Prototyping Environment Research Articles

Agro-Processing Value Chains Mapping and Technology Needs Assessment for Nyeri County, Kenya

A sectoral value chain mapping of the County Government of Nyeri, Kenya was initiated and implemented with regard to agro-processing of various forms of plant resources: food crops, textile/natural products, honey/animal feeds/ bio-energy/ICT, environment & biotechnology, essential oils, engineering services and Blue Economy. In this case, the following value-chain processes were assessed: Tea, Coffee; Oil crops; Banana & Dairy products with the support of service providers and prototypic environments. It also looked into value-addition practices and other cross-cutting factors. The cross-sectional study uncovered distinctions and issues including variability in the power supply, absence of Standard Operating Procedures (SOPs), minimal value addition, existence of counterfeit inputs, and unsuitable equipment. Recommendations include provision of standby power supply, provision of standard operating procedures, potential innovation in value addition, procurement of adequate equipment, and formulation of measures concerning genuine farm inputs. Therefore, by providing an enabling environment and putting into practice the strategies articulated in the study, sustainable agricultural-based growth and hence economic development in Nyeri County will be inevitably achieved under the Kenya Vision 2030.

SAAM II: A general mathematical modeling rapid prototyping environment.

Simulation Analysis and Modeling II (SAAM II) is a graphical modeling software used inlife sciences for compartmental model analysis, particularly, but not exclusively, appreciated in pharmacokinetics (PK) and pharmacodynamics (PD), metabolism, and tracer modeling. Its intuitive "circles and arrows" visuals allow users to easily build, solve, and fit compartmental models without the need for coding. It is suitable for rapid prototyping of models for complex kinetic analysis or PK/PD problems, and in educating students and non-modelers. Although it is straightforward in design, SAAM II incorporates sophisticated algorithms programmed in C to address ordinary differential equations, deal with complex systems via forcing functions, conduct multivariable regression featuring the Bayesian maximum a posteriori, perform identifiability and sensitivity analyses, and offer reporting functionalities, all within a single package. After 26 years from the last SAAM II tutorial paper, we demonstrate here SAAM II's updated applicability to current life sciences challenges. We review its features and present four contemporary case studies, including examples in target-mediated PK/PD, CAR-T-cell therapy, viral dynamics, and transmission models in epidemiology. Through such examples, we demonstrate that SAAM II provides a suitable interface for rapid model selection and prototyping. By enabling the fast creation of detailed mathematical models, SAAM II addresses a unique requirement within the mathematical modeling community.

Numerical study on local scour characteristics around submarine pipelines in the Yellow River Delta silty sandy soil under waves and currents

AbstractDue to their high reliability and cost‐efficiency, submarine pipelines are widely used in offshore oil and gas resource engineering. Due to the interaction of waves, currents, seabed, and pipeline structures, the soil around submarine pipelines is prone to local scour, severely affecting their operational safety. With the Yellow River Delta as the research area and based on the renormalized group (RNG) k‐ε turbulence model and Stokes fifth‐order wave theory, this study solves the Navier–Stokes (N–S) equation using the finite difference method. The volume of fluid (VOF) method is used to describe the fluid‐free surface, and a three‐dimensional numerical model of currents and waves–submarine pipeline–silty sandy seabed is established. The rationality of the numerical model is verified using a self‐built waveflow flume. On this basis, in this study, the local scour development and characteristics of submarine pipelines in the Yellow River Delta silty sandy seabed in the prototype environment are explored and the influence of the presence of pipelines on hydrodynamic features such as surrounding flow field, shear stress, and turbulence intensity is analyzed. The results indicate that (1) local scour around submarine pipelines can be divided into three stages: rapid scour, slow scour, and stable scour. The maximum scour depth occurs directly below the pipeline, and the shape of the scour pits is asymmetric. (2) As the water depth decreases and the pipeline suspension height increases, the scour becomes more intense. (3) When currents go through a pipeline, a clear stagnation point is formed in front of the pipeline, and the flow velocity is positively correlated with the depth of scour. This study can provide a valuable reference for the protection of submarine pipelines in this area.

