Search Results (41)

(1) Background: With the deep integration of e-commerce and video technology, live-streaming marketing has emerged globally and maintained rapid growth. However, most of the current research on live-streaming e-commerce marketing focuses on merchants’ sales strategies and consumers’ purchase intentions, and there is relatively little research related to the risks of live-streaming e-commerce marketing. Nevertheless, with the development of live-streaming e-commerce marketing and its integration with technologies such as artificial intelligence and virtual reality (VR), live-streaming e-commerce marketing still faces challenges such as unclear subject responsibility, difficulty in verifying the authenticity of marketing information, and uneven product quality. It also harbors problems such as the ethical misbehavior of AI anchors and the excessive beautification of products by VR technology. (2) Methods: This study systematically analyzes the scenarios of live-streaming marketing to elucidate the mechanisms of risk formation. Utilizing fault tree analysis (FTA) and risk checklist methods, risks are identified based on the three core elements of live-streaming marketing: “people–products–scenes”. Subsequently, the Delphi method is employed to refine the initial risk indicator system, resulting in the construction of a comprehensive risk indicator system comprising three first-level indicators, six second-level indicators, and 16 third-level indicators. A hesitant fuzzy multi-attribute group decision-making method (HFMGDM) is then applied to calculate the weights of the risk indicators and comprehensively assess the live-streaming marketing risks in live broadcast rooms of three prominent celebrity anchors in China. Furthermore, a detailed analysis is conducted on the risks associated with the six secondary indicators. Based on the risk evaluation results, targeted recommendations are proposed. This study aims to enhance consumers’ awareness of risk prevention when conducting live-streaming transactions and pay attention to related risks, thereby safeguarding consumer rights and fostering the healthy and sustainable development of the live-streaming marketing industry. (3) Conclusions: The results show that the top five risk indicators in terms of weight ranking are: Ethical Risk of the AI Anchor (A4), VR Technology Promotion Risk (F3), Anchor Reputation (A1), Product Quality (D1), and Logistics Distribution Service Quality (D2). The comprehensive live-streaming marketing risk of each live broadcast room is Y > L > D. Based on the analysis results, targeted recommendations are provided for anchors, MCN institutions, merchants, supply chains, and live-streaming platforms to improve consumer satisfaction and promote sustainable development of the live-streaming marketing industry. Full article

This paper analyzes the exploitability, economic viability, and impact of fleet-based 5G network extensions implemented in maritime environments, focusing on the Baltic Sea and Mediterranean as a case study. Through cost–benefit analysis and business model validation, we demonstrate how multi-hop 5G connectivity can reduce communication costs while improving service quality for maritime operators. Our findings indicate that implementing vessel-based 5G relay stations can achieve 80–90% coverage in key maritime corridors with a break-even period of 2–3 years. The study reveals that combining vessel-to-vessel relaying with strategic floating base stations can reduce connectivity costs by up to 40% compared to traditional satellite solutions, while enabling new revenue streams through premium services. We provide a detailed economic framework for evaluating similar implementations across different maritime routes and suggest policy recommendations for facilitating cross-border 5G maritime networks and introduce key use cases value creation for network extension. Full article

Cancer patient navigation has emerged as a patient-centric intervention enabling equitable cancer care, by mitigating barriers patients encounter throughout their cancer journey. Cancer Care Alberta (CCA) implemented a professional navigation model over a decade ago and commissioned a program evaluation in response to evolving operational demands. The objectives were (1) to better understand the current state of CCA’s cancer patient navigation program; (2) to explore the need for other specialized streams; and (3) to provide key recommendations to strengthen and grow the program. A mixed methods approach, including a survey, administrative data, and semi-structured interviews, captured patient-, staff-, and system-level insights. Findings revealed difficulties in identifying complex patients needing navigation, along with inconsistencies regarding intake practices, program awareness, referral pathways, standardized workflows, and a lack of programmatic supports, which contributed to variability in service delivery. A need for enhanced palliative navigation support also emerged. Approximately 25% of surveyed patients reported being unable to access perceived needed support before their first oncology consultation. These findings underscore the importance of early, targeted navigation for equity-deserving populations. Recommendations include harmonizing program structure, refining navigator roles, expanding navigation streams, standardizing processes, and enhancing equity-focused competencies. These findings offer a roadmap with which to improve person-centered cancer care. Full article

