Usual Strategy Research Articles

Understanding public opinion and discussion dynamics of digital humans on social media: an analysis of sentiment, themes and user characteristics

PurposeThe emergence of artificial intelligence-generated content (AIGC) technology has markedly enhanced the capabilities of digital human content generation and natural language processing, thus further advancing the development of digital humans. To enable enterprises and governments to effectively address the challenges and opportunities arising from the rapid development of digital humans, it is imperative to understand the public opinion and discussion dynamics of digital humans.Design/methodology/approachThis study initially analyzed the trends and distribution patterns of public attention to digital humans. By utilizing word cloud technology, we explored the primary focal points of public interest and conducted a topic analysis using latent Dirichlet allocation (LDA) techniques. Subsequently, content analysis was conducted on the popular application domains of digital humans. Finally, this study examined the influence of user characteristics on emotional scores toward digital humans and the presence of differences in focus across user groups.FindingsThe results indicate a sustained increase in public attention toward digital humans, accompanied by notable geographic disparities in the distribution of discussions. Discussions on Weibo are primarily focused on four domains, whereas areas within the digital human application domain that provoke widespread discussion include live streaming, service, cultural entertainment and digital avatars. Significant impacts of user characteristics on sentiment scores were observed, revealing divergent focal points of interest among different user groups toward digital humans.Originality/valueThrough the deep analysis of Weibo data, this study offers new insights into the digital human industry, enabling governments and businesses to understand industry trends and develop targeted digital human customization strategies based on customer characteristics.

An approach based on data mining and genetic algorithm to optimizing time series clustering for efficient segmentation of customer behavior

In today's highly competitive market, organizations face significant challenges in accurately understanding and segmenting customer behavior due to the inherently dynamic and evolving nature of customer interactions over time. Traditional customer segmentation methods often neglect these temporal variations, leading to ineffective business strategies and missed opportunities. This research addresses this critical gap by introducing an innovative time series-based approach for customer behavior segmentation. By modeling each customer's behavior as a time series capturing key metrics such as purchase frequency, transaction amounts, and customer lifecycle costs the proposed method dynamically adapts to behavioral changes over time. To enhance segmentation precision, a genetic algorithm is employed to optimize feature weights, ensuring that the most relevant factors are emphasized. These optimized features are then clustered using spectral clustering to identify distinct and meaningful customer segments. The effectiveness of the proposed method is validated using 30 months of transactional data from a payment services company. The results demonstrate that the proposed approach, particularly when combined with spectral clustering and optimally weighted features, significantly surpassing the performance of traditional static segmentation techniques. This research not only provides a more accurate framework for uncovering hidden patterns in customer behavior but also delivers actionable insights for targeted marketing and personalized customer strategies.

문자중계와 몬테카를로 시뮬레이션 기반 배구 경기 승패 예측에 관한 연구

With advancements in science and technology, a vast amount of data from various media is being generated in the field of sports. Applying statistical methods to extract meaningful information from this data has become essential. This study aimed to predict volleyball match outcomes by performing Monte Carlo simulations based on live text commentary data from the 2023-2024 women's professional volleyball season. By generating the necessary variables for the simulation from the text commentary data and conducting 10,000 iterations of Monte Carlo simulations, the study confirmed that the simulations accurately mirrored actual match outcomes. In particular, when one team demonstrated overwhelming performance, the accuracy of set score predictions was notably high, and the simulation results demonstrated the potential for developing customized strategies. However, limitations were observed in predicting outcomes in specific scenarios, such as when a team with a high total score loses 3-2. To address these limitations, future work should focus on more precisely modeling the complex interactions between various situational and variable factors by converting qualitative factors, which have been challenging to quantify, into objective indicators for integration into the model.

