Multiple Platforms Research Articles

Machine Learning Algorithms Identify Target Genes and the Molecular Mechanism of Matrine against Diffuse Large B-cell Lymphoma.

Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin's lymphoma worldwide. Novel treatment strategies are still needed for this disease. The present study aimed to systematically explore the potential targets and molecular mechanisms of matrine in the treatment of DLBCL. Potential matrine targets were collected from multiple platforms. Microarray data and clinical characteristics of DLBCL were downloaded from publicly available database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were applied to identify the hub genes of DLBCL using R software. Then, the shared target genes between matrine and DLBCL were identified as the potential targets of matrine against DLBCL. The least absolute shrinkage and selection operator (LASSO) algorithm was used to determine the final core target genes, which were further verified by molecular docking simulation and receiver operating characteristic (ROC) curve analysis. Functional analysis was also performed to elucidate the potential mechanisms. A total of 222 matrine target genes and 1269 DLBCL hub genes were obtained through multiple databases and machine learning algorithms. From the nine shared target genes of matrine and DLBCL, five final core target genes, including CTSL, NR1H2, PDPK1, MDM2, and JAK3, were identified. Molecular docking showed that the binding of matrine to the core genes was stable. ROC curves also suggested close associations between the core genes and DLBCL. Additionally, functional analysis showed that the therapeutic effect of matrine against DLBCL may be related to the PI3K-Akt signaling pathway. Matrine may target five genes and the PI3K-Akt signaling pathway in DLBCL treatment.

Omni-channel customer experience framework: enhancing service delivery in SMEs

The omni-channel customer experience framework offers a comprehensive strategy for enhancing service delivery in Small and Medium Enterprises (SMEs). This review examines how the integration of multiple communication and interaction channels—both online and offline—creates seamless, consistent, and personalized customer journeys, thereby improving overall service delivery. For SMEs, adopting an omni-channel approach provides significant competitive advantages by enabling customers to engage with businesses through their preferred platforms, including websites, mobile apps, social media, in-store visits, and customer service hotlines. This strategy fosters customer satisfaction, retention, and brand loyalty. By leveraging technology and data analytics, SMEs can synchronize customer interactions across channels, ensuring that customers receive a cohesive experience regardless of their entry point. Omni-channel frameworks allow businesses to track customer preferences, behaviors, and interactions, which in turn helps in delivering personalized services and timely responses. Automation tools such as chatbots and AI-driven systems further support this by providing 24/7 customer service, enhancing responsiveness and efficiency, particularly for resource-constrained SMEs. The integration of physical and digital touchpoints is crucial in creating a seamless experience. For example, customers may research a product online, visit a physical store to experience it firsthand, and then complete the purchase through a mobile app. SMEs that effectively adopt an omni-channel strategy can cater to evolving customer expectations and deliver more flexible, adaptive service offerings. Moreover, this framework helps in gathering and analyzing customer data from various touchpoints, offering valuable insights into customer behavior and preferences. These insights enable SMEs to continuously refine their service delivery, anticipate customer needs, and innovate their business models. In conclusion, implementing an omni-channel customer experience framework empowers SMEs to enhance service delivery by providing consistent, personalized, and responsive interactions across multiple platforms. This approach is key to maintaining competitiveness, improving customer satisfaction, and fostering long-term growth.

Age of air from in situ trace gas measurements: insights from a new technique

Abstract. The age of air is an important transport diagnostic that can be derived from trace gas measurements and compared to global chemistry climate model output. We describe a new technique to calculate the age of air, measuring transport times from the Earth's surface to any location in the atmosphere based on simultaneous in situ measurements of multiple key long-lived trace gases. The primary benefits of this new technique include (1) optimized ages of air consistent with simultaneously measured SF6 and CO2; (2) age of air from the upper troposphere through the stratosphere; (3) estimates of the second moment of age spectra that have not been well constrained from measurements; and (4) flexibility to be used with measurements across multiple instruments, platforms, and decades. We demonstrate the technique on aircraft and balloon measurements from the 1990s, the last period of extensive stratospheric in situ sampling, and several recent missions from the 2020s, and compare the results with previously published and modeled values.