Benchmarking signal quality and spatiotemporal distribution of interictal spikes in prolonged human iEEG recordings using CorTec wireless brain interchange

Neuromodulation through implantable pulse generators (IPGs) represents an important treatment approach for neurological disorders. While the field has observed the success of state-of-the-art interventions, such as deep brain stimulation (DBS) or responsive neurostimulation (RNS), implantable systems face various technical challenges, including the restriction of recording from a limited number of brain sites, power management, and limited external access to the assessed neural data in a continuous fashion. To the best of our knowledge, for the first time in this study, we investigated the feasibility of recording human intracranial EEG (iEEG) using a benchtop version of the Brain Interchange (BIC) unit of CorTec, which is a portable, wireless, and externally powered implant with sensing and stimulation capabilities. We developed a MATLAB/SIMULINK-based rapid prototyping environment and a graphical user interface (GUI) to acquire and visualize the iEEG captured from all 32 channels of the BIC unit. We recorded prolonged iEEG (~ 24 h) from three human subjects with externalized depth leads using the BIC and commercially available clinical amplifiers simultaneously in the epilepsy monitoring unit (EMU). The iEEG signal quality of both streams was compared, and the results demonstrated a comparable power spectral density (PSD) in all the systems in the low-frequency band (< 80 Hz). However, notable differences were primarily observed above 100 Hz, where the clinical amplifiers were associated with lower noise floor (BIC-17 dB vs. clinical amplifiers < − 25 dB). We employed an established spike detector to assess and compare the spike rates in each iEEG stream. We observed over 90% conformity between the spikes rates and their spatial distribution captured with BIC and clinical systems. Additionally, we quantified the packet loss characteristic in the iEEG signal during the wireless data transfer and conducted a series of simulations to compare the performance of different interpolation methods for recovering the missing packets in signals at different frequency bands. We noted that simple linear interpolation has the potential to recover the signal and reduce the noise floor with modest packet loss levels reaching up to 10%. Overall, our results indicate that while tethered clinical amplifiers exhibited noticeably better noise floor above 80 Hz, epileptic spikes can still be detected successfully in the iEEG recorded with the externally powered wireless BIC unit opening the road for future closed-loop neuromodulation applications with continuous access to brain activity.

Phase separation of multicomponent peptide mixtures into dehydrated clusters with hydrophilic cores

Phase separation of biomolecules underlies the formation and regulation of various membraneless condensates in cells. How condensates function reliably while surrounded by heterogeneous and dynamic mixtures of biomolecular components with specific and nonspecific interactions is yet to be understood. Studying multicomponent biomolecular mixtures with designer peptides has recently become an attractive avenue for learning about physicochemical principles governing cellular condensates. In this work, we employed long-timescale atomistic simulations of multicomponent tripeptide mixtures with all residue substitutions to illuminate the nature of direct and water-mediated interactions in a prototypical cellular condensate environment. We find that peptide mixtures form clusters with inverse hydrophobic order. Most multivalent and charged residues are localized in the cluster’s core, with a large fraction of nonaromatic hydrophobic residues remaining on the surface. This inverse hydrophobic order in peptide clusters is partly driven by the expulsion of nonspecifically bound water molecules following peptide cluster growth. The growth of clusters is also accompanied by the formation of increasing numbers of specific water-mediated interactions between polar and charged residues. While the present study focused on the condensation of short peptide motifs, the general findings and analysis techniques should be helpful for future studies on larger peptides and protein condensation.

Object Capability Model for Tee: A Cheri Based Compartmentalization Approach

In this paper, we introduce a capability-driven approach to bolster security and granularity within Trusted Execution Environments (TEEs) [1]. By delivering precise privilege control and fine-grained compartmentalization, we aim to improve TEE security standards. To address vulnerabilities within Trusted Execution Environments (TEEs) and enable selective privilege management and secure object sharing between secure and normal worlds, we introduce a TEE compartmentalization framework based on the CHERI object-capability model. Leveraging DSbD technologies, our framework provides an efficient prototyping environment for developing trusted applications while safeguarding against existing threats. At Verifoxx Ltd, our architecture relies on TEEs to handle sensitive data, encompassing tasks such as extracting client secrets, managing commitments, sharding and executing cryptographic operations for zero-knowledge responses. The proposed approach holds promise where TEEs can enhance transaction security and enterprises seeking data protection. Our approach introduces in-enclave compartments with controlled communication, facilitating domain transitions through sealed data capability delegations and hardware-assisted call/return mechanisms. This enables application layer compartmentalization by modularly separating concerns within the secure world, emphasising single responsibility, least privileges, and information hiding from unprivileged compartments. Furthermore, we ensure the integrity of lower-layer hardware and OS properties, effectively thwarting compromise attempts.

