Critical System Components Research Articles

CHLORINE RESIDUALS IN WASTEWATER TREATMENT: IMPLICATIONS FOR PATHOGEN INACTIVATION AND ENVIRONMENTAL IMPACT

Chlorine residuals are a critical component of wastewater treatment systems. Inactivation of pathogens is a crucial goal to protect public health and stop the spread of waterborne illnesses. The purpose of this study is to find out more about the use of chlorination to dominate all pathogenic bacteria in a safe environment. The method used in this paper is an approach to literature review, which is carried out by publishing articles, including browsing the internet, Google Scholar, and related journals. The study highlights the importance of understanding the relationship between the chlorine doses given and the resulting concentration of the residuals. In addition, the study emphasizes the potential consequences of an ecosystem, such as excessive algae growth and the contribution of methane gas production to greenhouse effects. Our findings focus on the need for a comprehensive approach to managing chlorine residuals, considering aspects of health in humans, effective inactivation that is pathogenic, and preserving the environment for long-term sustainability.

A compilation of histidine kinase genes in Enterobacter hormaechei strain HCF3 isolated from the North West Province of South Africa

This study investigates the histidine kinase (HK) gene repertoire of Enterobacter hormaechei strain HCF3, isolated from fresh cow dung in Mogosane Village, Northwest Province, South Africa. Histidine kinases are critical components of bacterial two-component signal transduction systems, enabling bacteria to sense and adapt to diverse environmental conditions. Given the growing concern over antimicrobial resistance (AMR) associated with E. hormaechei, this research elucidates the genetic components that facilitate its environmental adaptability. After isolating the strain, genomic sequencing using Illumina technology, resulting in high-quality sequence data, was conducted. The assembled genome was meticulously annotated and deposited in the National Center for Biotechnology Information (NCBI) under BioProject number PRJNA991313, with additional accession numbers for raw reads (JAUOLV000000000.1) and BioSample (SAMN36292742). Histidine kinase genes were identified based on conserved domains, particularly HisKA and HATPase. This led to compiling a comprehensive HK gene catalogue with locus tags, protein accession numbers, and functional annotations. To validate the HK gene set of E. hormaechei HCF3, we conducted a rigorous comparative analysis with other strains. This revealed that strain HCF1 contains 21 histidine kinase genes, HCF2 has 25, while HCF4 has 19. These findings underscore the diversity and conservation of HK genes across different Enterobacter species, providing a new perspective on their evolutionary significance. The assembled dataset provides valuable insights into the signalling pathways of E. hormaechei, highlighting the potential roles of HKs in environmental sensing, adaptation, and pathogenicity. Furthermore, this research lays the groundwork for future studies on the applications of these genes in agriculture and biotechnology, offering new avenues for understanding and managing E. hormaechei in various ecological contexts.

Travel time prediction for an intelligent transportation system based on a data-driven feature selection method considering temporal correlation

Travel-time prediction is a critical component of Intelligent Transportation Systems (ITS), offering vital information for tasks such as accident detection, congestion management, and traffic flow optimisation. Accurate predictions are highly dependent on the selection of relevant features. In this study, a two-stage methodology is proposed which consists of two layers of Optimisation Algorithm and one Data-Driven method (OA2DD) to enhance the accuracy and efficiency of travel-time prediction. The first stage involves an offline process where interconnected optimisation algorithms are employed to identify the optimal set of features and determine the most effective machine learning model architecture. In the second stage, the real-time process utilises the optimised model to predict travel times using new data from previously unseen parts of the dataset. The proposed OA2DD method was applied to a case study on the M50 motorway in Dublin. Results show that OA2DD improves the convergence curve and reduces the number of selected features by up to 50%, leading to a 56% reduction in computational costs. Furthermore, using the selected features from OA2DD, reduced the prediction error by up to 29% compared to the full feature set and other feature selection methods, demonstrating the method's effectiveness and robustness.