Cloud computing is now established as a viable alternative to on-premise infrastructure from both a system administration and cost perspective. This research provides insight into cluster computing performance and variability in cloud-provisioned infrastructure from two popular public cloud providers, Amazon Web Services (AWS) and Google Cloud Platform (GCP). In order to evaluate the perforance variability between these two providers, synthetic benchmarks including Memory bandwidth (STREAM), Interleave or Random (IoR) performance, and Computational CPU performance by NAS Parallel Benchmarks-Embarrassingly Parallel (NPB-EP) were used. A comparative examination of the two cloud platforms is provided in the context of our research methodology and design. We conclude with a discussion of the results of the experiment and an assessment of the suitability of public cloud platforms for certain types of computing workloads. Both AWS and GCP have their strong points, and this study provides recommendations depending on user needs for high throughput and/or performance predictability across CPU, memory, and Input/Output (I/O). In addition, the study discusses other factors to help users decide between cloud vendors such as ease of use, documentation, and types of instances offered. Full article

PVC has become an indispensable material worldwide. However, its production method (suspension) presents significant sustainability challenges, such as negative environmental impacts and high operational costs due to energy consumption. For this reason, a combined analysis was conducted involving energy integration using Aspen Energy Analyzer™ V14 software and a technical process analysis. This methodology aims to reduce industrial utility consumption and assess the sustainability performance of this alternative. The integration through pinch analysis revealed that it is possible to reduce the energy consumption of the process by 29% in heating utilities and 6% in cooling utilities. The minimum utility requirements were 21 GJ/h for heating (down from 29 GJ/h) and 131 GJ/h for cooling (down from 139 GJ/h). This reduction resulted in approximately a 41% decrease in utility costs. Additionally, the reduction in burner energy consumption led to lower greenhouse gas emissions, with a decreased natural gas consumption of approximately 279 m³. However, only two streams could be integrated due to technical process limitations; therefore, it is recommended to explore integrations with complex operations such as reactors and phase-change processes. In addition to this, the WEP technical evaluation yielded promising results showing a decrease in the specific energy intensity by 3219.506 MJ/t (being 4681.8 MJ/t), which represents an economic saving in industrial services (energy purposes) of approximately USD 886.000 per year, satisfying the optimization of the process despite the limitations when integrating it energetically. Finally, a more in-depth analysis should be conducted to further integrate other streams of the process to reduce utilities consumption. Full article

The rural digital governance platform is closely related to rural sustainable development. By playing the role of the rural digital governance platform, it can optimize the allocation of rural resources, improve the efficiency of rural governance, promote the development of rural industries, improve the quality of life of rural residents, promote the inheritance and innovation of rural culture, and provide a strong guarantee for the sustainable development of rural areas. Through the continuous advancement of the rural digital governance platform, it is anticipated to achieve the modernization of rural governance, promote industrial prosperity, optimize public services, encourage talent return, and foster cultural inheritance and innovation. This will provide a robust foundation for the implementation of the rural revitalization strategy. Guided by the “digital village” strategy, digital platforms serve as pivotal vehicles for the transformation of rural digital governance. Taking the policymaking process facilitated by the “JuHaoban” platform as a case study, this paper integrates theoretical frameworks with practical applications to construct a “Mixed-Scanning–Multiple-Stream” framework. This framework elucidates the policy innovation process at the local-decision-making level under the influence of the central strategy. The findings indicate that the problem stream can be generated through both proactive scanning and reactive response mechanisms, which can operate concurrently. Decision makers at various levels function as policy entrepreneurs, leading the policymaking community, and the policy window can open either opportunistically or continuously, driven by these decision makers. The policy establishment process of Julu County’s “JuHaoban” platform exemplifies an “up-and-down” dynamic, primarily influenced by political streams. By proactively identifying social issues and responding to emergencies, county-level decision makers implement policy innovations in alignment with the “digital village” strategy. The “Mixed-Scanning–Multiple-Stream” framework provides substantial explanatory power regarding local policy innovation processes within central–local interactions. The conclusions and recommendations offer significant policymaking implications for the development of rural digital governance platforms. Full article

