Accessible Platform Research Articles

More eyes on the prize: open-source data, software and hardware for advancing plant science through collaboration.

Automating the analysis of plants using image processing would help remove barriers to phenotyping and large-scale precision agricultural technologies, such as site-specific weed control. The combination of accessible hardware and high-performance deep learning (DL) tools for plant analysis is becoming widely recognised as a path forward for both plant science and applied precision agricultural purposes. Yet, a lack of collaboration in image analysis for plant science, despite the open-source origins of much of the technology, is hindering development. Here, we show how tools developed for specific attributes of phenotyping or weed recognition for precision weed control have substantial overlapping data structure, software/hardware requirements and outputs. An open-source approach to these tools facilitates interdisciplinary collaboration, avoiding unnecessary repetition and allowing research groups in both basic and applied sciences to capitalise on advancements and resolve respective bottlenecks. The approach mimics that of machine learning in its nascence. Three areas of collaboration are identified as critical for improving efficiency, (1) standardized, open-source, annotated dataset development with consistent metadata reporting; (2) establishment of accessible and reliable training and testing platforms for DL algorithms; and (3) sharing of all source code used in the research process. The complexity of imaging plants and cost of annotating image datasets means that collaboration from typically distinct fields will be necessary to capitalize on the benefits of DL for both applied and basic science purposes.

Sailboat Test Arena (STAr): A Remotely Accessible Platform for Robotic Sailboat Research

Autonomous sailing robots have attracted much attention, but challenges arise due to the sailing tests require an environment with both aerodynamic and hydrodynamic fields and a sufficient number of sailing robots in readiness. A remotely accessible platform with the advantages of low cost, easy operation and high efficiency is the preferred method to solve this dilemma. Consistent with this goal is the design of Sailboat Test Arena (STAr), a remotely accessible platform for sailing robot design verification, autonomous algorithms validation and sailing control practices. All three parts require extensive testing in real water environments. Hereby, for testers around the world, STAr can be accessed without time difference. A variety of local and remote tests have been conducted in the STAr platform at various location around the world. The results show that STAr is a remotely accessible and effective tool in data collection and skill transfer. With continuous adoption and optimization, STAr will continue to serve as a tool to further promote low-cost, high-efficiency and diverse sailing research, and provide opportunities for more people to experience sailing.

Multi-Tier Hybrid Offloading for Computation-Aware IoT Applications in Civil Aircraft-Augmented SAGIN

Satellites and civil aircrafts (CAs) with computing ability are valuable access platforms, making it possible for Internet of Things (IoT) devices to offload their computation-intensive tasks in remote areas without network infrastructures. Unlike existing works mainly focused on the static scenarios or the interaction between any two types of local, edge and cloud nodes, we propose an innovative multi-tier hybrid parallel computation architecture in CA-augmented space-air-ground integrated networks (CAA-SAGIN). Specifically, devices perform local computing, CAs and satellites act as edge servers, and ground stations of satellite networks operate cloud computing. Aiming to minimize the weighted sum of end-to-end (E2E) delay and energy consumption, we formulate a partial computation offloading problem by jointly considering access strategy, transmit power, computing resource allocation, offloading ratio and delay tolerance. The platform selection exists both within and between layers, and there are inner- and inter-coupling relationships between communication and computing resources. The issue is solved by the proposed multi-tier partial task offloading (MPTO) algorithm. The original problem is firstly decomposed into primal and master subproblems by generalized benders decomposition (GBD) method, and parallel successive convex approximation (SCA) theory is utilized to transform the multi-variable NP-hard master problem into a convex one. Simulation results demonstrate the convergence and optimality of the MPTO algorithm and the advantages of this multi-tier hybrid computation offloading system. Also, the optimal tradeoff between E2E delay and energy consumption can be achieved by the MPTO algorithm.

