Related Design Parameters Research Articles

Nonmedical cannabis legalization policy in Canada: Has commercialization been a pivotal mistake for public health?

AbstractCanada implemented the legalization of nonmedical cannabis use and supply in 2018. Initial blueprints for the legalization policy framework emphasized public health protection as a priority principle and objective, including related policy design parameters and regulatory restrictions (e.g., strict access and distribution control, advertisement/promotion ban, etc.) also as informed by adverse experiences from alcohol/tobacco control. Conversely, Canada's present legalization ecology is characterized by increasingly far‐reaching commercialization; this includes an extensive for‐profit cannabis production and retail industry producing large sales volumes that centrally include high‐risk cannabis products, with many public health‐oriented provisions hollowed out or circumvented in practice. While key cannabis‐related health problem indicators have increased through legalization, mounting evidence suggests that these adverse outcome dynamics, to a crucial extent, have been accelerated by commercialization aspects of legalization. Meanwhile, since legalization the cannabis industry has pushed for further rollbacks of public health‐oriented restrictions for benefits of increased competitiveness. Using the Canadian case study, we focus on the possible pitfalls and adverse effects of commercialization dynamics for public health‐oriented cannabis legalization. Also since commercialization‐related developments and outcomes are hard to reverse, we urge jurisdictions planning cannabis legalization reforms to carefully take consider related evidence and dynamics when assembling their legalization policy frameworks.

Intelligent generation method of infection risk map and management system in hospital waiting room for respiratory infectious diseases

Respiratory infectious diseases, such as seasonal influenza, pose significant risks to public health safety through airborne transmission. This study aims to explore methods for controlling airborne infection risk in waiting rooms, a hotspot for cross-infection. A database of 990 samples was established by integrating the results of computational fluid dynamics and social force model simulations into the Wells-Riley equation. We introduced two indicators, Pr (Particle Removal Efficiency) and Hr (High-risk Area Ratio), to evaluate the cleanliness performance of waiting room spaces. Additionally, the Sci (Space Cleanliness Index) was proposed as a criterion for evaluating architectural designs and fine-tuning fresh air system operational parameters. We also unveiled the mapping relationship between spatial morphology parameters and ventilation design parameters in relation to the cleanliness performance of waiting rooms. An infection-risk prediction proxy model was established based on machine learning methods, suitable for rapid prediction of spatial infection risks in waiting rooms and optimization of fresh air system operational parameters. The findings can be applied to the selection of hospital design schemes and the optimization of ventilation operational control strategies, aiding in strengthening infectious disease control and reducing the public health safety risks of cross-infection from respiratory infections during peak seasons in hospitals.

Knit-Mycelium Hybrids: Textile Design Strategies for Biofabrication Systems Based on Mycocrete Injection in 3D Knitted Tubular Formworks

This paper investigates the design potential of monolithically grown Mycelium-based Composites (MBCs), focusing on integrating 3D knitted formworks within the fabrication process. Mycelium has gained attention as a sustainable, biodegradable construction material with insulating and self-healing capabilities. While there have been advancements in MBC, challenges remain regarding geometrical complexity and design potential at scale through in situ practices. Recent interest has emerged in digital fabrication methods and mould creation using textile logic and CNC knitting. However, fabricating mycelium within tubular soft moulds present a lack of stability and uneven growth distribution. The novel technique using injection filling of Mycocrete, a viscous mycelium paste, overcomes this structural disintegration. This method enables the creation of complex designs within a two-staged fabrication process while providing high structural performance. The paper presents three case studies demonstrating the impact of hanging, draping, and internal shape integration as fabrication steps as design tools for geometrical possibilities and diversity of expressions. It examines the relationship between knitted soft preform design and biofabrication parameters in relation to design expression, scale and mycelium growth. The paper concludes by emphasising the significance of 3D knitted formworks in scaling up MBCs in the built environment and its potential for implementing textile design parameters in MBC expressions and geometries. The fabrication steps provide newfound design opportunities, opening possibilities for aesthetical, material-saving and functionally graded design approaches. Further research in this area holds promise for advancing sustainable construction and textile design practices.