A Virtual Prototyping Platform for Exploration of Vehicular Electronics

A critical requirement for robust, optimized, and secure design of vehicular systems is the ability to do system-level exploration, i.e., comprehend the interactions involved among ECUs, sensors, and communication interfaces in realizing system-level use cases and the impact of various design choices on these interactions. This must be done early in the system design to enable the designer to make optimal design choices without requiring a cost-prohibitive design overhaul. In this paper, we develop a virtual prototyping environment for the modeling and simulation of vehicular systems. Our solution, is modular and configurable, allowing the user to conveniently introduce new system-level use cases. Unlike other related simulation environments, our platform emphasizes coordination and communication among various vehicular components and just the abstraction of the necessary computation of each electronic control unit. We discuss the ability of to explore the interactions between a number of realistic use cases in the automotive domain. We demonstrate the utility of the platform, in particular, to create real-time in-vehicle communication optimizers for various optimization targets. We also show how to use such a prototyping environment to explore vehicular security compromises. Furthermore, we showcase the experimental integration and validation of the platform with a hardware setup in a real-time scenario.

Estimation and interpretation of equilibrium scour depth around circular bridge piers by using optimized XGBoost and SHAP

Most bridge failures result from scouring around bridge piers, resulting in economic losses and risks to public safety. The conventional equations for predicting the depth of scour at bridge piers have several limitations: (1) They mainly use regression-based techniques that cannot robustly capture the nonlinear relationship between the scour depth and its effective variables; (2) they are applicable only to a narrow range of variability of data; and (3) they are typically calibrated using laboratory data rather than field measurements and thus cannot simulate the prototype environment. To overcome these limitations, in this study, three novel hybrid machine learning methods: particle swarm optimization - extreme gradient boosting (PSO – XGBoost), red fox optimization - XGBoost (RFO – XGBoost), and relativistic particle swarm optimization - XGBoost (RPSO – XGBoost) are applied to estimate the scour depth around circular bridge piers, and their effectiveness is validated using three statistical metrics, i.e. the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). RPSO – XGBoost generates the best results for both dimensional and dimensionless data. Moreover, the proposed approaches outperform the state-of-the-art techniques. The SHapley Additive exPlanations (SHAP) method is used to assess the relative significance of the contributing factors for predicting the scour depth.

Innovation hub a venture for students’ entrepreneurial talents: a case of college of business education

Worldwide, unemployment is a challenging aspect that faces most of the graduate students. Based on that, students in colleges and universities currently, are clearly aware that they have to engage in self-employment through entrepreneurship. Moreover, the establishment of innovation hubs during collaging has propounded to help upbringings different entrepreneurial and creativity treats among students. Apart from performing incubation of the business ideas, the innovation hubs could be a free innovative centre where students, stakeholders, lecturers, and alumnae meet, work, and collaborate on the range of innovative augmented ideas to stimulate entrepreneur intention. Colleges and universities do not have a proper plan to expose students to practical business environments while studying. Therefore, through mixed research design, this study entails to design and develop a participatory contextual model that will enhance the College of Business Education to establish a free space as the innovation hub for incubating talents, entrepreneurial ideas and research focused on solution-based for students and lecturers. A total of 132 respondents consisting of 20 instructors; 100 students and 12 entrepreneurial practitioners were involved in this study. Data was collected through questionnaires, in-depth interviews, Focus Group Discussions (FGD) and documentary review. The obtained data were analysed through descriptive statistics and thematic content analysis. Based on the findings, it is evident that establishing an incubation and innovation hub within the CBE community is necessary. The findings provide valuable insights into the participants' perceptions and highlight various aspects that should be considered during establishing and implementing the hub. Moreover, the findings stipulate that the incubation and innovation hub should incorporate the prototype environment that includes mindset change programmes, regular training, internship programmes, effective incubation practices, strong mentorship and coaching programmes, and a clear exit strategy that can enhance the entrepreneurial skills and capabilities of incubates

