Performance Of Platform Research Articles

Simulation of a Self-Balancing Platform on the Mobile Car


 In the last years, the self-balancing platform has become one of the most common candidates to use in many applications such as flight, biomedical fields, industry. This paper introduced the simulated model of a proposed self-balancing platform that described the self–balancing attitude in (X-axis, Y-axis, or both axis) under the influence of road disturbance. To simulate the self-balanced platform's performance during the tilt, an integration between Solidworks, Simscape, and Simulink toolboxes in MATLAB was used. The platform's dynamic model was drawn in SolidWorks and exported as a STEP file used in the Simscape Multibody environment. The system is controlled using the proportional-integral-derivative (PID) controller to maintain the platform leveled and compensate for any road disturbances. Several road disturbances scenarios were designed in the x-axis, y-axis, or both axis (the pitch and roll angles) to examine the controller effectiveness. The simulation results indicate that that the platform completed self-balancing under the effect of disturbance (10° and -10°) on the X-axis, Y-axis, and both axes in less than two milliseconds. Therefore, a proposed self-balancing platform's simulated model has a high self-balancing accuracy and meets operational requirements despite its simple design.

To Interfere or Not To Interfere: Information Revelation and Price-Setting Incentives in a Multiagent Learning Environment

We consider a platform in which multiple sellers offer their products for sale over a time horizon of T periods. Each seller sets its own price. The platform collects a fraction of the sales revenue and provides price-setting incentives to the sellers to maximize its own revenue. The demand for each seller's product is a function of all sellers' prices and some customer features. Initially, neither the platform nor the sellers know the demand function, but they can learn about it through sales observations: each seller observes its own sales, whereas the platform observes all sellers' sales as well as the customer feature information. We measure the platform's performance by comparing its expected revenue with the full-information optimal revenue, and design policies that enable the platform to judiciously manage information revelation and price-setting incentives. Perhaps surprisingly, a simple do-nothing policy does not always exhibit poor revenue performance and can perform exceptionally well under certain conditions. With a more conservative policy that reveals information to make price-setting incentives more effective, the platform can always protect itself from large revenue losses caused by demand model uncertainty. We develop a strategic-reveal-and-incentivize policy that combines the benefits of the aforementioned policies and thereby achieves asymptotically optimal revenue performance as T grows large.

Investigating the mix of strategic choices and performance of transaction platforms: Evidence from the crowdfunding setting

AbstractResearch SummaryThe platform literature offers keen insights on the pricing and non‐pricing strategies that transaction platforms undertake. We supplement this work by studying how platforms mix together their strategic choices and the association with platforms’ performance. To that end, we focus on crowdfunding platforms; a prominent setting of transaction platforms. We present an inductive large‐N study of the population of 788 crowdfunding platforms that operated in EU‐15 countries up to 2018. Our contribution is threefold: (a) identifying common mixes of strategic choices; (b) tracking deviations from these mixes; and (c) associating these with platforms’ survival and growth. We discuss our findings and how they advance knowledge at the intersection of the platform and strategic management literatures.Managerial SummaryNotable transaction‐platforms such as eBay, LinkedIn, and Tencent have an aggregate market‐value in the hundreds of billions of dollars. We know that platforms’ success is driven by the strategic choices they undertake. Yet, we know less about how they mix together these choices and the association with platforms’ performance. Our study addresses this gap by focusing on a prominent setting: crowdfunding. Using data on the population of 788 crowdfunding platforms in EU‐15 countries, we show that these platforms cluster around three common mixes of strategic choices. Moreover, crowdfunding platforms do not strictly adhere to the strategy mix they are affiliated with. Interestingly, there is a positive association between the degree to which a platform's choices differentiate from its strategy mix and platform's subsequent performance.