Unsupervised machine learning, QSAR modelling and web tool development for streamlining the lead identification process of antimalarial flavonoids

ABSTRACT Identification of lead compounds with the traditional laboratory approach is expensive and time-consuming. Nowadays, in silico techniques have emerged as a promising approach for lead identification. In this study, we aim to develop robust and predictive 2D-QSAR models to identify lead flavonoids by predicting the IC50 against Plasmodium falciparum. We applied machine learning algorithms (Principal component analysis followed by K-means clustering) and Pearson correlation analysis to select 9 molecular descriptors (MDs) for model building. We selected and validated the three best QSAR models after execution of multiple linear regression (MLR) 100 times with different combinations of MDs. The developed models have fulfilled the five principles for QSAR models as specified by the Organization for Economic Co-operation and Development. The outcome of the study is a reliable and sustainable in silico method of IC50 (Mean ± SD) prediction that will positively impact the antimalarial drug development process by reducing the money and time required to identify potential antimalarial lead compounds from the class of flavonoids. We also developed a web tool (JazQSAR, https://etflin.com/news/4) to offer an easily accessible platform for the developed QSAR models.

Development and Evaluation of a Low‐Cost LEGO 3D Bioprinter: From Building‐Blocks to Building Blocks of Life

AbstractThe development of low‐cost accessible technologies for rapid prototyping of mechanical components has democratised engineering tools for hobbyists and researchers alike. The development of analogous approaches to fabrication of soft‐matter, and biologically compatible materials containing living cells, is anticipated to be similarly enabling across multiple fields of biological research. LEGO toy construction bricks represent low‐cost, precision engineered, and versatile construction materials for rapid prototyping. This study demonstrates construction of a benchtop LEGO 3D bioprinter for additive layer manufacture of a 3D structure containing viable human skin cells within a hydrogel scaffold. 3D bioprinted structures are formed from the deposition of microfluidically generated bio‐ink droplets containing live keratinocyte skin cells, representing components toward an artificial skin model. Fluid flow rates and printer speed, together with bio‐ink gelation rate, determine droplet packing arrangement in the bioprinted structures. The printing of 3D structures containing multiple bio‐inks is demonstrated and live cells are imaged in the resulting bioprints. Fluid delivery can be achieved using LEGO pumps and readily available, or home‐3D‐printed, microfluidic components, therefore avoiding the need for any specialist microfluidic hardware. Build instructions are described to enable easy uptake, modification and improvement by other laboratories, as well provide an accessible platform for learning and education. Affordable, accessible, and easy to use tools for 3D bioprinting are anticipated to open opportunities for a greater number of research labs to work with 3D cell culture and bio‐printed materials, with bioprinting expected to assist in better understanding of disease, contribute to tissue engineering and repair, and enable personalised medicine through the printing of cultured patient cells. The presented approach is not only an easily accessible laboratory tool for bioprinting, but also provides a learning system for mechanical construction, robotics, coding, microfluidics and cell biology, making it a versatile platform for research, education, and science engagement.

From e-commerce to m-commerce: An analysis of the user’s experience with different access platforms

The e-commerce market size is increasing day by day. In this context, m-commerce has become one of the fastest growing forms of e-commerce in the world. Considering the diversity of platforms and devices, it is important to understand what influences the intention to continue using e-commerce access platforms. We explore different factors predicting the user’s experience with the accessed platform through a free-simulation experiment. Participants were separated into three access platforms, one using traditional e-commerce (through notebooks or PCs) and two others involving m-commerce (accessing the website browser or the app). We verified that intention to continue using the online platform is strongly influenced by the consumers’ perceived value, being driven especially by its benefits and not sacrifices associated with the platform. We found differences among the three studied platforms regarding the significance, order of the predictors, and control variables such as platform previous experience, gender, and age, suggesting the existence of significant differences on the side of the consumer as to the platform they use on their online purchases; revealing, for example, that consumers who use mobile apps are influenced essentially by utilitarian than hedonic motives, differently from those who use website browsers. Finally, we outline some theoretical and practical implications for researchers and developers.

Improving Performance of Hardware Accelerators by Optimizing Data Movement: A Bioinformatics Case Study

Modern hardware accelerator cards create an accessible platform for developers to reduce execution times for computationally expensive algorithms. The most widely used systems, however, have dedicated memory spaces, resulting in the processor having to transfer data to the accelerator-card memory space before the computation can be executed. Currently, the performance increase from using an accelerator card for data-intensive algorithms is limited by the data movement. To this end, this work aims to reduce the effect of data movement and improve overall performance by systematically caching data on the accelerator card. We designed a software-controlled split cache where data are cached on the accelerator and assessed its efficacy using a data-intensive Bioinformatics application that infers the evolutionary history of a set of organisms by constructing phylogenetic trees. Our results revealed that software-controlled data caching on a datacenter-grade FPGA accelerator card reduced the overhead of data movement by 90%. This resulted in a reduction of the total execution time between 32% and 40% for the entire application when phylogenetic trees of various sizes were constructed.

