Frequent Updates Research Articles

Factors influencing public participation behavior relating to government microblogs on COVID-19 updates.

During the global COVID-19 pandemic, densely populated megacities engaged in active international exchanges have faced the most severe impacts from both the disease and the associated infodemic. This study examines the factors influencing public participation behavior on government microblogs in these megacities during the pandemic. It guides megacities in disseminating epidemic information, promoting knowledge on epidemic prevention, managing public opinion, and addressing related matters. Utilizing the elaboration likelihood model's central and peripheral routes, drawing on an empirical analysis of 6,677 epidemic-related microblogs from seven Chinese megacities, this study analyses the influence mechanisms influencing public participation behavior and reveals the regulatory role of confirmed case numbers. Meanwhile,a qualitative comparative analysis examines and discusses diferent confgurations of ixn fuential factors. The study reveals that microblog content richness demonstrates a U-shaped impact on public participation behavior. Conversely, content interaction, content length, and the number of fans positively impact participation, while update frequency has a negative impact. Additionally, the number of new confrmed cases positively regulates the impact of microblog content and publisher characteristics on public participation behavior. Public participation behavior also varies based on publishing time and content semantic features. This study further revealed the different confgurations of influential factors by QCA method. This study reveals the impact mechanism of the microblog content and publisher characteristics on public participation behavior. It also demonstrates the regulatory role of newly confrmed cases in the way content and publishers' characteristics influence public participation behavior. This study is of great significance for the operation of government microblogs, the release of emergency information, and the promotion of public participation.

Regional Formularies in Italy: current state and future perspectives

Regional Formularies (RF) are considered part of pharmaceutical policies implemented by regions to govern access of medicines to regional market. However, they have been actually challenged, because of their presumed impact on differences of patient’s access across the regions. The paper aimed at investigating the current status of RF and Regional Therapeutic Committees (CTR) and at suggesting/recommending possible reforms. The current status was investigated through a questionnaire administered to the regional pharmaceutical departments. Recommendations were retrieved from a multi-stakeholder work group carried out on 30-31 March 2023, embedded into a Forum focused on the regional pharmaceutical policies. Nineteen out of twenty-one regions responded to the survey: 12 use RF, mainly managed by the CTR; the RF frequency of update and the time needed for drugs listing greatly vary across regions; pharmacists, specialists and general practitioners are always represented in CTR, whereas other healthcare professionals and experts are more rarely involved; in 3 regions the CTR does not publish any RF update; the CTR mainly rely, to take decisions, on the dimension of the target population, the cost of therapy compared to alternative treatments and the impact on pharmaceutical expenditure. The working group recommended to overcome the RFs, if they are merely considered a list of available drugs at regional level, focusing CTR activities to ensure market access and to govern the prescribing behaviour, and strengthening/anticipating the flow of information from the Italian Medicines Agency (AIFA) to the regions, to enable a more efficient approach to local access to drugs.