Novel imbalanced multi-class fault diagnosis method using transfer learning and oversampling strategies-based multi-layer support vector machines (ML-SVMs)

For health monitoring and fault diagnosis of critical mechanical system components, historical data related to equipment failures are often limited and exhibit varying imbalanced multi-class characteristics (e.g., with noisy and time-series data). Moreover, fault diagnosis frameworks based on traditional resampling algorithms (e.g., SMOTE) mostly heavily rely on manual feature extraction, making them difficult to adapt to diverse working conditions or objects. To address these challenges, we propose a novel end-to-end imbalanced multi-class fault diagnosis architecture using transfer learning and oversampling strategies-based multi-layer support vector machines (ML-SVMs). ML-SVMs utilize a VGG-based deep migration feature extraction method to extract features from original time-domain vibration signals, employing natural source domain weights to reduce dependence on human experience and sample size. Then, ML-SVMs introduce ISCOTE (i.e., the first and second layers of ML-SVMs), an improved version of the sample-characteristic over-sampling technique (SCOTE). ISCOTE generates more effective and reasonable samples for each fault class through a scaling factor and iterative optimization mechanism, whether in noisy feature spaces with fuzzy boundaries or in clear boundary feature spaces. Finally, in the third layer of ML-SVMs, multi-class SVMs (e.g., LS-SVMs and standard SVMs) are utilized to train balanced feature samples and derive classification models with strong generalization ability. The effectiveness of ML-SVMs is demonstrated through 16 fault diagnosis instances using CWRU and IMS bearing data, PHM 2010 and TTWD tool wear data. Results indicate that ML-SVMs outperform 8 well-known oversampling-based algorithms in fault diagnosis recognition rates and algorithm robustness. It has offered a feasible architecture for multi-class imbalanced fault scenarios with limited data and multiple adverse features.

The application of improved SURF optical flow algorithm in seismic support safety monitoring system

Frequent earthquakes pose a major threat to the safety of building structures. As a critical component of earthquake-resistant systems, dynamic health monitoring of seismic isolation bearings is essential. However, existing monitoring methods are limited by high cost and poor environmental adaptability, and it is difficult to meet real-time requirements under complex conditions. Therefore, this study combines the stable acceleration feature point matching with the optical flow algorithm, and uses the proportional purification method and the bidirectional purification method to effectively match and purify the feature points. By optimizing the feature point matching and dynamic tracking accuracy, a seismic bearing safety monitoring system based on the improved feature optical flow algorithm is designed. The experimental results show that the improved model achieves a feature recognition rate of >90 %, and the number of feature tracking losses is <70. At the same time, the displacement time history curve of the seismic isolation bearing and the laser displacement sensor has the highest degree of fit, the error of the horizontal displacement peak is 2.13 %, and the absolute error is <0 .3mm. Compared with traditional methods, this system achieves high-precision real-time monitoring at a low cost, providing reliable technical support for the health management of seismic isolation bearings, and also opens up a new direction in the field of structural health monitoring.

Mathematical modeling and CFD investigation of the novel oscillating shear valve generating sinusoidal signals by fluid pressure fluctuation

The oscillating shear valve pulser is a critical component of advanced downhole high-speed transmission systems, playing a vital role in efficient drilling operations. It is highly valued for its flexible and accurate source signal modulation capabilities, as well as its ability to operate safely without blockages. Specifically, how to ensure that the oscillatory shear valve pulse generator produces high-quality sinusoidal differential pressure source signals is particularly crucial. Consequently, this paper initially considers the motion characteristics of the shear valve in acceleration-uniform speed-deceleration, derives the calculation formula of the throttle area of the valve hole based on the fan-arc-fillet-line, and adopts the latest unsteady-liquid thin-walled cutting-edge throttling mechanism (UTCTM) we discovered, innovatively proposing the mechanism model of the differential pressure signal generation of the oscillatory shear valve. Based on this model, a typical optimized four-blade oscillatory shear valve was obtained. Through a series of CFD computational fluid dynamics studies, the amplitude variation characteristics and sinusoidal morphology evolution laws of the differential pressure signal at different frequencies were revealed, forming a unique design method for oscillatory shear valves, which provides a solid theoretical basis and technical support for the development of a new generation of pulsers.