Real-Time Implementation of Self-Adaptive Salp Swarm Optimization-Based Microgrid Droop Control

This article presents a new version of the salp swarm inspired algorithm (SSIA) for the optimal design of the microgrid droop controller. The new version of SSIA is originated from the hybridizing of SSIA with the updating features of the particle swarm optimization (PSO). The development of SSIA is achieved by applying referential integrity between leaders and followers' candidates via employing both position and velocity update property of PSO. The hybrid SSIA-PSO also has a self-adaptive mechanism to avoid the necessity of refining the algorithm parameters for each optimization problem. Twenty-three benchmark test systems are tested to validate the superiority of the improved SSIA over the original PSO and SSIA. The proposed SSIA-PSO based control strategy is experimentally tested in a real-time environment. The control platform's performance is experimentally tested by using the Texas Instruments Launchpad TMS320F28379D. The developed real-time hardware-in-the-loop setup is a real investigation for implementing the suggested SSIA-PSO based control strategy with a low-cost control platform. The attained results prove the efficacy of the hybrid SSIA-PSO algorithm over the presented techniques. The introduced hybrid SSIA-PSO is employed to tackle the real microgrid droop control uncertainties such as inaccuracy in controller gains, deterioration of system parameters, multi-sources energy sharing challenge and system dynamics.

Extended validation and verification of XPS/AVL-Fire™, a computational CFD-DEM software platform

The goal of this work is an comprehensive experimental validation and verification of a computational CFD-DEM software platform. The software platform's modules and performance requirements specifically address the needs of pharmaceutical industry in terms of batch size (i.e., number of particles) and related phenomena (i.e., physical complexity). Moreover, we critically assessed the numerical models implemented. In addition to the review of relevant literature and selection of validation experiments, novel analytical solutions for the spray class in question are presented. As the presented method is independent of the spray model, the solution can be used to prove the correct implementation of various available spray models. The final outcome of this work is a validated software framework, which can be further used to investigate large-scale processes by resolving single particle trajectories in a coupled environment, including heat, mass and momentum transfer.

Point-of-care cervical cancer screening using deep learning-based microholography.

Most deaths (80%) from cervical cancer occur in regions lacking adequate screening infrastructures or ready access to them. In contrast, most developed countries now embrace human papillomavirus (HPV) analyses as standalone screening; this transition threatens to further widen the resource gap.Methods: We describe the development of a DNA-focused digital microholography platform for point-of-care HPV screening, with automated readouts driven by customized deep-learning algorithms. In the presence of high-risk HPV 16 or 18 DNA, microbeads were designed to bind the DNA targets and form microbead dimers. The resulting holographic signature of the microbeads was recorded and analyzed.Results: The HPV DNA assay showed excellent sensitivity (down to a single cell) and specificity (100% concordance) in detecting HPV 16 and 18 DNA from cell lines. Our deep learning approach was 120-folder faster than the traditional reconstruction method and completed the analysis in < 2 min using a single CPU. In a blinded clinical study using patient cervical brushings, we successfully benchmarked our platform's performance to an FDA-approved HPV assay.Conclusions: Reliable and decentralized HPV testing will facilitate cataloguing the high-risk HPV landscape in underserved populations, revealing HPV coverage gaps in existing vaccination strategies and informing future iterations.

Discriminative potential of ion mobility spectrometry for the detection of fentanyl and fentanyl analogues relative to confounding environmental interferents.

The opioid crisis and emergence of fentanyl, fentanyl analogues, and other synthetic opioids has highlighted the need for sensitive and robust detection for interdiction at screening points, notably vehicles at border crossings and packages at postal facilities. This work investigates the discriminative potential, sensitivity and specificity, of ion mobility spectrometry (IMS) for the detection of fentanyl and fifteen (15) fentanyl-related compounds (analogues, other opioids, and metabolites) relative to confounding environmental interferents. The environmental background interferent levels, frequency and intensity, were derived from over 10 000 screening samples collected from delivery vehicles entering a federal site. A receiver operating characteristic (ROC) curve methodology was employed to quantify the relationship between sensitivity and specificity for these target compounds on two instruments/configurations. These instrument configurations differed in desorption and drift tube temperatures, reactant ion dopant chemistry, and analysis time. This work identified reduced mobility areas of high interference that resulted in increased false positive rates (FPR), effectively reducing sensitivity (true positive rate: TPR) in those regions. Except for a few target compounds on either of the instruments that exhibited elevated FPRs, detection of fentanyl and fentanyl-related species was achieved at single to tens of nanograms with ≥90% TPR and ≤2% FPR. This work established the importance of systematic environmental background characterization at each specific screening setting in evaluating a platform's true performance.