SIMULATeQCD: A simple multi-GPU lattice code for QCD calculations

The rise of exascale supercomputers has fueled competition among GPU vendors, driving lattice QCD developers to write code that supports multiple APIs. Moreover, new developments in algorithms and physics research require frequent updates to existing software. These challenges have to be balanced against constantly changing personnel. At the same time, there is a wide range of applications for HISQ fermions in QCD studies. This situation encourages the development of software featuring a HISQ action that is flexible, high-performing, open source, easy to use, and easy to adapt. In this technical paper, we explain the design strategy, provide implementation details, list available algorithms and modules, and show key performance indicators for SIMULATeQCD, a simple multi-GPU lattice code for large-scale QCD calculations, mainly developed and used by the HotQCD collaboration. The code is publicly available on GitHub. Program summaryProgram Title: SIMULATeQCDCPC Library link to program files:https://doi.org/10.17632/ytgycy9nc3.1Developer's repository link:https://github.com/LatticeQCD/SIMULATeQCDLicensing provisions: MITProgramming language: C++, CUDA, HIPNature of problem: Quantum chromodynamics (QCD) is the fundamental theory behind the strong force, one of the four fundamental forces in nature. It describes interactions, mediated by gluons, between quarks, the elementary particles that build up protons, neutrons and other hadrons. One of its unique features is asymptotic freedom: with increasing energy, the interaction strength or coupling between quarks and gluons weakens, which enables studying QCD with peturbation theory. At lower energy scales, however, the strong coupling between quarks and gluons causes perturbation theory to break down, and other tools, such as lattice QCD, need to be employed to obtain QCD predictions.Solution method: Lattice QCD is a technique to solve QCD non-perturbatively by discretizing space and time onto a four-dimensional grid. The QCD path integral is thereby rendered finite dimensional and can be evaluated numerically via Monte Carlo methods. In this paper, we present SIMULATeQCD, a C++ code to perform lattice QCD calculations on multiple GPUs, supporting multiple APIs. SIMULATeQCD implements various Monte Carlo sampling methods ranging from sampling of purely gluonic configurations to fully dynamical quark simulations with the Highly Improved Staggered Quark (HISQ) discretization. Modules for measuring various physical observables on these samples are also available.

Determinants of the operational performance of community-managed handpumps in rural Mozambique: An analysis of five years of cross-sectional sustainability assessments

Improving rural water supply sustainability in sub-Saharan Africa remains a major challenge. This study analysed five-years of cross-sectional sustainability check data collected annually between 2011 and 2016 (except 2015) to investigate factors influencing the operational performance of community-managed handpumps in rural Mozambique. Logistic and negative binomial regression was performed using multiple outcome measures of functionality, reliability and continuity. Average marginal effects (AMEs) were estimated to assess the effects of the predictors and mediation analysis was conducted to examine the role of women's participation.Higher levels of handpump performance were associated with financial records quality and update frequency, water committees with spare parts in stock, and committee annual revenue. The AME of high quality or recently updated financial records for non-functionality was estimated to be −0.40 (95% confidence interval [CI], −0.56 to −0.25), which is interpreted as a 40% lower probability of a water point being non-functional compared to the reference group. Water committees with a spare part stock exhibited a preventive effect on both non-functionality and breakdowns in the past 12 months, reducing their probability by 10% (AME, 95% CI, −0.17 to −0.03) and by 18% (AME, 95% CI, −0.30 to −0.05), respectively. Annual committee revenue up to 2,000 MZN (57 USD) reduced the probability of breakdowns by 25% (AME, 95% CI, −0.45 to 0.05). Handpumps were 10% (AME, 95% CI, 0.03 to 0.17) more likely to be non-functional when women were in the committee management positions. Conversely, female maintenance group members were associated with reduced breakdowns in the multivariate regression analysis (odds ratio, 0.48; 95% CI, 0.24 to 0.95).Increasing outreach of post-construction support on capacity building for water committees, particularly on user fee collection, maintaining financial records, replenishing spare parts, and increased participation of women in the maintenance group, may contribute to greater sustainability of rural water points.

Practical Finite-Time Event-Triggered Control of Underactuated Surface Vessels in Presence of False Data Injection Attacks

Abstract The results of studies on a trajectory-tracking problem affected by false data injection attacks (FDIAs) and internal and external uncertainties are presented in this paper. In view of the FDIAs experienced by the system, we compensate for the serious navigation deviation caused by malicious attacks by designing an online approximator. Next, we study the internal and external uncertainties introduced by environmental factors, system parameter fluctuations, or sensor errors, and we design adaptive laws for these uncertainties to approximate their upper bounds. To further enhance the response velocity and stability of the system, we introduce finite-time technology to ensure that the unmanned underactuated surface vessels (USVs) reach the predetermined trajectory-tracking target within finite time. To further reduce the update frequency of the controller, we introduced event-triggered control (ETC) technology. This saves the system’s communication resources and optimizes the system. Through Lyapunov stability theory, a strict and complete stability analysis is provided for the control scheme. Finally, the effectiveness of the control scheme is verified using two sets of simulations.

