Implementation Of Platform Research Articles

Digitization Planning for Large Collections and Agencies with Specify 7

The Specify Collections Consortium (SCC) supports research collections around the world with an intuitive, robust, and highly-customizable software platform to digitize their holdings and manage their collections. For over 25 years, Specify has provided and sustained biological research museums and biorepositories with software for managing, integrating, and publishing collections information (Fig. 1). The SCC has increasingly engaged with very large collections managed by institutions ranging from universities to individual government departments to entire federal agencies, to envision a framework that supports standard computerization workflows and best practices. From our involvement in working with these institutions, which have successfully navigated the transition to Specify, we have gathered insights and learned lessons that may be helpful to others to better anticipate and address key data-related issues in their own institution when embarking on a Specify (or any collections management system) implementation. Understanding how to approach data preparation for import into a collections management system is crucial, as collections often face significant challenges in this area. We will discuss some of the key hurdles that collections encounter and methods to overcome them such as unstandardized data within a single collection and between multiple collections, unparsed names for taxonomy and people, and reconciling pre-existing custom data structures with the fixed structure of a collections management system. We will also cover the decision-making process for determining what collection data should be shared between collections versus what should be managed separately (scoping, data sharing, and data exporting). Large institutions often have multiple collections with diverse needs, and finding the right balance between centralized coordination and individual collection autonomy can be complex. The presentation will discuss the factors considered and the strategies employed to navigate this balance, as it can have significant implications for the overall success of a Specify deployment (Fig. 2). In addition to the technical considerations, the presentation will also delve into the human side of transitioning to a new collections management system. Successful collections management platform implementations require not only technical expertise, but also effective communication and collaboration among the collections staff, volunteers, and management. This presentation offers a framework to help other large collections successfully transition to Specify. Our team at the Specify Collections Consortium has extensive experience in supporting the transition of large research collections to their robust and highly customizable software platform. We have gained valuable insights into the key challenges and best practices for data preparation, system implementation, and change management. By sharing the lessons learned from these institutions and the SCC's experience, the presentation provides valuable insights and guidance to support other organizations in their collections management system implementation projects. A better understanding of this process will ultimately contribute to preserving and improving access to natural history collections (and soon geoscience collections) worldwide.

A blockchain-based platform for incentivizing customer reviews in the grocery industry

Nowadays, user-generated content is pivotal for many companies: people trust other customers' opinions more than any brand advertisement. Brands are aware of this and try to promote and motivate their customers to create high-quality content. However, this way of operating is still at an early stage: there is a lack of fairness, as companies typically do not provide a validation system, or if they do, it is not based on a transparent solution, and often there is no reward for creating unique and high-quality content. In this paper, we focus on the problem of incentivizing the user's creation of content in the form of customer reviews in the online grocery industry. Specifically, we illustrate the solution to the problem devised in the Re-Taled project by relying on blockchain technology. We developed a decentralized ecosystem of consumers, influencers, and manufacturers, where content creators are rewarded for their contribution according to a framework that provides incentives in the form of both reputation and monetization. Blockchain technology is used to certify the content's authenticity and compensate content creators with a cryptographic token. We illustrate the technical choices of the solution together with its software architecture and implemented platform. In particular, we introduce the framework used to validate the trustworthiness of user-generated content and favor fairness and transparency within the platform.

Assessing and harnessing updated polyketide synthase modules through combinatorial engineering

The modular nature of polyketide assembly lines and the significance of their products make them prime targets for combinatorial engineering. The recently updated module boundary has been successful for engineering short synthases, yet larger synthases constructed using the updated boundary have not been investigated. Here we describe our design and implementation of a BioBricks-like platform to rapidly construct 5 triketide, 25 tetraketide, and 125 pentaketide synthases to test every module combination of the pikromycin synthase. Anticipated products are detected from 60% of the triketide synthases, 32% of the tetraketide synthases, and 6.4% of the pentaketide synthases. We determine ketosynthase gatekeeping and module-skipping are the principal impediments to obtaining functional synthases. The platform is also employed to construct active hybrid synthases by incorporating modules from the erythromycin, spinosyn, and rapamycin assembly lines. The relaxed gatekeeping of a ketosynthase in the rapamycin synthase is especially encouraging in the quest to produce designer polyketides.