Food security and dietary patterns in the Tajik Pamirs

For mountain regions in the so-called ‘developing world’, an increase in the number of people considered vulnerable to food insecurity has been noted since the turn of the millennium. Challenging environmental settings, as well as difficult societal conditions have been identified as sources of aggravation to mountain food systems. This paper applies a case study approach, conducted in a remote settlement in the Western Pamirs of Tajikistan, to underpin these general insights with detailed accounts of the complexity of the subject under study, and examines the question of how food security plays out in a specific local setting. The goal is to illustrate how socio-ecological conditions influence the food situation in a specific location, how different parameters generate unequal access to food and effect the nutrition patterns of local households, and what dynamics these patterns exhibit over time. Empirical material was analyzed using Cannon’s food systems approach. The findings exemplify how ensuring food security is made more difficult by the concurrence of challenging conditions, that there is a relationship between the socio-economic status and the dietary patterns of individual households, and how consumption patterns are subject to fluctuations over time. Based upon this, I argue that customized strategies are vital in order to tackle food security challenges in specific settings effectively, and that differentiated, empirically grounded analyses are essential to developing such approaches.

Successful Coronary Interventions with the Ingenious Vector\(\circledR\) Balloon in Chronic Total Occlusion (CTO): A Preliminary Study

Aims: To assess the safety and plausibility of successful coronary interventions in Chronic Total Occlusion (CTO) with the innovative Vector\(\circledR\) balloon through balloon-assisted crossing technique. Study Design: Observational, prospective study. Place and Duration of Study: Department of Cardiology, King Edward Memorial Hospital and Seth G. S. Medical College, Mumbai. The study was carried out between the months of March and June, 2024. Methodology: A total of 20 patients were included in the study. All patients with chronic coronary syndrome were being planned for Percutaneous Coronary Intervention (PCI) for CTO. During PCI, the standard protocol of intervening CTO lesions was followed. When the operator was not able to cross the lesion with standard CTO guidewires, Vector\(\circledR\) balloon-assisted crossing was planned. This technique was used in a total of seven (35%) patents. Success was defined as the possibility of advancing the guidewire further to the target lesion and being able to deploy a drug eluting stent. Safety was assessed as reporting complications like kinking or rupture of the balloon shaft, loss of balloon inside the guide or the coronary artery and the balloon burst rate after inflation exceeding the Rated Burst Pressure (RBP) according to the manufacturer’s reference table. Results: The mean age of the patients was 58.1 ± 3.6 years. The balloon-assisted crossing was successful in all seven patients. The most commonly used diameter of the balloon was 1.25 mm (42.85%). Conclusion: The novel Vector\(\circledR\) balloon catheter showed excellent result when deployed to treat the CTO after the usual strategy of guidewire escalation had failed. The proximal shaft with polytetrafluoroethylene (PTFE) coating brings the flexibility to navigate complex and tortuous vascular pathways with ease due to its laser welded core-wire. A study with a larger sample size and a comparator arm would rather clarify if such a balloon-assisted crossing technique in CTO would decrease the need of costly devices like double lumen microcatheters.

How Supplier Customization Influences Its Performance? A Moderated Mediation Model Based on the Contract Theory

ABSTRACT Purpose This study aims to investigate the underlying psychological mechanism and the boundary conditions of the effects supplier customization (SC) on its performance in supply chain management (SCM). Methodology/approach Based upon the contract theory, the study explores how SC influences its performance by revealing mediating role of customer entitlement (CE), and examines the boundary conditions of contractual governance (CG). The dyadic questionnaire survey data of 111 supplier and customer firms in China were used to validate a moderated mediation model. Findings It is found that SC enhances its performance through the increased CE. Furthermore, the above consequential link is moderated by both contractual completeness (CCP) rather than contractual enforcement (CEF). Specifically, CCP negatively moderates the indirect effect of SC on SP through CE. However, CEF negatively moderates the direct effect of SC on CE. Research implications This research are really limitations, including its focus on single industry; single-country system; its cross-sectional design; and the oversight of the possible impact of relational governance (RG) on the link between buyers and sellers. Practical implications Executives should always attach importance to the development of psychological contract (PC), and managers should identify the underlying psychological mechanisms of a customization strategy in B2B and use contractual design and execution with caution. Originality/value/contribution The study extends the literature of the psychological mechanism and more granular characterization of CG, and provides insights for managers to recognize reasonable governance tactics in the context of SCM.