Integrated entry guidance with no-fly zone constraint using reinforcement learning and predictor-corrector technique

This paper presents an integrated entry guidance law for hypersonic glide vehicles with no-fly zone constraint. Existing methods that employ predictor-corrector technique and lateral guidance logic for both guidance and avoidance, may have limitations in response time and maneuverability when facing sudden threats, because the guidance cycle is limited by computational efficiency and the bank angle magnitude cannot be adjusted according to the urgency of the avoidance. To overcome these challenges, the proposed method divides the entry process into safe flight stages and no-fly zone avoidance stages, and introduces reinforcement learning to develop an intelligent avoidance strategy for the latter. This division reduces the complexity of the learning problem by restricting the state space and increases the applicability in the presence of multiple no-fly zones. The trained avoidance strategy can directly output continuous bank angle command through a single forward calculation, considering both guidance and avoidance requirements. This enables the full utilization of the vehicle’s maneuverability and supports a high command update frequency to effectively handle threats. Additionally, a network trained via supervised learning is employed to generate reference commands, accelerating the training convergence of reinforcement learning. Simulation results demonstrate the effectiveness of the proposed guidance law, highlighting its high computational efficiency, command stability, and robustness. Importantly, the approach offers convenience in extending to multiple no-fly zones and accommodating vast initial state spaces.

Contract Theory-Based Collection, Updating, and Packaging Combination Strategies in Data Trading

In the data transaction process, where a single supplier collects and sells data to heterogeneous data buyers, its transaction strategy relies on the data’s value. In this paper, we measure the value of data in terms of volume and currency and analyze the whole process from data collection and update management to sales. The data supplier determines the maximum amount of data to be collected based on the willingness to pay and the cost of collection and decides the optimal update frequency based on the update cost paid to improve the data utility. Because data sets can be replicated and split nearly costlessly, data suppliers maximize revenue by combining data sets of different sizes. In the information asymmetry scenario, contract theory is applied to obtain the optimal data collection quantity, update frequency, and packaging combination scheme to maximize the profit of data suppliers under data buyer incentive compatibility and individual rationality and compare them with the information symmetry scenario. The results show that data suppliers will only provide personalized data sets to some data buyers, and the needs of some buyers will not be met. Information asymmetry causes data suppliers to reduce the number of data collections and the actual purchases by data buyers, and data suppliers’ profits tend to decrease. The surprising conclusion is that data suppliers should set up more frequent update strategies in the information asymmetry case than in the information symmetry case to sustain the decrease in data utility caused by insufficient quantity. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72071042 and 72201113]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/serv.2023.0018 .

Investigating the Application of Thematic Progression in EFL Writing: A Systematic Review

A systematic literature review was conducted to examine the studies on the application of thematic progression (TP) to EFL writing. Based on three criteria of research relevance, coverage and update frequency(Shaheen,2023), five databases were chosen, covering Web of Science, Scopus, Eric, Google Scholars and ProQuest. A total of 32 research articles published from 2014 to 2023 were categorized and reviewed. The research methodology mainly follows the guidelines of PRISMA to address the three research questions: 1) What are the development trends in TP -based EFL writing studies including research publications, countries and methodologies? 2) How is TP applied to EFL writing? 3)How does TP influence EFL writing performance? It was discovered that in the past decade there was a steady increase and expansion in the number of research articles and the range of research countries and methods. Moreover, different TP patterns were found to have been distributed across six writing types. These TP patterns established stronger relations with and strengthened writing organization, language use, and content. From the above findings, it can be suggested that TP-based EFL writing researches have regained their development momentum. It can also be inferred that the distribution of TP patterns varies from genre to genre and the patterns improve the overall EFL writing performance by enhancing writing organization, content and language use.

