Large User Research Articles

The Achilles heel of interconnected user networks: Network firms and the vulnerability of rapid decline

Digital technologies have amplified firms' ability to gain and sustain competitive advantages by leveraging network effects and large user bases. They allow cheaper and better building, scaling, and operation of user networks, more efficient data collection, lower transaction costs, and ever-stronger scale advantages. Yet, the seemingly stable competitive advantage of large-scale user networks may have an “Achilles heel” that creates a strategic vulnerability. We examine this overlooked effect by studying how structural network characteristics affect the risk of rapid decline after users start to depart. We find that for user networks with small-world and scale-free properties, the departure of a few influential users can prompt strong negative network effects and accelerate the speed of a decline. To arrive at these insights, we use an analytical approach inspired by the computational networks literature to assess the impact of different structural characteristics on the likelihood of rapid collapse. Our research enriches the platform and network literature by providing fresh insights into the vulnerabilities of interconnected user networks, highlighting the risk of rapid decline. We also suggest practical implications for firms that depend on such networks for a competitive edge.

Biometric linkage of longitudinally collected electronic case report forms and confirmation of subject identity: an open framework for ODK and related tools.

The availability of low-cost biometric hardware sensors and software makes it possible to rapidly, affordably and securely sample and store a unique and invariant biological signature (or biometric "template") for the purposes of identification. This has applications in research and trials, particularly for purposes of consent, linkage of case reporting forms collected at different times, and in the confirmation of participant identity for purposes of safety monitoring and adherence to international data laws. More broadly, these methods are applicable to the needs of the billion people who live in resource-restricted settings without identification credentials. The use of mobile electronic data collection software has recently become commonplace in clinical trials, research and actions for public good. A raft of tools based on the open-source ODK project now provide diverse options for data management that work consistently in resource-restricted settings, but none have built-in functionality for capturing biometric templates. In this study, we report the development and validation of a novel open-source app and associated method for capturing and matching biometric fingerprint templates during data collection with the popular data platforms ODK, KoBoToolbox, SurveyCTO, Ona and CommCare. Using data from more than 1,000 fingers, we show that fingerprint templates can be used to link data records with high accuracy. The accuracy of this process increases through the linkage of multiple fingerprints to each data record. By focussing on publishing open-source code and documentation, and by using an affordable (<£50) and mass-produced model of fingerprint sensor, we are able to make this platform freely available to the large global user community that utilises ODK and related data collection systems.

Biomarkers of Toxic Exposure and Oxidative Stress Among U.S. Adult Users of Premium Cigar Versus Other Cigar Subtypes: 2013-2019.

Cigars are currently the second-highest-used combustible tobacco product among U.S. adults, but knowledge about health effects of premium cigars versus other cigar subtype use is limited. This study analyzed the biospecimen data (n = 31 875) from Waves 1-5 of the Population Assessment of Tobacco and Health Study, collected during 2013-2019. Multivariable generalized estimation equations, accounting for within-person clustering, were conducted to examine differences in urine biomarkers of exposure (BOE) from five classes of harmful and potentially harmful constituents along with a biomarker of oxidative stress (urine 8-isoprostane) among exclusive users of premium cigars versus other exclusive cigar subtypes (ie, non-premium large cigars, cigarillos, and filtered cigars), cigarettes, and non-tobacco users. In comparison to non-tobacco users, exclusive premium cigar users had higher geometric mean concentrations of the nicotine metabolite cotinine (5.8 vs. 0.5ng/mg, p < .0001), tobacco-specific nitrosamine (TSNA) (4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL): 7.8 vs. 1.3pg/mg, p < .0001), and volatile organic compound (VOC) (N-Acetyl-S-(2-cyanoethyl)-L-cysteine (CYMA, acrylonitrile): 4.7 vs. 1.6ng/mg, p < .0001). Exclusive premium cigar users were less likely to be daily users than other tobacco user groups and had comparable BOEs with exclusive non-premium large cigar users but generally lower BOEs than exclusive cigarillo, filtered cigar, and cigarette smokers. Daily exclusive premium cigar users had similar nicotine and TSNA exposure but lower exposure to polycyclic aromatic hydrocarbons and volatile organic compounds than exclusive cigarillo and filtered cigar users. Premium cigar use exhibits different exposure to toxicants from other cigar subtype users. Regulations of premium cigars need to formalize product definition and take the population's health effects into consideration. This population study provides important information on BOE and potential harm with premium cigar use and its potential health effects. At present, premium cigars appear to pose a relatively low overall population health risk due to low frequency of use. However, future regulation of other tobacco products might change the landscape of premium cigar use and alter the overall health impact.