Universality of light thermalization in multimoded nonlinear optical systems

Recent experimental studies in heavily multimoded nonlinear optical systems have demonstrated that the optical power evolves towards a Rayleigh–Jeans (RJ) equilibrium state. To interpret these results, the notion of wave turbulence founded on four-wave mixing models has been invoked. Quite recently, a different paradigm for dealing with this class of problems has emerged based on thermodynamic principles. In this formalism, the RJ distribution arises solely because of ergodicity. This suggests that the RJ distribution has a more general origin than was earlier thought. Here, we verify this universality hypothesis by investigating various nonlinear light-matter coupling effects in physically accessible multimode platforms. In all cases, we find that the system evolves towards a RJ equilibrium—even when the wave-mixing paradigm completely fails. These observations, not only support a thermodynamic/probabilistic interpretation of these results, but also provide the foundations to expand this thermodynamic formalism along other major disciplines in physics.

Improving CBCA synthase activity through rational protein design

Global interest for the minor cannabinoid cannabichromene (CBC) is growing steadily, as potential pharmaceutical applications continue to emerge. Due to low-yielding and unspecific extraction processes from its plant host Cannabis sativa, a biotechnological production is desirable. The complete heterologous biosynthesis of several other cannabinoids has recently been demonstrated as an accessible platform. However, the enzyme involved in the biosynthesis of CBC precursor cannabichromenic acid (CBCA) suffers from comparatively low catalytic efficiency, has not been crystallized, and remains poorly characterized. This study contributes to overcoming these challenges in three unique aspects. A deep‑learning‑assisted prediction of the CBCA synthase crystal structure using DeepMinds AlphaFold is performed and evaluated. The predicted CBCA synthase structure scored considerably higher in various quality assessments than the alternative template‑based homology modeling approach. A robust and practical understanding of crucial structure-function relationships for CBCA synthase is provided and a new binding mode for the substrate uncovered. Rational design approaches and computational analyses to suggest CBCAS variants with facilitated activity are applied. Through subsequent screening the substrate conversion of those variants is compared to the native enzyme. The best variant presented in this study increases CBCA production from crude lysate 22-fold and is one of five positions where substitutions had a significantly favorable impact on product formation.

Civic Reporting Indicators and Biocultural Conservation: Opportunities and Challenges for Sustainable Tourism

Citizen science (CS) within sustainable tourism is an underutilized tool for biocultural conservation. The aims of this research integrate conceptual and applied approaches to situate post-positivist and interpretive paradigms within CS and sustainable tourism. The aims are fulfilled by the creation of the new Civic Reporting Indicators (CRIs), developed through analysis of the 174 Global Sustainable Tourism Council Destination (GSTC-D) criteria and indicators. It was determined that 114 indicators are perceivable audibly and/or visibly by untrained citizens. The rearticulation of GSTC-D criteria into the CRIs utilizes embodied perceptions and observations reportable by untrained visitors and residents. The CRIs are framed within ethical principles of research, CS, and sustainable tourism. The inclusion of interpretive paradigms within CS provides epistemological innovation that validates lived experiences and embodied knowledge, fostering agency and empowerment within sustainability narratives. The CRIs may harness end-user observations by utilizing information communication technologies (ICTs) to amass longitudinal and real-time data for smart, sustainable destination management and biocultural conservation. Engaging citizens through the CRIs has the potential to contribute valid observations that amass democratic, longitudinal, and cost-effective data. Designing accessible ICT platforms for destination management may enact civic agency and critical social reflection to democratize sustainability engagement.