Exploring multi-omics and clinical characteristics linked to accelerated biological aging in Asian women of reproductive age: insights from the S-PRESTO study

BackgroundPhenotypic age (PhenoAge), a widely used marker of biological aging, has been shown to be a robust predictor of all-cause mortality and morbidity in different populations. Existing studies on biological aging have primarily focused on individual domains, resulting in a lack of a comprehensive understanding of the multi-systemic dysregulation that occurs in aging.MethodsPhenoAge was evaluated based on a linear combination of chronological age (CA) and 9 clinical biomarkers in 952 multi-ethnic Asian women of reproductive age. Phenotypic age acceleration (PhenoAgeAccel), an aging biomarker, represents PhenoAge after adjusting for CA. This study conducts an in-depth association analysis of PhenoAgeAccel with clinical, nutritional, lipidomic, gut microbiome, and genetic factors.ResultsHigher adiposity, glycaemia, plasma saturated fatty acids, kynurenine pathway metabolites, GlycA, riboflavin, nicotinamide, and insulin-like growth factor binding proteins were positively associated with PhenoAgeAccel. Conversely, a healthier diet and higher levels of pyridoxal phosphate, all-trans retinol, betaine, tryptophan, glutamine, histidine, apolipoprotein B, and insulin-like growth factors were inversely associated with PhenoAgeAccel. Lipidomic analysis found 132 lipid species linked to PhenoAgeAccel, with PC(O-36:0) showing the strongest positive association and CE(24:5) demonstrating the strongest inverse association. A genome-wide association study identified rs9864994 as the top genetic variant (P = 5.69E-07) from the ZDHHC19 gene. Gut microbiome analysis revealed that Erysipelotrichaceae UCG-003 and Bacteroides vulgatus were inversely associated with PhenoAgeAccel. Integrative network analysis of aging-related factors underscored the intricate links among clinical, nutritional and lipidomic variables, such as positive associations between kynurenine pathway metabolites, amino acids, adiposity, and insulin resistance. Furthermore, potential mediation effects of blood biomarkers related to inflammation, immune response, and nutritional and energy metabolism were observed in the associations of diet, adiposity, genetic variants, and gut microbial species with PhenoAgeAccel.ConclusionsOur findings provide a comprehensive analysis of aging-related factors across multiple platforms, delineating their complex interconnections. This study is the first to report novel signatures in lipidomics, gut microbiome and blood biomarkers specifically associated with PhenoAgeAccel. These insights are invaluable in understanding the molecular and metabolic mechanisms underlying biological aging and shed light on potential interventions to mitigate accelerated biological aging by targeting modifiable factors.

Communication in the Era of Media Convergence and Tourism Enhancement in Samut Songkhram Province Based on the Digital Economy

This qualitative research aims to explore the problems and obstacles of communication in the era of media convergence for tourism promotion and the ability to enhance tourism in Samut Songkhram Province based on the digital economy. An in-depth interview was used to collect data from 27 people: Samut Songkhram representatives or community leaders, tourists, and communication and tourism scholars. The data was analyzed by content analysis and interpretation to present a descriptive narrative. The findings revealed that problems and obstacles to communication in the era of media convergence include the need for a more concise communication process in tourism. Many steps in coordinating cause inconvenience and slowness, which is considered a high cost and expenditure for communication in tourism. As for communication, the era of media convergence and the ability to enhance the provincial tourism of Samut Songkhram based on the digital economy, the respondents thought that they should use integrated communication that combines all platform media to foster innovation in development and support the economy from the grass root which creates and distributes income to residents in Samut Songkhram community. They should develop technological skills and creativity to achieve excellence so the community can adapt to the current and future changes. In this regard, the discussion revealed interesting information that communication in the era of media convergence, where content is delivered across multiple platforms simultaneously, can be used as a guideline to elevate Samut Songkhram tourism with the concept of "Digital Economy Creative Tourism," which Samut Songkhram communities must adjust to keep up with changes that occur all the time.

Exploring Activity-Sharing Response Differences Between Broad-Purpose and Dedicated Online Social Platforms

People often leverage multiple platforms to share activities they undertake in their lives, from music listening to eating. Broad-purpose platforms, which people use to share a wide variety of activities with a diverse audience, and dedicated platforms, which often focus on tracking and sharing a specific activity with connections with similar interests, both help individuals seeking social benefits from sharing their activity. Researchers designing systems for activity sharing have often reflected on whether to support sharing on dedicated or broad-purpose platforms, suggesting a need to better understand their relative utility. We collected and compared the responses received between 700,000 pairs of activity-sharing posts on four sets of broad-purpose and dedicated platforms across two domains: physical activity (Strava, MapMyRun) and creativity (Dribbble, Behance). Results showed that dedicated platforms were more likely to receive responses (likes and comments), and comments were more likely to be encouraging and refer to specific qualities of the activities being shared. We reflect on the tradeoff between sheer audience volume and likelihood of response, and discuss how to design prompts and templates into sharing features which better align with the norms of respective platforms.