Sustainability Through Collaborative Consumption Practices

"Online fashion renting" is how the sharing economy concept has emerged in the fashion sector. Online fashion rentals are now acceptable and accessible thanks to the growth of collaborative consumption in the clothes sector and technological advancements, accelerating consumer demand for more reasonably priced and environmentally friendly fashion items. The current study determines how the expectancy-value plays a vital role in developing behavioral beliefs that may cause favorable attitudes and intentions toward online renting. The research applies a quantitative approach; a conceptual framework is proposed based on the theory of planned behavior and expectancy value theory. Data was gathered from 320 fashion users renting items from social media and rental outlets via survey. The results revealed that growing awareness of online fashion rental in Pakistan has not only made it convenient for the customer, but these rental services also support giving a second life to fashion items that have been disposed of after the low frequency of use due to short life cycles and frequent updates of fashion. This study provides guidelines for renters and rentals as the success of fashion renting depends on consumer awareness, and its acceptance in the Pakistani market for customer communication can play a vital role in creating trust regarding online fashion renting.

STUDY OF TEMPORAL CHANGES IN THE TOPOGRAPHIC INFORMATION OF 1:50,000 SCALE MAPS FOR THE CITY OF RIVNE AND ITS SURROUNDINGS

According to the State Geo Cadastre of Ukraine, 98.4% of topographic maps reflect information from more than 10 years ago, and 70.3% of maps show the state of the terrain from more than 20 years ago. This negative situation with the provision of cartographic materials has become particularly noticeable since the beginning of the war, which has been going on in Ukraine for almost two years. The purpose of this paper is to study the temporal changes in topographic information of 1:50,000 scale maps, which is an urgent and very important scientific and practical task. The object of the study is the territory of Rivne city and its outskirts within two nomenclature sheets of the 1:50,000 map. The changes in topographic information that occurred between 1979 and 2021 were analysed. A digital vector map of the area was created in ArcGIS Pro software from a 1979 map using raster scanning technology. The data was updated based on satellite imagery of 11.06.2021. The results of the research indicate intensive temporal changes in topographic information - almost 50% of the territory has changed over 42 years. There has been an aggressive expansion of settlements into the surrounding agricultural land - towns and villages have more than doubled their territory. The rate of change in topographic information is 2.3 square kilometres per year. This applies to maps with a scale of 1:50,000. On maps of larger scale, such changes occur much faster. Therefore, in order to keep the information on topographic maps up-to-date, a differentiated approach to the frequency of map updates is proposed, depending on the scale and territory. Recommendations on update periods require further research for other map scales and different territories.

Fast 50 Hz Updated Static Infrared Positioning System Based on Triangulation Method.

One of the important issues being explored in Industry 4.0 is collaborative mobile robots. This collaboration requires precise navigation systems, especially indoor navigation systems where GNSS (Global Navigation Satellite System) cannot be used. To enable the precise localization of robots, different variations of navigation systems are being developed, mainly based on trilateration and triangulation methods. Triangulation systems are distinguished by the fact that they allow for the precise determination of an object's orientation, which is important for mobile robots. An important feature of positioning systems is the frequency of position updates based on measurements. For most systems, it is 10-20 Hz. In our work, we propose a high-speed 50 Hz positioning system based on the triangulation method with infrared transmitters and receivers. In addition, our system is completely static, i.e., it has no moving/rotating measurement sensors, which makes it more resistant to disturbances (caused by vibrations, wear and tear of components, etc.). In this paper, we describe the principle of the system as well as its design. Finally, we present tests of the built system, which show a beacon bearing accuracy of Δφ = 0.51°, which corresponds to a positioning accuracy of ΔR = 6.55 cm, with a position update frequency of fupdate = 50 Hz.

