Complicated Operations Research Articles

Design of smart home environment based on wireless sensor system and artificial speech recognition

In the context of the rapid development of intelligent technology, smart home has become the focus of attention. However, the traditional smart home system has some problems such as complicated operation and inconvenient information interaction. In order to solve these problems and improve the user experience of smart home environment, this study establishes a fast and efficient smart home environment design scheme by introducing wireless sensor system and artificial speech recognition technology. By arranging multiple wireless sensor nodes, the monitoring and data acquisition of environmental parameters are realized, and then the voice control and command interaction of smart home devices are realized by using artificial voice recognition technology. Through the effective combination of wireless sensor system and artificial voice recognition technology, users can realize the control of smart home environment through voice commands, and improve the convenience and intelligence of the system. The experimental results show that the design scheme can accurately and stably realize the recognition of user voice commands and the control of smart home devices, and users' evaluation of the degree of intelligence and user experience has also been significantly improved.

A SERS nanocellulose-paper-based analytical device for ultrasensitive detection of Alzheimer's disease

BackgroundAlzheimer's disease (AD), one of the most prevalent neurodegenerative diseases, results in severe cognitive decline and irreversible memory loss. Early detection of AD is significant to patients for personalized intervention since effective cure and treatment methods for AD are still lacking. Despite the severity of the disease, existing highly sensitive AD detection methods, including neuroimaging and brain deposit-positive lesion tests, are not suitable for screening purposes due to their high cost and complicated operation. Therefore, these methods are unsuitable for early detection, especially in low-resource settings. Although regular paper-based microfluidics are cost-efficient for AD detection, they are restricted by a poor limit of detection (LOD). ResultsTo address the above limitations, we report the ultrasensitive and low-cost nanocellulose paper (nanopaper)-based analytical microfluidic devices (NanoPADs) for detecting one of the promising AD blood biomarkers (glial fibrillary acidic protein, GFAP) using Surface-enhanced Raman scattering (SERS) immunoassay. Nanopaper offers advantages as a SERS substrate, such as an ultrasmooth surface, high optical transparency, and tunable chemical properties. We detected the target GFAP in artificial serum, achieving a LOD of 150 fg mL-1. SignificanceThe developed NanoPADs are distinguished by their cost-efficiency and ease of implementation, presenting a promising avenue for effective early detection of AD's GFAP biomarker with ultrahigh sensitivity. More importantly, our work provides the experimental routes for SERS-based immunoassay of biomarkers on NanoPADs for various diseases in the future.

Deep Transfer Learning Based Surrogate Modeling for Optimal Investment Decision of Distribution Networks

At present, the distribution network investment is faced with complicated operation strategies and various new investment elements. It's difficult to evaluate the impact of different investment measures on the final benefits due to the complicated physical modeling and potential coupling correlation, which contributes to that the traditional model-driven decision-making method may not work. This paper proposes a novel deep transfer learning based surrogate-assisted method to address the challenge. The key idea is to develop a surrogate model to replace the input-output correlation between the certain investment measure and its benefits, which can be further reformulated into mixed-integer linear constraints and embedded into the classical investment decision-making model for the target distribution network. As the model is trained through a designed two-stage adaptive transfer learning algorithm, which can extract the similar rules from other distribution networks, the dilemma of insufficient effective samples is alleviated, while a neural network constrained algorithm is designed to linear the surrogate model for simplifying the optimization. Numerical cases prove the effectiveness of the surrogate model on correlation mining and show the superiority of the surrogate-assisted method in achieving the precision investment for target distribution network.

Technique of Colon Interposition for Oesophageal Replacement for Oesophageal Cancer

Nowadays, it is only relatively rare and in selected situations that colonic interposition is chosen rather than the stomach as a reconstructive organ for replacing the oesophagus. The colon is a reliable organ for tubular replacement of the oesophagus when the stomach is not available for reconstruction. Colon interposition is a complex and complicated operation. It requires a specific indication and thorough preoperative preparation. From a technical point of view, colon interposition places high demands on the selection and surgical dissection of the vascular supply to the reconstructed organ. The reconstruction route and elevation of the interposition graft to the proximal oesophagus and the need to create 3 or 4 gastrointestinal anastomoses also place significantly higher demands than reconstruction using a gastric tube. Overall, despite the significant surgery-related morbidity, good functional results and a good quality of life can usually be achieved. The surgical technique applied in our own practice is described in detail. An overview from literature on the results of colonic interposition is given, particularly with regard to surgical complications and quality of life after colon interposition.