Pricing and Job Allocation in Online Labor Platforms

Problem Definition: We model, analyze, and optimize the operations of an online labor platform that matches jobs to workers. The arrival of jobs and workers to the platform is stochastic and the job processing time is random. The platform chooses the fees per job and assigns jobs to workers with the goal of (i) maximizing platform revenues, (ii) minimizing the unpredictability in workers' profits, and (iii) minimizing any delay in processing the incoming jobs. Workers are sensitive to the revenue they make in the platform and, therefore, the worker arrival rate depends on the platform's pricing and job allocation strategy. Academic/Practical Relevance: We contribute to the fields of online platform operations and stochastic systems. The asymptotically optimal policy that we introduce is simple and practical. Our analysis provides new insights into the management of uncertainty in online platforms and how uncertainty impacts objectives (i), (ii), and (iii) above. Methodology: We propose a continuous-time stochastic model that describes the mechanics of the platform. We introduce a policy that sequentially allocates jobs to workers in a rotating manner. The optimal parameters of this policy are corrected versions of the fluid solution. Then, through an asymptotic analysis, we prove that this policy is optimal as the platform scales. Finally, we examine the policy's performance through a discrete event simulation. Results: We introduce a policy class called Uniform Allocation (UA) and provide an analytical characterization of the platform's behavior and performance under this policy class. Then, we design a UA policy that simultaneously optimizes objectives (i), (ii), and (iii) as the system scales. We obtain insights into the UA policy's behavior through a discrete event simulation and find that it leads to similar profits but much lower worker income variability compared to other policies. Implications: Our proposed pricing and assignment policy align objectives (i), (ii), and (iii) above. From an academic standpoint, we demonstrate how the platform's revenue maximizing pricing and allocation policies can also act as a form of risk pooling. From a practical standpoint, we determine that platform revenue maximization is not incompatible with predictable profits and stable schedules for workers.

A system for the hydrodynamic design of tension leg platforms of floating wind turbines

A system that allows testing new concepts quickly and with higher precision is developed and demonstrated through an example of a tension leg platform. The process goes through the mass matrix estimations, hydrostatics, mooring system design, and dynamic analysis. It starts from a multi-purpose 3D model and does not require user intervention or moving data between software. Instead, the same quadrilateral mesh is used for all calculations after modifying a format used by panel method codes. The need to use simplified tables and spreadsheets is eliminated. The frequency domain wave responses are obtained with a potential flow solver and assessed to approximate the platform's performance with the wind turbine. The assessment includes the detection of slack mooring occurrences. The setup allows completing the initial design with minimal amount of input data. A time domain model for simulations is also built within the system. The level of agreement between these two solutions is examined. The platform is assumed to operate in Galicia, Spain, to discuss sets of environmental conditions considering the mooring line responses. Two primary variables are used to illustrate a sizing algorithm on 231 hull variations. A measure of performance is given to clarify the required time for the design space assessment with this method.

Abstract LB-174: Novel AC electrokinetic platform for rapid isolation and characterization of extracellular vesicles from NSCLC patients

Abstract We have developed a novel Alternating Current Electrokinetics (ACE) platform to isolate macromolecules such as circulating cell-free DNA (ccfDNA) and nanoparticles, including cell-derived extracellular vesicles (EVs). ccfDNA and EV isolation, and antibody staining using the platform required less than two hours to complete. EVs isolated by ultracentrifugation from the pancreatic cancer cell line ASPC-1 were used to validate the ACE platform's performance. EVs from pancreatic cancer ASPC-1 cells were detected using an anti-CD63 antibody and an antibody to the pancreatic cancer specific membrane protein Glypican-1, confirming that the ACE platform could isolate EVs and identify membrane-bound proteins. Recovered EVs were isolated and EV-associated RNA was amplified by RT-PCR. EVs from isolated from NSCLC patient plasma on the ACE platform contained higher levels of PD-L1 compared to healthy individuals (p&amp;lt;0.001). In parallel on the ACE platform, ccfDNA was isolated and quantified from the same samples, with increased levels measured in cancer patients. Improved differentiation between the NSCLC patients and the healthy normals was achieved when both markers were included in the analysis. Therefore, we believe the ACE platform has the potential to become a multimodal analytics tool for cancer diagnostics and treatment monitoring. Citation Format: Rajaram Krishnan, Juan P. Hinestrosa, David Searson, Robert Turner, Robert Kovelman, Heath Balcer, James Madsen, David Bodkin. Novel AC electrokinetic platform for rapid isolation and characterization of extracellular vesicles from NSCLC patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr LB-174.

