Design Of Systems Research Articles

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.

Transition State Stabilizing Effects of Oxygen and Sulfur Chalcogen Bond Interactions.

Non-covalent chalcogen bond (ChB) interactions have found utility in many fields, including catalysis, organic semiconductors, and crystal engineering. In this study, the kinetic effects of ChB interactions of oxygen and sulfur were experimentally measured using a series of molecular rotors. The rotors were designed to form ChB interactions in the bond rotation transition states. This enabled their kinetic influences to be assessed by monitoring changes in rotational barriers. Despite forming weaker ChB interactions, the smaller chalcogens were able to stabilize transition states and had measurable kinetic effects. Sulfur stabilized the bond rotation transition state by as much as -7.2 kcal/mol without electron-withdrawing groups. The key was to design a system where the sulfur𝜎-hole was aligned with the lone pairs of the chalcogen bond acceptor. Oxygen rotors also could form transition state stabilizing ChB interactions but required electron-withdrawing groups. For both oxygen and sulfur ChB interactions, a strong correlation was observed between transition state stabilizing abilities and electrostatic potential (ESP) of the chalcogen, providing a useful predictive parameter for the rational design of future ChB systems.

Phase equilibria of CaO–Al2O3–CaO·TiO2 system at 1500 °C in hydrogen atmosphere

The phase diagram of CaO–Al2O3-TiOx system is of great importance in guiding the design of metallurgical slag systems for Al–Ti alloy steel, controlling Ti-bearing inclusions in molten steel, and regulating the mineral phase of vanadium-titanium magnetite. In the present work, the high temperature equilibration-quench technique was adopted, and the types and compositions of equilibrium phases were identified by Electron Probe Micro Analysis (EPMA) and X-ray diffraction (XRD). The isothermal section of CaO–Al2O3–CaO·TiO2 system at 1500 °C in H2 atmosphere was constructed for the first time. There are 7 three-phase fields, 10 two-phase fields, and a single liquid phase field. On this basis, according to the theoretical analysis of the crystal structure, the formation energy analysis based on first-principles calculations, and the related theories of defect chemistry, the formation mechanisms of CaTiO3 and Ca3Ti2O7 solid solution with were determined, respectively. Additionally, the experimental phase diagram was utilized to discuss the dissolution type and the equilibrium solubility of typical inclusions (Al2O3, Ti3O5, and CaO·TiO2) in Al–Ti alloy steel.

Supporting medical and nursing activities with AI: recommendations for responsible design and use

Clinical decision support systems (CDSS) based on artificial intelligence (AI) are complex socio-technical innovations and are increasingly being used in medicine and nursing to improve the overall quality and efficiency of care, while also addressing limited financial and human resources. However, in addition to such intended clinical and organisational effects, far-reaching ethical, social and legal implications of AI-based CDSS on patient care and nursing are to be expected. To date, these normative-social implications have not been sufficiently investigated. The BMBF-funded project DESIREE (DEcision Support In Routine and Emergency HEalth Care: Ethical and Social Implications) has developed recommendations for the responsible design and use of clinical decision support systems. This article focuses primarily on ethical and social aspects of AI-based CDSS that could have anegative impact on patient health. Our recommendations are intended as additions to existing recommendations and are divided into the following action fields with relevance across all stakeholder groups: development, clinical use, information and consent, education and training, and (accompanying) research.

Design and Analysis of a Novel Fractional-Order System with Hidden Dynamics, Hyperchaotic Behavior and Multi-Scroll Attractors

The design of chaotic systems with complex dynamic behaviors has always been a key aspect of chaos theory in engineering applications. This study introduces a novel fractional-order system characterized by hidden dynamics, hyperchaotic behavior, and multi-scroll attractors. By employing fractional calculus, the system’s order is extended beyond integer values, providing a richer dynamic behavior. The system’s hidden dynamics are revealed through detailed numerical simulations and theoretical analysis, demonstrating complex attractors and bifurcations. The hyperchaotic nature of the system is verified through Lyapunov exponents and phase portraits, showing multiple positive exponents that indicate a higher degree of unpredictability and complexity. Additionally, the system’s multi-scroll attractors are analyzed, showcasing their potential for secure communication and encryption applications. The fractional-order approach enhances the system’s flexibility and adaptability, making it suitable for a wide range of practical uses, including secure data transmission, image encryption, and complex signal processing. Finally, based on the proposed fractional-order system, we designed a simple and efficient medical image encryption scheme and analyzed its security performance. Experimental results validate the theoretical findings, confirming the system’s robustness and effectiveness in generating complex chaotic behaviors.