In today’s digital era, the abundance of online services presents users with a daunting array of choices, spanning from streaming platforms to e-commerce websites, leading to decision fatigue. Recommendation algorithms play a pivotal role in aiding users in navigating this plethora of options, among which collaborative filtering (CF) stands out as a prevalent technique. However, CF encounters several challenges, including scalability issues, privacy implications, and the well-known cold start problem. This study endeavors to mitigate the cold start problem by harnessing the capabilities of natural language processing (NLP) applied to user-generated reviews. A unique methodology is introduced, integrating both supervised and unsupervised NLP approaches facilitated by sci-kit learn, utilizing benchmark datasets across diverse domains. This study offers scientific contributions through its novel approach, ensuring rigor, precision, scalability, and real-world relevance. It tackles the cold start problem in recommendation systems by combining natural language processing (NLP) with machine learning and collaborative filtering techniques, addressing data sparsity effectively. This study emphasizes reproducibility and accuracy while proposing an advanced solution that improves personalization in recommendation models. The proposed NLP-based strategy enhances the quality of user-generated content, consequently refining the accuracy of Collaborative Filtering-Based Recommender Systems (CFBRSs). The authors conducted experiments to test the performance of the proposed approach on benchmark datasets like MovieLens, Jester, Book-Crossing, Last.fm, Amazon Product Reviews, Yelp, Netflix Prize, Goodreads, IMDb (Internet movie Database) Data, CiteULike, Epinions, and Etsy to measure global accuracy, global loss, F-1 Score, and AUC (area under curve) values. Assessment through various techniques such as random forest, Naïve Bayes, and Logistic Regression on heterogeneous benchmark datasets indicates that random forest is the most effective method, achieving an accuracy rate exceeding 90%. Further, the proposed approach received a global accuracy above 95%, a global loss of 1.50%, an F-1 Score of 0.78, and an AUC value of 92%. Furthermore, the experiments conducted on distributed and global differential privacy (GDP) further optimize the system’s efficacy. Full article

Modern technology and analysis of emotions play a crucial role in enabling intelligent healthcare systems to provide diagnostics and self-assistance services based on observation. However, precise data predictions and computational models are critical for these systems to perform their jobs effectively. Traditionally, healthcare monitoring has been the primary emphasis. However, there were a couple of negatives, including the pattern feature generating the method’s scalability and reliability, which was tested with different data sources. This paper delves into the Discriminant Input Processing Scheme (DIPS), a crucial instrument for resolving challenges. Data-segmentation-based complex processing techniques allow DIPS to merge many emotion analysis streams. The DIPS recommendation engine uses segmented data characteristics to sift through inputs from the emotion stream for patterns. The recommendation is more accurate and flexible since DIPS uses transfer learning to identify similar data across different streams. With transfer learning, this study can be sure that the previous recommendations and data properties will be available in future data streams, making the most of them. Data utilization ratio, approximation, accuracy, and false rate are some of the metrics used to assess the effectiveness of the advised approach. Self-assisted intelligent healthcare systems that use emotion-based analysis and state-of-the-art technology are crucial when managing healthcare. This study improves healthcare management’s accuracy and efficiency using computational models like DIPS to guarantee accurate data forecasts and recommendations. Full article

In the complex dynamics of modern information systems such as e-commerce and streaming services, managing uncertainty and leveraging information theory are crucial in enhancing session-aware recommender systems (SARSs). This paper presents an innovative approach to SARSs that combines static long-term and dynamic short-term preferences within a counterfactual causal framework. Our method addresses the shortcomings of current prediction models that tend to capture spurious correlations, leading to biased recommendations. By incorporating a counterfactual viewpoint, we aim to elucidate the causal influences of static long-term preferences on next-item selections and enhance the overall robustness of predictive models. We introduce a dual-tower architecture with a novel data augmentation process and a self-supervised training strategy, tailored to tackle inherent biases and unreliable correlations. Extensive experiments demonstrate the effectiveness of our approach, outperforming existing benchmarks and paving the way for more accurate and reliable session-based recommendations. Full article

Technological progress has led to significant advancements in Earth observation and satellite systems. However, some services associated with remote sensing face issues related to timeliness and relevance, which affect the application of remote sensing resources in various fields and disciplines. The challenge now is to help end-users make precise decisions and recommendations for relevant resources that meet the demands of their specific domains from the vast array of remote sensing resources available. In this study, we propose a remote sensing resource service recommendation model that incorporates a time-aware dual LSTM neural network with similarity graph learning. We further use the stream push technology to enhance the model. We first construct interaction history behavior sequences based on users’ resource search history. Then, we establish a category similarity relationship graph structure based on the cosine similarity matrix between remote sensing resource categories. Next, we use LSTM to represent historical sequences and Graph Convolutional Networks (GCN) to represent graph structures. We construct similarity relationship sequences by combining historical sequences to explore exact similarity relationships using LSTM. We embed user IDs to model users’ unique characteristics. By implementing three modeling approaches, we can achieve precise recommendations for remote sensing services. Finally, we conduct experiments to evaluate our methods using three datasets, and the experimental results show that our method outperforms the state-of-the-art algorithms. Full article

eHealth services require continuous data streaming and a stable level of quality of service. However, wireless network connections can be characterized by variable bandwidths. This requires continuous adaptation of systems, including adapting the bit rates of data streamed by sensors. Assigning appropriate rates to the data represents a main task in congestion control. Most of the current methods look for proper sensor data rates within continuous domains. We examine the case when sensors can generate data streams with several different qualities (e.g., sampling rates, sampling accuracies, etc.). For this reason, the domain of the data rate values can be restricted to the discrete values representing the data rates of the possible quality variations. This paper examines the optimization of the utility of the delivered data under resource constraints by selecting an appropriate variation of the provided data from a discrete set. We provide a formal model for delivering data streams in WBANs and recommend an optimization algorithm to solve the problem. Our recommended solutions are related to the multiple-choice multidimensional knapsack problem. By comparing the proposed algorithms, we found that the greedy method closely approximates the optimum in a short running time. Full article