Targeted next-generation sequencing for the molecular diagnosis of hereditary angioedema due to C1-inhibitor deficiency

SERPING1 genotyping of subjects suspicious for hereditary angioedema due to C1-INH deficiency (C1-INH-HAE) is important for clinical practice as well as for research reasons. Conventional approaches towards the detection of C1-INH-HAE-associated SERPING1 variants are cumbersome and time-demanding with many pitfalls. To take advantage of the benefits of next-generation sequencing (NGS) technology, we developed and validated a custom NGS platform that, by targeting the entire SERPING1 gene, facilitates genetic testing of C1-INH-HAE patients in clinical practice. In total, 135 different C1-INH-HAE-associated SERPING1 variants, out of the approximately 450 reported, along with 115 negative controls and 95 randomly selected DNA samples from affected family members of C1-INH-HAE index patients, were included in the forward and reverse validation processes of this platform. Our platform's performance, i.e. analytical sensitivity of 98.96%, a false negative rate of 1.05%, analytical specificity 100%, a false positive rate equal to zero, accuracy of 99.35%, and repeatability of 100% recommends its implementation as a first line approach for the genetic testing of C1-INH-HAE patients or as a confirmatory method. A noteworthy advantage of our platform is the concomitant detection of single nucleotide variants and copy number variations throughout the whole length of the SERPING1 gene, moreover providing information about the size and the localization of the latter. During our study, 15 novel C1-INH-HAE-related SERPING1 variants were detected.

Third-party apps (TPAs) and software platform performance: The moderating role of competitive entry

Noting the increasing importance of software platform, this article examines the effects of third-party apps (TPAs) on platform performance (PP) and how such effects differ before versus after competitive entry. The authors validate the hypotheses using data from the web browser market. The results show that the quantity of new TPAs leads to higher PP before than after competitive entry, whereas the quantity of TPA updates contributes a higher PP after than before competitive entry. In addition, the quality is more important and diversity is less important to PP after than before competitive entry.

Order dispatch in price-aware ridesharing

With the prevalence of car-hailing applications, ridesharing becomes more and more popular because of its great potential in monetary saving and environmental protection. Order dispatch is the key problem in ridesharing, which has a strong impact on riders' experience and platform's performance. Existing order dispatch research works fail to consider the price of the orders, which can be an important reference because it directly relates to the platform's profit. Our work takes the order price into concern, and formulates a constrained optimization problem, which takes platform's profit as the optimization objective and performs controls on riders' detour distance and waiting time. We prove the problem is NP-hard, thus, we propose approximation methods. We further develop a simulation framework based on real ridesharing order and vehicle data. We conduct experiments with this simulation framework to evaluate the effectiveness and efficiency of the proposed methods.

Direct Measurement of a Biomarker's Native Optimal Frequency with Physical Adsorption Based Immobilization.

