Design Of Systems Research Articles

Realtime Optimization and Management System (ROAM): A Decision Support System for Digital Agriculture Systems

The growing disparity between food supply and demand requires innovative Digital Agriculture (DA) systems to increase farm sustainability and profitability. However, current systems suffer from problems of complexity stemming from the challenge of integrating diverse, often non-interoperable hardware and software components. In order to tackle these complexities to increase farm efficiency and understand the tradeoffs of these new DA innovations we developed Realtime Optimization and Management System (ROAM), which is a decision-support system developed to find a Pareto optimal architectural design to build DA systems. To find the Pareto optimal solution, we employed the Rhodium Multi-Objective Evolutionary Algorithm (MOEA), which systematically evaluates the trade-offs in DA system designs. Based on data from five live deployments at Cornell University, each DA design can be analyzed based on user defined objectives and evaluated under uncertain farming environments with ROAM. Paired with this, we develop a web interface that allows users to define personalized decision spaces and visualize decision tradeoffs. To help validate ROAM, it was deployed to a commercial farm where the user was recommended a DA architecture design method to increase farm efficiency. ROAM allows users to quickly make key decisions in designing their DA systems to increase farm profitability.

Latest Developments of OLYMPE Platform for Optimizing Design of Fusion Cryomagnetic Systems

Latest Developments of OLYMPE Platform for Optimizing Design of Fusion Cryomagnetic Systems

A full operating conditions ejector model for refrigeration systems driven by low-grade heat sources

The ejector refrigeration technology driven by low-grade heat sources constitutes as one of the crucial means to solve the energy shortage in present-day society and to reduce the global environmental pollution. Thermodynamics ejector models are the basis of performance prediction, system control and structural design in ejector refrigeration systems driven by low-grade heat sources. However, the majority of present ejector models are unable to simultaneously meet the general feasibility of full operating conditions and the level of prediction accuracy for improving the energy efficiency of the refrigeration system. In this study, a novel theoretical model of ejectors is successfully derived based on the foundation of compound-choking theory and gas dynamic relations, which easily predicts the entrainment performance under the on-design and off-design conditions in ejector refrigeration systems. Indeed, a direct solution algorithm is presented to quickly and accurately solve this model and a linear correlation is employed between the mixing loss coefficient and the back pressure at the subcritical mode. The developed model is verified with experimental results in R245fa, R134a and R600a ejector refrigeration system. The results show that the model provides a precise estimation of the entrainment performance with the mean relative errors of 2.45%, 5.49% and 3.67% for R245fa, R134a and R600a refrigerants at full working conditions, while the prediction of critical condensing temperatures is considerably approximated by the experimental measurements. In the comparative analysis with the previous model, this model demonstrates a superior prediction performance, especially in the subcritical mode using the linear function mixing loss coefficient. This study will be a valuable aid in the improvement of energy efficiency in ejector refrigeration systems.

Circular Systems Navigator: A Process Tool for Circular Systems Design and Rebound Effect Mitigation

Circular Systems Navigator: A Process Tool for Circular Systems Design and Rebound Effect Mitigation

Circular Systems Navigator: A Process Tool for Circular Systems Design and Rebound Effect Mitigation

Circular Systems Navigator: A Process Tool for Circular Systems Design and Rebound Effect Mitigation

Design of the pistol P1 weapon storage system shelf using fingerprint electronic system in the TNI-AD units

One of the most important aspects of preventing illegal usage of firearms is safe storage. An inventive way to guarantee that only authorized owners can access the firearms is to incorporate fingerprint technology into the design of electric firearm storage systems. The use of fingerprint technology to increase the security of gun storage is desperately needed. However, issues with the finger's health, such as stains, cuts, and dampness, can impair the accuracy of the detection. This study employs a quantitative methodology using five fingerprint detection situations to test the accuracy of the system. The Arduino ESP was used as the foundation for the system implementation throughout the nine months that the research was conducted at the Kodiklatad Poltekad Electronics Laboratory. According to the test results, the system has 100% accuracy in identifying five different fingerprint detecting scenarios. But limitations are implied by the system's incapacity to identify damp, damaged, or dusty fingertips. Consequently, further thought must be given to these circumstances in order to improve system reliability. The design of an electric gun storage unit using Arduino ESP-based fingerprint technology has the potential to increase the security of gun storage, but more work is needed to address some issues that limit detection accuracy

Design and optimization of piezoelectric energy harvesting systems for enhanced performance in wireless sensor networks

Design and optimization of piezoelectric energy harvesting systems for enhanced performance in wireless sensor networks

Inverse design of topological photonic time crystals via deep learning

Photonic time crystals are a new kind of photonic system in modern optical physics, leading to devices with new properties in time. However, so far, it is still a challenge to design photonic time crystals with specific topological states due to the complex relations between time crystal structures and topological properties. Here, we propose a deep-learning-based approach to address this challenge. In a photonic time crystal with time inversion symmetry, each band separated by momentum gaps can have a non-zero quantized Berry phase. We show that the neural network can learn the relationship between time crystal structures and Berry phases, and then determine the crystal structures of photonic time crystals based on given Berry phase properties. Our work shows a new way of applying machine learning to the inverse design of time-varying optical systems and has potential extensions to other fields, such as time-varying phononic devices.