Event-triggered error-constrained switching guidance-based marine surface vessel path-following control and collision avoidance

For marine surface vessel with input saturation, model uncertainties, and unknown disturbances, this article proposes a path-following control and collision avoidance method based on error-constrained event-triggered switching line-of-sight guidance. This method can autonomously switch between path-following and collision avoidance modes, constrain the position and heading errors to satisfy safe navigation in confined waters, and reduce the computational frequency of the controller and the mechanical wear of the actuator. In the path-following mode, we construct an asymmetric modified barrier Lyapunov function to constrain the position errors and use the relative threshold event-triggered mechanism to reduce the update frequency of the guidance heading angle. In the collision avoidance mode, static or dynamic obstacles are avoided through the response of collision avoidance radius. In the heading and velocity control design, asymmetric modified barrier Lyapunov function and event-triggered mechanism are also applied, so that the marine surface vessel can track the guided heading angle and meet the heading error-constrained while reducing the update frequency of the controller. The adaptive auxiliary systems are used to compensate for the input saturation, and radial basis function neural networks and adaptive laws are used to approximate the model uncertainties and composite disturbances. According to the Lyapunov stability theory, all signals are semi-globally uniformly ultimately bounded and the Zeno phenomenon is avoided. Finally, the comparative experiment shows the superiority of the designed control strategy.

MVDet: Encrypted malware traffic detection via multi-view analysis

Detecting encrypted malware traffic promptly to halt the further propagation of an attack is critical. Currently, machine learning becomes a key technique for extracting encrypted malware traffic patterns. However, due to the dynamic nature of network environments and the frequent updates of malware, current methods face the challenges of detecting unknown malware traffic in open-world environment. To address the issue, we introduce MVDet, a novel method that employs machine learning to mine the behavioral features of malware traffic based on multi-view analysis. Unlike traditional methods, MVDet innovatively characterizes the behavioral features of malware traffic at 4-tuple flows from four views: statistical view, DNS view, TLS view, and business view, which is a more stable feature representation capable of handling complex network environments and malware updates. Additionally, we achieve a short-time behavioral features construction, significantly reducing the time cost for feature extraction and malware detection. As a result, we can detect malware behavior at an early stage promptly. Our evaluation demonstrates that MVDet can detect a wide variety of known malware traffic and exhibits efficient and robust detection in both open-world and unknown malware scenarios. MVDet outperforms state-of-the-art methods in closed-world known malware detection, open-world known malware detection, and open-world unknown malware detection.

Sequential management of energy and low-carbon portfolios

This study explores the ability of clean energy and European Union Allowance (EUA) assets to diminish portfolio risk when mixed with unclean energy assets. We use a family of Asymmetric Dynamic Conditional Correlation-Generalized AutoRegressive Conditional Heteroskedastic (ADCC-GARCH) models and provide a flexible and adaptive estimation and model selection framework based on a sequential strategy with differently sized estimation and validation windows, as well as different model update frequencies. Through this procedure, we obtain accurate estimations of the conditional covariance matrices of day-to-day asset returns and build adequate optimal minimum variance portfolios. The analyzed period (Jan. 2010–May. 2022) includes the latest crisis episodes (Sovereign debt crisis, Brexit, COVID-19, and the Russian–Ukrainian war). Our findings show that since the 2015 Paris Agreement (the only exception being the pandemic period), investing in clean energy companies and EUAs is an attractive investment in terms of return-risk. These results should provide investors with more incentives to decarbonize their portfolios.

Reduction in size-at-maturity in unprecedentedly strong cohorts of redfish (Sebastes mentella and S. fasciatus) in the Gulf of St. Lawrence and Laurentian Channel.