MBF-Net: Multi-scale boundary-aware aggregation for bi-directional information exchange and feature reshaping for medical image segmentation

As a critical component of computer-aided diagnosis systems, medical image segmentation plays a vital role in assisting clinicians in making rapid and accurate decisions and formulating treatment plans. Nevertheless, precise medical image segmentation still presents a number of challenges, including insufficient feature extraction capabilities in the presence of limited sample sizes, blurred segmentation boundaries, and information loss between the encoder and decoder. In order to address these issues, we propose a Multi-Scale Boundary-Aware Aggregation Network with Bidirectional Information Exchange and Feature Refinement (MBF-Net) for medical image segmentation. Initially, we design a Multi-Scale Boundary-Aware Aggregation Encoder (MBAE) that aggregates features from different scales and pixel levels within the input images, capturing fine-grained boundary information in deep features and establishing comprehensive global and local multi-scale contextual dependencies. This design significantly enhances the model's understanding of the overall image structure and its ability to discern subtle differences between lesions and background. Subsequently, a Multi-Scale Bidirectional Information Transmission (MBIT) module is introduced, which integrates bidirectional information flow between low-level and high-level features, enabling multi-scale features to flow bidirectionally across different layers. The MBIT module effectively preserves crucial boundary details during cross-layer information transmission, thereby bridging the semantic gap between the encoder and decoder, and thereby improving the clarity of the segmentation boundaries. Finally, we develop a Feature Refinement and Aggregation Fusion (FRAF) module, designed to integrate feature information from various semantic levels, which alleviates discrepancies between features at varying scales, thus enhancing the segmentation accuracy of the network. The generalisation and effectiveness of MBF-Net are validated through comprehensive experiments on a range of tasks, including nuclear segmentation, breast cancer segmentation, polyp segmentation and skin lesion segmentation. Both subjective and objective evaluations demonstrate that MBF-Net significantly outperforms current state-of-the-art methods, achieving average Dice Similarity Coefficient (DSC) and Intersection over Union (IoU) scores of 86.34 % and 78.37 %, respectively. The superior performance of MBF-Net in terms of segmentation accuracy and quality is demonstrated across five public datasets.

Computer Vision-Based Research on the Mechanism of Stick–Slip Vibration Suppression and Wear Reduction in Water-Lubricated Rubber Bearing by Surface Texture

Water-lubricated rubber bearings are a critical component of the propulsion systems in underwater vehicles. Particularly under conditions of low speed and high load, friction-induced vibration and wear often occur. Surface texturing technology has been proven to improve lubrication performance and reduce friction and wear; however, research on how different texture parameters affect friction-induced vibration and wear mechanisms remains insufficient. In this study, various texture patterns with different area ratios and aspect ratios were designed on the surface of water-lubricated rubber bearings. By combining these designs with an in situ observation system based on computer vision technology, the effects of texture parameters on bearing friction, vibration, and wear were thoroughly investigated. The experimental results show that surface textures play a critical role in improving hydrodynamic effects and stabilizing the lubrication film at the friction interface. Specifically, textures with a high area ratio (15%) and aspect ratio (3:1) exhibited the best vibration suppression effect, primarily due to the reduction in actual contact area. However, excessively high area ratios may lead to increased surface wear. This study concludes that a reasonable selection of texture area and aspect ratios can significantly reduce frictional force fluctuations and vibration amplitude, minimize surface wear, and extend bearing life.

Intelligent Fault Diagnosis of Planetary Gearbox Across Conditions Based on Subdomain Distribution Adversarial Adaptation.