Study of a wall-climbing robot based on chain feet with negative pressure adhesion

In this paper, a wall-climbing robot based on chain feet with negative pressure adhesion is designed. The robot uses chain feet with negative pressure adhesion as the motion units and has the characteristics of adhesion and barrier crossing. Analyzing the power consumption is an important aspect of robot design. The influences of robot design parameters on mechanical characteristics are analyzed with the kinetic method. The robot power model is constructed by combining the energy model of the drive motor and the power model of the transmission system. The relationship between the key parameters and robot power consumption is established by discussing the effects of the robot load, motor angular velocity, and other related design parameters on the robot power consumption. Simulations and experiments show that the established robot power model can be used as a theoretical basis for the optimal design of robots and provides a reference for establishing an optimal model for the motion control of robots.

Parameter effect on the novel swashplateless rotor control

To achieve underactuated control, a novel swashplateless rotor configuration of asymmetric inclined hinges which can be driven by periodic sinusoidal torque is studied, hopefully eliminating the conventional swashplate mechanism and multiple actuators. Therefore, the traditional mechanism can be intelligently replaced by software. However, high-frequency torque variation will invariably result in vibration noise and energy loss.Thus it is critical to properly design the swashplateless rotor in order to achieve a control process with less torque variation. As a result, in both numerical simulation and experiments, we performed sensitivity analyses on related design parameters such as hinge eccentricity, hinge inclined angle, and wingtip hammer position. The parameter effect on swashplateless rotor control was discovered through the analyses, and the basic law of its design was obtained.

Discrete Optimization on Unsteady Pressure Fluctuation of a Centrifugal Pump Using ANN and Modified GA

Pressure fluctuation due to rotor-stator interaction in turbomachinery is unavoidable, inducing strong vibration in the equipment and shortening its lifecycle. The investigation of optimization methods for an industrial centrifugal pump was carried out to reduce the intensity of pressure fluctuation to extend the lifecycle of these devices. Considering the time-consuming transient simulation of unsteady pressure, a novel optimization strategy was proposed by discretizing design variables and genetic algorithm. Four highly related design parameters were chosen, and 40 transient sample cases were generated and simulated using an automatic program. 70% of them were used for training the surrogate model, and the others were for verifying the accuracy of the surrogate model. Furthermore, a modified discrete genetic algorithm (MDGA) was proposed to reduce the optimization cost owing to transient numerical simulation. For the benchmark test, the proposed MDGA showed a great advantage over the original genetic algorithm regarding searching speed and effectively dealt with the discrete variables by dramatically increasing the convergence rate. After optimization, the performance and stability of the inline pump were improved. The efficiency increased by more than 2.2%, and the pressure fluctuation intensity decreased by more than 20% under design condition. This research proposed an optimization method for reducing discrete transient characteristics in centrifugal pumps.

Road Accident Hotspots on Jordan’s Highway Based on Geometric Designs Using Structural Equation Modeling

One of the primary objectives of transportation engineering is to increase the safety of road infrastructure. This study seeks to determine the relationship between geometric design parameters in relation to road accident criteria based on accident hotspots on Jordan’s Desert Highway. The road accident data (from 2016 to 2019) were collected from the Jordan Traffic Department. The spatial pattern of hotspots was identified using a GIS tool named Getis-Ord Gi* based on the severity index of road accidents. A topographic survey was conducted to investigate the road alignment and intersections at hotspot locations. The study utilized the Structural Equation Modeling (SEM) technique via SmartPLS to highlight the correlation between geometric designs in relation to road accidents. The hotspot analysis (Gits-Ord Gi) discovered 80 road accident hotspots along the highway. The study found that horizontal alignment and road intersections significantly impact road accidents in hotspot locations. Furthermore, vertical alignment has no effect on road accidents in hotspot areas. The study enhanced the comprehension of the factors associated with road geometrics and intersections that affect the occurrence of road accidents.

