Turnkey Solution Research Articles

The adaptation of a single institution diabetes care platform into a nationally available turnkey solution.

Digital decision support and remote patient monitoring may improve outcomes and efficiency, but rarely scale beyond a single institution. Over the last 5 years, the platform Timely Interventions for Diabetes Excellence (TIDE) has been associated with reduced care provider screen time and improved, equitable type 1 diabetes care and outcomes for 268 patients in a heterogeneous population as part of the Teamwork, Targets, Technology, and Tight Control (4T) Study (NCT03968055, NCT04336969). Previous efforts to deploy TIDE at other institutions continue to face delays. In partnership with the diabetes technology non-profit, Tidepool, we developed Tidepool-TIDE, a clinic-agnostic, turnkey solution available to any clinic in the United States. We present how we overcame common technical and operational barriers specific to scaling digital health technology from one site to many. The concepts described are broadly applicable for institutions interested in facilitating broader adoption of digital technology for population-level management of chronic health conditions.

Fly-scan high-throughput coded ptychographic microscopy via active micro-vibration and rolling-shutter distortion correction.

Recent advancements in ptychography have demonstrated the potential of coded ptychography (CP) for high-resolution optical imaging in a lensless configuration. However, CP suffers imaging throughput limitations due to scanning inefficiencies. To address this, we propose what we believe is a novel 'fly-scan' scanning strategy utilizing two eccentric rotating mass (ERM) vibration motors for high-throughput coded ptychographic microscopy. The intrinsic continuity of the 'fly-scan' technique effectively eliminates the scanning overhead typically encountered during data acquisition. Additionally, its randomized scanning trajectory considerably reduces periodic artifacts in image reconstruction. We also developed what we believe to be a novel rolling-shutter distortion correction algorithm to fix the rolling-shutter effects. We built up a low-cost, DIY-made prototype platform and validated our approach with various samples including a resolution target, a quantitative phase target, a thick potato sample and biospecimens. The reported platform may offer a cost-effective and turnkey solution for high-throughput bio-imaging.

Key4hep: Progress Report on Integrations

Key4hep: Progress Report on Integrations

Lensless polarimetric coded ptychography for high-resolution, high-throughput gigapixel birefringence imaging on a chip

Polarimetric imaging provides valuable insights into the polarization state of light interacting with a sample. It can infer crucial birefringence properties of specimens without using labels, thereby facilitating the diagnosis of diseases such as cancer and osteoarthritis. In this study, we present a novel polarimetric coded ptychography (pol-CP) approach that enables high-resolution, high-throughput gigapixel birefringence imaging on a chip. Our platform deviates from traditional lens-based systems by employing an integrated polarimetric coded sensor for lensless coherent diffraction imaging. Utilizing Jones calculus, we quantitatively determine the birefringence retardance and orientation information of biospecimens from the recovered images. Our portable pol-CP prototype can resolve the 435 nm linewidth on the resolution target, and the imaging field of view for a single acquisition is limited only by the detector size of 41 mm×41 mm. The prototype allows for the acquisition of gigapixel birefringence images with a 180 mm×180 mm field of view in ∼3.5 min, a performance that rivals high-end whole slide scanner but at a small fraction of the cost. To demonstrate its biomedical applications, we perform high-throughput imaging of malaria-infected blood smears, locating parasites using birefringence contrast. We also generate birefringence maps of label-free thyroid smears to identify thyroid follicles. Notably, the recovered birefringence maps emphasize the same regions as autofluorescence images, underscoring the potential for rapid on-site evaluation of label-free biopsies. Our approach provides a turnkey and portable solution for lensless polarimetric analysis on a chip, with promising applications in disease diagnosis, crystal screening, and label-free chemical imaging, particularly in resource-constrained environments.

Structured modulation multi-height microscopy for high-resolution imaging.

Conventional multi-height microscopy techniques introduce different object-to-detector distances to obtain multiple measurements for phase retrieval. However, surpassing the diffraction limit imposed by the numerical aperture (NA) of the objective lens remains a challenging task. Here, we report a novel structured modulation multi-height microscopy technique for quantitative high-resolution imaging. In our platform, a thin diffuser is placed in between the sample and the objective lens. By translating the diffuser to different axial positions, a sequence of modulated intensity images is captured for reconstruction. The otherwise inaccessible high-resolution object information can thus be encoded into the optical system for detection. In the construction process, we report a ptychographic phase retrieval algorithm to recover the existing wavefront of the complex object. We validate our approach using a resolution target, a phase target, and various biological samples. We demonstrate a ∼4-fold resolution gain over the diffraction limit. We also demonstrate our approach to achieve a 6.5 mm by 4.3 mm field of view and a half-pitch resolution of 1.2 µm. The reported methodology provides a portable, turnkey solution for quantitative high-resolution imaging with potential applications in disease diagnosis, sample screening, and other fields.