Design, construction, and validation of obstetric risk classification systems to predict intensive care unit admission.

To develop and validate a support tool for healthcare providers, enabling them to make precise and critical decisions regarding intensive care unit (ICU) admissions for high-risk pregnant women, thus enhancing maternal outcomes. This retrospective study involves secondary data analysis of information gathered from 9550 pregnant women, who had severe maternal morbidity (any unexpected complication during labor and delivery that leads to substantial short-term or long-term health issues for the mother), collected between 2009 and 2010 from the Brazilian Network for Surveillance of Severe Maternal Morbidity, encompassing 27 obstetric reference centers in Brazil. Machine-learning models, including decision trees, Random Forest, Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost), were employed to create a risk prediction tool for ICU admission. Subsequently, sensitivity analysis was conducted to compare the accuracy, predictive power, sensitivity, and specificity of these models, with differences analyzed using the Wilcoxon test. The XGBoost algorithm demonstrated superior efficiency, achieving an accuracy rate of 85%, sensitivity of 42%, specificity of 97%, and an area under the receiver operating characteristic curve of 86.7%. Notably, the estimated prevalence of ICU utilization by the model (11.6%) differed from the prevalence of ICU use from the study (21.52%). The developed risk engine yielded positive results, emphasizing the need to optimize intensive care bed utilization and objectively identify high-risk pregnant women requiring these services. This approach promises to enhance the effective and efficient management of pregnant women, particularly in resource-constrained regions worldwide. By streamlining ICU admissions for high-risk cases, healthcare providers can better allocate critical resources, ultimately contributing to improved maternal health outcomes.

Enhance Kinesthetic Experience in Perceptual Learning for Welding Motor Skill Acquisition with Virtual Reality and Robot-based Haptic Guidance.

Welding is an important operation in many industries, including construction and manufacturing, which requires extensive training and practices. Although welding simulators have been used to accommodate welding training, it is still challenging to enable novice trainees to effectively understand the kinesthetic experience of the expert in an egocentric manner, such as the proper way of force exertion in complex welding operations. This study implements a robot-assisted perceptual learning system to transfer the expert welders' experience to trainees, including both the positional and force control actions. A human-subject experiment (N = 30) was performed to understand the motor skill acquisition process. Three conditions (control, robotic positional guidance with force visualization, and force perceptual learning with position visualization) were tested to evaluate the role of robotic guidance in welding motion control and force exertion. The results indicated various benefits related to task completion time and force control accuracy under the robotic guidance. The findings can inspire the design of future welding training systems enabled by external robotic systems.

Enhancing Ultrasound Power Transfer: Efficiency, Acoustics, and Future Directions.

Implantable medical devices (IMDs), like pacemakers regulating heart rhythm or deep brain stimulators treating neurological disorders, revolutionize healthcare. However, limited battery life necessitates frequent surgeries for replacements. Ultrasound power transfer (UPT) emerges as a promising solution for sustainable IMD operation. Current research prioritizes implantable materials, with less emphasis on sound field analysis and maximizing energy transfer during wireless power delivery. This review addresses this gap. A comprehensive analysis of UPT technology, examining cutting-edge system designs, particularly in power supply and efficiency is provided. The review critically examines existing efficiency models, summarizing the key parameters influencing energy transmission in UPT systems. For the first time, an energy flow diagram of a general UPT system is proposed to offer insights into the overall functioning. Additionally, the review explores the development stages of UPT technology, showcasing representative designs and applications. The remaining challenges, future directions, and exciting opportunities associated with UPT are discussed. By highlighting the importance of sustainable IMDs with advanced functions like biosensing and closed-loop drug delivery, as well as UPT's potential, this review aims to inspire further research and advancements in this promising field.