Predictive Clinical Neuroscience Portal (PCNportal): instant online access to research-grade normative models for clinical neuroscientists.

The neurobiology of mental disorders remains poorly understood despite substantial scientific efforts, due to large clinical heterogeneity and to a lack of tools suitable to map individual variability. Normative modeling is one recently successful framework that can address these problems by comparing individuals to a reference population. The methodological underpinnings of normative modelling are, however, relatively complex and computationally expensive. Our research group has developed the python-based normative modelling package Predictive Clinical Neuroscience toolkit (PCNtoolkit) which provides access to many validated algorithms for normative modelling. PCNtoolkit has since proven to be a strong foundation for large scale normative modelling, but still requires significant computation power, time and technical expertise to develop. To address these problems, we introduce PCNportal. PCNportal is an online platform integrated with PCNtoolkit that offers access to pre-trained research-grade normative models estimated on tens of thousands of participants, without the need for computation power or programming abilities. PCNportal is an easy-to-use web interface that is highly scalable to large user bases as necessary. Finally, we demonstrate how the resulting normalized deviation scores can be used in a clinical application through a schizophrenia classification task applied to cortical thickness and volumetric data from the longitudinal Northwestern University Schizophrenia Data and Software Tool (NUSDAST) dataset. At each longitudinal timepoint, the transferred normative models achieved a mean[std. dev.] explained variance of 9.4[8.8]%, 9.2[9.2]%, 5.6[7.4]% respectively in the control group and 4.7[5.5]%, 6.0[6.2]%, 4.2[6.9]% in the schizophrenia group. Diagnostic classifiers achieved AUC of 0.78, 0.76 and 0.71 respectively. This replicates the utility of normative models for diagnostic classification of schizophrenia and showcases the use of PCNportal for clinical neuroimaging. By facilitating and speeding up research with high-quality normative models, this work contributes to research in inter-individual variability, clinical heterogeneity and precision medicine.

Psychiatric disorders in patients with type 2 diabetes mellitus on sodium-glucose cotransporter-2 inhibitors-a nationwide retrospective cohort study.

To compare the potential role of sodium-glucose cotransporter-2 inhibitors (SGLT2I) in the development of psychiatric disease among patients with type 2 diabetes mellitus (DM). Using a large population-based database, SGLT2I users and non-SGLT2I users were 1:1 matched according to the covariates of sex, age, comorbidities, adapted diabetes complications severity index (DCSI), medications, and index year using propensity score matching and a logistic regression model. We calculated the incidence of major psychiatric disorders and adjusted hazard ratios (HR) with 95% confidence interval (CI) for SGLT2I users and the non- SGLT2I users using a Cox proportional hazards model. SGLT2I were associated with a lower risk for psychiatric disorders than those not treated with SGLT2I (HR 0.80 and 95% CI 0.72-0.88). Among patients with DM, SGLT2I were associated with a lower risk of psychiatric disease.

Simulation-based joint user assignment and edge resource allocation optimization for hybrid tasks in vehicular edge computing

With the swift advancement of 5G communication technology and the widespread prevalence of intelligent connected vehicles, a multitude of applications for the Internet of Vehicles (IoV) have been steadily burgeoning. Nevertheless, owing to vehicular mobility and the variety of tasks generated by IoV applications competing for restricted edge resources, the task of assigning users to edge nodes, in addition to appropriating edge node resources to hybrid tasks within edge environments, persists as a formidable challenge. In addressing this predicament, we establish a model delineating the execution duration of hybrid tasks engendered by IoV applications, and introduce a joint mechanism for user assignment and edge resource allocation. Within this mechanism, we architect an optimal strategy for edge resource allocation, predicated upon given user assignment outcomes. In order to ascertain the user assignment strategy, we employ an enhanced genetic algorithm in scenarios of a large user base, and utilize the branch-and-bound algorithm when dealing with a small user base. Finally, we design and implement an experimental setup for joint user assignment and edge resource allocation. The comprehensive simulation results provide robust verification of the effectiveness of our proposed mechanism.