Analysis of antimicrobial resistance and genetic diversity of Acinetobacter baumannii in a tertiary care hospital in Haikou City.

This study addresses the distribution and antimicrobial resistance of Acinetobacter baumannii (A. baumannii) in a medical facility in Haikou City, aiming to provide essential insights for enhancing in-hospital treatment and prevention strategies. We conducted a retrospective analysis of 513 A. baumannii isolates collected from a tertiary care hospital in Haikou between January 2018 and December 2020, focusing on their antimicrobial resistance patterns. Random Amplified Polymorphic DNA (RAPD) analysis was performed on 48 randomly selected A. baumannii strains. Using Gel-pro4.0 and NTSYSspc2.10 software, we constructed dendrograms to assess the genetic diversity of these strains. Our results indicate that males between 60 and 70 years old are particularly vulnerable to A. baumannii infections, which are most frequently detected in sputum samples, with a detection rate exceeding 70%. Alarmingly, over 50% of the isolates were identified as multi-drug resistant. The RAPD-PCR fingerprinting cluster analysis demonstrated substantial genetic diversity among the strains. Using primer OPA-02 at a 45% similarity coefficient, the strains were categorized into four groups (A-D), with group A being predominant (39 strains). high-prevalence areas like the Neurosurgery and Intensive Care Medicine Wards require enhanced surveillance and targeted interventions to manage Group C infections effectively. Additionally, the varied presence of other groups necessitates customized strategies to address the specific risks in each ward. Similarly, primer 270 at a 52% similarity coefficient classified the strains into five groups (E-I), with group E being most common (36 strains). The study highlights a concerning prevalence of antimicrobial resistance, particularly multi-drug resistance, among A. baumannii strains in the Haikou hospital. The significant genetic diversity, especially within groups A and E, underscores the need for tailored hospital treatment protocols and prevention measures. These findings contribute to the growing body of research on antimicrobial resistance, emphasizing the urgent need for effective management strategies in healthcare settings.

Joint optimization of production, inspection, and maintenance under finite time for smart manufacturing systems

Given the flexible and configurable characteristics of smart manufacturing systems with a limited time per manufacturing task, the assumption of infinite time for prostems is no longer applicable to the joint-control strategy. Consequently, a joint-control model that considers production, inspection, and maintenance within a finite-time scenario for smart manufacturing systems is proposed in this paper. The objective is to optimize overall production and maintenance functions to minimize the total system cost. Comparing the joint strategy under infinite time with the proposed finite-time approach reveals significant differences in unit costs between the two scenarios. To enhance the effectiveness of the model, a discrete iterative algorithm with multiple loops was developed. Through a case study, it was observed that 1) joint strategies implemented within a finite time horizon were more cost-effective than those under infinite time, thus emphasizing the need for business managers to develop strategies within a finite time frame; 2) different production planning and efficiency levels had varying effects on the final joint strategy, necessitating customized strategies based on different production durations. Overall, the research gap regarding joint strategies within a finite-time context was addressed in this research, serving as a methodological foundation for practitioners to develop various strategies that minimize total costs across diverse real-world scenarios.

Technologies for customized housing in modular construction

In the modular construction, the range of customization options is limited compared to traditional construction, which can be seen as a negative aspect. Different customization strategies can be adopted by companies, but the choice of the appropriate strategy is related to the technologies used in the operational process, with emphasis on the design and production stages. This study aims to compare the technologies used in the design, production and exchange of information in the development of customized housing in modular construction companies in Brazil and England. To achieve that, three case studies were conducted in English modular construction companies operating in the residential sector. The results of the case studies were compared with the literature on technologies used by Brazilian companies. It was noted that English companies, regardless of their size and type of customization offered, use Revit software to develop the design, and communication between different designers is done via cloud computing. However, communication between designers and production is often done on paper, which leads to errors. The machinery used on the shop floor varies between companies. The results found are similar to the Brazilian reality. It was noted that companies do not make use of all available potential; however, larger companies that are able to perform mass customization invest more in technologies for control and integration between operations. To ensure greater productivity and efficient use of technologies, companies should adopt mass customization.