Digitizing operating theater data in resource-limited settings: Understanding surgical care delivery post-implementation at Tanzanian referral hospital.

Digitizing surgical data infrastructure is critical for policymakers to make informed decisions. The implementation of the first web-based operating theater (OT) recordings at Muhimbili Orthopedic Institute (MOI) represents significant advancements in data management for Tanzania. This study aims to share post-platform implementation outcomes, challenges, and insights gained offering guidance to settings facing similar data repository challenges. In July 2023, after training clinicians, the platform was deployed at MOI operating theaters (OTs) to facilitate prospective data entry following procedures, ensuring timely updates of perioperative outcomes. Semi-structured interviews were conducted with key stakeholders to gather insights into the platform's functionality and efficient data management systems. We presented data from August 2023 to February 2024 along with platform insights. Over 4449 procedures were conducted, comprising 1321 emergencies and 3128 electives, with orthopedics/trauma accounting for the majority (3606). Trauma-related emergencies (921) predominate among interventions. General anesthesia was prevalent; 60.56% in emergencies and 44.51% in electives. Orthopedics/trauma utilized 90.91% of assigned operating days in electives, while neurosurgery utilized 93.39% (p<0.011). The cancellation rate was 7.5%, primarily due to emergency interferences (32%). Of procedures, 96.76% were discharged, while 2.81% died. Challenges encountered during platform implementation included securing local support, integrating technology, and navigating administrative adjustments. Lessons learned emphasized continuous communication for stakeholder buy-in and training for platform familiarity. The web-based OT recordings at MOI succeeded with local support and showed promise for wider scalability. To ensure sustainability, ongoing follow-up, monitoring of platform functionality, local funding establishment, and strengthening global partnerships are recommended.

Need for Data Standardization and Infrastructure of Research Data Management to Promote Using Real-world Data

Real-world data (RWD) have been increasingly used for regulatory decision-making and as a control group for new drug approval applications. RWD is also helpful in understanding information such as risk factors (e.g., pre-existing medical conditions, personal protective equipment, travel, contacts, smoking, and exposure to animals) and vaccination status for the coronavirus disease 2019 (COVID-19). The methodology of utilizing RWD is inconsistent across healthcare institutions. However, there are possible solutions to standardize RWD for clinical data use, which include the use of Clinical Data Interchange Standards Consortium (CDISC) standards, tools, and concepts. This study examines the availability of CDISC and other international standards for the utilization of RWD with concrete examples and presents the potential platform for implementation. We consider the solution currently available to temporarily convert clinical data-warehouse (DWH) data into the Fast Healthcare Interoperability Resources (FHIR) format to comply with the CDISC standard. This approach would allow for converting institution-level standards to national standards as an interim solution until FHIR is supported, mapping national standards to international standards. We believe that the ideal research environment is a data platform that complies with national and international regulations related to RWD applications. Within such a platform, users can share data freely, rather than rely on a specific facility or vendor. Data platform developments are progressing in Japan and globally. In Japan, initiatives to use research data on research data platforms are being conducted. We are experimenting with implementing tools and knowledge shared by CDISC.

Research on Student Management Platform Based on Big Data Under Low-Carbon Environment

This paper makes a comprehensive investigation on the development and implementation of low-carbon student management platform in educational institutions. The platform adopts advanced information technology, including cloud computing and big data analysis, aiming at solving urgent environmental problems by analyzing the dynamic learning data and daily behavior data of college students. The implementation of the platform has greatly reduced carbon emissions, especially greenhouse effect variables and dormitory electricity consumption. The observed impact highlights the effectiveness of using academic management platform to promote carbon emission reduction. However, this study acknowledges the existing limitations and predicts the long-term challenges, and emphasizes the need for continuous innovation and research in the intersection of artificial intelligence and environmental sustainable development. Generally speaking, the development and deployment of low-carbon student management platform is a key step to promote the sustainable development of educational environment.