Digital exposure notification tools: A global landscape analysis.

As the COVID-19 pandemic continues, digital exposure notification systems are increasingly used to support traditional contact tracing and other preventive strategies. Likewise, a plethora of COVID-19 mobile applications have emerged. Objective: To characterize the global landscape of pandemic related mobile applications, including digital exposure notification and contact tracing tools. The following queries were entered into the Google search engine: "(*country name* COVID app) OR (COVID app *country name*) OR (COVID app *country name*+) OR (*country name*+ COVID app)". The App Store, Google Play, and official government websites were then accessed to collect descriptive data for each application. Descriptive data were qualified and quantified using standard methods. COVID-19 Exposure Notification Systems (ENS) and non-Exposure Notification products were categorized and summarized to provide a global landscape review. Our search resulted in a global count of 224 COVID-19 mobile applications, in 127 countries. Of these 224 apps, 128 supported exposure notification, with 75 employing the Google Apple Exposure Notification (GAEN) application programming interface (API). Of the 75 apps using the GAEN API, 15 apps were developed using Exposure Notification Express, a GAEN turnkey solution. COVID-19 applications that did not include exposure notifications (n = 96) focused on COVID-19 Self-Assessment (35·4%), COVID-19 Statistics and Information (32·3%), and COVID-19 Health Advice (29·2%). The digital response to COVID-19 generated diverse and novel solutions to support non-pharmacologic public health interventions. More research is needed to evaluate the extent to which these services and strategies were useful in reducing viral transmission.

External catalyst-free InGaN photoelectrode for highly efficient energy conversion and H2 generation

GaN is a well-known material whose energy band edges can straddle the redox potentials deep in the visible and infrared wavelengths, thereby promising a drastically improved photon-to-current efficiency under applied bias. However, the material is still limited by the half-reactions of water splitting due to its high defect density, low light absorption, small reaction area, and large energy band bending. Here, our study provides a turn-key solution to all these issues. The synergistic effect of InGaN/GaN quantum pyramids on nanowires (QPs-NWs) directly addresses the performance degradation of the photocathodes (PCs). New InGaN QP structures on non-polar GaN nanowire show a unique tunable energy band (Eg: ∼2 eV to ∼1.36 eV) by quantum-sliding interface recombination effect. Without the use of external catalysts, the photoelectrochemical water splitting (PEC-WS) of QPs-NWs PC demonstrated enhanced performance with a current density of 34.36 mA cm−2 and a photon-to-current efficiency of 13.75 % under the −0.8 to 0 V applied biasing condition, which is much higher than in previous reports. The current density and the H2 production were measured to be ∼61.81 mA cm−2 and 11.5 mmol cm−2 for 10 h. The external catalyst-free electrode and the metal organic chemical vapor deposition (MOCVD) process will open a new platform for the commercialization of III-nitride based water splitting hydrogen technology.

Modeling and Operation of Series-Parallel Resonant Load in Industrial RF Dielectric Heating Application

Radio Frequency (RF) based heating application is used in multiple industries like drying cross-laminated woods, food and packaging, melting silicon etc. These industrial RF heating plants operate at frequencies of 6.78 MHz, 13.56 MHz, or 27.12 MHz (ISM band). Historically, the high frequency requirement has forced the industry to rely on vacuum tube based RF generator, which are only 60% efficient. Modern semiconductors like wide bandgap (WBG) devices have achieved the high frequency operation that is required to replace the inefficient vacuum tubes. However, a comprehensive study of load network for an industrial RF heating plant has not been undertaken such that an existing plant can be retrofitted with semiconductor based turn-key solution. In this paper the authors present design equations for an industrial RF dielectric heating load structure and suggest necessary modifications required to replace vacuum tube RF generator with voltage source converter (VSC). A new figure of merit (FOM) is defined that impacts the load structure efficiency. Finally, a small-scale prototype load is manufactured as a case study. The prototype demonstrated an output power of 750 W at 6.76 MHz with an overall efficiency of 90% from DC input to RF load which can not be achieved with a vacuum tube technology.