Crash Frequency Minimization with Severity Mitigation in Road Geometric Design Using Chance Constraint Programming Optimization

Crash frequency and crash severity are two major aspects of transportation safety. In this paper, we propose a decision-making scheme combining statistical analysis and optimization modeling to be used in transportation safety study. We conduct a safety analysis, a travel speed analysis, and an optimization analysis to develop a two-stage decision scheme to minimize crash frequency while mitigating crash severity, using data collected in urban environments in Lincoln, Nebraska. In the safety analysis and the travel speed analysis, we study the impact of lane width and other related road geometric design parameters on annual crash frequency and vehicle travel speed using count models and linear regression models, respectively. In the optimization analysis, the proposed two-stage stochastic programming model determines the lane width and other road geometric design parameters in the first stage, and then the posted road speed limit in the second stage for each scenario. To mitigate crash severity, we use a chance constraint to restrict a certain percentile of the vehicle travel speed to comply with the posted road speed limit. This two-stage decision scheme is shown to be effective for the data collected in Lincoln, Nebraska, when restricting the vehicle travel speed of up to two positive standard deviations from the mean travel speed to be under the posted speed limit. The application of a stochastic programming model that utilizes regression analysis results serves as an innovative decision scheme that effectively connects statistical analysis and optimization studies in road geometric design for transportation safety. Its objective is to minimize crash frequency while simultaneously mitigating crash severity. This methodology has extensive potential for application in various environments to assist in the reduction of both crash frequency and crash severity.

Reduced-Order High-Gain Observer (ROHGO)-Based Neural Tracking Control for Random Nonlinear Systems With Output Delay

Compared with stochastic differential equations (SDEs) driven by white noise, random differential equations (RDEs) generated by colored noise are claimed to be more practical. This article considers reduced-order high-gain observer (ROHGO)-based neural tracking control on random nonlinear systems having output delay. In order to foster the design and analysis, the estimated states and the estimation errors are scaled by the high gain of the observer. Based on neural network (NN) approximation and state observation, an adaptive controller is designed for the overall system using the backstepping method. It is proved that all the closed-loop signals are bounded almost surely, letting alone the tracking error. By tuning the related design parameters, the asymptotic tracking error could be regulated arbitrarily small. Within the best of our knowledge, this article serves as the first attempt for NN-based control on RDE systems. Finally, the validity of main results is confirmed by a simulation example.

Global sensitivity analysis and optimisation of design parameters for low GHG emission lifecycle of multifamily buildings in India

In India, nearly 25% of citizens live in slums, unfit for a decent living. Replacing the existing residential building stock and the rapid development of the economy will significantly increase energy consumption and related greenhouse gases (GHG) emissions in the coming decades. Consequently, there is a high need of developing low lifecycle GHG emission, affordable and comfortable multifamily building designs, which can be widely implemented in the Indian construction market.The objective of this article is first to explore the most influential design parameters of the multifamily building located in the in: warm and humid (Bhubaneswar), hot and dry (Jodhpur), and composite (New-Delhi) climate zones of India on life cycle GHG emissions and indoor thermal comfort, and secondly, to perform a multi-objective optimisation considering the life cycle GHG emissions, life cycle cost and initial material investment cost based on the set of the most sensitive design parameters. The study combines a two-step global sensitivity analysis based on the Morris and Fast method with a multi-objective genetic algorithm integrated into one framework based on the parametric multifamily building model with extensive building performance simulations.The global sensitivity analysis results indicated that for all investigated locations: the apartment’s floor area, equipment load, windows-to floor ratio, mechanical ventilation airflow, and cooling temperature setpoint were the most influential design parameters in relation to the lifecycle GHG emissions. Finally, based on the multi-objective optimization, significant reductions in the range of 62–75% in terms of life cycle GHG emissions and 40–54% in terms of life cycle cost were achieved compared to the baseline 7-storey multifamily design scenario based on the minimum requirements of the Indian energy conservation code. At the same time, the initial material investment cost was 25–34% higher. The optimal set of the design strategies, resulting in the lowest life cycle GHG emissions and life cycle cost was found to be minimisation of the apartment’s floor area and windows to floor ratio, maximisation of the on-site renewable energy use, and design of a mechanical ventilation system combined with ceiling fans thus enabling the energy-efficient and thermally comfortable operation of the multifamily building with a high cooling temperature setpoint.