Acutely enhancing affective state and social connection following an online dance intervention during the COVID-19 social isolation crisis

The COVID-19 pandemic has forced many throughout the world to isolate themselves from their respective communities to stop the spread of disease. Although this form of distancing can prevent the contraction of a virus, it results in social isolation and physical inactivity. Consequently, our communities have become heavily reliant on digital solutions to foster social connection and increase physical activity when forced to isolate. Dance is a multidimensional form of physical activity that includes sensory, motor, cognitive, rhythmic, creative, and social elements. Long-term, interventional studies in dance have shown positive effects on both mental and social health; however, little has been done to examine the acute effects and no studies to date have explored the relationship between the affective state and social outcomes of dance. We examined the hypothesis that online dance is associated with improvements in affective state and social connection during a time of social isolation, namely, the COVID-19 crisis. Healthy adults (age ≥ 18; n = 47) engaged in a single session of 60 min of self-selected online dance, completing a series of validated self-reported questionnaires before and after class. We found that online dance was associated with improvements in affective state as measured by increased positive affect and self-esteem and decreased negative affect and depressive symptoms. Additionally, online dance was associated with improvements in social and community connectedness. Further, we found that those who experienced the largest increases in self-esteem and decreases in negative affect demonstrated the largest gains in social connectivity. Although in-person dance classes may be optimal for formalized dance training, online dance instruction offers an accessible platform that can provide mental and social health benefits during the COVID-19 social isolation crisis. We conclude that through online dance, individuals can experience a connection between the body, mind, and community.

FLUIDOT: A Modular Microfluidic Platform for Single-Cell Study and Retrieval, with Applications in Drug Tolerance Screening and Antibody Mining.

Advancements in lab-on-a-chip technologies have revolutionized the single-cell analysis field. However, an accessible platform for in-depth screening and specific retrieval of single cells, which moreover enables studying diverse cell types and performing various downstream analyses, is still lacking. As a solution, FLUIDOT is introduced, a versatile microfluidic platform incorporating customizable microwells, optical tweezers and an interchangeable cell-retrieval system. Thanks to its smart microfluidic design, FLUIDOT is straightforward to fabricate and operate, rendering the technology widely accessible. The performance of FLUIDOT is validated and its versatility is subsequently demonstrated in two applications. First, drug tolerance in yeast cells is studied, resulting in the discovery of two treatment-tolerant populations. Second, B cells from convalescent COVID-19 patients are screened, leading to the discovery of highly affine, in vitro neutralizing monoclonal antibodies against SARS-CoV-2. Owing to its performance, flexibility, and accessibility, it is foreseen that FLUIDOT will enable phenotypic and genotypic analysis of diverse cell samples and thus elucidate unexplored biological questions.

Continuous Monitoring for Noisy Intermediate-Scale Quantum Processors

We present a continuous monitoring system for intermediate-scale quantum processors that allows extracting estimates of noisy native gate and read-out measurements based on the set of executed quantum circuits and resulting measurement outcomes. In contrast to standard approaches for calibration and benchmarking quantum processors, the executed circuits, which are input to the monitoring system, are assumed to be out of any control. We provide the results of applying our system to the synthetically generated data obtained from a quantum emulator, as well as to the experimental data collected from a publicly accessible cloud-based quantum processor. In both cases, we demonstrate that the developed approach provides valuable results about inherent noises of emulators and processors. Considering that our approach uses only already accessible data from implemented circuits without the need to run additional algorithms, the monitoring system can complement existing approaches. We expect that our monitoring system can become a useful tool for various quantum computers in the near-term horizon, including publicly accessible cloud-based platforms, and reduce resources that are required for their benchmarking and calibration.

Precision-Driven Multi-Target Path Planning and Fine Position Error Estimation on a Dual-Movement-Mode Mobile Robot Using a Three-Parameter Error Model

The multi-target path planning problem is a universal problem to mobile robots and mobile manipulators. The two movement modes of forward movement and rotation are universally implemented in integrated, commercially accessible mobile platforms used in logistics robots, construction robots, etc. Localization error in multi-target path tracking is one of the crucial measures in mobile robot applications. In this article, a precision-driven multi-target path planning is first proposed. According to the path's odometry error evaluation function, the precision-optimized path can be discovered. Then, a three-parameter odometry error model is proposed based on the dual movement mode. The error model describes localization errors in terms of the theoretical motion command values issued to the mobile robot, the forward moving distances, and the rotation angles. It appears that the three error parameters follow the normal distribution. The error model is finally validated using a mobile robot prototype. The error parameters can be identified by analyzing the actual moving trajectory of arbitrary movements. The experimental localization error is compared to the simulated localization error in order to validate the proposed error model and the precision-driven path planning method. The OptiTrack motion capture device was used to capture the prototype mobile robot's pose and position data.