The optimal frequency (OF) of a biomarker in electrochemical impedance spectroscopy (EIS) is the frequency at which the EIS response best reflects the binding of the biomarker to its molecular recognition element. Commonly, biosensors rely on complicated immobilization chemistry to attach biological molecules to the sensor surface, making the direct study of a biomarker's native OF a challenge. Physical adsorption presents a simple immobilization strategy to study the native biomarker's OF, but its utility is often discouraged due to a loss in biological activity. To directly study a biomarker's native OF and investigate the potential of OF to overcome the limitations of physical adsorption, a combination of EIS and glutaraldehyde-mediated physical adsorption was explored. The experimental sensing platform was prepared by immobilizing either anti-lactoferrin (Lfn) IgG or anti-immunoglobulin E (IgE) onto screen printed carbon electrodes. After characterizing the native OFs of both biomarkers, investigation of the platform's specificity, stability, and performance in complex medium was found to be sufficient. Finally, a paper-based tear sampling component was integrated to transform the testing platform into a prototypical point-of-care dry eye diagnostic. The investigation of native OFs revealed a correlation between the native OFs (57.44 and 371.1 Hz for Lfn and IgE, respectively) and the molecular weight of the antibody-antigen complex. Impedance responses at the native OFs have enabled detection limits of 0.05 mg/mL and 40 ng/mL for Lfn and IgE, respectively, covering the clinically relevant ranges. The native OFs were found to be robust across various testing mediums and conditions.

Prosocial Crowdlending in Kenya

The crowdfunding industry has emerged in the past few years as one of the most promising alternative financing options. Lending and donating operations accounted for 81% of the crowdfunding industry's $34.4 billion total funding volume in 2015. Kiva Zip, a prosocial program, created an online platform that provides 0% interest peer-to-peer loans and has features in common with lending and donating crowdfunding platforms. This program is a spin-off of Kiva.org. Although both platforms have a similar objective and modus operandi, they differ primarily because Kiva.org has a figure called a field partner, who is the intermediary between borrowers and lenders, while Kiva Zip has no field partners but does have so-called trustees, who provide support to borrowers but do not act as intermediaries for resources or charge for their services. The authors thoroughly analyzed Kiva Zip's operations in Kenya for the years from 2011 to 2015. Kiva Zip has stopped posting new campaigns in Kenya but has continued to collect payment for previously delivered loans. We studied in detail the impact that lenders, borrowers and trustees had on the platform's performance. In addition, we analyzed the different stages of a campaign on Kiva Zip: when a campaign is posted, when it is funded and when the loan is paid back. This analysis may provide insight into the levers that drove the performance of Kiva Zip in Kenya, which in turn could have enabled the organization to identify areas for improvement in order to continue operating in the country. In addition, we have identified relevant theoretical frameworks for analyzing prosocial crowdlending in greater depth.

A new MEMS gyroscope drift suppression method for the low‐cost untwisting spin platform

The microelectromechanical system (MEMS) gyroscope provides a new method to design a low‐cost untwisting spin platform to be used in a single‐axis, stable strap‐down inertial navigation system. However, the MEMS gyroscope's drift reduces the effectiveness of the closed‐loop feedback control. Thus, a new method of drift suppression is proposed in this paper based on phase space reconstruction in order to improve the platform's performance. The feasibility of the MEMS gyroscope's drift suppression is analyzed using linear decomposition based on phase space. The system drift is estimated by phase space reconstruction. The optimal embedding dimension is found through a grid search. The number of dimensions for dimension reduction analysis is selected according to the minimum eigenvalue. The mapping from the high‐dimensional phase space onto the low‐dimensional phase space is obtained by minimizing the variance. A Kalman filter is used to compensate the residual sequence further. The proposed method is applied to an untwisting spin platform based on the MEMS gyroscope L3G4200D. The experimental results show that it can reduce the platform drift rate effectively.

Analytical Validation and Capabilities of the Epic CTC Platform: Enrichment-Free Circulating Tumour Cell Detection and Characterization.

The Epic Platform was developed for the unbiased detection and molecular characterization of circulating tumour cells (CTCs). Here, we report assay performance data, including accuracy, linearity, specificity and intra/inter-assay precision of CTC enumeration in healthy donor (HD) blood samples spiked with varying concentrations of cancer cell line controls (CLCs). Additionally, we demonstrate clinical feasibility for CTC detection in a small cohort of metastatic castrate-resistant prostate cancer (mCRPC) patients. The Epic Platform demonstrated accuracy, linearity and sensitivity for the enumeration of all CLC concentrations tested. Furthermore, we established the precision between multiple operators and slide staining batches and assay specificity showing zero CTCs detected in 18 healthy donor samples. In a clinical feasibility study, at least one traditional CTC/mL (CK+, CD45-, and intact nuclei) was detected in 89 % of 44 mCRPC samples, whereas 100 % of samples had CTCs enumerated if additional CTC subpopulations (CK-/CD45- and CK+ apoptotic CTCs) were included in the analysis. In addition to presenting Epic Platform's performance with respect to CTC enumeration, we provide examples of its integrated downstream capabilities, including protein biomarker expression and downstream genomic analyses at single cell resolution.