A Wideband Low-RCS Metasurface-Inspired Circularly Polarized Slot Array Based on AI-Driven Antenna Design Optimization Algorithm

A metasurface (MS)-inspired low-profile circularly polarized (CP) slot array with a wide CP band and broadband low radar cross section (RCS) is proposed in this communication. The slot array consists of four element antennas, four grounded substrates, and a sequential-rotated feeding network. In terms of radiation performance, the array yields a wide CP band resulting from the CP element antenna and the sequential-rotated feeding network. The CP element antenna is achieved due to the polarization conversion property of the MS-based superstrate. The feeding network with multiple related design parameters is optimized by an artificial intelligence (AI)-driven antenna design method to find the widest bandwidth. In terms of scattering performance, broadband RCS reduction is achieved by using a hybrid RCS reduction technique that combines two destructive interference principles. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\vert \text{S}_{11}\vert &lt; -10$ </tex-math></inline-formula> dB bandwidth reaches 53.2%, the AR < 3 dB bandwidth reaches 50%, and the RCS reduction bandwidth reaches 147.8% for a low-profile structure with a relatively low number of MS unit cells. A prototype was fabricated and measured. The measured and simulated results are in good agreement.

Design of Desanding Facilities for Hydropower Schemes Based on Trapping Efficiency

Turbine wear caused by hydro-abrasion due to suspended sediment in the turbine water has a negative impact on the power production and revenue of hydropower schemes. Efficiency of desanding facilities that reduce suspended sediment load ahead of turbines is key to limiting hydro-abrasion. Existing facilities built according to common design approaches often show lack of performance, in particular under non-ideal site-specific inlet and outlet conditions. Consequently, a new design concept that allows for the optimized design of desanding facilities at hydropower schemes has been developed based on prototype field measurements and CFD modeling. The effects of facility layout and components, such as tranquilizing racks, transition zone and outflow weir and related design parameters on the flow field and involved particle settling have been investigated in a comprehensive numerical model study. Hence, length adjustment terms were deduced from resulting changes in trapping efficiency, which allow for the estimation of the required basin length and an optimized facility layout. Compared to design approaches considering linear settling trajectories of sediment particles, the proposed procedure leads to longer settling basins in general and thus to increased trapping efficiencies of desanding facilities. Furthermore, the findings about the effect of specific facility components may allow for the optimisation of existing desanding facilities in an economical way.

Vibration mitigation of an integrated structure consisting of a monopile offshore wind turbine and aquaculture cage under earthquake, wind, and wave loads

To realize multi-purpose exploration of marine resources, a novel design consisting of a monopile offshore wind turbine and steel aquaculture cage in an integrated structure is proposed in this paper; the structure is denoted as MOWTAC. Based on the theories of the screen model, a hydrodynamic analysis module is developed in the OWT simulation tool FASTv8. Then, in combination with a seismic analysis module, the fully coupled analysis model of the MOWTAC is established in the updated FAST v8. Based on the coupled model, the dynamic characteristics and coupling mechanisms of the MOWTAC under earthquake, wind, and wave loads are investigated. A tuned mass damper (TMD) is applied to the MOWTAC under earthquakes, and the influence of the TMD parameters on the mitigation effects is studied. Furthermore, a multiple tuned mass damper (MTMD) with various tuning frequencies is applied, and comparisons with the TMD are carried out. Based on the reduction of seismic responses using the TMD and MTMD, vibration control methods and related design parameters are recommended for the MOWTAC under earthquake, wind, and wave loads.

Review on Local Buckling of Hollow Box FRP Profiles in Civil Structural Applications.