CROSS: A framework for cyber risk optimisation in smart homes

This work introduces a decision support framework, called Cyber Risk Optimiser for Smart homeS (CROSS), which advises both smart home users and smart home service providers on how to select an optimal portfolio of cyber security controls to counteract cyber attacks in a smart home including traditional cyber attacks and adversarial machine learning attacks. CROSS is based on a multi-objective bi-level two-stage optimisation. In stage-one optimisation, the problem is modelled as a multi-leader-follower game that considers both security and economic objectives, where the provider selects a security portfolio to protect both itself and its users, while rational attackers target the weakest path. Stage-two optimisation is a Stackelberg security game that focuses on additional user security controls under the remit of smart home users. While CROSS can potentially be applied to other similar use cases, in this paper, our aim is to address threats against artificial intelligence (AI) applications as the use of AI in smart Internet of Things (IoT) devices introduces new cyber threats to home environments. Specifically, we have implemented and assessed CROSS in a smart heating use case in a prototypical AI-enabled IoT environment that combines characteristics and vulnerabilities currently present on existing commercial off-the-shelf (COTS) devices, demonstrating the selection of optimal decisions.

Using Gesture Recognition for AGV Control: Preliminary Research.

In this paper, we present our investigation of the 2D Hand Gesture Recognition (HGR) which may be suitable for the control of the Automated Guided Vehicle (AGV). In real conditions, we deal with, among others, a complex background, changing lighting conditions, and different distances of the operator from the AGV. For this reason, in the article, we describe the database of 2D images created during the research. We tested classic algorithms and modified them by us ResNet50 and MobileNetV2 which were retrained partially using the transfer learning approach, as well as proposed a simple and effective Convolutional Neural Network (CNN). As part of our work, we used a closed engineering environment for rapid prototyping of vision algorithms, i.e., Adaptive Vision Studio (AVS), currently Zebra Aurora Vision, as well as an open Python programming environment. In addition, we shortly discuss the results of preliminary work on 3D HGR, which seems to be very promising for future work. The results show that, in our case, from the point of view of implementing the gesture recognition methods in AGVs, better results may be expected for RGB images than grayscale ones. Also using 3D imaging and a depth map may give better results.

Demonstrating industrial smart product-service systems: Industry 4.0 maturity through stakeholder-oriented development and testing

Servitization and the increasing use of digital technologies have led to the emergence of smart product-service systems (sPSS). Although sPSS offer wide-ranging potentials for manufacturing firms, the heterogeneity of technological stacks and their cross-disciplinary nature pose challenges concerning their development and the articulation of value propositions. This paper proposes a stakeholder-oriented, prototypical environment for developing and testing sPSS scenarios using a modular demonstrator. The demonstrator offers a concrete representation of manufacturing processes and allows different stakeholder groups to communicate their requirements and expectations. Three scenarios with increasing technological complexity are implemented to present the value-creating potential of industrial sPSS.

Autonomous Dozer Sand Grading Under Localization Uncertainties

Surface grading, the process of leveling an uneven area containing pre-dumped sand piles, is an important task in the construction site pipeline. This labour-intensive process is often carried out by a dozer, a key machinery tool at any construction site. Current attempts to automate surface grading assume perfect localization. However, in real-world scenarios, this assumption fails, as agents are presented with imperfect perception, which leads to degraded performance. In this work, we address the problem of autonomous grading under uncertainties. First, we implement a simulation and a scaled real-world prototype environment to enable rapid policy exploration and evaluation in this setting. Second, we formalize the problem as a partially observable markov decision process and train an agent capable of handling such uncertainties. We show, through rigorous experiments, that an agent trained under perfect localization will suffer degraded performance when presented with localization uncertainties. However, an agent trained using our method will develop a more robust policy for addressing such errors and, consequently, exhibit a better grading performance.

