. The literature explored the potential of speech feature analysis in diagnosis, differentiating between, assessing the severity and monitoring the treatment of neurological conditions. The common speech tasks used were sustained phonations, diadochokinetic tasks, reading tasks, activity-based tasks, picture descriptions, and prompted speech tasks. From these tasks, conventional speech features (such as fundamental frequency, jitter, and shimmer), advanced digital signal processing-based speech features (such as wavelet transformation-based features), and spectrograms in the form of audio images were analyzed. Traditional machine learning and deep learning approaches were used to build predictive models, whereas statistical analysis assessed variable relationships and reliability of speech features. Model evaluations primarily focused on analytical validations. A significant research gap was identified: the need for a structured research process to guide studies toward potential technological intervention in clinical settings. To address this, a research framework was proposed that adapts a design science research methodology to guide research studies systematically. The findings highlight how data science techniques can enhance speech signal analysis to support clinical decision-making. By combining knowledge from clinical practice, speech science, and data science within a structured research framework, future research may achieve greater clinical relevance.

YeeZzzy does it: Using Kanye West’s tweets to identify sleep and emotional disturbances through digital rest-activity rhythms analysis

YeeZzzy does it: Using Kanye West’s tweets to identify sleep and emotional disturbances through digital rest-activity rhythms analysis

Wiki warriors: language editors counter knowledge hierarchies during the pandemic

ABSTRACT The volunteerism of Wikipedia editors has been conceptualised as an altruistic form of social production and a form of social activism. However, notions of linguistic activism and cultural identity as motivators, and the ways in which editors representing the Global South have sought to counter knowledge inequities online, have not been addressed. Drawing on theoretical frameworks of digital colonialism and social justice; a grounded theory approach that included semi-structured interviews with prominent editors; and digital data analysis from Wikipedia, we present a culturally situated case study of Indian language Wikipedia editors during the Covid-19 pandemic. We identify them as ‘Wiki Warriors’ and offer their motivations and work practices as instructive for rethinking assumptions about digital colonialism. We find that during the urgency of the global health crisis, the editors in the Global South developed a range of decolonialist practices as they negotiated the tensions of western medicine and traditional non-Western healing systems, such as Ayurveda and Unnani, ‘universal’ facts, and the spreadability of vernacular, localised health information in the digital space. Such decolonialist strategies were shaped by the responses towards religious disharmony and misinformation.

Ultrafast enzyme-responsive hydrogel for real-time assessment and treatment optimization in infected wounds

Monitoring wound infection and providing appropriate treatment are crucial for achieving favorable outcomes. However, the time-consuming nature of laboratory culture tests may delay timely intervention. To tackle this challenge, a simple yet effective HDG hydrogel, composed of hydrogen peroxide (H₂O₂), dopamine, and GelMA polymer, is developed for the ultrafast detection and treatment of Staphylococcus aureus (SA) infections. The HDG hydrogel detects SA by exploiting its secreted catalase to catalyze H₂O₂, producing oxygen, which in turn accelerates the polymerization of colorless dopamine into deep brown polydopamine (PDA). The bacterial detection process takes only 10 min with high sensitivity, and the results can be readily recognized by the naked eye or quantified using a cell phone-based digital analysis. Moreover, the HDG hydrogel provides a dual antibacterial mechanism through chemical and photothermal therapies via the generated PDA, significantly improving bacterial clearance. In animal experiments, the HDG hydrogel demonstrated promising capabilities in monitoring and eliminating bacteria, enhancing collagen deposition, reducing inflammation, and promoting the healing of infected wounds. This multifunctional design offers an enzyme-responsive strategy for the rapid assessment and management of infections, simplifying infection evaluation and facilitating the development of advanced wound dressings.Graphical

Using digital pathology to standardize and automate histological evaluations of environmental samples