Hollow box pultruded fibre-reinforced polymers (PFRP) profiles are increasingly used as structural elements in many structural applications due to their cost-effective manufacturing process, excellent mechanical properties-to-weight ratios, and superior corrosion resistance. Despite the extensive usage of PFRP profiles, there is still a lack of knowledge in the design for manufacturing against local buckling on the structural level. In this review, the local buckling of open-section (I, C, Z, L, T shapes) and closed-section (box) FRP structural shapes was systematically compared. The local buckling is influenced by the unique stresses distribution of each section of the profile shapes. This article reviews the related design parameters to identify the research gaps in order to expand the current design standards and manuals of hollow box PFRP profiles and to broaden their applications in civil structures. Unlike open-section profiles, it was found that local buckling can be avoided for box profiles if the geometric parameters are optimised. The identified research gaps include the effect of the corner (flange-web junction) radius on the local buckling of hollow box PFRP profiles and the interactions between the layup properties, the flange-web slenderness, and the corner geometry (inner and outer corner radii). More research is still needed to address the critical design parameters of layup and geometry controlling the local buckling of pulwound box FRP profiles and quantify their relative contribution and interactions. Considering these interactions can facilitate economic structural designs and guidelines for these profiles, eliminate any conservative assumptions, and update the current design charts and standards.

Numerical study on the influence of waveform channel and related design parameters on the cold start of proton exchange membrane fuel cell

In order to enable the proton exchange membrane fuel cell to be used under outdoor conditions, research on the cold start of the fuel cell is essential. In this paper, numerical simulation is used to study the influence of fuel cell cathode channel geometry and porosity on the fuel cell cold start process. By analyzing the internal parameters of the fuel cell at different times, including current density, ice volume fraction, temperature and water content, the optimal fuel cell design was obtained. From a comprehensive comparison, although the current density and temperature rise of the ordinary wave channel fuel cell with higher porosity increase slowly at the beginning of the cold start, the decline process and icing process of it are also relatively slow. Therefore, a fuel cell with an ordinary wave channel and a higher porosity may perform better at cold start. This research contributes to the application and commercialization of fuel cells in a wider range of practical conditions.

Application of heuristic algorithms for design optimization of industrial heat pump

The design parameters of heat pumps are related to each other nonlinearly or in a complicated manner; therefore, it is difficult to determine the optimal combination of design parameters, such as superheat, subcooling, and refrigerant type, analytically. To address this limitation, three representative heuristic algorithms, namely the genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA), are applied to optimize a heat pump under the given process conditions. Heuristic algorithms are driven based on randomness; thus, the consistency of the calculation results and computational time represent the decision criteria for the appropriate optimizer. The GA is unsuitable as a heat pump optimizer because it requires an excessive number of iterations. In contrast, PSO and SA have a similar capability of consistency and calculation time with a rational number of iterations. In conclusion, PSO exhibits a slightly better consistency and use of computational resources; therefore, PSO is selected as the heat pump design optimization algorithm in this study. The novelty of this work lies in that the related design parameters of the heat pump are simultaneously globally optimized with minimal physical background, and the heuristic algorithm that is most applicable to heat pump design optimization is determined.

A Study of Commonly Used Design Solutions for Green Roofs in Dhaka City and their Comparative Analysis

Although architects, landscape designers and building engineers have been designing and building green roofs in Dhaka, Bangladesh, there is a dearth of evidence based research regarding the best practices of setting design parameters, available solution types and performance. Based on the descriptive survey method, this study identifies three basic types of green roofs. The common construction and use related design parameters used by the local practitioners were identified. The three types of green roofs are compared on the basis of their performance against these design parameters. It was concluded that further research, proper policies and research based development in the practice is necessary to materialize the true potential of green roofs and roof top gardening in Dhaka which will address the UN sustainable development goals.