Recent advances and innovations in the design and fabrication of wearable flexible biosensors and human health monitoring systems based on conjugated polymers

Recent advances and innovations in the design and fabrication of wearable flexible biosensors and human health monitoring systems based on conjugated polymers

Calculation and Analysis of TVMS Considering Profile Shifts and Surface Wear Evolution Process of Spur Gear

Profile shift is a highly effective technique for optimizing the performance of spur gear transmission systems. However, tooth surface wear is inevitable during gear meshing due to inadequate lubrication and long-term operation. Both profile shift and tooth surface wear (TSW) can impact the meshing characteristics by altering the involute tooth profile. In this study, a tooth stiffness model of spur gears that incorporates profile shift, TSW, tooth deformation, tooth contact deformation, fillet-foundation deformation, and gear body structure coupling is established. This model efficiently and accurately determines the time-varying mesh stiffness (TVMS). Additionally, an improved wear depth prediction method for spur gears is developed, which takes into consideration the mutually prime teeth numbers and more accurately reflects actual gear meshing conditions. Results show that consideration of the mutual prime of teeth numbers will have a certain impact on the TSW process. Furthermore, the finite element method (FEM) is employed to accurately verify the values of TVMS and load sharing ratio (LSR) of profile-shifted gears and worn gears. This study quantitatively analyzes the effect of profile shift on the surface wear process, which suggests that gear profile shift can partially alleviate the negative effects of TSW. The contribution of this study provides valuable insights into the design and maintenance of spur gear systems.

Efficient Error-Rate Estimation for Optical Transmission Systems Using Artificial Neural Networks

ABSTRACT With the advancement in modulation formats, optical transmission systems are becoming more adaptable and dynamic. Predicting bit errors of higher-order modulation formats is a challenging problem for the optimum design of communication systems. The existing simulation methods have persisted by employing iterative procedures that frequently incur high computing expenses and time consumption. On the other hand, deep learning (DL) algorithms have demonstrated remarkable efficacy as practical computing tools, presenting a viable approach to accelerate modulation simulations. In this paper, an artificial neural network (ANN) based bit error rate (BER) estimation scheme is proposed for the popular modulation forms including 56 Gbps 16-quadrature amplitude modulation (16QAM), 100 Gbps 32QAM, and 120 Gbps 64QAM optical system. Based on constellation diagrams (CDs) acquired with different launch power, laser linewidth, transmission distance, and OSNR, amplitude histograms (AHs) are generated via a manual preprocessing method. The properly trained ANN architecture exhibits a computational speed that surpasses traditional simulation methods by a factor exceeding 347. Moreover, the design considerations including the number of layers, nodes, activation functions, learning rate, optimizers, and evolution epochs are also investigated in detail. This research paves the way for optical transmission systems to use fast DL-based optimization strategies.

Composite flat-sheet membrane adsorbent of Li2TiO3-Ethylene-co-vinyl alcohol (LTO-EVAL) for lithium extraction

Composite bead adsorbents have been prevalent for the extraction of Li+ from brines. However, adsorbent beads generally demonstrate declining adsorption capacity and require extended equilibrium time when used repeatedly in absorption/desorption cycles. To overcome this challenge, a novel composite flat-sheet membrane adsorbent based on ethylene-co-vinyl alcohol (EVAL) and Li2TiO3 (LTO) is reported herein for highly efficient Li+ extraction from a geothermal brine. At an optimal LTO loading and membrane thickness, the resulting LTO-EVAL membrane was characterized by a large open macrovoid structure for fast ion transport, a fast adsorption rate that reached equilibrium in 0.5 h, and a high capacity of 96 % of that of the powder. The new membrane adsorbent showed adsorption behavior consistent with the Langmuir model and pseudo-second-order kinetic model. For a geothermal brine with a low lithium concentration, the LTO-EVAL membrane showed stable performance in 13 rapid adsorption/desorption cycles, with adsorption capacity at an average adsorption capacity of 7.3 mg/g and a 70 % adsorption efficiency. The average dissolution rate of Ti4+ was below 0.6 % in each cycle. The structure of the adsorbent material showed no discernable changes in chemical structure as confirmed by XRD and SEM results. With the improved extraction efficiency, the reusable membrane adsorbents would be economically viable in the long run and could potentially serve as the next generation of scalable Li+ adsorption material for brines. Further utilization of membrane adsorbent would lead to new design of adsorption modules and systems with reduced footprint. We need to further optimize the stability of the membrane adsorbents so that loss of Li2TiO3 remain at low level to reduce the lost for lithium extraction from the geothermal brine.