A Survey on Neuromorphic Architectures for Running Artificial Intelligence Algorithms

Neuromorphic computing, a brain-inspired non-Von Neumann computing system, addresses the challenges posed by the Moore’s law memory wall phenomenon. It has the capability to enhance performance while maintaining power efficiency. Neuromorphic chip architecture requirements vary depending on the application and optimising it for large-scale applications remains a challenge. Neuromorphic chips are programmed using spiking neural networks which provide them with important properties such as parallelism, asynchronism, and on-device learning. Widely used spiking neuron models include the Hodgkin–Huxley Model, Izhikevich model, integrate-and-fire model, and spike response model. Hardware implementation platforms of the chip follow three approaches: analogue, digital, or a combination of both. Each platform can be implemented using various memory topologies which interconnect with the learning mechanism. Current neuromorphic computing systems typically use the unsupervised learning spike timing-dependent plasticity algorithms. However, algorithms such as voltage-dependent synaptic plasticity have the potential to enhance performance. This review summarises the potential neuromorphic chip architecture specifications and highlights which applications they are suitable for.

Feasibility evaluation of the Versius surgical system: robot-assisted hysterectomy for benign and malignant gynaecological lesions.

The application of minimally invasive surgery allows for radical and precise removal of the gynaecological lesion while simultaneously reducing the side effects and complications associated with surgical treatment. This paper aims to share our direct experience with the implementation of the CMR Versius robotic platform in the treatment of benign and malignant gynaecological lesions. This study included patients who underwent hysterectomy in the Department of Obstetrics, Gynaecology, and Gynaecologic Oncology at the Regional Polyclinical Hospital in Grudziadz, Poland. A total of 50 patients were included in the study: 29 underwent laparoscopic surgery and 21 underwent robot-assisted surgery using the CMR Versius system. It was found that in the case of non-radical hysterectomy, the duration of surgery differed significantly (96.5 vs. 134.6min, p < 0.01) in the groups of patients undergoing laparoscopic and robotic surgery. There were also no statistically significant differences in loss of blood parameters, rate of complications and conversions to other type of surgery after the laparoscopic and robotic surgeries. Both groups did not differ significantly in terms of hospitalisation time after surgery. Versius CMR surgical robot assistance provides safe and effective support for MIS procedures in gynaecology.

Using the Electronic Health Record to Implement Expedited Partner Therapy in the Pediatric Emergency Department.

Expedited partner therapy (EPT) is a partner treatment strategy for sexually transmitted infections (STIs) including gonorrhea and chlamydia as well as trichomoniasis in some states. The process allows healthcare providers to write prescriptions for STI treatment among partners of infected patients without a previous medical evaluation. The Centers for Disease Control (CDC) has recommended EPT as a useful option to facilitate partner treatment, particularly male partners of women with chlamydia or gonorrhea infections. Our institution implemented EPT in 2016 after Ohio legislation was passed to authorize its use. We aim to describe the implementation process and descriptive outcomes of EPT adoption in a pediatric emergency department. This study describes use of the electronic health record for implementation of EPT in our institution. We conducted a retrospective review of EPT utilization from implementation. Electronic records from the implementation date of January 1, 2017, through December 31, 2021, were reviewed. We describe basic demographics and overall uptake of the intervention. Fisher exact tests were used for categorical variables and two-sample t-tests for continuous variables. There was a total of 3275 positive test results and 739 EPT prescriptions written. Adolescent patients who received prescriptions for EPT were more likely to be female (78.7% of all EPT prescriptions, P = 0.007) and older than other patients (average age 17.7 vs 17.4 years, P = 0.004). There was no significant difference in race, insurance, or ethnicity among adolescent patients receiving and not receiving EPT. The percentage of positive STI tests associated with an EPT prescription ranged between 11.4% and 18.2%. Metronidazole was the most prescribed EPT medication. The use of the electronic health record provides a platform for implementation of EPT. Our study highlights a potential strategy for increasing treatments of STIs through EPT prescribing in the emergency department setting.