Abstract Histological evaluations of tissues are commonly used in environmental monitoring studies to assess the health and fitness status of populations or even whole ecosystems. Although traditional histology can be cost-effective, there is a shortage of proficient histopathologists and results can often be subjective between operators, leading to variance. Digital pathology is a powerful diagnostic tool that has already significantly transformed research in human health but has rarely been applied to environmental studies. Digital analyses of whole slide images introduce possibilities of highly standardized histopathological evaluations, as well as the use of artificial intelligence for novel analyses. Furthermore, incorporation of digital pathology into environmental monitoring studies using standardized bioindicator species or groups such as bivalves and fish can greatly improve the accuracy, reproducibility, and efficiency of the studies. This review aims to introduce readers to digital pathology and how it can be applied to environmental studies. This includes guidelines for sample preparation, potential sources of error, and comparisons to traditional histopathological analyses.

AI-assisted Digital Video Analysis Reveals Changes in Gait Among Three-Day Event Horses During Competition.

The value and welfare of a performance horse are closely tie to locomotor behaviors, but we lack objective and quantitative measures for these characteristics, and qualitative approaches for assessing gait do not provide measures suitable for large-scale biomechanical research studies. Digital video analysis utilizing artificial intelligence-based strategies promise to meet the need for an economical, accurate, repeatable and objective technique for field quantification of equine locomotion. Here we describe pilot work using a consumer-level digital video camera to capture high-resolution and high-speed videos of horses moving at the trot during mandatory inspections for international-level eventing competitions. We assessed 194 horses from five different competition venues, recorded at pre-competition (first) and post-cross-country (second) inspections as a model of gait change following exertion. We labeled twenty-six keypoints on each frame with DeepLabCut and processed the resulting tracking data using MatLab to derive quantitative gait parameters. Once trained, the DeepLabCut model labeled the 388 videos in just minutes, a task that would have otherwise taken months of human effort to complete. A Generalized Linear Mixed Model (GLMM) examining seven gait parameters identified significant changes in duty factor, speed, and forelimb swing range following the completion of the cross-country phase (P≤0.05). Despite some limitations, video analysis through artificial intelligence proved capable of quantifying several gait parameters quickly, efficiently, and without the need for specialized equipment, making this tool a promising option for future biomechanical research in the athletic horse.

Paenibacillus zeirhizosphaerae sp. nov., isolated from surface of the maize (Zea mays) roots in a horticulture field, Hungary.

A novel Gram-stain-positive, rod-shaped, endospore-forming bacterium with peritrichous flagella, designated as P96T was isolated from the surface of maize roots. Strain P96T grew optimally at 28 °C, pH 7.0. The strain contained A1γ meso-Dpm-direct in the cell-wall peptidoglycan. The dominant polar lipids were diphosphatidylglycerol, phosphatidylethanolamine and phosphatidylglycerol. The genome size of strain P96T was 4.8 Mb, and the G+C content was 50.01%. Phylogenomic analyses based on the whole-genome sequences classified the strain into the genus Paenibacillus. The digital DNA-DNA hybridization and average nucleotide identity relatedness analysis resulted in values below the threshold for prokaryotic species delineation, with the highest values observed for Paenibacillus enshidis KCTC 33519T (29.4 and 85.2%, respectively). Genotypic data together with phenotypic properties supported the classification of strain P96T as representative of a novel species of the genus Paenibacillus, for which the name Paenibacillus zeirhizosphaerae sp. nov. is proposed. The type strain is P96T (=LMG 32802T = NCAIM B 02678T).

Forecasting coastal stability: Digital shoreline analysis system and machine learning techniques in evaluating Impacts of cyclones

Forecasting coastal stability: Digital shoreline analysis system and machine learning techniques in evaluating Impacts of cyclones

Fish Tracking, Counting, and Behaviour Analysis in Digital Aquaculture: A Comprehensive Survey