Continuous High-Precision Positioning in Smartphones by FGO-Based Fusion of GNSS-PPK and PDR.

The availability of raw Global Navigation Satellites System (GNSS) measurements in Android smartphones fosters advancements in high-precision positioning for mass-market devices. However, challenges like inconsistent pseudo-range and carrier phase observations, limited dual-frequency data integrity, and unidentified hardware biases on the receiver side prevent the ambiguity resolution of smartphone GNSS. Consequently, relying solely on GNSS for high-precision positioning may result in frequent cycle slips in complex conditions such as deep urban canyons, underpasses, forests, and indoor areas due to non-line-of-sight (NLOS) and multipath conditions. Inertial/GNSS fusion is the traditional common solution to tackle these challenges because of their complementary capabilities. For pedestrians and smartphones with low-cost inertial sensors, the usual architecture is Pedestrian Dead Reckoning (PDR)+ GNSS. In addition to this, different GNSS processing techniques like Precise Point Positioning (PPP) and Real-Time Kinematic (RTK) have also been integrated with INS. However, integration with PDR has been limited and only with Kalman Filter (KF) and its variants being the main fusion techniques. Recently, Factor Graph Optimization (FGO) has started to be used as a fusion technique due to its superior accuracy. To the best of our knowledge, on the one hand, no work has tested the fusion of GNSS Post-Processed Kinematics (PPK) and PDR on smartphones. And, on the other hand, the works that have evaluated the fusion of GNSS and PDR employing FGO have always performed it using the GNSS Single-Point Positioning (SPP) technique. Therefore, this work aims to combine the use of the GNSS PPK technique and the FGO fusion technique to evaluate the improvement in accuracy that can be obtained on a smartphone compared with the usual GNSS SPP and KF fusion strategies. We improved the Google Pixel 4 smartphone GNSS using Post-Processed Kinematics (PPK) with the open-source RTKLIB 2.4.3 software, then fused it with PDR via KF and FGO for comparison in offline mode. Our findings indicate that FGO-based PDR+GNSS-PPK improves accuracy by 22.5% compared with FGO-based PDR+GNSS-SPP, which shows smartphones obtain high-precision positioning with the implementation of GNSS-PPK via FGO.

Analysis of the Effect of Security, Effectiveness, and Ease of BTN Mobile Services on the Mobile Banking Marketing Strategy of PT Bank Tabungan Negara (Persero) Tbk Yogyakarta Branch

This study aims to determine the influence of service security factors, service effectiveness, and service security in marketing strategies for PT Bank Tabungan Negara or Bank BTN KC Yogyakarta customers. This study uses a quantitative approach using the Accidental Sampling technique, which produces 100 samples. The data analysis technique is processed using IBM SPSS 26 software. The results of the study indicate that service security and service effectiveness at BTN Mobile have a significant positive effect on mobile banking marketing strategies, while ease of service does not have an influence on the mobile banking marketing strategy. This research is useful to help banks understand what factors influence marketing strategies in using mobile banking services to improve their mobile banking services so that they better meet customer needs and desires such as security, effectiveness, and ease of mobile banking services.