Leveraging Participatory Science for Tackling Water Supply Challenges in Water-Scarce Developing Regions

This paper explores how participatory science helps address water supply challenges in developing countries suffering from water shortages. In the absence of technical capacity to monitor the water supply system, this participation constitutes an excellent alternative to involve citizens in data collection and evaluation of the quality of the water service. The paper presents the design and implementation of a participatory science platform that enables data about the quality of the water supply in the city of Kharas in Palestine to be collected. Thanks to significant citizen participation, the study demonstrates that participatory science enhances the management of the water supply system and public awareness and engagement. Temporal and spatial analysis of collected data helped us to better understand the water supply system and identify the causes of the water service degradation. The study shows that the success of citizen participation requires cooperation between the water staff and citizens, including continuous dialogue and training and the construction of a friendly citizen participation platform.

Traffic safety implementation at local level in line with 2030 Agenda for Sustainable Development: synergies, challenges and enablers

This study explores how traffic safety implementation at local level in line with the 2030 Agenda for Sustainable Development is characterised. By investigating interactions between traffic safety and other sustainability goals (synergies), as well as identifying contradictions between goals and conflicting interests (challenges), the study aims to pinpoint the main enablers for implementing traffic safety as part of sustainability. The emphasis lies on local authorities in Sweden representing a mature traffic safety context when viewed globally. To delve into this issue, interviews were conducted with 37 municipal employees from the city of Gothenburg, encompassing diverse roles that exemplify the significant influence Swedish local authorities wield in shaping traffic safety: as road manager and planner, in maintenance and construction phases, as employer and purchaser, and as influencer of behaviour. The study reveals several synergies between traffic safety and other sustainability goals, as well as contradictions between goals and conflicting interests posing challenges to traffic safety implementation. Main enablers for implementation of traffic safety as part of sustainability at the local level concern, for example, an integrated approach on strategic level where traffic safety could be handled as either a prerequisite (e.g. for active mobility) or a framework condition (e.g. for green cars). The necessity to bridge gaps between strategic and operational levels is also emphasised. Contradicting goals and interests are inevitable and must be managed. The study identifies the traffic strategy and strategic analyses, in the European context referred to as Sustainable Urban Mobility Planning, as a platform for traffic safety implementation as part of sustainability. Such platform highlights implementation areas in common for different target areas. The significance of integrating traffic safety in relevant activities and processes, alongside fostering horizontal collaborations across target areas, becomes apparent when striving to embed traffic safety in a wider sustainability context. While Swedish municipalities may not always recognize traffic safety as part of the sustainability agenda, existing platforms within municipalities serve as a foundation for adopting an integrated approach to traffic safety implementation. However, there is a need for a central coordinating function to ensure robust leadership in integrating traffic safety as a vital component of sustainability.

Unveiling the heterogeneous utilisation of the same digital patient management platform: case studies in primary healthcare in Sweden

BackgroundThe utilisation of digital technology in primary healthcare, particularly digital patient management platforms, has gained prominence, notably due to the global pandemic. These platforms are positioned as substitutes for face-to-face consultations and telephone triage. They are seen as a potential solution to the escalating costs associated with an aging population, increasing chronic conditions, and a shrinking healthcare workforce. However, a significant knowledge gap exists concerning the practical aspects of their implementation and their effect on the utilisation of digital patient management in primary healthcare.MethodsThis study addresses this gap by conducting a comprehensive analysis of three case studies involving the implementation of a specific digital patient management platform. Over a period of three years, we examine how the practicalities of implementation shape the adoption and utilisation of a digital patient management platform in three different clinics.ResultsOur findings revealed that differences in implementation strategies directly influenced variations in utilisation. The successful utilisation of the platform was achieved through a bottom-up decision-making process that involved the employees of the primary healthcare clinics. Onsite training, close collaboration with the eHealth provider, and a structured patient onboarding process played crucial roles in this utilisation. In contrast, a top-down approach at two of the primary healthcare clinics led to limited utilisation of the platform into daily workflows. Furthermore, making the platform a part of everyday work meant putting accessibility, by working as a team of physicians, at the forefront of continuity of care, with patients being managed by their designated physician. Additionally, it was observed that digital patient management proved most effective for addressing simple patient issues such as skin rashes, rather than complex cases, and did not reduce the demand for phone triage.ConclusionOnly one of the three clinics studied effectively integrated digital patient management into its daily operations, and did so by aligning objectives among management and all categories of healthcare professionals, employing a bottom-up decision-making process, collaborating with the eHealth service provider for regular platform adjustments to clinic needs, and implementing active patient onboarding. This sociotechnical integration resulted in high platform utilisation. In contrast, the other two clinics faced challenges due to incoherent objectives among diverse healthcare professional employees and top management, a top-down decision-making approach during implementation, limited collaboration with the eHealth service provider, and passive patient onboarding. The findings indicate that these factors negatively affected utilisation and led to low platform adoption as well as disrupted the sociotechnical balance.