Gapless assembly of complete human and plant chromosomes using only nanopore sequencing.

The combination of ultra-long (UL) Oxford Nanopore Technologies (ONT) sequencing reads with long, accurate Pacific Bioscience (PacBio) High Fidelity (HiFi) reads has enabled the completion of a human genome and spurred similar efforts to complete the genomes of many other species. However, this approach for complete, "telomere-to-telomere" genome assembly relies on multiple sequencing platforms, limiting its accessibility. ONT "Duplex" sequencing reads, where both strands of the DNA are read to improve quality, promise high per-base accuracy. To evaluate this new data type, we generated ONT Duplex data for three widely studied genomes: human HG002, Solanum lycopersicum Heinz 1706 (tomato), and Zea mays B73 (maize). For the diploid, heterozygous HG002 genome, we also used "Pore-C" chromatin contact mapping to completely phase the haplotypes. We found the accuracy of Duplex data to be similar to HiFi sequencing, but with read lengths tens of kilobases longer, and the Pore-C data to be compatible with existing diploid assembly algorithms. This combination of read length and accuracy enables the construction of a high-quality initial assembly, which can then be further resolved using the UL reads, and finally phased into chromosome-scale haplotypes with Pore-C. The resulting assemblies have a base accuracy exceeding 99.999% (Q50) and near-perfect continuity, with most chromosomes assembled as single contigs. We conclude that ONT sequencing is a viable alternative to HiFi sequencing for de novo genome assembly, and provides a multirun single-instrument solution for the reconstruction of complete genomes.

Edge Computing for AI-Based Brain MRI Applications: A Critical Evaluation of Real-Time Classification and Segmentation.

Medical imaging plays a pivotal role in diagnostic medicine with technologies like Magnetic Resonance Imagining (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), and ultrasound scans being widely used to assist radiologists and medical experts in reaching concrete diagnosis. Given the recent massive uplift in the storage and processing capabilities of computers, and the publicly available big data, Artificial Intelligence (AI) has also started contributing to improving diagnostic radiology. Edge computing devices and handheld gadgets can serve as useful tools to process medical data in remote areas with limited network and computational resources. In this research, the capabilities of multiple platforms are evaluated for the real-time deployment of diagnostic tools. MRI classification and segmentation applications developed in previous studies are used for testing the performance using different hardware and software configurations. Cost-benefit analysis is carried out using a workstation with a NVIDIA Graphics Processing Unit (GPU), Jetson Xavier NX, Raspberry Pi 4B, and Android phone, using MATLAB, Python, and Android Studio. The mean computational times for the classification app on the PC, Jetson Xavier NX, and Raspberry Pi are 1.2074, 3.7627, and 3.4747 s, respectively. On the low-cost Android phone, this time is observed to be 0.1068 s using the Dynamic Range Quantized TFLite version of the baseline model, with slight degradation in accuracy. For the segmentation app, the times are 1.8241, 5.2641, 6.2162, and 3.2023 s, respectively, when using JPEG inputs. The Jetson Xavier NX and Android phone stand out as the best platforms due to their compact size, fast inference times, and affordability.

Multi-platform biomarkers of response to an immune checkpoint inhibitor in the neoadjuvant I-SPY 2 trial for early-stage breast cancer.

Only a subset of patients with breast cancer responds to immune checkpoint blockade (ICB). To better understand the underlying mechanisms, we analyze pretreatment biopsies from patients in the I-SPY 2 trial who receive neoadjuvant ICB using multiple platforms to profile the tumor microenvironment. A variety of immune cell populations and markers of immune/cytokine signaling associate with pathologic complete response (pCR). Interestingly, these differ by breast cancer receptor subtype. Measures of the spatial distributions of immune cells within the tumor microenvironment, in particular colocalization or close spatial proximity of PD-1+ Tcells with PD-L1+ cells (immune and tumor cells), are significantly associated with response in the overall cohort as well as the in the triple negative (TN) and HR+HER2- subtypes. Our findings indicate that biomarkers associated with immune cell signaling, immune cell densities, and spatial metrics are predictive of neoadjuvant ICB efficacy in breast cancer.