ROS responsive hydrogel for inhibition of MUC5AC against allergic rhinitis: A new delivery strategy for Ipratropium Bromide

Allergic rhinitis (AR) is a chronic inflammatory disease of the nasal mucosa mediated by immunoglobulin E (IgE) after exposure to allergens. The bothersome symptoms of AR, such as runny nose and nasal congestion, affect millions of people worldwide. Ipratropium Bromide (IB), commonly used in clinical practice for treating AR, requires frequent administration through nasal spray and may cause significant irritation to the nasal mucosa. The induction of ROS is closely related to the initiation and symptoms of AR, and ROS will continue to accumulate during the onset of AR. To address these challenges, we have designed a drug delivery system that can be administered in liquid form and rapidly crosslink into a ROS-responsive gel in the nasal cavity. This system enables sustained ROS responsive release of IB in a high-concentration ROS environment at AR lesions, thereby alleviating AR symptoms. The gel demonstrated prolonged release of IB for up to 24 hours in rats. In the treatment of AR rat models, it improved their symptoms, reduced the expression of various inflammatory factors, suppressed MUC5AC protein expression, and decreased mucus secretion through a ROS responsive IB release pattern. Overall, this system holds promise as a better option for AR treatment and may inspire the design of nanogel-based nasal drug delivery systems.

Scaled experiment and numerical study on the effect of a novel makeup air system on smoke control in atrium fires

In the field of fire safety engineering, makeup air systems design for atriums is always a concern for researchers and engineers. A novel makeup air system integrating breathing zone and underfloor air supply (BUMS) was applied in the investigated atrium in this study. However, the flow rates proportional relationship of makeup air required for effective smoke control on each floor remains unclear. Scaled experiments and numerical simulations were carried out to address this problem in this study. The results show that the BUMS effectively controlled temperature, followed by visibility, with minimal impact on carbon monoxide (CO) concentrations. When the flow rate of makeup air was equal on each floor, the sizes of the safe evacuation passageways created by the BUMS on the third and fourth floors were not conducive to the safe evacuation of personnel. Based on the proportion of the CO concentration of each floor under natural ventilation, distribution proportions of makeup air flow rate were obtained. The effective action area was used to help determine the upper limit flow rate ratio for each floor. Calculation results revealed that when the flow rate distribution proportion for the first to fourth floors was 15 %, 27 %, 28 % and 30 %, respectively, the smoke control effectiveness has been improved. These findings are expected are expected to provide a theoretical basis for designing makeup air systems in atrium and large-space buildings.

An improved Udwadia–Kalaba approach for controller design in underactuated mechanical systems

An improved Udwadia–Kalaba approach for controller design in underactuated mechanical systems

The Use of fMRI Regional Analysis to Automatically Detect ADHD Through a 3D CNN-Based Approach.

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by a reduced attention span, hyperactivity, and impulsive behaviors, which typically manifest during childhood. This study employs functional magnetic resonance imaging (fMRI) to use spontaneous brain activity for classifying individuals with ADHD, focusing on a 3D convolutional neural network (CNN) architecture to facilitate the design of decision support systems. We developed a novel deep learning model based on 3D CNNs using the ADHD-200 database, which comprises datasets from NeuroImage (NI), New York University (NYU), and Peking University (PU). We used fractional amplitude of low-frequency fluctuations (fALFF) and regional homogeneity (ReHo) data in three dimensions and performed a fivefold cross-validation to address the dataset imbalance. We aimed to verify the efficacy of our proposed 3D CNN by contrasting it with a fully connected neural network (FCNN) architecture. The 3D CNN achieved accuracy rates of 76.19% (NI), 69.92% (NYU), and 70.77% (PU) for fALFF data. The FCNN model yielded lower accuracy rates across all datasets. For generalizability, we trained on NI and NYU datasets and tested on PU. The 3D CNN achieved 69.48% accuracy on fALFF outperforming the FCNN. Our results demonstrate that using 3D CNNs for classifying fALFF data is an effective approach for diagnosing ADHD. Also, FCNN confirmed the efficiency of the designed model.

Investigation of normal zone propagation velocity in AgSn/Ag/Ba122 superconducting tapes using a conduction cooling test system

The investigation of the behavior of quench propagation in iron-based superconducting (IBS) tapes is important for their application in magnets and the design of quench protection systems. However, little research has been done in this area. In this work, a conduction cooling test system for IBS tapes and coils has been built, which has functions such as charging test, temperature and voltage data acquisition, pulse current heating, quench detection and protection. A series of experiments were performed on several 27 cm long IBS tapes to measure their normal zone propagation velocity (NZPV). The variation trends of NZPV with different operating temperatures, and with different ratios of operating current to critical current were systematically explored. In addition, monitoring all the voltages between different positions of an IBS tape, including two joints, has provided more detailed results and findings.