Enhancing Food Safety and Sanitation Education: Developing an Immersive Learning Platform Using Virtual Simulation

ABSTRACT In hospitality management education, diminishing interest in food safety and sanitation education poses challenges compounded by limited awareness of its importance. An immersive learning platform with virtual simulation environments for food safety and sanitation education was developed and integrated into a foodservice management course on a Bachelor of Science in Hospitality Management curriculum. This paper describes the development and implementation of the immersive learning platform, highlighting its positive impact on learning outcomes to date. The paper underscores the practical use of immersive technology in food safety and sanitation education and suggests avenues for further research to optimize student learning and engagement.

Towards a Blockchain Hybrid Platform for Gamification of Healthy Habits: Implementation and Usability Validation

(1) Background: In developed countries, public health faces a number of problems, including sedentary lifestyles and poor diets, which collectively contribute to the occurrence of preventable diseases. Noncommunicable diseases represent the leading cause of global mortality. Despite the promotion of healthy living, compliance remains a significant challenge. The integration of gamification into health apps has been demonstrated to facilitate behavioral change. Blockchain technology enhances the effectiveness of gamification by providing data trustability and support for auditable incentives. This feature is possible and easy due to the inherent characteristics of blockchain automating processes through Smart Contracts, rewarding participants and creating leaderboards in a transparent and reliable manner. The use of smart contracts and events enhances the traceability and reliability of decentralized applications, including healthcare. Interoperability in blockchain tools facilitates the deployment of complex environments. The aim of this research is the deployment of a tool for the implementation and testing of a gamification platform based on blockchain technology. (2) Methods: Pre-experimental research was carried out to assess the usability of the decentralized application developed. (3) Results: A decentralized application was developed with the objective of gamifying healthy habits. The application was evaluated using the System Usability Scale, obtaining a score of 80.49, and the Cronbach’s Alpha score, which was found to be 0.75. (4) Conclusions: A prototype of a decentralized application connected with a blockchain network to reward challenge fulfilment was deployed. Despite being in early development, it demonstrated high usability. Employing blockchain technology guarantees transparency and traceability while remaining in compliance with legal requirements like the General Data Protection Regulation.

A Comprehensive Review on Diabetic Retinopathy Detection Techniques using Neural Network Architectures

Diabetic retinopathy (DR) is a significant complication arising from diabetes, affecting the eyes and potentially causing vision loss if not identified and addressed promptly. Over the years, there has been a significant advancement in the field of DR detection, primarily driven by advancements in imaging techniques and machine learning algorithms. This review paper presents a comprehensive overview of different techniques and advancements in the detection of diabetic retinopathy using deep learning and several neural network architectures. The comparative study of the existing datasets for the DR detection with the benefits, challenges and possible solutions for each dataset is also provided. The paper discusses the methods, preprocessing, implementation platforms and results of various implementation of CNN architectures like Deep CNN, CNN with Transfer Learning, Capsule Networks and DNN. The objective of this paper is to furnish researchers and clinicians with a thorough understanding of the present status of diabetic retinopathy detection, highlight the strengths and limitations of existing approaches, and identify future research directions in this vital area of healthcare.

Impact of visibility limiting conditions on satellite and high-altitude platform quantum key distribution links.