A SIMBA CoMICs Initiative to Cocreating and Disseminating Evidence-Based, Peer-Reviewed Short Videos on Social Media: Mixed Methods Prospective Study.

Social media is a powerful platform for disseminating health information, yet it is often riddled with misinformation. Further, few guidelines exist for producing reliable, peer-reviewed content. This study describes a framework for creating and disseminating evidence-based videos on polycystic ovary syndrome (PCOS) and thyroid conditions to improve health literacy and tackle misinformation. The study aims to evaluate the creation, dissemination, and impact of evidence-based, peer-reviewed short videos on PCOS and thyroid disorders across social media. It also explores the experiences of content creators and assesses audience engagement. This mixed methods prospective study was conducted between December 2022 and May 2023 and comprised five phases: (1) script generation, (2) video creation, (3) cross-platform publication, (4) process evaluation, and (5) impact evaluation. The SIMBA-CoMICs (Simulation via Instant Messaging for Bedside Application-Combined Medical Information Cines) initiative provides a structured process where medical concepts are simplified and converted to visually engaging videos. The initiative recruited medical students interested in making visually appealing and scientifically accurate videos for social media. The students were then guided to create video scripts based on frequently searched PCOS- and thyroid-related topics. Once experts confirmed the accuracy of the scripts, the medical students produced the videos. The videos were checked by clinical experts and experts with lived experience to ensure clarity and engagement. The SIMBA-CoMICs team then guided the students in editing these videos to fit platform requirements before posting them on TikTok, Instagram, YouTube, and Twitter. Engagement metrics were tracked over 2 months. Content creators were interviewed, and thematic analysis was performed to explore their experiences. The 20 videos received 718 likes, 120 shares, and 54,686 views across all platforms, with TikTok (19,458 views) and Twitter (19,678 views) being the most popular. Engagement increased significantly, with follower growth ranging from 5% on Twitter to 89% on TikTok. Thematic analysis of interviews with 8 out of 38 participants revealed 4 key themes: views on social media, advice for using social media, reasons for participating, and reflections on the project. Content creators highlighted the advantages of social media, such as large outreach (12 references), convenience (10 references), and accessibility to opportunities (7 references). Participants appreciated the nonrestrictive participation criteria, convenience (8 references), and the ability to record from home using prewritten scripts (6 references). Further recommendations to improve the content creation experience included awareness of audience demographics (9 references), sharing content on multiple platforms (5 references), and collaborating with organizations (3 references). This study demonstrates the effectiveness of the SIMBA CoMICs initiative in training medical students to create accurate medical information on PCOS and thyroid disorders for social media dissemination. The model offers a scalable solution to combat misinformation and improve health literacy.

Advancing Sustainable Mobility: A Data Acquisition System for Light Vehicles and Active Mobility

Active mobility and light vehicles, such as e-bikes, are gaining increasing attention as sustainable transportation alternatives to internal combustion solutions. In this context, collecting comprehensive data on environmental conditions, vehicle performance, and user interaction is crucial for improving system efficiency and user experience. This paper presents a data acquisition system designed to collect data from multiple sensor platforms. The architecture is optimized to maintain low power consumption and operate within limited computational resources, making it suitable for real-time data acquisition on light vehicles. To achieve this, a data acquisition module was developed using a single-board computer integrated with a custom shield, which also captures data related to the assistance of an e-bike motor through a wireless interface. The paper provides an in-depth discussion of the architecture and software development, along with a detailed overview of the sensors used. A demonstrator was created to verify the system architecture idea and prove the potentialities of the system overall. The demonstrator has been qualified by professional and semi-professional riders in the framework of the Giro-E, a cyclist event which took place in May 2024, on the same roads of the Giro d’Italia. Finally, some preliminary analyses on the data acquired are provided to show the performance of the system, particularly in reconstructing the user behavior, the environmental parameters, and the type of road.

Scalar and vectorized implementations of the density functional theory via Geant4 and GeantV project

In this paper, we present the implementation of the Density Functional Theory (DFT) method using the Geant4-DNA framework in the Single Instruction Single Data (SISD) mode. Furthermore, this implementation is improved in terms of execution time within the GeantV project with vectorization techniques such as Single Instruction Multiple Data (SIMD). Within this framework, a set of SIMD strategies in molecular calculation algorithms such as one-electron operators, two-electron operator, quadrature grids, and functionals, was implemented using the VecCore library. The applications developed in this work implement two DFT functionals, the Local Density Approximation (LDA) and the General Gradient Approximation (GGA), to approximate the molecular ground-state energies of small molecules and amino acids. To assess the performance of the implementations, a standard test simulation was performed in multiple CPU platforms. The SIMD vectorization strategy significantly accelerates DFT calculations, leading to time ratios ranging from 1.6 to 5.4 in either individual steps or entire implementations when compared with the scalar process within Geant4.