Recurrence following percutaneous exclusion of giant coronary pseudoaneurysm: a case report

BackgroundEmergence of coronary giant pseudoaneurysm (PSA) after stent implantation is potentially catastrophic and may end up with life threatening complications if not managed promptly. There is scarcity of data in existing literature with respect to guidelines on the management of coronary PSA following stent implantation. We report the recurrence of coronary PSA following initial percutaneous management of a giant coronary PSA using multiple stent grafts.Case presentationA 38-year-old male who underwent primary angioplasty of the right coronary artery (RCA) about a month back, presented with dull aching precordial chest pain for the last 15 days. A repeat coronary angiography revealed giant coronary PSA in proximal to mid RCA. Considering the significantly large size of the coronary PSA with symptoms of impending rupture, the giant coronary PSA was successfully excluded by implanting three sequentially coronary stent grafts. However, after one and a half months, the patient again presented with a similar kind of dull aching chest pain. We found a recurrence of coronary PSA in a segment of the coronary artery distal to the portion excluded by stent grafts. This recurrent coronary PSA was once again successfully excluded by redeploying two more stent grafts with the help of a guide extension catheter.ConclusionsIn this case, vessel wall injury as a result of aggressive post dilatation using an oversized balloon during the index procedure was the contributor to the giant coronary PSA formation. It usually appears early after the index procedure (within 4 weeks). Though the usual strategy used to exclude coronary aneurysm is by using the minimal number of stent grafts (due to the inherent increased risk of restenosis/thrombosis in stent grafts) in post angioplasty traumatic aneurysm it is prudent to exclude the entire damaged artery by placing stent grafts to prevent recurrence in segments with even minimal dilatation on initial evaluation.

Linking SMEs’ customer strategy to firm growth: the case of manufacturing suppliers in South Korea

PurposeThis study examines the customer strategies for small- and medium-sized manufacturing suppliers (SMMSs) in customer dominance markets, particularly regarding power asymmetry. It has two key objectives: (1) to identify factors impacting SMMSs' customer strategies, including technological and marketing capabilities and the importance of the main customer and (2) to examine the impact of these strategies on firm growth.Design/methodology/approachUsing the survey data on 279 Korean manufacturing suppliers in B2B markets, nine hypotheses were verified through a structural equation model (SEM). It involved capturing the varied influence of organizational capabilities on the two types of customer strategies and the link between customer strategy and firm growth.FindingsTechnology and marketing capabilities affect new customer acquisition directly, while in the case of main customer retention, marketing capability affects main customer retention through the overall satisfaction about the existing relationship. Although the importance of the main customer suppresses new customer acquisition strategies, SMMSs should actively pursue both customer acquisition and retention to promote firm growth.Originality/valueThe findings reveal how SMMSs, leveraging technological and marketing capabilities, can concurrently pursue new customer acquisition and main customer retention. Additionally, it empirically demonstrates the impact of the main customer’s importance on these customer strategies and underscores the positive impact of both strategies on firm growth. This provides a blueprint for a value-creation process linking capabilities, customer strategies and firm performance.

Advanced informatic technologies for intelligent construction: A review

Since there is a growing interest in driving the digital transformation of the construction industry, the goal of this paper is to provide a full picture and understanding of the emerging research front on intelligent construction. Through the quantitative and qualitative analysis, relevant pieces of literature are thoroughly reviewed to extract useful knowledge to raise awareness of intelligent construction, resulting in the summary of research status and the determination of future needs. In light of the research findings, the rapid development of intelligent construction follows the strategies of standardization, digitization, intellectualization, collaboration, greenization, and customization, which mainly support “4S” technologies. The popular research topics covering intelligent engineering services for the project's full lifecycle (i.e., design, construction, operation, and maintenance) and even the city level have been deeply explored, which are proven to dramatically improve project efficiency, safety, and automation. Moreover, challenges exist in three potential paths for future research, including the digital twin-enabled engineering cloud platform, man-machine-environment interaction, great sustainability under carbon peaking and carbon neutrality goals. As for the practical value, a synthesized point of up-to-date reference is created to guide managers to promote intelligent construction in real-world engineering.