Satellite and aerial platforms are critical in the deployment of global quantum communications networks. Currently, there remain significant challenges including operation during daytime and robustness to visibility limiting conditions. In this work we investigate, through simulation, the impact of visibility limiting conditions on low-Earth orbit CubeSat dimensioned satellites, small satellites and high-altitude platform implementations. Three different operational wavelengths were considered: currently used near-infrared (at 850 nm); next-generation short-wave infrared (at 1550 nm); and a candidate longer wavelength (at 2133 nm). We present channel attenuation and consider quantum key distribution (QKD) system performance parameters. Results indicate that the "best wavelength" for an implementation depends on the minimum visibility rated and the single-photon detector technology utilized. In the cases where tolerated meteorological visibility is short, 1550 nm and 2133 nm wavelengths provide better performance. In cases when the visibility is long, the operational wavelength of 850 nm provides better QKD system performance.

Optimizing High School English Language Learning: The Impact of Modern Learning Apps and Technology Platforms

The aim of this research is to gain a thorough understanding of how the most recent information technology has affected English language learning in the present, as well as to determine what kinds of platforms and applications are thought to be most useful for helping students improve their command of the language. Additionally, a detailed analysis of the English language learning platform's implementation in Muhammadiyah schools in the Metro will be included in this research. A survey design is used in this study as a quantitative research methodology. The participants in this survey research will be drawn from MTs Muhammadiyah Metro, SMP Muhammadiyah Metro, and SMP Muhammadiyah Ahmad Dahlan, three Muhammadiyah secondary schools located in Metro City. Once the pre-survey is completed, samples will be chosen and the methodology will be decided upon by researchers at the schools and madrasas that have been identified as the research population. The result of the study shows that Female research participants make up the majority (90.09%) of the sample, while male subjects make up just 9.01%), according to the findings of the demographic analysis of the study data. The demographic information provided above indicates that there may be gender biases or trends in the research that could affect the analysis's findings or the dynamics of student participation in the use of technology in the classroom. Moreover, the research focus was at a certain grade level or educational unit stage in high school, as the bulk of research subjects (90.08%) were 17 years old.

BCI Toolbox: An open-source python package for the Bayesian causal inference model.

Psychological and neuroscientific research over the past two decades has shown that the Bayesian causal inference (BCI) is a potential unifying theory that can account for a wide range of perceptual and sensorimotor processes in humans. Therefore, we introduce the BCI Toolbox, a statistical and analytical tool in Python, enabling researchers to conveniently perform quantitative modeling and analysis of behavioral data. Additionally, we describe the algorithm of the BCI model and test its stability and reliability via parameter recovery. The present BCI toolbox offers a robust platform for BCI model implementation as well as a hands-on tool for learning and understanding the model, facilitating its widespread use and enabling researchers to delve into the data to uncover underlying cognitive mechanisms.

Design and Implementation of the Teaching Quality Evaluation Data Platform of Teachers' Teaching Innovation Team in Vocational Education

Design and Implementation of the Teaching Quality Evaluation Data Platform of Teachers' Teaching Innovation Team in Vocational Education

Operational reliability evaluation integrating prediction models for enhanced situational awareness in wind-integrated system

The extensive implementation of wind power generation (WPG) challenges power systems' flexibility and reliability during operations. Fluctuating wind speeds lead to variability in generation within wind energy systems, directly impacting operational reliability. Therefore, evaluating wind energy systems' situational awareness (SA) is imperative to forecast two-day-ahead operational reliability, facilitating effective power system planning. The foundation of SA for wind energy systems lies in monitoring the wind profile. This involves gaining insights into the system's state through perceiving wind speed and WPG, comprehending the dynamics of its state, and subsequently predicting operational reliability. To achieve this, a Simulink model of the wind system has been developed and integrated with dSPACE hardware through a real-time interface for real-time validation and implementation of a cloud-based IoT platform. This IoT platform aids in acquiring real-time data on wind profiles, enhancing the assessment of SA. The representation of the wind energy system utilizes a multi-state wind speed model, incorporating the inherent randomness of wind energy generation. A time-series-based non-linear autoregressive with exogenous input (NARX) artificial neural network (ANN) model is employed to predict wind speed and WPG for SA-based operational reliability. This model proves instrumental in forecasting the dynamic nature of wind speed and WPG. The proposed approach for operational planning is validated by implementing the wind energy system in real-time on the dSPACE platform and integrating it with IoT. This approach considers the uncertainties associated with wind generation, providing a comprehensive framework for assessing SA and optimizing power system operations.