Research on the heat–humidity coupling transfer model of the CRTS Ⅱ type track slab and its characteristics

After operation for more than 10 years, CRTSⅡtype track slab structures have developed interlayer diseases in continuous extreme high-temperature environments, which are dominated by track slab-CA mortar layer cracking. To reveal the formation mechanism of track slab-CA mortar layer cracking under complicated operation conditions, a heat–humidity coupling transfer model, which is utilized to analyze track slab deformation, was developed. Furthermore, to enhance the precision of the model, we investigated the variation law that regulates relevant parameters of concrete materials with respect to temperature and humidity. Moreover, a testing apparatus was established to validate the accuracy of the transfer model under natural conditions. The analysis revealed that the maximum temperature error in the track slab was 3.33 K, and that the maximum error of relative humidity (RH) was 6.04 %. The distribution characteristics of temperature and humidity fields in the track slab during 10 consecutive rainless days under different seasonal climates in a hot-summer and cold-winter zone (Hangzhou) in China were calculated and analyzed using COMSOL Multiphysics® software. The results indicated that in the distribution of the summer temperature field, the maximum negative temperature gradient could attain -48.67 K/m, and that the maximum positive temperature gradient could attain 76.37 K/m. Moreover, RH values at 10 mm, 20 mm, 30 mm, and 40 mm decreased by 30.85 %, 15.51 %, 9.17 %, and 5.9 %, respectively. In a year, a higher track slab temperature, a greater temperature fluctuation range, and more apparent positive and negative temperature gradients were observed during summer, and the influencing depth of water also attained the maximum value during summer.

Revealing the Denoising Principle of Zero-Shot N2N-Based Algorithm from 1D Spectrum to 2D Image.

Denoising is a necessary step in image analysis to extract weak signals, especially those hardly identified by the naked eye. Unlike the data-driven deep-learning denoising algorithms relying on a clean image as the reference, Noise2Noise (N2N) was able to denoise the noise image, providing sufficiently noise images with the same subject but randomly distributed noise. Further, by introducing data augmentation to create a big data set and regularization to prevent model overfitting, zero-shot N2N-based denoising was proposed in which only a single noisy image was needed. Although various N2N-based denoising algorithms have been developed with high performance, their complicated black box operation prevented the lightweight. Therefore, to reveal the working function of the zero-shot N2N-based algorithm, we proposed a lightweight Peak2Peak algorithm (P2P) and qualitatively and quantitatively analyzed its denoising behavior on the 1D spectrum and 2D image. We found that the high-performance denoising originates from the trade-off balance between the loss function and regularization in the denoising module, where regularization is the switch of denoising. Meanwhile, the signal extraction is mainly from the self-supervised characteristic learning in the data augmentation module. Further, the lightweight P2P improved the denoising speed by at least ten times but with little performance loss, compared with that of the current N2N-based algorithms. In general, the visualization of P2P provides a reference for revealing the working function of zero-shot N2N-based algorithms, which would pave the way for the application of these algorithms toward real-time (in situ, in vivo, and operando) research improving both temporal and spatial resolutions. The P2P is open-source at https://github.com/3331822w/Peak2Peakand will be accessible online access at https://ramancloud.xmu.edu.cn/tutorial.