Life-history traits, such as size-at-maturity, are key parameters to model population dynamics used to inform fisheries management. Fishery-induced evolution, density-dependent effects, and global warming have been shown to affect size- and age-at-maturity, and resulting spawning stock biomass (SSB) in a wide range of commercial fish stocks. Marked changes in redfish biomass and environmental conditions in the Gulf of St. Lawrence and Laurentian Channel over the past decade called for a review and update of size-at-maturity for commercially important deepwater redfish Sebastes mentella and Acadian redfish Sebastes fasciatus stocks. Following a 25-year moratorium, local redfish biomass has recently reached unprecedented levels, co-occurring with an overall warming of bottom water temperatures. Our objectives were (1) to perform a histological assessment of redfish reproduction stages, including the validation and fine-tuning of a robust visual chart to facilitate monitoring of size-at-maturity and SSB in a transforming environment, and (2) to evaluate changes in size-at-maturity in unprecedentedly strong cohorts of redfish, and consequences for stock status assessment and fisheries management. Each specimen was genetically identified to species, and gonad reproduction stages were determined by histology and macroscopic appearances. The present study enabled a robust visual chart for continued and cost-effective monitoring of redfish reproduction stages to be refined and validated, and has shown a large decrease in redfish length when 50% of the individuals are considered mature that led to an increase in estimates of SSB during the 2011-2021 period for S. mentella and S. fasciatus. These changes modified the perception of stock status, thus having significant implications for fisheries management. Given that fishery-induced evolution and community structure changes along with global warming are affecting numerous stocks worldwide, the present study outlines a major and global challenge for scientists and resources managers. As shown by our results, the monitoring and frequent updates of life-history traits in transforming environments are needed to provide reliable science advice for sustainable fisheries.

Telemedicine Trends and Lessons Learned During the COVID-19 Pandemic-World Trade Center Health Program, 2020-2021.

The World Trade Center (WTC) Health Program, a limited federal health care program for eligible people exposed to the terrorist attacks on September 11, 2001, expanded telemedicine services during the COVID-19 pandemic (2020-2021). We analyzed service use trends from January 2020 through December 2021 to describe how the program implemented telemedicine services. About three-quarters (75%) of telemedicine visits were for mental health-related services. In the second quarter of 2020 (April-June), the number of telemedicine visits per 1000 members (n = 367) increased, exceeding in-person visits (n = 152) by 1.4-fold. The number of telemedicine visits per 1000 members decreased gradually during the rest of the study period but still represented 38% of total visits by the end of 2021. Changes in telemedicine visits were offset by comparable changes for in-person visits, such that the rate of total visits was essentially constant during the study period. Multivariate logistic regression models showed differences in telemedicine visit rates by member type and by demographic characteristics. Survivor members (vs responder members), those self-identified as non-Hispanic Other races (vs non-Hispanic White), those with preferred language not English (vs preferred language English), and those not living in the New York metropolitan area (vs living in the New York metropolitan area) were less likely to use telemedicine. Implementing telemedicine services in the WTC Health Program during the COVID-19 pandemic underscored the importance of extensive collaboration among partners, the capacity to rapidly develop necessary technical guidance, and the flexibility to address frequent regulatory guidance updates in a timely fashion. These lessons learned may guide similar health care providers posed with time-sensitive disruptions of in-person services.

Creating a common notation of the x86 processor software interface for automated disassembler construction

The subject of the study is the process of reverse engineering of programs in order to obtain their source code in low- or high-level languages for processors with x86 architecture, the software interface of which is developed by Intel and AMD. The object of the study is the technical specifications in the documentation produced by these companies. The intensity of updating documentation for processors is investigated and the need to develop technological approaches aimed at automated disassembler construction, taking into account regularly released and frequent updates of the processor software interface, is justified. The article presents a method for processing documentation in order to obtain a generalized, formalized and uniform specification of processor commands for further automated translation into the disassembler program code. The article presents two main results: the first is an analysis of the various options for describing commands presented in the Intel and AMD documentation, and a concise reduction of these descriptions to a monotonous form of representation; the second is a comprehensive syntactic analysis of machine code description notations and the form of representation of each command in assembly language. This, taking into account some additional details of the description of the commands, for example, the permissible operating mode of the processor when executing the command, made it possible to create a generalized description of the command for translating the description into the disassembler code. The results of the study include the identification of a number of errors in both the documentation texts and in the operation of existing industrial disassemblers, built, as shown by the analysis of their implementation, using manual coding. The identification of such errors in the existing reverse engineering tools is an indirect result of the author's research.