Control algorithm for coordinated operation of wind-solar microgrid standalone generation system

ABSTRACT In this paper, a wind-driven self-excited induction generator (SEIG) combining solar photovoltaic (SPV) and battery storage has been successfully demonstrated. A second-order sequence filter-based frequency locked loop (SOSF-FLL) control is implemented for operating the voltage source converter (VSC). The SOSF-FLL method is designed by replacing two second-order low pass filter (LPF) pure integrators with two reduced order generalized integrators (ROGIs). The SOSF-FLL has a nonlinear control feedback system. The major contributions in this study include: 1. Implementation of SOSF-FLL control to generate reference source currents for VSC and evaluate the amplitude of load current component under intermittent condition of renewable energy sources (RESs) and nonlinear load, 2) The control algorithm with VSC performs numerous functions such as voltage/frequency regulation, load leveling, harmonic reduction, active/reactive power compensation and reduces the overall power quality problems, 3) Through DC-link voltage management, a bidirectional converter (BDC) with battery storage optimizes the best power flow to provide power balancing between the sources and the unbalanced load, 4) Under diverse operating situations, an incremental conductance (INC)-based maximum power point tracking approach with BDC control provides quick system convergence and support frequency. Moreover, a comparison is carried out between SOSF-FLL control and conventional control algorithms. The settling time of the proposed control is very less than one cycle (20 ms) within 2% described limit, whereas the conventional control algorithms show oscillations of more than 5% during the load dynamics. Despite the load current THD 24.41%, the proposed control is able to obtain supply current THD at 4.23%, which is less than 5% as per IEEE-519 standard. The proposed microgrid (MG) is constructed in MATLAB/Simulink and experimental prototype environment, and its dynamic response is investigated under dynamic conditions of RESs and nonlinear load conditions.

PER4Mance Prototyping environment for research on human-machine interactions for alarm floods management: the case study of a chemical plant process control

Alarm floods are dangerous because the quantity of alarms triggered is too numerous for operators to reliably implement the right corrective action. Process operators of complex systems, such as chemical plants or nuclear power production, are faced with alarm management systems that can be better built in consideration of human capabilities and limitations. Developing human-machine interfaces (HMIs) that better support operators is critical for ensuring the safe and reliable operation of critical systems and processes. The research team has developed an accessible and adaptable prototyping environment dedicated for research on alarm management and human-machine interactions in the process industry. The method used was to build on the Tennessee Eastman Process (TEP) simulator and incorporate Human-Machine design guidelines. The results are an open-sourced prototyping environment that incorporates data from a real chemical plant and integrates true alarm data and thresholds. At the end of this article, we share the Github link to the entire MATLAB, Simulink and App Designer files of PER4Mance: a prototyping environment for research on human-machine interactions for alarm flood management.

Spatial ability of transitioning 2D to 3D designs in virtual environment: understanding spatial ability in apparel design education

As the apparel industry has been transforming through digital-driven technologies in the apparel design process, the need for students to learn spatial visualization skills specified within a three-dimensional virtual environment is critical. By applying an apparel spatial visualization test into a virtual environment (ASVT-V), the purpose of this study is to examine the three-dimensional visualization skills in a virtual prototype environment of students who are majoring in apparel design and product development at the college level. The dataset was collected from 70 college students and analyzed by adopting correlation and variance analysis (ANOVA). Results from an online survey indicate that their ASVT-V scores were positively related to their general spatial visualization skills measured by a paper folding test. The results also indicate that students who received higher scores on the ASVT-V performed better in apparel design-related courses, while there was no influence on their perceived performance from the number of apparel design-related courses taken. This study provides practical implications for professionals in apparel design and product development education in that they can consider ASVT-V scores to understand and enhance domain-specific spatial visualization skills.