ABSTRACTDigital aquaculture leverages advanced technologies and data‐driven methods, providing substantial benefits over traditional aquaculture practices. This article presents a comprehensive review of three interconnected digital aquaculture tasks, namely, fish tracking, counting, and behaviour analysis, using a novel and unified approach. Unlike previous reviews which focused on single modalities or individual tasks, we analyse vision‐based (i.e., image‐ and video‐based), acoustic‐based, and biosensor‐based methods across all three tasks. We examine their advantages, limitations, and applications, highlighting recent advancements and identifying critical cross‐cutting research gaps. The review also includes emerging ideas such as applying multitask learning and large language models to address various aspects of fish monitoring, an approach not previously explored in aquaculture literature. We identify the major obstacles hindering research progress in this field, including the scarcity of comprehensive fish datasets and the lack of unified evaluation standards. To overcome the current limitations, we explore the potential of using emerging technologies such as multimodal data fusion and deep learning to improve the accuracy, robustness, and efficiency of integrated fish monitoring systems. In addition, we provide a summary of existing datasets available for fish tracking, counting, and behaviour analysis. This holistic perspective offers a roadmap for future research, emphasizing the need for comprehensive datasets and evaluation standards to facilitate meaningful comparisons between technologies and to promote their practical implementations in real‐world settings.

Preliminary characterisation of the spatial immune and vascular environment in triple negative basal breast carcinomas using multiplex fluorescent immunohistochemistry.

Triple negative breast cancers often contain higher numbers of tumour-infiltrating lymphocytes compared with other breast cancer subtypes, with their number correlating with prolonged survival. Since little is known about tumour-infiltrating lymphocyte trafficking in triple negative breast cancers, we investigated the relationship between tumour-infiltrating lymphocytes and the vascular compartment to better understand the immune tumour microenvironment in this aggressive cancer type. We aimed to identify mechanisms and signaling pathways responsible for immune cell trafficking in triple negative breast cancers, specifically of basal type, that could potentially be manipulated to change such tumours from immune "cold" to "hot" thereby increasing the likelihood of successful immunotherapy in this challenging patient population. We characterised the spatial immune environment in 10 basal breast cancers showing a range of tumour-infiltrating lymphocytes using multiplex fluorescent immunohistochemistry and quantitative digital analysis of CD3+ T cells. We examined their relationship to blood vessels and their activation status as defined by VCAM-1, ICAM-1 and PD-L1. Confirmation of the relationship between tumour-infiltrating lymphocytes and endothelial activation was performed through in silico analysis on TCGA BRCA RNA-seq data (N = 808). Significantly higher CD3+ T cell densities were observed in the stromal compartment compared with the neoplastic cell compartment (P = 0.003). ICAM-1 activated blood vessels were spatially associated with higher CD3+ T cell densities only within 30 microns of blood vessels compared with more distal activated and non-activated blood vessels (P = 0.041). In silico analysis confirmed higher numbers of tumour-infiltrating lymphocytes in basal breast cancers and that higher numbers were significantly associated with endothelial cell activation molecules, co-clustering with upregulated ICAM-1 and VCAM-1 amongst others. PD-L1 was also identified in a subset of blood vessels, suggesting an additional immune regulatory mechanism in endothelial cells. Regulating the activation status of tumour-associated vascular endothelial cells may improve T cell trafficking into basal breast tumours and enhance immunotherapeutic response.

Multiplex Digital Nucleic Acid Analysis by a LAMP-Argonaute Coupling Assay via a Parallel Droplet Fusion SlipChip.

Multiplex digital nucleic acid analysis (NAA) allows the precise quantification of multiple target nucleic acids with single-molecule sensitivity, making it highly appealing for life science research and clinical diagnostics. Nucleic acid-guided endonucleases, such as CRISPR, have demonstrated great potential in digital NAA. However, performing multiplex digital NAA with an endonuclease remains challenging. The thermophilic Argonaute protein (Ago) enables specific targeting of multiple sequences by a single enzyme, exhibiting superior potential in multiplex detection. Here, we developed a multiplex digital NAA by coupling nucleic acid amplification and Ago-specific detection using parallel droplet fusion facilitated by a SlipChip. The SlipChip can generate a series of droplets to perform multiplex digital loop-mediated isothermal amplification (LAMP), followed by a series of droplets containing Ago reagents for parallel mixing and reactions, resulting in three distinct digital fluorescence signals (FAM, ROX, and Cy5) corresponding to each specific target sequence. We performed viral load analysis of respiratory viruses, including influenza A, influenza B, and SARS-CoV-2, within 60 min. In addition, we used this digital LAMP-Ago assay to analyze viral loads in 34 clinical samples. The system provides a multiplex digital NAA capable of precise nucleic acid quantification with high sensitivity and specificity.