Multi-Hop and Mesh for LoRa Networks: Recent Advancements, Issues, and Recommended Applications

A comprehensive review is presented on the latest approaches to solutions focusing on multi-hop and mesh LoRa networks through the evaluation of simulations and real-world experiments, based on papers published between 2015 and 2023. The approaches are systematically classified into four (4) aspects: energy awareness, concurrent access and duty cycle regulations, routing protocols, and security. The first aspect encompasses parameter selection, distance, and clustering. The second aspect focuses on network segregation and time synchronisation. The third aspect covers proactive, reactive, and hybrid routing protocols. The aspect of security includes privacy and Denial-of-Service (DoS). Findings show a consensus that multi-hop networks and adherence to radio duty cycle are able to lower average power consumption. Concurrent access method can improve network performance, but more research is needed due to different conclusions. Reactive protocols have a slight advantage over proactive protocols in terms of node deployment scalability and lower power consumption due to less frequent routing table updates. Hybrid protocols are only beneficial in specific topology deployments. Security is still a major concern for LoRa networks, particularly on attacks such as Man-In-The-Middle (MITM), spoofing, wormholes, flooding, packet replay, and information disclosure. The upcoming trend of implementing machine learning algorithms to multi-hop and mesh LoRa networks opens up a wide range of possibilities, ranging from airspace efficiency, efficient route selection, and improving data throughput. A detailed discussion of the aspects’ issues and recommended industry applications that will benefit from multi-hop and mesh LoRa networks are also provided.

Configuring mobile app update strategy for growth: An empirical analysis of a landscape search model

PurposeThis study attempts to discover effective strategies for mobile commerce applications (apps) to grow their consumer base by releasing app strategic updates. Drawing on the landscape search model from strategy research, this study conceptualizes mobile app update strategy as three interdependent decisions, i.e. what business elements are changed in an app strategic update, how substantial the changes are and when strategic updates are released relative to the competitive environment.Design/methodology/approachUsing a field data set of 1,500 strategic updates of seven rival apps in the mobile travel market, this study integrated fuzzy set qualitative comparative analysis (fsQCA) with econometric analysis to analyze how app strategic update decisions interdependently influence app performance.FindingsThis study identified three effective and one ineffective mobile app update strategies from the mixed-method analysis, which verified the complex interdependency of app strategic update decisions. A general takeaway from these strategies is that a complex strategy problem on the mobile platform must be solved with respect to the constraints and capabilities of mobile technology.Originality/valueThis study moves beyond a linear view of the relationship between app update frequency and app performance and provides a holistic view of how and why app strategic update decisions mutually influence one another in their impact on app performance. This work makes contributions by identifying interdependency as a conceptual bridge between strategy and mobile app literature and developing an empirically testable version of the landscape search model.

A connectivity-preserving performance guarantee for cooperative control method with improved switching event-triggered mechanism

An adaptive tracking control scheme is designed for multiagent systems. To ensure a certain communication range between agents, a connectivity-preserving performance guarantee mechanism is proposed, which increases the consideration of the connection information between agents and the leader to further improve the cooperativity. Meanwhile, different from traditional constraint error methods, a boundary-based prescribed performance control scheme is constructed, which has the characteristic of getting the variable preset boundary. Furthermore, the improved switching event-triggered mechanism is developed, and the threshold parameters can be adjusted in real-time to adaptively change the update frequency of the controller. All signals are bounded in the closed-loop system. Finally, some simulation results affirm the validity of the control scheme.