(1) Background: The rising prices of medical infusions have resulted in the increased utilization of policies for payors to manage costs. These policies can be disruptive to the continuity of care, and health systems should develop a systematic strategy to address market changes and prevent patient leakage. (2) Methods: A quality improvement study was conducted by an interdisciplinary workstream to assess the current state of infusion services in an academic medical center in the Midwest and to provide recommendations for immediate access improvement and long-term system planning. An organizational assessment of the value stream was completed, which analyzed the available infusion capacity, billing strategy, patient mix/volumes, payor mix, staffing levels, and current policies. The interventions implemented after developing the infusion system strategy were triaging patients to the appropriate site of care to increase infusion capacity and eliminating paper orders in one of the health system’s Infusion Centers. (3) Results: Patients receiving medical infusions for oncologic conditions warrant unique considerations in evaluating the Infusion Center’s efficiency due to the infusion regimen’s length, complexity, and tolerability. The management of the payor site of care also poses a challenge for health systems to triage patients effectively without fragmenting care. (4) Conclusions: An organizational strategy around infusion services must include broad stakeholder representation to address the clinical, operational, and financial challenges to provide timely care to patients. Full article

The shift towards lean production is gradually replacing traditional mass production, and lean accounting is also being mentioned to evaluate operational efficiency based on the lean philosophy, eliminating waste, and simplifying direct cost aggregation along the value stream to improve productivity, distribution, quality, and service. This study aims to evaluate the impact of various factors on the adoption of lean accounting in Vietnamese garment firms based on data collected from 242 survey questionnaires completed by managers and accountants of Vietnamese garment firms. Through Cronbach’s Alpha test, EFA test, and multiple regression analysis to verify and forecast information, eight determinants affecting the adoption of lean accounting in Vietnamese garment firms are arranged in descending order of influence, including leadership, size, cost of implementation, resources, accounting department, education and training, culture, and competitive pressure. Based on the findings, recommendations are proposed to management businesses and agencies to address shortcomings in the process of applying lean accounting, contributing to making it one of the most effective tools in promoting product development and continuous improvement, enhancing quality and production efficiency. Full article

With the development and popularization of remote sensing earth observation technology and the remote sensing satellite system, the problems of insufficient proactiveness, relevance and timeliness of large-scale remote sensing supporting services are increasingly prominent, which seriously restricts the application of remote sensing resources in multi-domain and cross-disciplinary. It is urgent to help terminal users make appropriate decisions according to real-time network environment and domain requirements, and obtain the optimal resources efficiently from the massive remote sensing resources. In this paper, we propose a recommendation algorithm using fusion of attention and multi-perspective (MRS_AMRA). Based on MRS_AMRA, we further implement an active service recommendation model (MRS_ASRM) for massive multi-source remote sensing resources by combining streaming pushing technology. Firstly, we construct value evaluation functions from multi-perspective in terms of remote sensing users, data and services to enable the adaptive provision of remote sensing resources. Then, we define multi-perspective heuristic policies to support resource discovery, and fusion these policies through the attention network, to achieve the accurate pushing of remote sensing resources. Finally, we implement comparative experiments to simulate accurate recommendation scenarios, compared with state-of-the-art algorithms, such as DIN and Geoportal. Furthermore, MRS_AMRA achieves an average improvement of 10.5% in the recommendation accuracy NDCG@K, and in addition, we developed a prototype system to verify the effectiveness and timeliness of MRS_ASRM. Full article

Live commerce is creating a new paradigm for e-commerce platform economy. This study aims to provide effective strategies that can be used for live commerce platform operation and management by analysing factors that influence consumer intentions to purchase. As a combination of e-commerce and live streaming, live commerce allows businesses to demonstrate products, answer customer questions, and provide personalized recommendations in a way that replicates the in-store experience. Thus, this study aims to provide effective strategies that can be used for the new e-commerce platform economy and market competition that live commerce platforms build by analysing factors that influence consumer intentions to purchase. This study was conducted using structural equation modeling (SEM) based on a survey of 237 live commerce users. The results of this study lend platform-providers a more comprehensive understanding of the functional and social factors that influence consumers’ decisions to make purchases on such platforms. The outcomes can further serve as a basis for improving platforms, providing consumers with a better consumption experience, and offering theoretical support for consumers to use related services rationally and efficiently. Full article