Revolutionizing construction: A cutting-edge decision-making model for artificial intelligence implementation in sustainable building projects

This study examines how certain artificial intelligence (AI) drivers affect the industry's adoption of this technology in the construction industry. The research methods comprised a comprehensive analysis of previous studies to pinpoint the primary factors influencing AI adoption in the construction industry. Data collection was carried out through a well-structured survey involving relevant stakeholders in the building construction sector. The three main constructs of technological devices, advancement, and knowledge were found from the set of drivers with the technique of exploratory factor analysis. The deployment of AI in construction has the potential to improve health and safety and expedite project completion, as this research has evaluated. To figure out how these factors relate to the adoption of AI in the construction industry, partial least squares structural equation modeling was used. The study's conclusions showed that the influence of AI installation in the construction industry is reasonably significant thanks to the technology, advancement, and knowledge, contributing around 15 % of the effects that have been directly witnessed. The practical implications of AI for policy makers, engineers, and construction stakeholders are extensive and provide valuable insights for customized strategies aimed at using AI's potential to improve projects, promote sustainability, and elevate health and safety standards.

Sustainable Plastic Waste Management in Thanjavur District: An Evaluation Using Fuzzy AHP and Neutrosophic TOPSIS

Introduction: The amount of plastic garbage has increased in tandem with the steady growth in the use of plastic products worldwide. Since plastics are lightweight, inexpensive, and durable, they are highly appreciated in a variety of industries, including electronics, building, packaging, healthcare, and agriculture). Plastic's cost, adaptability, resistance to corrosion, durability, and ease of production all contribute to its rising demand.With changes to the Plastic Waste Management Rules, India has made managing single-use plastics a top priority. Objectives: The research provides an extensive examination of the methods used to manage plastic garbage in Thanjavur's rural villages. Examining household behaviors and waste production trends, it reveals a wide range of plastic kinds, including polystyrene (PS), polypropylene (PP), high-density polyethylene (HDPE), low-density polyethylene (LDPE), and polyethylene terephthalate (PET). Methods: The research uses a Multi-Criteria Decision Making (MCDM) approach that combines the Neutrosophic Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) for alternative ranking with the Fuzzy Analytic Hierarchy Process (FAHP) for criteria weighting. Although it uses fuzzy logic to address ambiguity and uncertainty in decision-making processes, the Fuzzy AHP approach is applied in a manner similar to that of AHP. Results: Egestas diam in arcu cursus euismod quis viverra nibh. Convallis aenean et tortor at risus viverra. Sit amet justo donec enim diam. Sem et tortor consequat id. Purus gravida quis blandit turpis. Consectetur adipiscing elit duis tristique sollicitudin nibh sit amet commodo. Eget duis at tellus at urna condimentum mattis pellentesque. Auctor elit sed vulputate mi sit amet. Conclusions: This study determines that, among the available options, Polyethylene Terephthalate (PET) is the best option by applying the fuzzy analytic hierarchy process (Fuzzy AHP) to balance these criteria. PET exhibits potential because of its economic and environmental advantages, but its effective application necessitates customized strategies that take into account the infrastructure constraints and socioeconomic characteristics of the local area. Programs for raising community knowledge and participation are crucial in promoting sustainable waste management techniques, as the study emphasizes.

A study on the relationship between high-quality development of the logistics industry and rural revitalization across different levels of industrial structure

Due to regional differences in industrial structure levels, the development of the logistics industry in rural areas faces various challenges and opportunities. The objective is to explore how the high-quality development of the logistics industry influences rural revitalization at various tiers of industrial structure. It employs a benchmark regression model to dissect the influence that the logistics industry's high-quality development exerts on fostering rural revitalization. Moreover, a panel threshold model is employed to examines this impact across different levels of industrial structure. It demonstrates that enhancing the logistics industry significantly supports rural revitalization, with its impact varying across different levels of industrial structure. Hence, it is imperative to develop customized strategies for the logistics industry that account for the varying industrial structure in rural areas. It emphasizes the logistics industry's role in facilitating rural growth and recommends adopting tailored development strategies corresponding to the industrial structure's evolution to boost rural revitalization. Despite valuable insights, this study has limitations in reflecting regional trends and exploring other factors beyond the direct impact of logistics on rural revitalization, pointing to potential avenues for future research.