Data Security of Mobile Cloud Computing on Cloud Server

Mobile cloud computing refers to the technology that allows services, such as software, virtual hardware, and bandwidth, to be delivered over the Internet. This technology primarily benefits mobile devices, particularly smartphones. The popularity of mobile cloud computing is rapidly increasing among consumers, and major companies like Apple, Google, Facebook, and Amazon have a large user base in this field. With the help of cloud storage services, users can conveniently access their data anytime, anywhere, and from any device, including mobile devices. While this provides flexibility and scalability in data management, it also introduces new security risks that need to be addressed. However, these security concerns can be mitigated by implementing appropriate data handling practices. Cloud server providers can enhance data security by employing encryption and decryption techniques when storing data in the cloud. In this study, we propose various encryption and decryption methods to safeguard data in the cloud, ensuring that confidential information remains inaccessible to unauthorized individuals or machines due to its encrypted form.

Youtube Video TrendingAnalysis Based on Machine Learning

Social media platforms play an important role in commerce, entertainment, marketing, education, media and communication. YouTube has a large and active user base, making it the center of corporate digital marketing efforts. There are always videos that attract a lot of attention in a short period of time and become trending videos on YouTube, and these videos can be displayed as trending videos on YouTube and updated daily. In this article, we analyzed data on YouTube trending videos in the US region and analyzed the targets that captured the attention of users in a relatively short period of time. We conduct exploratory data analysis on each aspect to gain data insights and find statistical similarities between them to understand viewing patterns across video categories. We present our analysis by measuring, mining, analyzing, and doing ANOVA comparisons of four scales of different categories of likes, dislikes, opinions, and comments. In addition, the study compared the accuracy of three machine learning methods on YouTube data, which counts the number of views and viewer reactions of popular videos on YouTube.

Filament‐Based Melt Electrowriting Enables Dual‐Mode Additive Manufacturing for Multiscale Constructs

Melt electrowriting (MEW) is an electric‐field‐assisted fiber‐forming biofabrication strategy for the additive manufacturing (AM) of precisely defined 3D microarchitectures. MEW is based on pressure‐driven extrusion of a polymer melt pool, currently mainly implemented at laboratory scale with specialized machine technology and limited to only few materials. This precludes the accessibility of MEW to a broader user group and can become the bottleneck of MEW's technological advancement. In contrast to conventional MEW, a filament‐based approach (F‐MEW) is introduced that exploits the technological ecosystem of fused filament fabrication (FFF), a globally used transformative AM technique. In this work, a polymer filament serves as feedstock material and is melted just on demand. By upgrading existing FFF systems, MEW of polymer microfibers is enabled, as validated with polycaprolactone (PCL) and demonstrated with direct writing of thermosensitive polydioxanone (PDO). Finally, FFF and F‐MEW are hybridized in a dual‐mode AM process. This enables multiscale constructs featuring both FFF struts and one order of magnitude smaller F‐MEW microfibers. This work opens the accessibility of F‐MEW to the large FFF user group, potentially benefitting from the plethora of filaments available for FFF, while, at the same time, expanding the FFF fabrication window.

Modular zk-rollup on-demand

The rapid expansion of the use of blockchain-based systems often leads to a choice between customizable private blockchains and more secure, scalable and decentralized but expensive public blockchains. This choice represents the trade-off between privacy and customization at a low cost and security, scalability, and a large user base but at a high cost. In order to improve the scalability of secure public blockchains while enabling privacy and cost reduction, zk-rollups, a layer 2 solution, appear to be a promising avenue. This paper explores the benefits of zk-rollups, including improved privacy, as well as their potential to support transactions designed for specific applications. We propose an innovative design that allows multiple zk-rollups to co-exist on the same smart contracts, simplifying their creation and customization. We then evaluate the first implementation of our system highlighting a low overhead on existing transaction types and on proof generation while strongly decreasing the cost of new transaction types and drastically reducing zk-rollup creation costs.