Landed property as collateral to access credit for housing development in Ghana: The case of Northern Region of Ghana

Property in the commons, or the Washington Consensus, as it is known in other circles, posits that landed property and access to formal credit are directly related. Whether landed property improves access to formal credit or not has been at the centre of the debate, with varying practical evidence, especially for the Global South. Another related dimension of the debate concerns the implications of family-help-mortgage arrangements using intra-family transfer of land as collateral to support a member's mortgage by placing a charge on the collective property. This paper investigates the use of landed property as collateral to access formal credit from financial institutions for housing development in the Tamale Metropolitan Area (TMA) in the Northern Region of Ghana. The primary research question is: does formal documentation of landed property as collateral help in accessing formal credit for housing development in TMA? The study involves the use of in-depth interviews to investigate the opinions of estate developers, credit officers of universal banks, and land title holders on the relative importance of landed property and access to credit for housing development. The data are analysed by means of thematic content analysis. The findings indicate that even though landed property may contribute to the decision to grant formal credit, it is not a turn-key solution easily accepted in the final lending decision. This study adds to the body of knowledge primarily by evaluating the effects of reforms to increase credit access with landed property as collateral in Ghana, investigating the implications of family-help mortgage arrangements, including the use of intra-family assets as collateral in obtaining loans for investment at the micro-level in Ghana. The paper concludes that while formal documentation of land rights matters in the broad scheme of estate financing, it is not sufficient to bring about more immediate access to credit, especially for those who lack regular or reliable systems of income that could be critical in ensuring the payback of loans contracted using landed property as collateral for housing development.

Online Program Management Companies: Friend or Foe of Communication Sciences and Disorders Higher Education?

Purpose: Online Program Management companies (OPMs) provide a turn-key solution for higher education institutions that want to build new or move existing programs into an online learning environment. The purposes of this article are to overview the current state of OPM usage by higher education institutions and to identify the benefits and drawbacks of using an OPM. Method: This conceptual viewpoint article is based on a review of the literature and informal interviews with administrators who had previous experience in working with OPMs. Results: The processes and structures of OPMs, including service offerings and contractual arrangements, are explained. Numerous benefits of OPMs were identified, including increased enrollments, speed to market, and marketing expertise, among others. Several drawbacks were highlighted, such as yielding control to the OPM, a lack of transparency, aggressive recruitment practices, and more. Conclusions: The future of OPMs is discussed, especially with respect to the financial and legal implications. Implications for Communication Sciences and Disorder programs are also highlighted.

Partitioning for Easy Multiplexing: A Versatile Droplet PCR Application for Clone Monitoring in Tumors

Clinical genome-wide next-generation sequencing (NGS) has brought new challenges to genetic laboratories. The identification of numerous patient-specific variants that may require to be screened for on multiple other samples poses an issue when striving for time and cost-effectiveness. Here, we propose d-multiSeq, a straightforward method utilizing the advantages of droplet PCR for multiplexing combined with amplicon-based NGS. By comparing d-multiSeq with a standard multiplex amplicon-based NGS, it was shown that partitioning prevents the amplification competition seen when multiplexing and leads to a homogeneous representation of each target in the total read count for up to a 40-target multiplex without the need for prior adjustment. Variant allele frequency was reliably evaluated with a sensitivity of 97.6% for variant allele frequency up to 1%. The applicability of d-multiSeq was also tested on cell-free DNA with the successful amplification of an eight-target multiplex panel. Preliminary application of the technique to assess the clonal evolution in a childhood leukemia harboring high interpatient variability in its somatic variants is shown. d-multiSeq represents a turnkey solution for analyzing large sets of patient-specific variants on low DNA amounts and cell-free DNA.

A Practical Fault Location Algorithm With Increased Adaptability for Active Low Voltage Grids

Quick power restoration has always been one of the grid operators' major goals and could be facilitated by the application of automatized fault location methods. The need for such methods is particularly visible in the low voltage (LV) grids which have been overlooked until now. Hence, this paper presents a novel turnkey solution to the fault location problem in LV grids. The proposed algorithm takes advantage of the growing data availability and employs a machine learning (ML) model for the prediction of the faulted point. In order to ensure the applicability of the method all the steps of its implementation are analyzed. The study begins by addressing the algorithm's data collection requirements and proposing a smart storage strategy. It continues with the implementation of advanced data processing and minimization techniques, such as the feature selection, and addresses the generalizability limitations of ML–based algorithms by proposing a retraining scheme. Finally, the algorithm's performance during load changes and data contamination is studied. For the validation of the algorithm a modified version of the CIGRE European LV benchmark was simulated. The results verify the algorithm's robustness against the main influencing parameters and reveal low mean absolute errors throughout the tests; the default study case error was 0.5 m.