Sensory data are the basis for the intelligent health state awareness of planetary gearboxes, which are the critical components of electromechanical systems. Despite the advantages of intelligent diagnostic techniques for detecting intricate fault patterns and improving diagnostic speed, challenges still persist, which include the limited availability of fault data, the lack of labeling information and the discrepancies in features across different signals. Targeting this issue, a subdomain distribution adversarial adaptation diagnosis method (SDAA) is proposed for faults diagnosis of planetary gearboxes across different conditions. Firstly, nonstationary vibration signals are converted into a two-dimensional time-frequency representation to extract intrinsic information and avoid frequency overlapping. Secondly, an adversarial training mechanism is designed to evaluate subclass feature distribution differences between the source and target domain. A conditional distribution adaptation is employed to account for correlations among data from different subclasses. Finally, the proposed method is validated through experiments on planetary gearboxes, and the results demonstrate that SDAA can effectively diagnose faults under crossing conditions with an accuracy of 96.7% in diagnosing gear faults and 95.2% in diagnosing planet bearing faults. It outperforms other methods in both accuracy and model robustness. This confirms that this approach can refine domain-invariant information for transfer learning with less information loss from the sub-class level of fault data instead of the overall class level.

Research on Microscale Vehicle Logo Detection Based on Real-Time DEtection TRansformer (RT-DETR).

Vehicle logo detection (VLD) is a critical component of intelligent transportation systems (ITS), particularly for vehicle identification and management in dynamic traffic environments. However, traditional object detection methods are often constrained by image resolution, with vehicle logos in existing datasets typically measuring 32 × 32 pixels. In real-world scenarios, the actual pixel size of vehicle logos is significantly smaller, making it challenging to achieve precise recognition in complex environments. To address this issue, we propose a microscale vehicle logo dataset (VLD-Micro) that improves the detection of distant vehicle logos. Building upon the RT-DETR algorithm, we propose a lightweight vehicle logo detection algorithm for long-range vehicle logos. Our approach enhances both the backbone and the neck network. The backbone employs ResNet-34, combined with Squeeze-and-Excitation Networks (SENetV2) and Context Guided (CG) Blocks, to improve shallow feature extraction and global information capture. The neck network employs a Slim-Neck architecture, incorporating an ADown module to replace traditional downsampling convolutions. Experimental results on the VLD-Micro dataset show that, compared to the original model, our approach reduces the number of parameters by approximately 37.6%, increases the average accuracy (mAP@50:95) by 1.5%, and decreases FLOPS by 36.7%. Our lightweight network significantly improves real-time detection performance while maintaining high accuracy in vehicle logo detection.

Fault migration diagnosis method for rolling bearing based on 2D convolutional neural network

ABSTRACT In recent years, the development of mechanical systems has gradually become larger and more complex. As one of the most critical components of mechanical systems, the health status of rolling bearings is crucial for the safe and orderly operation of the entire mechanical system. Therefore, this paper constructs a transfer diagnosis model based on two-dimensional convolutional neural networks, achieving efficient and fast cross working condition fault transfer diagnosis of rolling bearings. In summary, this article takes rolling bearings as the research object and constructs a fault diagnosis model under working condition migration by combining two-dimensional convolutional neural networks with maximum mean difference. Based on the constructed model, experiments are designed and signal data is collected for model training, validation, and testing. This article aims to achieve cross working condition transfer diagnosis of rolling bearings by combining the constructed model with real data from the test bench, providing reference and guidance for cross domain fault diagnosis of rolling bearings in practical engineering. The research results indicate that the constructed transfer diagnosis model based on two-dimensional convolutional neural network has achieved efficient and fast fault transfer diagnosis of rolling bearings across working conditions. The accuracy of the model in the frequency domain of transfer under 6 working conditions is higher than 98.15%.