Measurement of Steiner analysis conventionally and digitally at the age of 12-18 years at RSGM-P Universitas Airlangga

Background: Malocclusion is generally not a pathological process but a deviation process that occurs in the growth and development of the craniofacial system starting in childhood, affecting function and aesthetics, thereby impacting quality of life and social interactions. Lateral cephalometry is used to study individual growth changes within a population. Lateral cephalometric analysis measurements can be performed using conventional or digital methods. With technological advancements, several measurement methods using digital and computer applications for cephalometric analysis have been developed. Consequently, digital measurement methods are gradually replacing conventional methods. Purpose: To analyze the results of conventional and digital Steiner analysis measurements on cephalometric radiographs in individuals aged 12-18 years. Methods: A total of 50 lateral cephalometric samples (19 males; 31 females) were selected according to inclusion and exclusion criteria from patients in the Department of Pediatric Dentistry. Steiner analysis was used for each radiographic measurement. The SNA, SNB, and ANB angles were first measured conventionally, followed by digital methods using the Android-based OneCeph application. The results obtained from both methods were then compared. Results: The data used in this study are not normally distributed; therefore, the non-parametric Mann-Whitney test is used to analyze the data. There is no significant difference between the results of Steiner analysis measurements using conventional and digital methods (OneCeph app). Conclusion: The results of this study indicate that there is no difference between the Steiner analysis measurement results for individuals aged 12-18 years using the conventional method (hand-tracing) and the digital method (One-Ceph app).

Diagnostic accuracy of pancreatic fibrosis grading with artificial intelligence: a single-center experience

Background: Chronic pancreatitis is a long-term fibro-inflammatory disease of the pancreas that worsens the quality of life of patients and is associated with life-threatening complications. Chronic pancreatitis is based on fibro-inflammatory changes in the tissue of the pancreas. The necessity of a more accurate identification of early fibrosis is related to discrepancies in assessments done with conventional visual scales. Aim: To evaluate the diagnostic accuracy of existing scales for visual assessment of the degree of pancreatic fibrosis compared to the techniques of whole-slide images (WSI) digital analysis. Methods: The morphometric analysis of the assessment of pancreatic fibrosis grade was carried out in the postoperative material from 118 patients with the use of two assessment semi-quantitative visual scales (those proposed by G. Klöppel B. Maillet in 1991 and by O. V. Paklina et al. in 2011), as well as with the use of the digital image marking in the ASAP software and an artificial intelligence algorithm in the QuPath software. Both intralobular and perilobular fibrosis grades were assessed, as well as their integrative index (II). Results: The assessment of pancreatic fibrosis with a 12-point rating system (II) according to Klöppel Maillett showed the following distribution: low grade fibrosis (LF), 55.9% of the cases, moderate fibrosis (MF), 25.4%, severe fibrosis (SF), 16.1%, no fibrosis (NF), 2.5% of the cases. According to the Paklina et al. scale, the results were as follows: LF, 66.1%, MF, 15.2%, SF, 12.7%, and NF, 5.9% of the cases. The comparison of the classical systems revealed a mismatch in the fibrosis grade in 27.9% of the cases; this resulted in the use of more objective methods for the WSI digital analysis. When comparing the average percentage of fibrosis area detected with ASAP with the fibrosis level according to Klöppel Maillett, the data were distributed as follows: with LF, the mean fibrosis area by ASAP was 6.9%, with MF, the fibrosis area was 30%, and with SF, 65%. Similarly, when counting cells in QuPath the following values were obtained: LF, 8.2%, MF, 27.9%, SF, 69.3%. The use of the WSI digital image analysis showed a higher concordance between them (R = 0.937) relative to the comparison between the classical rating scales (R = 0.811). When comparing the methods for assessment of fibrosis for the accurate diagnosis of chronic pancreatitis with ROC analysis, the best result was shown by the cell counting method with QuPath (AUC = 0.943). The method of area calculation with ASAP showed the AUC of 0.910, the classical Klöppel Maillett method demonstrated the AUC of 0.879 and the assessment according to Paklina et al. showed the AUC of 0.808. Conclusion: the WSI digital analysis has higher and significant concordance and diagnostic accuracy; therefore it can be used in clinical practice.