Effect of pipe flattening on pressure drop in vapor core and thermal characteristic of miniature round and flat-shape heat pipe with sintered fiber wick

The effect of pipe flattening on pressure drop in vapor core and also heat transfer characteristic of the sintered fiber heat pipes has been investigated. The original diameter of heat pipes were 2 mm and 3 mm, respectively, with the effective length of 100 mm. Thermal performance of the flat-shaped heat pipe with the sintered fiber bundle wick was experimentally investigated under a horizontal orientation in this study. The proper fiber bundle wicks were fabricated at porosity of 0.54, and firmly filled within internal heat pipe wall by the sintering process. The heat pipes were pressed into two final thickness by the flattened process which results in vapor core minimizing. The numerical method was established in modified forms to represent the related wick-heat pipe design parameters, for example, wick thickness, wick porosity, wall thickness, flattened width and height, corresponding to thermal performance of fiber wick heat pipes. The numerical analysis for pressure drop was only focused on vapor core region. The vapor flow was assumed to be laminar and incompressible. It is found that when the heat pipe was flattened, the pressure drop increased. The flattening of heat pipe caused the smaller space of vapor core, therefore the working fluid in vapor phase could not easily transfer heat. In this case, the critical final thickness of 0.45 mm (for original diameter of 2 mm) which the vapor core area nearly approaches to zero, has an effect on the high value of thermal resistance considered in vapor core (z5). In addition, the overall thermal resistance estimated from ESDU79012 agrees well with that obtained from the experiment with the standard deviation (STD) of ±15 %, and it shows that the thermal resistance model can provides a reasonable prediction for the thermal resistance of flat-shaped heat pipes with sintered fiber wicks. Moreover, the new normalized parameter h′v/hv is proposed to represent the appearance of vapor core after pipe flattening which can be used to suggest a desirable normal region of flattened heat pipe with the sintered fiber wick. The critical remaining vapor core height after pipe flattening has been further analyzed to be 0.06 (about 74% from original diameter).

Improving Gerotor Pump Performance Trough Design, Modeling and Simulation

Gerotor pumps are well known by a compact design, simple structure and low noise level, which makes them suitable for use in the automotive industry, and especially in hydraulic systems for engine lubrication. One of the main disadvantages of gerotor pumps is the inability to adjust to wear, which significantly reduces the pump efficiency. In order to mitigate the negative effect of the inevitable wear process, this paper presents a methodology for determining the optimal combination of trochoid gears design parameters for a defined aspect. An appropriate mathematical model has been developed to analyze the effect of changes in gear design parameters in relation to maximum contact stresses, pressure changes in gerotor pump chambers and wear rate proportional factor (WRPF). Verification of the developed models was performed by realizing physical pairs of gears and laboratory experiments with simulation of pump operating conditions. The results and conclusions presented in this paper, with an emphasis on the actual work processes, bring very important perspectives for the gerotor pumps design with improved performance.

Design and Analysis of a Novel Finite-Time Convergent and Noise-Tolerant Recurrent Neural Network for Time-Variant Matrix Inversion

Matrix inversion ubiquitously arises in engineering. The so-called zeroing neural network (ZNN) is an effective recurrent neural network for solving time-variant matrix inversion. Without considering noises, the ZNN approach requires finite time or infinitely long time to converge to the exact solution. When perturbed by additive noises, the existing ZNN models exhibit limited ability to reject disturbances and are susceptible to be divergent. For instance, under time-variant bounded noises, steady-state residual errors of the existing ZNNs would be bounded. To make the steady-state residual errors arbitrarily small, infinitely long time is required and related design parameters must be set large enough or infinitely large, which is not realistic in practice. To overcome this situation, this paper for the first time systematically designs and analyses a finite-time convergent and noise-tolerant ZNN (FTNTZNN) that is capable of completely converging to the theoretical solution in finite time even under various types of noises. Theoretically, the finite-time convergence and disturbance-rejection properties of the FTNTZNN are rigorously proved. Comparative numerical results substantiate that the FTNTZNN model delivers superior convergence and robustness performance in solving time-variant matrix inversion and kinematic control of a robotic arm as compared with the existing ZNN models. The FTNTZNN model expands the current knowledge for designing neural-dynamic systems to solve matrix inversion, which can provide inspiration for other problems solving under noises.