Risk Prediction of Digital Human Resource Management Based on Artificial Intelligence

The latest information technologies have greatly accelerated the digitalization progress of Human Resource Management (HRM) and many useful techniques and tools have been developed for that purpose. However, in terms of risk management, effective enough tools and methods are still insufficient. Existing studies generally fail to give a turnkey solution to the operational risks in digital HRM system, and the macro measurement models are not suitable for dealing with the risks in the digital HRM system of each single enterprise. In view of these defects, this paper studied the prediction of risks in digital HRM systems based on Artificial Intelligence (AI). Firstly, the paper outlined the functions of a digital HRM system, defined the risk management mechanism of a HRM system, and built a conceptual model for it. Then, this paper proposed a novel method for predicting the risks in the digital HRM system, which innovatively integrates the digital HRM risk event chains with the risk event graph. After that, the paper elaborated on the structures and building principles of the risk event representation layer, risk event chain module, risk event graph module, and attention fusion module. At last, experimental results verified that the proposed model has obvious advantages in digital HRM risk prediction in terms of both stability and accuracy.

Hydrogen-powered refrigeration system for environmentally friendly transport and delivery in the food supply chain

Urban population and the trend towards online commerce leads to an increase in delivery solution in cities. The growth of the transport sector is very harmful to the environment, being responsible for approximately 40% of greenhouse gas emissions in the European Union. The problem is aggravated when transporting perishable foodstuffs, as the vehicle propulsion engine (VPE) must power not only the vehicle but also the refrigeration unit. This means that the VPE must be running continuously, both on the road and stationary (during delivery), as the cold chain must be preserved. The result is costly (high fuel consumption) and harmful to the environment. At present, refrigerated transport does not support full-electric solutions, due to the high energy consumption required, which motivates the work presented in this article. It presents a turnkey solution of a hydrogen-powered refrigeration system (HPRS) to be integrated into standard light trucks and vans for short-distance food transport and delivery. The proposed solution combines an air-cooled polymer electrolyte membrane fuel cell (PEMFC), a lithium-ion battery and low-weight pressurised hydrogen cylinders to minimise cost and increase autonomy and energy density. In addition, for its implementation and integration, all the acquisition, power and control electronics necessary for its correct management have been developed. Similarly, an energy management system (EMS) has been developed to ensure continuity and safety in the operation of the electrical system during the working day, while maximizing both the available output power and lifetime of the PEMFC. Experimental results on a real refrigerated light truck provide more than 4 h of autonomy in intensive intercity driving profiles, which can be increased, if necessary, by simply increasing the pressure of the stored hydrogen from the current 200 bar to whatever is required. The correct operation of the entire HPRS has been experimentally validated in terms of functionality, autonomy and safety; with fuel savings of more than 10% and more than 3650 kg of CO2/year avoided.

Scalable Remote Cloud Data Center for Vessel Equipment Predictive Maintenance Service-as-a-Product (SaaP)

Predictive maintenance is gradually replacing conventional preventive maintenance, through an informed decision-making process for a fleet operator to proactively monitor the health status of the sea-based equipment and machinery. As a result, the fleet operator will only need to ask for on-time repair and maintenance services instead of periodic maintenance, which saves both time and cost significantly. While moving into this new maritime business model, which is also known as Service-as-a-Product (SaaP), both fleet operators and maintenance, repair, and overhaul (MRO) companies could also build a stronger collaboration in the maritime industries. With the advent of digital transformation towards Industry 4.0 (I4.0), the Industrial Internet-of-Things (IIoT) renders the massive collection of operational and machinery process data from the vessel equipment through sensorization, where these collected real-time information are useful for advanced analytics to predict equipment failure and to avoid unplanned downtime. Unlike other industries, there are a few challenges in the maritime industries when developing a centralized smart vessel equipment monitoring platform. One of the key challenges, is the lack of a feasible data management system, requires the centralized host must be able to handle and manage the telemetry IoT data that is transmitted over the satellite communication, by complying with all cybersecurity considerations and regulations in maritime sector. In this paper, we present a scalable, hybrid cloud-based data management framework that can connect to multiple edge systems where each system is deployed on a physical vessel, to establish a scalable SaaP business model. Hence, the ship-to-shore sensorized data can be processed and monitored via a real-time visualization dashboard through the centralized cloud-based platform. We argue that our approach, would be a turn-key solution that can be implemented for mostly all types of marine equipment and machinery, and thus to further improve the prediction tools to support advanced decision-making techniques, such as optimal time repair of vessel equipment.