Recursos Educativos Abiertos y metodologías activas para la enseñanza de STEM en Educación Primaria

This research is part of the CREATEskills European project, focused on STEM teaching in primary education. The aim of this article is to present the main results of an evaluative research, developed with a quantitative method and a single case design, in which 5 teachers, 117 students and 55 parents of students from a public school have participated. The participants answered questionnaires designed ad hoc after the implementation of an educational innovation experience in which active methodologies, Open Educational Resources and an open access platform for teaching STEM were used. After the evaluation of the experience, a very positive assessment was made by the teachers and students involved, as the vast majority highlighted the quality, originality, creativity, variety and relevance of the open resources used, as well as the learning outcomes promoted with the active methods. Most of the parents agreed that STEM teaching promotes real-life problem-solving skills and improves student motivation, according to previous researchers. Although more than half used online resources to work on STEM at home with their children, only a minority used the CREATEskills platform.

Digital Technologies Easing the Learning Curve in the Transition to Practicum

Aim/Purpose: This study aims to explore the value of utilizing non-immersive virtual reality (VR) to create virtual learning environments (VLEs) to support and prepare optometry students in their transition into preclinical and clinical teaching spaces. Background: Digital education is widely integrated into university curricula with the use of online simulators, immersive VR, and other digital technologies to support student learning. This study focuses on non-immersive VR as an accessible and low-friction means of accessing VLEs to reduce students’ learning burden. Methodology: Current optometry students were invited to explore 360° 3D panoramic virtual learning environments of preclinical and clinical teaching spaces. Students were recruited to participate in an online Qualtrics survey and individual semi-structured interviews. Quantitative data was analyzed, and thematic analysis was conducted on qualitative data from students’ responses to identify key takeaways on the accessibility and impact of VLEs on students’ learning. Contribution: Non-immersive VR has utility in alleviating student stress and helping transition students into practicum. The VLEs have the means to supplement the curriculum to provide support to students entering the preclinical and clinical teaching spaces. Findings: Students engaged voluntarily with the novel VLEs and utilized the resources to help familiarize themselves with the preclinical and clinical teaching spaces. The open-access resource supported students in their preparation for practical learning and helped to reduce self-reported stress and build confidence prior to entering practical classes. Many of the students enjoyed the experience of navigating through the spaces, which helped to appease their curiosity and reduce the learning curve associated with entering new spaces. The VLEs did not replace attending practical spaces but rather were supportive learning resources that aided students due to limited face-to-face contact hours. For students with existing familiarity with the spaces, through their in-person attendance in pre-clinical and clinical teaching sessions prior to accessing the VLEs, the digital resources were not as beneficial compared to students who were still transitioning into practicum. Recommendations for Practitioners: Introductory digital resources like non-immersive VR are accessible platforms that help to orient and familiarize students with new environments. VLEs can potentially help to relieve student stress and reduce the learning load associated with entering practicum or new learning spaces. Recommendation for Researchers: More work needs to be done on how student preparation can translate to feeling less stressed and more confident in relation to transitioning from traditional learning environments to practical learning spaces. Impact on Society: A broader application of non-immersive VR can be implemented as an introductory learning preparation tool across different disciplines to alleviate student stress and maximize the limited time in practicum to allow focus on learning outcomes and practical skills. Future Research: Future studies should consider different cohorts to study, with a focus on objective measures of engagement with VLEs. The effect of VLEs on students’ cognitive load should be assessed and assessment of self-perceived stress can be evaluated with instruments such as Cohen’s Perceived Stress Scale.

Hands-on training in industrial cybersecurity for a multidisciplinary Master's degree

As a response to the scarcity of workforce with essential competences for Industry 4.0, academic institutions are making an effort to propose specialized educational programs. In this context of digitalization, industrial cybersecurity is an increasingly important aspect. Nevertheless, industrial cybersecurity is a challenging topic that requires the understanding of both information technologies and the operation of industrial facilities. Furthermore, practical training requires realistic environments to be useful. For this reason, in this work, we present a hands-on activity on a remotely accessible training platform to complement the theoretical concepts of a Master's degree course that deals with the introduction to industrial cybersecurity. This platform presents the students a realistic automation environment with industrial hardware and software. The educational experience has been assessed with regard to the students’ perception and its technical operation. The platform was found useful for learning and motivating, although the perceived degree of difficulty needs to be adjust to promote students’ self-confidence.