Laboratory studies of fish entrainment sampling using a screw centrifugal impeller pump

AbstractObjectiveRegulatory legislation in both the United States and Canada requires water users with medium to high intake volumes to monitor entrainment losses to determine effects and to assess potential mitigation strategies to reduce harm to fish. Recent studies have recommended the use of both increased sampling frequency and sampling volume to characterize fish entrainment for large power plants on the Great Lakes. There is little doubt that increasing the flow rate and volume of water collected for sampling reduces the uncertainty in estimates of annual entrainment. Depending on the type of pump and the pumping flow rate used for entrainment sampling, considerable damage to fish larvae can occur. A screw‐type centrifugal pump has many qualities that make it ideal for entrainment sampling, but it has not yet been used as a sampling device for fish entrainment applications at power plants or other large industrial water users. The objective of this study was to determine mortality of entrainable fish eggs and larvae due to passage through a screw‐type centrifugal pump.MethodsIn a laboratory setting, Lake Trout Salvelinus namaycush eggs and larvae, Lake Whitefish Coregonus clupeaformis eggs, and Rainbow Trout Oncorhynchus mykiss juveniles were passed through a screw‐type centrifugal pump at three treatment flow rates: 39.5, 47.3, and 59.1 m3/h. Fish and eggs were monitored for mortality posttreatment and again at 24 and 48 h.ResultAcross treatments, very few mortalities were observed. Species specific moralities were four larval Lake Trout, one Lake Whitefish egg, and one juvenile Rainbow Trout. No mortality was observed for Lake Trout eggs.ConclusionThe present study identified high egg and larval survival (≥99%) after passage through a screw‐type centrifugal pump. High survival reduces error associated with identification and counting of entrained organisms, which provides more accurate estimates of annual entrainment losses.

A fusion recommendation model based on mutual information and attention learning in heterogeneous social networks

Heterogeneous social networks generate vast amounts of data due to their large user base. In response, recommendation systems have been developed as an effective tool to mitigate the problem of information overload. Nonetheless, standalone recommendation methods are often limited by their own inherent deficiencies. Consequently, deep learning has emerged as a powerful approach to address these issues. By integrating diverse recommendation methods, the shortcomings of individual methods can be partially compensated for and improved recommendation outcomes can be achieved. This paper presents a novel user interest fusion recommendation model called MIAL, which leverages mutual information and attention-based learning mechanisms. Specifically, a user’s posts are first transformed into word sequences via a Convolution Neural Network (CNN) based interest recommendation model for pre-processing. To enhance the multi-label prediction model, this study adopts a label selection method based on the label maximum mutual information spanning tree (LMMIST) to determine the final recommended label set. To identify reliable user similarity, the concept of pseudo-similar friends is introduced to construct heterogeneous networks. During the network embedding phase, a new random walk algorithm termed MRWalk and a pseudo-similar propagation mechanism are proposed. Experimental results using the Twitter dataset and public Amazon product dataset demonstrate that the proposed MIAL algorithm outperforms various state-of-the-art algorithms. The comparative analysis shows that MIAL has significantly improved performance.

Sentiment Analysis on Social Media with Glove Using Combination CNN and RoBERTa

Twitter is a popular social media platform that allows users to share short message’s opinion and engage in real-time conversations on a wide range of topics known as tweet. However, tweets often have a complicated and unclear context, which makes it difficult to determine the actual emotion. Therefore, sentiment analysis is required to see the tendency of an opinion, whether the opinion tends to be positive, negative, or neutral. Researchers or institutions can find out how the response and emotions of an issue are happening and make good decisions. With the large user of Twitter social media in Indonesia, sentiment analysis will be carried out using deep learning Convolutional Neural Network (CNN), Term Frequency-Inverse Document Frequency (TF-IDF), Robustly Optimized BERT Pretraining Approach (RoBERTa), Synthetic Minority Over-sampling Technique (SMOTE), and Global Vector (Glove). In this research, the dataset used is trending topics with hashtags related to government policies on Twitter social media and obtained through crawling. By using 30.811 data, the result shows the highest accuracy of 95.56% using CNN with a split ratio of 90:10, baseline unigram, RoBERTa, SMOTE, and Top10 corpus tweet with an increase 10.1%.