Preconceptual Design of Multifunctional Gas-Cooled Cartridge Loop for the Versatile Test Reactor: Instrumentation and Measurement—Part II

An integrated effort by the Versatile Test Reactor (VTR) Gas-Cooled Fast Reactor (GFR) Team to develop an experiment vehicle or extended-length test assembly for the VTR experiments is led by Idaho National Laboratory and supported by an industrial partner, General Atomics, and university partners, including Texas A&M University, University of Michigan, Oregon State University, University of Houston, and University of Idaho. The focus of the effort is to design a helium gas-cooled cartridge loop (GCL) to assist with the testing of fuels, materials, and instrumentation to further support development of advanced reactor systems. This study is divided into two parts. Part I provides the functional requirements and critical irradiation data needs for advancing gas-cooled fast reactor (GFR) technologies. The objective of Part I is to describe the overall preliminary conceptual design of the VTR helium cartridge loop, the design of a fission product venting system, thermal-hydraulic effects of flow direction, and gamma-heating generation in the cartridge. This paper, Part II, includes the measurement techniques being developed to measure the thermophysical properties of the different materials that make up the GCL, as well as the instrumentation and control system within the cartridge required for advancing GFR technologies. The purpose of Part II is to describe the functionality and efficacy of the measuring systems being developed to support the GCL. These systems include (a) a unique measurement platform that joins ion irradiation and a laser beam with an infrared camera and X-ray detection equipment developed and used to investigate more accurately and efficiently the influence of radiation and fission gases on the material properties under high temperatures; (b) a laser-induced breakdown spectroscopy to demonstrate its capability of monitoring possible fuel failure by detecting sub–part-per-million levels of xenon in the helium coolant stream, providing experimental data to better understand the interactions of fuel elements and coolant at high temperature, pressure, and fast neutron flux; (c) fiber-optic sensors with the ability to measure both the temperature demonstrated using a three-dimensional printed heat exchanger and, potentially, the strain in harsh environments; and (d) surface emissivity measurement test rigs to understand the effect of temperature, radiation, and surface finish on the silicon carbide cladding surface emissivity. Additional analyses and development, as well as integrated out-of-pile testing, are planned to demonstrate and validate the accuracy of the measuring systems and instrumentation in a more prototypic environment prior to their implementation into the VTR.

Exploring object and multi-target instrument tracking for AR-guided interventions

Abstract The rapid development of available hard- and software for computer-assisted or augmented reality (AR) guided interventions creates a need for fast and inexpensive prototyping environments. However, intraoperative tracking systems in particular represent a high cost threshold. Therefore, this work presents a low-cost tracking method based on a conventional RGB camera. Here, a combined approach of multiple image targets and 3D object target recognition is implemented. The system is evaluated with a systematic accuracy assessment analyzing a total of 385 3D positions. On average, a deviation of 15,69+-9,95 mm was measured. In addition, a prototypical AR-based needle navigation visualization was developed using Microsoft HoloLens 2. This system’s feasibility and usability was evaluated positively in a pilot study (n=3).

PREVALENCE AND RISK FACTORS FOR ASTHMA IN SCHOOLS. IS THERE A DIFFERENCE BETWEEN RURAL AND URBAN AREAS?

The effect of urban life is a global problem in the face of population growth, industrialization, and pollution; Urban locations are believed to have the prototype environment for asthma predisposition.

Adaptive robust control of moving-target tracking for marching tank based on constraint following

This paper is devoted to the constraint-following scheme for the moving-target tracking control problem of tank on move. The mission of tank on the battlefield is to find and shoot the armored vehicle, both conditions are required to accomplish this task: complete the process from finding a moving target (time-varying constraints) to pointing to it; keep the barrel stable under highly nonlinear disturbance (which is caused by the battlefield environment). Considering modeling uncertainty and initial condition deviation, an adaptive robust strategy based on Udwadia-Kalaba scheme is presented to solve the matters of target tracking and stable following. Considering the limitation of the analytical model, a tracking system model and a target movement model are built in virtual prototyping environment, complicated road condition, and real target motion state are restored by this method. The model-based control system and the three-dimensional model are combined to verify the feasibility of the control algorithm by the method of RecurDyn/Matlab. By this way, the barrel responds and follows the movement of the target stably within [Formula: see text] s under the action of the stabilization system, and the constraints are approximately satisfied under complex perturbations.