Algorithms for evaluation of minimal cut sets

Objective:We propose a way to enhance the evaluation of minimal cut sets (MCSs) in biological systems modeled by Petri nets, by providing criteria and methodology for determining their optimality in disabling specific processes without affecting critical system components. Methods:This study concerns Petri nets to model biological systems and utilizes two primary approaches for MCS evaluation. First is the analyzing impact on t-invariants to identify structural dependencies. Second is assessing the impact on potentially starved transitions caused by the inactivity of specific MCSs. This approach deal with net dynamics. These methodologies aim to offer practical tools for assessing the quality and effectiveness of MCSs. Results:The proposed methodologies were applied to two case studies. In the first case, a cholesterol metabolism network was analyzed to investigate how local inflammation and oxidative stress, in conjunction with cholesterol imbalances, influence the progression of atherosclerosis. The MCSs were ranked, with the top sets presented, focusing on those that disabled the fewest number of t-invariants. In the second case, a carbohydrate metabolism disorder model was examined to understand its impact on atherosclerosis progression. The analysis aimed to identify MCSs that could inhibit the atherosclerosis process by targeting specific transitions. Both studies utilized the Holmes software for calculations, demonstrating the effectiveness of the proposed evaluation methodologies in ranking MCSs for practical biological applications. Conclusion:The algorithms proposed in this paper offer an analytical approach for evaluating the quality of MCSs in biological systems. By providing criteria for MCS optimality, these approaches have potential to enhance the utility of MCS analysis in systems biology, aiding in the understanding and manipulation of complex biological networks.Algorithm are implemented within Holmes software, an open-source project available at https://github.com/bszawulak/HolmesPN.

Viscoelastic study of cement and emulsified asphalt mortar under temperature variations based on a novel variable-order fractional Maxwell model

Cement and emulsified asphalt (CA) mortar is a critical viscoelastic component of ballastless track systems, exhibiting pronounced temperature sensitivity. However, accurately predicting the mechanical responses of CA mortar due to temperature fluctuations remains a challenge. This work proposes a novel variable-order fractional Maxwell (VOFM) model to investigate the viscoelastic properties of CA mortar under temperature variations. The VOFM model is numerically solved using a time-varying coefficient ladder (TVL) model. The VOFM model is validated by comparing with both numerical integration methods and experimental data. The basic properties of the VOFM model, the viscoelastic responses of CA mortar under varying temperatures and alternating stress are systematically investigated. The results show that the mechanical properties of VOFM model agree with the FM model when time-dependent parameters of the VOFM model correspond to that of the FM model. Moreover, high temperatures have a promoting effect on the deformation of CA mortar under both alternating and constant stress conditions. The peak amplitude of CA mortar strain under practical varying temperature and stress loading correlates with the trend of temperature changes.

Review on Preamble-Based Channel Estimation Method for FBMC/OQAM Toward 6G: Advantages, Challenges and Future Works

Filter bank multicarrier/offset quadrature amplitude modulation (FBMC/OQAM) is a multicarrier modulation that is expected to replace orthogonal frequency division multiplexing (OFDM) in future sixth-generation (6G) networks. FBMC/OQAM has high spectrum efficiency, cyclic prefix (CP)-free transmission, decreased out-of-band emission (OOBE), and asynchronous environment robustness. However, the orthogonality criteria of FBMC/OQAM are only in the real field. Therefore, imaginary components of complex-valued OQAM symbols cause imaginary interferences among subcarriers, affecting channel estimation (CE) processing operations. Channel estimation is a critical component of wireless communication systems. Channel estimate allows the receiver to approximate channel impulse response (CIR) to determine the impacts of the communication channel on the sent symbols. So, an accurate channel estimate is critical in FBMC/OQAM. In this review, we focus on the Preamble-based method, one of the basic methods for channel estimation in FBMC. Three preamble-based approaches have been studied: the interference approximation method (IAM), the interference cancellation method (ICM), and pairs of pilots (POP) using a single antenna and multiple input multiple outputs (MIMO). Compare them regarding bit error rate (BER) and mean square error. Also, it compares different interference approximation methods in terms of bit error rate (BER), magnitude, and peak average power ratio (PAPR). The review found the superiority of M-IAM and NPS in spectrum efficiency and PAPR. Future work that can help the researcher in this field.