Reddit sentiment analysis for natural language processing

In the Internet age, social media has fully penetrated into people's lives. As one of the well-developed online platforms with a large user base, Reddit allows users to independently publish current news, life experiences, and interesting life stories. However, sometimes it sends a negative tone that affects the brand of a company or individual and destroys profits and it is necessary to prevent Twitter by identifying hate words. The biggest innovation of this post is that we use reddit data to compare various methods simultaneously. As we process more data, trying deep learning will yield good results. Compared to other machine learning classifiers, the transformer classifier achieves the best results.

3E (energy, economic, environmental) multi-objective optimization of CCHP industrial plant: Investigation of the optimal technology and the optimal operating strategy

The article describes the application of an innovative multi-objective optimization model for tri-generative energy systems, based on three objective functions: technical, economic, and environmental. The proposed algorithm was used to simulate the daily operation of several tri-generative system configurations, counting for a total of 31,680 simulations. Thereafter, a critical examination of the data was carried out and then comparisons were made between the 22 studied technology sets, by considering internal combustion engines, solid oxide fuel cells, proton exchange membrane fuel cells, absorption heat pumps, and compression gas heat pumps, lithium-ion, and lead-acid batteries. For the analyzed case study, relating to a large industrial user, the average electrical load in the four reference periods is equal to 4970 kW. The optimal tri-generation plant for this type of user turned out to be composed of: SOFC fueled by methane with a size of 5 MWe, a lithium-ion storage system with a maximum size of 500 kW, an absorption heat pump with a size of 390 kW, gas compression heat pump for the generation of cooling energy with size equal to 390 kW, and gas compression heat pump for the generation of thermal energy with size equal to 1600 kW.

Electric vehicle charging and discharging scheduling strategy based on dynamic electricity price

The rapid growth in the number of electric vehicles (EVs) has significantly increased the demand for electricity for residents. In addition, because the charging time of EVs highly coincides with the peak period of user electricity consumption, the disorderly charging of EVs will lead to the overload of the power grid transformer. Traditional control methods lack certain robustness and do not fully consider the uncertainty of EVs. As a result, the V2G participation rate of electric vehicles cannot be determined, and the control reliability is low. To solve the above problems, this paper designs a reinforcement learning framework of Long Short-Term Memory network and Improved Linear programming algorithm (LSTM-ILP) to control the V2G of EVs.This paper comprehensively considers the overall electric vehicle charging demand, discharge potential, large grid electricity price, aggregator, and users’ interests demands. Firstly, aiming to minimize the charging and discharging fee of EVs and the load peak-to-valley difference of the power grid, a dynamic electricity price based on Long Short-Term Memory neural network (LSTM) is established. Then, the improved linear programming algorithm (ILP) is used to solve the charging and discharging optimization problem of EV, and the results are fed back to the input of the next iterative update of the LSTM, and finally, the optimal electricity price and EV charging and discharging schedule are achieved. The simulation results show that the LSTM-ILP framework can not only reduce the charging fee of electric vehicles, but also achieve the Peak and valley trimming of the grid load. Charging costs for EV users were reduced by 42.1% compared with unordered charging, and by 22% percent compared with orderly charging.

Study on the Fitting Method of Power User Load Curve Under the Condition of Spot Operation

With the acceleration and deepening of the power market reform, the National Development and Reform Commission has issued power system reform documents to promote all industrial and commercial users to enter the power market. For the spot pilot area, the clearing and settlement time is required to be more accurate. Except for a few large industrial users, a wide range of small and medium-sized users and residents do not have the meter conditions for time-sharing measurement, and the investment in large-scale transformation of measuring equipment is huge, so the settlement basis must be obtained by fitting. This paper proposes a reverse derivation method of the user load curve. First, deducting the user curve with metering conditions from the load curve, and then calculating different types of user curves by adding weight coefficient fitting. At the same time, it proposes a method to deal with fitting deviation, which realizes the fitting of user curves without time-sharing meters, supports the needs of spot market settlement, and effectively promotes the entry of all industrial and commercial users into the market.