Manipulative technologies in a large amount of linguistic data: Social media

The article presents the results of our study on manipulative techniques based on a large amount of linguistic data from social media and focused on the implementation of a major urban development project in the field of road transport construction in Moscow. The research was conducted using data from social networks, video hosting platforms, microblogs, blogs, forums and review websites (3,358,590 tokens, the audience of 17,688,221). Created during the study, the database contains user-generated content and their digital footprints reflecting user reactions. The data analysis was conducted from a cognitive perspective, employing a neural network text analysis, content analysis, sentiment analysis, digital aggression analysis and lexical association analysis. The Brand Analytics monitoring system was used for data collection, while GPT-4o, GPT-4o mini, GPT-4, TextAnalyst 2.32 and AutoMap were used for the data analysis and interpretation, with the Tableau platform used for visual analytics. The analysis of the data revealed mythological manipulation, speech and psychotechnologies. When manipulative techniques are employed in the media space, rhetorical tropes and figures, sophisms and eristic tricks are actively used. The adaptation of these techniques to the specifics of digital communication allows for indirect influence on the audience, significantly increasing the effectiveness of the impact, which can, in turn, lead to heightened conflict potential and social tension.

Understanding Taphonomy Through 3D and 2D Records: A Case Study from the Tropical Maya Area

Mortuary archaeology in the Maya region is complicated by both cultural and natural factors. Distinctive funerary depositional and post-depositional secondary activities, in addition to tropical climate, contribute to the complexity of pre-Hispanic Maya funerary practices. This paper proposes to merge 2D and 3D recording data to obtain a comprehensive understanding of the taphonomic phenomena that affect heavily altered burials at the site of Palenque, Mexico. Employing an archaeothanatological approach, we argue that careful 3D imaging, integrated with earlier produced legacy data, provides additional insight into the formation processes of funerary contexts compared to previous methods. Digital photogrammetric analyses improve our capacity to reconstruct joint articulations of the body and its original funerary deposition in situ. The results of this research elucidate the intentional activities that led to the archaeological arrangement of the grave assemblage. By noticing the degree of articulation and clarifying the pace at which bodies decomposed, these results showcase the depositional sequence of one collective mixed burial from Group IV, a domestic compound in Palenque. These results suggest the viability of 3D methodologies in assessing post-depositional disturbances and movements of the body, both for illuminating funerary taphonomic practices and serving as important recording procedures for the future.

Effects of dynamic elastomeric fabric orthoses in children with cerebral palsy: A single-blind randomized controlled trial.

The dynamic elastomeric fabric orthoses (DEFOs) are made of neoprene material, providing the right biomechanical alignment and afferent input in the trunk, pelvis, and extremities, potentially allowing individuals to actively participate in daily life. The aim of this study was to investigate the effects of DEFOs applied to the lower trunk and pelvis, on balance, gait parameters, and pelvic symmetry in children with cerebral palsy (CP). An evaluator-blinded randomized controlled trial. Twenty-two children (7 girls, 15 boys) aged 4-10 years, with spastic CP, Gross Motor Function Classification System levels 1 and 2 were included. Thirteen children were randomly assigned to the control group (group 1) and 13 to the orthosis group (group 2), but 4 withdrew before treatment commenced. There were 10 children in group 1 and 12 in group 2. Both groups received a physiotherapy and rehabilitation program twice a week for 8 weeks. Children in group 2 received lower body and pelvis DEFOs in addition to physiotherapy and rehabilitation sessions. Dynamic elastomeric fabric orthoses wearing tolerance was gradually built up over the first week, until the child was wearing the DEFO 8 h per day. The pediatric balance scale and timed up and go test were used to assess balance, while gait parameters and pelvic kinematic values were evaluated using the BTS G-Walk, a wireless mini digital gait analysis system. All of the measurement tools used were validated for CP. Only stride length among the gait parameters changed in the group using DEFO compared with the group not using it (p = 0.035). There was no change in gait speed and cadence nor were there any differences in the balance scores between the groups. However, the pelvic tilt in the frontal plane of the pelvis improved significantly in the group using DEFOs compared with the group not using it (p = 0.014). Dynamic elastomeric fabric orthoses used in the lower body and pelvis conferred superior stride length and the pelvic tilt in the frontal plane to conventional physiotherapy. All other outcomes were equally effective between groups.