Attention-guided hierarchical fusion U-Net for uncertainty-driven medical image segmentation

Small inaccuracies in the system components or artificial intelligence (AI) models for medical imaging could have significant consequences leading to life hazards. To mitigate those risks, one must consider the precision of the image analysis outcomes (e.g., image segmentation), along with the confidence in the underlying model predictions. U-shaped architectures, based on the convolutional encoder–decoder, have established themselves as a critical component of many AI-enabled diagnostic imaging systems. However, most of the existing methods focus on producing accurate diagnostic predictions without assessing the uncertainty associated with such predictions or the introduced techniques. Uncertainty maps highlight areas in the predicted segmented results, where the model is uncertain or less confident. This could lead radiologists to pay more attention to ensuring patient safety and pave the way for trustworthy AI applications. In this paper, we therefore propose the Attention-guided Hierarchical Fusion U-Net (named AHF-U-Net) for medical image segmentation. We then introduce the uncertainty-aware version of it called UA-AHF-U-Net which provides the uncertainty map alongside the predicted segmentation map. The network is designed by integrating the Encoder Attention Fusion module (EAF) and the Decoder Attention Fusion module (DAF) on the encoder and decoder sides of the U-Net architecture, respectively. The EAF and DAF modules utilize spatial and channel attention to capture relevant spatial information and indicate which channels are appropriate for a given image. Furthermore, an enhanced skip connection is introduced and named the Hierarchical Attention-Enhanced (HAE) skip connection. We evaluated the efficiency of our model by comparing it with eleven well-established methods for three popular medical image segmentation datasets consisting of coarse-grained images with unclear boundaries. Based on the quantitative and qualitative results, the proposed method ranks first in two datasets and second in a third. The code can be accessed at: https://github.com/AfsanaAhmedMunia/AHF-Fusion-U-Net.

Fault diagnosis for multiple redundancy aileron actuator based on parallel-SDP and polar sparse representation

Abstract As a critical component of aircraft flight control systems, the aileron actuator's fault directly affects the performance of flight control performance system and the overall flight safety of aircraft. Therefore, effective fault diagnosis of the aileron actuator becomes particularly important. However, with the development of redundancy design, the structure of aileron actuators more and more complex, and the fault modes are more and more diverse, which increases the difficulty of fault diagnosis significantly. To address this challenge, this study proposes a fault diagnosis method based on Parallel-SDP and Polar Sparse Representation.&amp;#xD;First, the current residual signals of force motors are obtained using bi-step observer. Then, the residual signals are transformed into Parallel-SDP images, where each of the four petals of Parallel-SDP image generated from the corresponding channel. The Parallel-SDP image presents global fault information and capture subtle changes of residual signals, which increases the sensitivity of fault diagnosis. Subsequently, rapid single-channel fault localization is achieved by barycenter analysis of Parallel-SDP images. Finally, based on the result of fault localization, the proposed polar sparse representation algorithm is utilized for fault diagnosis of mechanical and control faults separately. Based on the dataset obtained from physical-parameter-simulation, the proposed method was validated, and the accuracy of fault diagnosis of was 99.29%, which is better than conventional fault diagnosis, meanwhile, the computational resources consumption of the proposed method is much less than deeplearning-based method, which is suitable for embedded computing system in aircraft.&amp;#xD;

Novel intelligent defects detection of boiler water walls in thermal power plants based on OFM_SSD

The boiler is a critical component of conventional thermal power systems, where surface flaws in boiler water walls can significantly compromise safety and availability, potentially leading to substantial loss of life and property. Traditional detection methods, whether manual or based on machine learning, often prove costly, inefficient and time-consuming, failing to meet the stringent requirements for water wall inspection. Therefore, a novel surface defect detection model integrating an improved single shot multibox detector (SSD) with the optical flow method (OFM) (OFM_SSD) is proposed. The OFM enhances data sample diversity by augmenting the dataset derived from thermal power plants, while the incorporation of deconvolution techniques improves the model receptive field, thereby enhancing its ability to detect and classify small defects. Comprehensive experiments demonstrate that the OFM_SSD outperforms several existing models including the SSD model based on traditional expanded datasets (T_SSD), you only look once (YOLO), ordinary SSD, Regions with the CNN(R_CNN), and Deconvolution-only SSD (DSSD) in terms of accuracy in defect localization and classification. This advancement of the OFM_SSD not only reduces operational costs but also enhances detection capabilities, ultimately contributing to safer and more efficient operations within thermal power plants.