Team Tracker: A Project Management Tool

This paper introduces "Team Tracker," a project management tool developed using Asana and Next.js. The tool integrates key functionalities such as audio and video calling, real-time task tracking, and an embedded browser, creating an all-in-one solution for remote team collaboration. The objective of Team Tracker is to streamline workflow by eliminating the need for multiple platforms, thus improving team efficiency and communication. A comparative analysis with existing tools like Jira and Slack demonstrates how Team Tracker addresses the gaps in current project management solutions by offering enhanced real-time communication and task management capabilities. The system's architecture, challenges encountered during development, expected outcomes, and potential future enhancements are also discussed. Team Tracker is poised to provide a comprehensive, scalable solution for modern project management needs. Keywords: project management, Asana, Next.js, real-time communication, task tracking, Team Tracker.

A Review of UAV Path-Planning Algorithms and Obstacle Avoidance Methods for Remote Sensing Applications

The rapid development of uncrewed aerial vehicles (UAVs) has significantly increased their usefulness in various fields, particularly in remote sensing. This paper provides a comprehensive review of UAV path planning, obstacle detection, and avoidance methods, with a focus on its utilisation in both single and multiple UAV platforms. The paper classifies the algorithms into two main categories: (1) global and local path-planning approaches in single UAVs; and (2) multi-UAV path-planning methods. It further analyses obstacle detection and avoidance methods, as well as their capacity to adapt, optimise, and compute efficiently in different operational environments. The outcomes highlight the advantages and limitations of each method, offering valuable information regarding their suitability for remote sensing applications, such as precision agriculture, urban mapping, and ecological surveillance. Additionally, this review also identifies limitations in the existing research, specifically in multi-UAV frameworks, and provides recommendations for future developments to improve the adaptability and effectiveness of UAV operations in dynamic and complex situations.

Activation of Ms 6.9 Milin Earthquake on Sedongpu Disaster Chain, China with Multi-Temporal Optical Images

In recent years, disaster chains caused by glacier movements have occurred frequently in the lower Yarlung Tsangpo River in southwest China. However, it is still unclear whether earthquakes significantly contribute to glacier movements and disaster chains. In addition, it is difficult to measure the high-frequency and large gradient displacement time series with optical remote sensing images due to cloud coverage. To this end, we take the Sedongpu disaster chain as an example, where the Milin earthquake, with an epicenter 11 km away, occurred on 18 November 2017. Firstly, to deal with the cloud coverage problem for single optical remote sensing analysis, we employed multiple platform optical images and conducted a cross-platform correlation technique to invert the two-dimensional displacement rate and the cumulative displacement time series of the Sedongpu glacier. To reveal the correlation between earthquakes and disaster chains, we divided the optical images into three classes according to the Milin earthquake event. Lastly, to increase the accuracy and reliability, we propose two strategies for displacement monitoring, that is, a four-quadrant block registration strategy and a multi-window fusion strategy. Results show that the RMSE reduction percentage of the proposed registration method reaches 80%, and the fusion method can retrieve the large magnitude displacements and complete displacement field. Secondly, the Milin earthquake accelerated the Sedongpu glacier movement, where the pre-seismic velocities were less than 0.5 m/day, the co-seismic velocities increased to 1 to 6 m/day, and the post-seismic velocities decreased to 0.5 to 3 m/day. Lastly, the earthquake had a triggering effect around 33 days on the Sedongpu disaster chain event on 21 December 2017. The failure pattern can be summarized as ice and rock collapse in the source area, large magnitude glacier displacement in the moraine area, and a large volume of sediment in the deposition area, causing a river blockage.