Satellite Reveals the Accelerated Coastline Erosion of Sydney’s Sandy Beaches Since the 21st Century

Coastline change serves as a crucial indicator of environmental changes in coastal areas. By utilizing Landsat imagery time series from 2000 to 2022, we integrated the Modified Normalized Water Index, Support Vector Machine supervised classifier, and the Digital Coastline Analysis System to track coastline changes in Sydney’s sandy beaches over 2000-2022, and analyzed the potential factors. Since the beginning of the 21st century, Sydney’s sandy beaches exhibited an erosion trend of −0.17 m/a, resulting in a net coastline movement of −6.84 m, with over 80% of the coastline experiencing erosion. Before 2010, the sandy beaches, on average, accreted at a rate of 0.40 m/a. Then, from 2010 to 2019, the average beach accretion slowed down (0.07 m/a), with some beaches showing an erosion trend. However, after 2019, sandy coastline erosion in Sydney has greatly accelerated, with an average rate of −4.81 m/a. The primary factors influencing the spatial-temporal patterns of Sydney’s sandy coastline include sea level height, significant wave height, and storm events. This study provides valuable insights into the sustainable management and protection of sandy beaches, disaster response planning, and the sustainable development of coastal areas.

Digital analysis of discrete fractional order worms transmission in wireless sensor systems: performance validation by artificial intelligence

Digital analysis of discrete fractional order worms transmission in wireless sensor systems: performance validation by artificial intelligence

Three-Dimensional Prospective Modeling and Deep Metallogenic Prediction of the Lintan Gold Deposit in Guizhou Province, China

The Lintan gold deposit, located in the “gold triangle” of Qianxinan, Guizhou Province, has become a focal point for implementing the “exploring near existing deposits” strategy, aiming to identify another large-scale gold deposit within the region. This study addresses the challenges of deep-edge mineral exploration in the Lintan gold deposit by adopting a metallogenic system perspective. Using a multidisciplinary approach, it integrates geological, geophysical, and geochemical datasets to construct various three-dimensional (3D) visualization and prospectivity models. The research leverages geostatistical theories and methods, multisource digital information analysis, and advanced 3D modeling and visualization techniques to verify mineralization anomalies. These efforts expand the scope of prospectivity evaluation for the deep-edge regions of the Lintan gold deposit into 3D space. In the 3D spatial framework, this study elucidates the metallogenic geological characteristics, geophysical anomalies, and geochemical signatures within the study area. Building upon this foundation, it conducts a comprehensive analysis and evaluation of geological, geochemical, and geophysical prospecting indicators under multisource geoscience datasets. This approach transitions from known to unknown domains, effectively reducing the ambiguities and uncertainties associated with single-source data interpretations. The findings demonstrate that, under the guidance of geological prospectivity models, the effective integration and synthesis of geological, geophysical, and geochemical data can reveal the interrelationships between metallogenic geological bodies and the contributing factors of the metallogenic system. This enables the identification of anomalous information associated with metallogenic geological bodies and facilitates the spatial localization and prediction of target areas for deep-edge mineral resources. The proposed methodology provides novel insights and techniques for deep-edge mineral exploration. Comprehensive analysis indicates significant prospectivity for mineral resource exploration in the deep-edge regions of the Lintan gold deposit.