Inverse Hamiltonian design of highly entangled quantum systems

Solving inverse problems to identify Hamiltonians with desired properties holds promise for the discovery of fundamental principles. In quantum systems, quantum entanglement plays a pivotal role in not only characterizing the quantum nature but also developing quantum technology such as quantum computing. Nonetheless, the design principles of the quantum entanglement are yet to be clarified. Here we apply an inverse design framework using automatic differentiation to quantum spin systems, aiming to construct Hamiltonians with large quantum entanglement. We show that the method automatically finds the Kitaev model with bond-dependent anisotropic interactions, whose ground state is a quantum spin liquid, on both honeycomb and square-octagon lattices. On triangular and maple-leaf lattices with geometrical frustration, it generates numerous solutions with spatially inhomogeneous interactions rather than converging to a specific model, but it still helps to construct unprecedented models. The comparative study reveals that bond-dependent anisotropic interactions, rather than isotropic Heisenberg interactions, amplify quantum entanglement, even in the systems with geometrical frustration. The present study paves the way for the automatic design of new quantum systems with desired quantum nature and functionality. Published by the American Physical Society 2024

Biologically inspired piezoresistive MEMS acoustic vector sensor for underwater applications

Underwater acoustics has assumed a pivotal role in the detection, navigation, communication, and target identification of ships and submarines. In recent years, advancements in noise reduction from marine vessels have underscored the significance of underwater acoustic vector sensors. This paper presents a novel approach to the design, fabrication, and characterization of a two-dimensional Micro-Electro Mechanical Systems (MEMS) vector acoustic sensor tailored for underwater environments. Traditional underwater acoustic vector sensors often employ doped polysilicon or silicon/germanium sensing films on rigid silicon substrates. The inflexible nature of silicon substrates necessitates labor-intensive and costly chemical-mechanical polishing processes to achieve submicron thickness, often requiring extended clean-room usage. In contrast, this work proposes a simplified, cost-effective, and scalable alternative. The sensor design utilizes reduced graphene oxide (rGO) as the sensing material and Kapton as the substrate, chosen for their unique properties. rGO exhibits high carrier mobility and low intrinsic noise, ensuring clear signals with minimal external interference, thereby improving measurement accuracy in challenging environments. Kapton provides excellent thermal stability, mechanical strength, and chemical resistance, along with superior insulating properties that reduce electrical noise, enhancing the signal-to-noise ratio. The designed vector sensor leverages the piezoresistive transduction principle, incorporating a thin piezoresistive film of rGO on a flexible Kapton substrate via a straightforward drop-casting method. Inspired by the sensory mechanisms of aquatic life, the two-dimensional vector sensor not only detects pressure associated with acoustic signals but also discerns the direction from which these low and ultralow frequency signals originate. The device's performance characteristics, sensitivity, and directivity are comprehensively evaluated through static, dynamic, and acoustic tests conducted in both air and water mediums. Static tests reveal the piezoresistive behavior of rGO under applied displacement, yielding displacement sensitivities of 0.0279 V/mm and 0.0276 V/mm for axial and radial displacements, respectively. Dynamic and acoustic tests demonstrate the sensor's capability to detect low-frequency signals spanning from 0.25 Hz to 200 Hz, exhibiting a smooth and favorable frequency response. In air, the sensor exhibits receiving sensitivities ranging from −15.18 dB to −19.75 dB (X-channel) and −13.67 dB to −16.67 dB (Y-channel). In the underwater environment, these values range from −134.40 dB to −138.93 dB (X-channel) and −131.83 dB to −136.93 dB (Y-channel). Notably, the variation in receiving sensitivity across channels in both air and water is limited to within 6 dB. Furthermore, the device showcases a symmetrical directivity pattern resembling the shape of an "8," with an approximate sensitivity of −136.66 dB, a significant improvement compared to the −165 dB sensitivity of conventional polysilicon underwater vector sensors. These experimental findings affirm the feasibility of the proposed underwater vector sensor, based on the piezoresistive effect and MEMS technology, highlighting its two-dimensional directivity capabilities.

A closed-loop patch based on bioinspired infection sensor for wound management

Pathogenic wound infections remain a major global health challenge. When skin tissue is wounded, some colonizing pathogenic bacteria secrete large amounts of hyaluronidase (HAase) to degrade the extracellular matrix (ECM), enabling their diffusion and proliferation. Current clinical techniques for detecting infection rely on basic visual cues or analysis of wound fluids, enabling only reactive treatment once severe signs manifest. Herein, we present the use of a closed-loop patch (CLP) for advanced wound management. The patch contains a bio-inspired sensor based on an engineered hyaluronic acid (HA) hydrogel that detects HAase secreted by invading pathogens. This detection triggers a quantifiable change in the capacitance of the interdigital electrode and achieving in-time detection of Staphylococcus aureus (S. aureus) infection in a murine wound model before obvious visual signs appear. In response, the release of titanium hydride (TiH1.924) nanodots from hydrogel decomposition can eradicate pathogens under ultrasound (US) irradiation. Concurrently, the product, HA fragments further promoted cell migration and angiogenesis to significantly improve wound healing by ∼14.6-fold rates in 12 days. Overall, the CLP provides a sensory act-treat effect according to the wound status, which facilitate the closed-loop integration of monitoring and rehabilitation. This strategy may provide additional possibilities for the design of future wound management systems.