Tree-side Molecular Testing for DNA from the HLB Bacterium Candidatus Liberibacter asiaticus

HLB affects all citrus cultivars and causes tree decline resulting in greatly reduced citrus production in Asia, Africa, the Indian subcontinent, the Arabian Peninsula, Brazil, and the United States. To enable integrated pest management strategies for combating HLB, we have developed a sensitive nucleic acid-based HLB diagnostic test based on Mesa Tech International, Inc.’s (MTI's) new point-of-use diagnostic platform, MTIDx, and targeting hyvI/hyvII genes of Candidatus Liberibacter asiaticus (Las) (Zhou et al., 2011). The MTIDx platform integrates sample preparation, rapid nucleic acid amplification and sequence-specific hybridization-based detection. The simplicity of the test may offer end users with little or no specialized training the opportunity to obtain molecular test results comparable to laboratory-based PCR methods without costly instrumentation. Here, we will report on the efficacy of MTI's nucleic acid testing platform for the detection of Candidatus Liberibacter asiaticus bacteria in greenhouse and field collected citrus tissue and in the insect vector of the pathogen (Diaphorina citri Kuwayama). Comparative studies evaluating the MTIDx platform's performance relative to widely accepted laboratory PCR testing will be presented. Results from these studies will be discussed in the context of their implications for HLB management.

Platform competition: Strategic trade‐offs in platform markets

Because the literature on platform competition emphasizes the role of network effects, it prescribes rapidly expanding a network of platform users and complementary applications to capture entire markets. We challenge the unconditional logic of a winner‐take‐all (WTA) approach by empirically analyzing the dominant strategies used to build and position platform systems in the U.S. video game industry. We show that when platform firms pursue two popular WTA strategies concurrently and with equal intensity (growing the number and variety of applications while also securing a larger fraction of those applications with exclusivity agreements), it diminishes the benefits of each strategy to the point that it lowers platform performance. We also show that a differentiation strategy based on distinctive positioning improves a platform's performance only when a platform system is highly distinctive relative to its rivals. Our results suggest that platform competition is shaped by important strategic trade‐offs and that the WTA approach will not be universally successful. Copyright © 2013 John Wiley &amp; Sons, Ltd.

Performance analysis and workload characterization of the 3DMark05 benchmark on modern parallel computer platforms

With ever increasing CPU and graphics card speeds, and improved sophistication, stunning visual effects, and growing scene detail and real life-like content of 3D games, 3DMark® emerged as the leading PC benchmark for 3D gaming performance with several millions of worldwide downloads. Its tests are at the cutting edge of consumer graphics and push the limit of 3D rendering with spectacular scenes, and state of the art lighting techniques. The benchmark scores help quickly differentiate the platforms with state of the art graphic cards and processors from those with older components. In this paper, we analyze the scaling of the 3DMark®05 benchmark with CPU frequency, number of CPUs, number of GPUs, and number of threads supported by the hardware. We also characterize the benchmark's workload. These results reveal that the benchmark scales well indicating that 3D games if implemented with multiple Physics and Artificial Intelligence or other relevant content threads should show good scaling too on multi-CPU and multi-GPU platforms. The characterization results reveal the close dependence of 3D graphics applications on the memory subsystem's performance as 1 out of 2 instructions is a load or store instruction. The results also revealed that there is a direct correlation with the Game Tests' performance and the number of cache memory read misses per instruction retired, the number of stores retired per instruction retired, the number of polygons per Draw*Primitive; and the number of setvertexshader calls per frame. All these events relate to the memory subsystem performance generally linking the 3D graphics applications' performance and the 3DMark® overall score to the platform's memory performance. Salient microarchitectural performance events of the CPU tests were also memory-related.