Joint Trajectory Plan and Resource Allocation for UAV-Enabled C-NOMA in Air-Ground Integrated 6 G Heterogeneous Network

Leveraging unmanned aerial vehicles (UAVs) for access and high altitude platform stations (HAPSs) for data backhaul to construct the Air-Ground Integrated Network (AGIN), is a feasible solution to achieve seamless network coverage for remote IoT devices in future 6 G era. However, since the number of terminals increases exponentially, it is essential to improve spectrum efficiency and throughput of the system. In addition, the limited on-board energy storage of UAVs and finite battery of IoT terminals make the system energy efficiency (EE) a new concern. To cope with the above mentioned challenges, in this study we first put forward a clustered-NOMA (C-NOMA)-enabled heterogeneous AGIN model for remote areas including one HAPS for backhaul and multiple UAVs for access, where C-NOMA is used to obtain both improved system throughput and terminal complexity. Then, we study the joint UAV trajectory plan and resource allocation problem in order to maximize the system EE. Since it is a mixed integer nonlinear programming (MINLP) issue coupled with transmission power, subchannel allocation, UAV trajectory and speed control, this problem is decoupled into two subproblems and solved iteratively. For the first one, the optimal channel and power strategy is obtained by our subchannel allocation and power control for AGIN EE (SAPAE) maximum algorithm according to Lagrange dual decomposition method. For the second, for transforming the nonconvex problem into a convex one, we provide the successive convex approximation-based UAV trajectory and speed optimization for AGIN EE (SUTSAE) maximum algorithm, and then the near-optimal UAV trajectory and flight speed are obtained. We conduct extensive experiments to compare with other benchmark methods. It is shown that the proposed approach is better in EE and spectrum efficiency.

The Effects of the Infant Health Promotion Program for Mothers with Their Firstborn Infants

This study was intended to evaluate the effects of an Infant Health Promotion Program (IHPP) for mothers with their firstborn infants. This study employed a non-equivalent control group pretest-posttest design. The participants consisted of 17 mothers with their firstborn infants in the experimental group and 17 in the control group from two women's hospitals. The experimental group received eight sessions of the program for four weeks. The collected data were analyzed using the chi-square test and repeated-measures ANOVA using an SPSS/WIN ver. 22.0. The experimental group receiving the program had statistically significant higher levels of infant health promotion knowledge (F = 22.91, p < .001), social support (F = 27.64, p < .001), maternal role confidence (F = 8.25, p = .005) and health promotion behavior for infants (F = 16.85, p < .001) than the control group. The experimental group had a statistically significant lower level of parenting stress than the control group (F = 29.93, p < .001). The study's findings indicate that the IHPP is effective in improving health promotion knowledge, social support, and maternal role confidence and decreasing parenting stress among mothers with their firstborn infants. A method of delivering intervention, focused on readily accessible online platforms, coupled with intervention strategies grounded in the theory of self-efficacy, proves to be an advantageous approach for this particular target group.

“Guidelines for the classification and management of risk, for the evaluation of safety and for the monitoring of existing bridges”. An automatic calculation model of the seismic attention class optimized for the use of cloud computing services.

The Italian road network is complex due to the particular orography of the territories crossed, often characterized by areas of high seismicity. In addition, bridges and viaducts present a peculiar state of decay mainly due to their remote period of construction. The situational picture is also difficult to understand due to the absence or non-uniformity of infrastructural safety management systems (SMS). To fill this gap, the Italian Higher Council of Public Works has issued guidelines for risk management, that allow the classification, safety assessment and monitoring of existing bridges. The central focus of the new approach is the identification of the overall attention class, which allows to move from assessments on a territorial scale to safety assessments on a limited number of bridges. This work focuses on the development of a prototype for the automatic calculation of the seismic attention class that has been optimized for cloud computing services. In particular, a standardized procedure has been defined that guides the operator in the evaluation of the seismic attention class. The model also allows to carry out the validation of other software. Finally, the prototype created is easily extensible to other attention classes which are therefore manageable independently in the perspective of modular development of an open access and open source platform.