Global weak solutions to a time-periodic body-liquid interaction problem

We prove existence of time-periodic weak solutions to the coupled liquid-structure problem constituted by an incompressible Navier–Stokes fluid interacting with a rigid body of finite size, subject to an undamped linear restoring force. The fluid flow is generated by a uniform, time-periodic velocity field {\boldsymbol V} far from the body. We emphasize that our result is global, in the sense that no restriction is imposed on the magnitude of {\boldsymbol V} and, rather remarkably, the frequency of {\boldsymbol V} is entirely arbitrary. Thus, in particular, it can coincide with any multiple of a natural frequency of vibration of the body so that, with this model, resonance cannot occur. Although based on the classical “invading domains” technique, our approach requires several new ideas. Indeed, due to the lack of sufficient dissipation, it appears quite unfeasible to show the existence of a fixed point of the Poincaré map at the finite-dimensional level along the Galerkin approximant. Therefore, unlike the usual strategy, such a result must be proven directly in a class of weak solutions, and therefore in the infinite-dimensional framework.

Experimentally validating sabatier plot by molecular level microenvironment customization for oxygen electroreduction

Microenvironmental modifications on metal sites are crucial to tune oxygen reduction catalytic behavior and decrypt intrinsic mechanism, whereas the stochastic properties of traditional pyrolyzed single-atom catalysts induce vague recognition on structure-reactivity relations. Herein, we report a theoretical descriptor relying on binding energies of oxygen adsorbates and directly associating the derived Sabatier volcano plot with calculated overpotential to forecast catalytic efficiency of cobalt porphyrin. This Sabatier volcano plot instructs that electron-withdrawing substituents mitigate the over-strong *OH intermediate adsorption by virtue of the decreased proportion of electrons in bonding orbital. To experimentally validate this speculation, we implement a secondary sphere microenvironment customization strategy on cobalt porphyrin-based polymer nanocomposite analogs. Systematic X-ray spectroscopic and in situ electrochemical characterizations capture the pronounced accessible active site density and the fast interfacial/outward charge migration kinetics contributions for the optimal carboxyl group-substituted catalyst. This work offers ample strategies for designing single-atom catalysts with well-managed microenvironment under the guidance of Sabatier volcano map.

Research on sales strategies for target customers of electric vehicles based on gradient boosting ensemble trees

This paper focuses on the automotive industry, especially the new energy vehicle sector, which is vital for the economy and needs careful market sales decisions. It analyzes data from 1,964 potential customers, including their satisfaction and personal information. The study uses machine learning to create a math model and give sales advice. The data is first cleaned without changing its original structure, and then normalized using the Z-Score method. For any outlier values in the data, the mean of similar items is used to fill in gaps. Specifically, for 500 missing entries about the number of children (B7), the paper groups customers by age, calculates the frequency of children in each group, and uses these frequencies to fill in missing values.Using the cleaned data, the XGBoost algorithm ranks the factors influencing customer decisions. For Brand 1, the top factors are economy (a3), work experience (B10), and safety performance (a4). For Brand 2, comfort (a2), disposable income (B15), and mortgage expenses (B16) are key. For Brand 3, car loan expenses (B17), battery technology (a1), and again mortgage expenses (B16) are most important.The paper also addresses imbalanced data by using an XGBoost binary classification model. This model predicts the likelihood of buying an electric vehicle using the Sigmoid function on tree node weights. It evaluates the model using Precision, Recall, and Area Under the Receiver Operating Characteristic Curve (AUC), which works well even with imbalanced data. The Precision and Recall rates for all three brands are above 90%, with AUC over 85% for Brands 1 and 2, and over 75% for Brand 3. This shows that the model is effective and can be used in real-world situations.