DOMAIN: Explainable credibility assessment tools for empowering online readers coping with misinformation

Despite all the fact-checking initiatives on news and social media aimed at countering misinformation, they remain insufficient to promptly address the wide array of misleading information disseminated by both news and social media outlets. Rather than attempting to identify or filter misleading information, this work advocates new tools for assisting online readers in identifying misinformation among the massive online content pushed every day through multiple platforms. We introduce DOMAIN, an article assessment resource bundle comprising a multidimensional indicator to categorize articles into different types (hard news, soft news, opinion, satire, and conspiracy), a set of explanatory metrics to help users understand the results, a tool for verifying the reliability of the article’s source, and a text summary of the assessment. This work also studies how DOMAIN tools impact online readers, specifically focusing on i) understanding the extent to which computer-generated assessments influence human perceptions of credibility; ii) evaluating the effectiveness of automatic article categorization in human assessment of credibility; and iii) identifying the most relevant explanatory metrics for promoting informed and critical consumption of information.

Adding Highly Variable Genes to Spatially Variable Genes Can Improve Cell Type Clustering Performance in Spatial Transcriptomics Data.

Spatial transcriptomics has allowed researchers to analyze transcriptome data in its tissue sample's spatial context. Various methods have been developed for detecting spatially variable genes (SV genes), whose gene expression over the tissue space shows strong spatial autocorrelation. Such genes are often used to define clusters in cells or spots downstream. However, highly variable (HV) genes, whose quantitative gene expressions show significant variation from cell to cell, are conventionally used in clustering analyses. In this report, we investigate whether adding highly variable genes to spatially variable genes can improve the cell type clustering performance in spatial transcriptomics data. We tested the clustering performance of HV genes, SV genes, and the union of both gene sets (concatenation) on over 50 real spatial transcriptomics datasets across multiple platforms, using a variety of spatial and non-spatial metrics. Our results show that combining HV genes and SV genes can improve overall cell-type clustering performance.

AgriScan: Next.js powered cross-platform solution for automated plant disease diagnosis and crop health management

Plant diseases present a formidable challenge to the agricultural sector worldwide, leading to significant losses, with the US experiencing annual losses amounting to one-third of crop production. Diagnosis of crop diseases through optical observation of leaf symptoms is particularly daunting for farmers with limited resources. Therefore, there is an urgent need for enhanced detection, monitoring, and prediction methods to mitigate agricultural losses effectively. Harnessing the power of computer vision and deep learning, this paper introduces a cross-platform system designed to automate plant leaf disease diagnosis. The system employs convolutional neural networks to classify 46 disease categories, trained on a dataset comprising 96,206 images of healthy and infected plant leaves. The user interface, accessible across multiple platforms including Android, iOS, Windows, and Linux, allows farmers to capture photos of infected leaves and receive real-time disease classification along with confidence percentages. By empowering farmers to maintain crop health and prevent the application of incorrect fertilizers, the system aims to optimize crop productivity. Performance evaluation includes metrics such as classification accuracy and processing time, with the model achieving an impressive overall accuracy of 93.45% across 46 common disease classes spanning 16 crop species.

Modeling a ride-sourcing market with a third-party platform integrator under batch matching mechanisms

This paper develops a mathematical model to characterize a generalized ride-sourcing market with a third-party integrator and multiple competitive ride-sourcing platforms. Different from earlier works which normally assume instant matching strategies, our model can characterize the integrator’s and platforms’ decisions on choosing flexible matching strategies under batch matching mechanisms and their influences on the market equilibrium. Through theoretical analytics and numerical studies, this study reveals that the introduction of a third-party platform integrator is not always beneficial but may be detrimental to the system in certain situations. For example, an inefficient matching strategy chosen by the integrator may lead to a lose-lose situation where both the customers hailing rides through the integrator or the individual platforms experience longer waiting times. In addition, we show that a ride-sourcing platform with a high market share may refuse to join the integrator since the efficiency gains brought by the integrator do not outweigh the losses caused by its losing market share. The managerial insights obtained from this work can assist both the integrator and individual ride-sourcing platforms in developing more efficient operational strategies in terms of pricing and matching strategies.

The Communication about Destination Projected Image on Short-form Video Platforms: A Systematic Literature Review

With the continuous innovation of Internet technology, the way of communication is evolving rapidly. Social media enables the creation and dissemination of forms of expression. In today's environment, short videos play a key role in the dissemination of destination image projection, and it is particularly important to build and maintain the virtual image of the destination. This paper systematically reviews the relevant literature and comprehensively analyzes the research on destination image projection in short videos. In the past decade, the relevant research results have steadily increased, covering multiple tourism destinations and platforms. This study includes articles from English peer-reviewed academic journals and discusses them by topic. Through the literature review, this paper explores the direction of current research and outlines future research trends.