DRQN-based global optimal control of air conditioning water system

The intelligent control of the air conditioning water system, a critical component of HVAC systems, plays a crucial role in reducing building energy consumption. Reinforcement learning (RL), an emerging interactive learning method capable of self-optimization according to environmental changes, has been widely applied in the field of HVAC control. This paper focuses on an air conditioning water system with chiller group control. Initially, a load-based sequencing control strategy is adopted, and deadband ranges are set based on empirical knowledge to coordinate the sequencing control of multiple devices. Subsequently, cooling load, wet-bulb temperature, and the number of chillers are used as the state space. By incorporating the implicit temporal information associated with these changes, the Deep Recurrent Q-Network (DRQN) is introduced to effectively leverage historical data for optimizing operational parameters such as chiller supply water temperature, pump frequency, and cooling tower fan speed. Experimental results show that compared with traditional rule-based control and Deep Q-Network (DQN) control, the proposed control framework achieves energy savings of 20.38%-24.59% and 1.82%-3.48%, respectively, under different sequencing control strategies.

Research on Energy-Saving Control Strategies for Single-Effect Absorption Refrigeration Systems

The automatic control device is a critical component of absorption refrigeration systems. Its functional enhancement can reduce operating costs, improve energy efficiency, and ensure long-term stable unit operation. Given that absorption refrigeration systems operate under various dynamic conditions, the rational design of control strategies is particularly important. This study analyzes the influence of changes in the cooling water and heat source water flow rates on the outlet temperature of chilled water in the unit based on the open-loop response characteristics of absorption refrigeration systems. It proposes a dual-loop energy-saving control strategy for single-effect hot water lithium bromide absorption refrigeration systems based on the setpoint comprehensive optimization algorithm. Considering the multiple variables, strong coupling, large inertia, long time delay, and nonlinear characteristics of absorption refrigeration systems, as well as the difficulties in modeling these systems, this study applies a model-free adaptive control algorithm to the system’s control. It derives both SISO and MIMO model-free control algorithms with time-delay components. Through simulations comparing MFAC, improved MFAC, and traditional PID control, the dual-loop energy-saving control strategy is demonstrated to effectively reduce system heat consumption by approximately 20%, decrease power consumption by about 10%, and enhance the system’s SCOP by approximately 19.3%.

A Fully Edible Transistor Based on a Toothpaste Pigment.

Edible electronics is emerging in recent years motivated by a diverse number of healthcare applications, where sensors can be safely ingested without the need for any medical supervision. However, the current lack of stable and well-performing edible semiconductors needs to be addressed to reach technological maturity and allow the surge of a new generation of edible circuits. In the quest for good-performing edible semiconductors, this study has explored the possibility of considering materials that are not regulated for intentional ingestion, yet are daily swallowed with no adverse reactions, such as pigments contained in toothpaste. This work first elaborates on the basis of inadvertent ingestion data to estimate the quantity of daily ingested Copper(II) Phthalocyanine (CuPc), a whitening pigment and well-known organic semiconductor. Subsequently, CuPc is employed in the first demonstration of fully edible electrolyte-gated transistors operating at low voltage (<1V), displaying good reproducibility and stable performance for over a year. The results indicate that, with the daily ingested quantity of CuPc from toothpaste, more than 104 edible transistors can be realized, thus paving the way to edible circuits, a critical component of future edible electronic systems.