Rapid full-color Fourier ptychographic microscopy via spatially filtered color transfer

Full-color imaging is of critical importance in digital pathology for analyzing labeled tissue sections. In our previous cover story [Sci. China: Phys., Mech. Astron. 64, 114211 (2021)SCPMCL1674-734810.1007/s11433-021-1730-x], a color transfer approach was implemented on Fourier ptychographic microscopy (FPM) for achieving high-throughput full-color whole slide imaging without mechanical scanning. The approach was able to reduce both acquisition and reconstruction time of FPM by three-fold with negligible trade-off on color accuracy. However, the method cannot properly stain samples with two or more dyes due to the lack of spatial constraints in the color transfer process. It also requires a high computation cost in histogram matching of individual patches. Here we report a modified full-color imaging algorithm for FPM, termed color-transfer filtering FPM (CFFPM). In CFFPM, we replace the original histogram matching process with a combination of block processing and trilateral spatial filtering. The former step reduces the search of the solution space for colorization, and the latter introduces spatial constraints that match the low-resolution measurement. We further adopt an iterative process to refine the results. We show that this method can perform accurate and fast color transfer for various specimens, including those with multiple stains. The statistical results of 26 samples show that the average root mean square error is only 1.26% higher than that of the red-green-blue sequential acquisition method. For some cases, CFFPM outperforms the sequential method because of the coherent artifacts introduced by dust particles. The reported CFFPM strategy provides a turnkey solution for digital pathology via computational optical imaging.

Inspecting CAD/CAM ceramic dental prosthesis using X-ray micro-computed tomography

ABSTRACT The X-ray micro-computed tomography measurement protocol proposed in this paper aims to inspect the geometrical quality of ceramic dental prostheses manufactured by dental CAD/CAM systems. This measurement protocol sits at the interface of dental CAD/CAM technology and dental CAD/CAM engineering, which will interest to the dentistry community. The main objective of this paper is to provide a turnkey solution to researchers with a medical-education background for 3D inspection of dental prostheses. The proposed protocol hinges on 3D digitisation, obtained by X-ray micro-computed tomography acquisition and image processing. Before using it in a realistic case, the protocol has been validated by inspecting a certified ceramic sphere. The measurements demonstrated errors under the level of clinical tolerance. A realistic prosthesis was then designed and machined. Our protocol revealed under-milled areas, over-milled areas, and, particularly, chipping on thinnest areas. This protocol is crucial for overcoming the inherent difficulties in exchanging information between dentistry practice and dental CAD/CAM systems development.

About Algorithms of Target Positioning

Software has been developed for the system most suitable for wind shear detection. The formulation of the target designation problem is presented. The features of modern radars are considered. The aim of the work is to find the most suitable system for detecting wind shear and to develop software for this system that offers the pilot a turnkey solution for flying through the danger zone of wind shear. The paper presents the definitions of wind shear, statistics of air crashes caused by this phenomenon. The systems for determining wind shear are described and the most suitable system is selected. The principle of operation and the algorithm of operation of the Doppler radar station are given. A simulation of the detection of a dangerous wind shear zone was carried out. The pilot uses the proposed software solution. Thus, the task of determining the wind shear zone and the decisions proposed on this basis to fly around the dangerous zone have been completed. The results show that introducing random noise into labeled samples degrades the accuracy of the model, while introducing random noise into unlabeled data can, on the contrary, increase the accuracy of the model.

Cloud Functions and Serverless Computing

Abstract: Cloud Function in the simplest words is a Function-as-a-Service (FaaS). FaaS is actually a family of the serverless computing category. “Serverless” means that the user can focus on its application logic without dealing with infrastructure at all. Painless development, deployment, and maintenance of a web API is still not a turn-key solution, although modern web application frameworkshave improved dramatically in the last few years. Serverless is without any doubt a game-changer. The event-driven approach combined with a scalable and robust cloud ecosystem offered by the maintop cloud vendors opens endless opportunities. Cloud Functions is Google Cloud’s event-driven serverless compute platform. FaaS is a real NoOpstechnology, it completely abstracts away servers. Cloud Functions are developed with the sole purpose to build event-driven architectures. These can react to events like file changes in storage, messages in the queue, or any HTTP request. Index Terms: Cloud Computing, Serverless Technology, Function as a Service, LambdaFunctions and Functions

Inherent Quality Metrics for Continuous Software Quality Enhancement

Traditional software quality metrics based on bug number and pass rate can only provide us afterthought post validation & product release. In this paper, we propose some new inherent software quality metrics for proactive quality control during development phase, including Lines of Code (LOC#), Cyclomatic Complexity and Code Churn. In this paper, citing one ultra-large-scale software - Intel Media Driver as one example, we introduce the reason to choose those metrics, our experience on leveraging those metrics to improve the software quality, and our turn-key solution for automatic metrics data collection and analysis. We expect the identified metrics can help more researchers to form the corresponding research agendas and the experiences sharing can help following practitioners to apply similar enhancements.