Distributing Peace of Mind

In the multi-barrier approach that the water industry uses to protect public health, distribution systems are a vital link between water utilities and their customers. The more customers a system has, the more it can spread out costs and improve services, yet every customer expects to receive a safe, continuous water supply throughout the distribution system. In addition to minimizing system leaks and managing demands, water systems can do a lot to publicly reinforce the importance of the pipes that deliver safe drinking water to their community's homes and businesses. This month's Journal AWWA touches on a wide range of topics involving water distribution, starting with how utilities can help customers optimize their water use by implement measures that lower their bills. Various incentives can be “stacked” to extend the savings, and as our cover story highlights, the Metropolitan Water District of Southern California has worked diligently with its member agencies and retail agency customers on an approach that allows co-funding flexibility, ensures rebate accuracy, and maintains good results with consumers. Collaborating to improve water conservation allows utilities to help all customers, and it can better address water use efficiency in specific sectors like disadvantaged communities and for large water users. Another article this month focuses on how utilities can better establish and meet levels of service (LoS) within their asset management programs. Different metrics can improve overall service, especially those focused on customer expectations, organizational goals, and technical capabilities. For example, a utility may look at breaks in its distribution system and chart a course to lower the break rate from 15 to 10 breaks per 100 miles of pipe per year. As their experience with this framework grows, utilities can optimize their LoS to maximize resources so they better meet customer expectations and support life cycle decision-making. Further to water system asset management, another article this month explores how advanced analytics, particularly machine learning, can provide more accurate break predictions for water distribution systems. By improving these estimates, utilities can better plan spending on pipe replacement and better prioritize which pipes to replace. Machine learning models present a great opportunity to save money by creating a proactive pipe replacement plan that identifies the right pipes to replace at the right time. Distribution systems may be out of sight, but they are never out of mind for water professionals. If you have innovative projects or sustainable approaches that could benefit the water industry, please consider writing an article for Journal AWWA by contacting me at [email protected].

High efficiency, low cost, RF sources for accelerators and colliders

Accelerators for High Energy Physics (HEP) are large users of energy, much of it in the form of radiofrequency (RF) power to accelerate particles to very high energies. Proposed HEP projects will require even larger amounts of RF energy. Increasing concerns of the cost and availability of energy will require the HEP community to use energy as efficeintly as possible. Successful transfer of HEP technology to the public and private sectors will be most effective if it is highly efficient. Historically, the HEP community has utilized available RF power sources, mainly in the form of vacuum tube technology, much of which was developed during the cold war or is otherwized used in the private sector. The private sector is moving to solid-state RF sources which do not exibit the electrical efficiency that is needed for future HEP projects. Here, we summarize the state of the development of a number of RF sources that promise efficeincies of 80% and above. We also outline future efforts that are needed to fully realize the potential of these sources.

Translational argument technology: Engineering a step change in the argument web

Following the establishment in 2006 of a representational standard for the computational handling of structures of argumentation, the Argument Interchange Format, it became possible to develop a vision for the coherent integration of multifarious services, components and tools that create, consume, navigate, analyse, evaluate and manipulate arguments and debates. This vision was the Argument Web with theoretical foundations laid by Rahwan et al. (2007), and practical engineering work described by Bex et al. (2013). Over the intervening period, the key challenge has been to demonstrate the practical and societal value of the Argument Web by taking its tools and applications to larger audiences. This paper lays out three approaches by which the Argument Web has been scaled up in this way, each in partnership with the BBC, and each with different kinds of evaluation and impact. Transitioning these technologies to large user groups paves the way for broader-scale uptake of the Argument Web and heralds the translation from lab to real-world application for a substantial research community working in argument technology.