Research on an Intelligent Identification Method for Cable-Stayed Force with a Damper Based on Microwave Radar Measurements

Aiming at the shortcomings of traditional contact cable force monitoring technology in accuracy, efficiency, and applicability, an assessment method based on microwave radar measurements is proposed to measure a sloping cable with a damper for lengths greater than 200 m in this study. A formula for calculating the cable-stayed force with a damper is derived, and an intelligent cable force monitoring platform is developed based on cloud technology. Based on the Chongqing Nanjimen Railway Bridge, a real bridge test was carried out. It was indicated that the microwave radar method could be used to freely adjust the measurement angle and possessed high applicability and penetration. It significantly improved the measurement accuracy and efficiency of cables with a damper for lengths greater than 200 m. It has good application value for the solution of the problems of complicated operation and high costs in the monitoring of cables with a damper. The formula for calculating the cable force with a damper was proven to be reliable and accurate when compared to the results of direct calculation and the equivalent cable length method. It was able to significantly reduce the calculation error of the cable force caused by the influence of the damper. Additionally, the intelligent cable force monitoring platform was utilized to enhance the level of digitization, providing technical support for the scientific management and maintenance of bridges.

An equivalent heat source model for inducted heating forming with single-circuit inductor

ABSTRACT Induction line heating is a suitable method for plate bending in shipbuilding, and the rapid prediction of this forming process is required. However, the existing coupled magnetic-thermal prediction method is time-consuming and requires complicated modelling operation, which makes it difficult to rapidly estimate the thermal conduction of large-size ship hull plate bending. This paper proposes an equivalent heat source (EHS) model for a single-circuit inductor and establishes its quantitative relationship with heating parameters. First, based on a local yet accurate magnetic-thermal coupling model, an EHS model was constructed with three governing parameters. Second, the results with the EHS model were consistent with the experiments, while the thermal analysis was accelerated eight times over current magnetic-thermal coupling analysis. Finally, the effects of heating parameters - current, frequency, air gap and plate thickness on the three governing parameters were investigated, and the relationship between the heating parameters and the EHS model was identified with dimensional analysis.

Robotis Can Create Numerical Inverse Kinematic Problems Because Continuously Replace Humans

Robotic process automation is an smart way to manage work across those machines which AI can easily be categorize. Robot Automation is a sequence of steps that gives result in a meaningful action without interference of human being. robotics, design, construction, and use of machines (robots) to perform tasks done traditionally by human beings. Robots are widely used in such industries as automobile manufacture to perform simple repetitive tasks, and in industries where work must be performed in environments hazardous to humans. Medical robotics is an interdisciplinary field that focuses on developing electromechanical devices for clinical applications. The goal of this field is to enable new medical techniques by providing new capabilities to the physician or by providing assistance during surgical procedures. robotics is revolutionizing the future of work in a variety of industries. From manufacturing and logistics to healthcare and education, robots are improving efficiency, productivity, and safety. The application of the inverse kinematics method will herald a new approach in the robot nondestructive testing industry. Meanwhile, complicated matrix operations greatly limit traditional inverse kinematics methods.

An Efficient and Fast Method for Labeling and Analyzing Mouse Glomeruli.

The glomeruli are fundamental units in the kidney; hence, studying the glomeruli is pivotal for understanding renal function and pathology. Biological imaging provides intuitive information; thus, it is of great significance to label and observe the glomeruli. However, the glomeruli observation methods currently in use require complicated operations, and the results may lose label details or three-dimensional (3D) information. The clear, unobstructed brain imaging cocktails and computational analysis (CUBIC) tissue clearing technology has been widely used in renal research, allowing for more accurate detection and deeper detection depth. We found that mouse glomeruli can be rapidly and effectively labeled by tail vein injection of medium molecular weight FITC-Dextran followed by the CUBIC clearing method. The cleared mouse kidney could be scanned by a light-sheet microscope (or a confocal microscope when sliced) to obtain three-dimensional image stacks of all the glomeruli in the entire kidney. Processed with appropriate software, the glomeruli signals could be easily digitized and further analyzed to measure the number, volume, and frequency of the glomeruli.

Automatic reconstruction of geological reservoir models based on conditioning data constraints and BicycleGAN

For geological reservoir units with different sizes of pore spaces and relatively stronger anisotropic heterogeneities, Generative-Adversarial-Network-based (GAN) modeling methods can overcome limitation of numerical-simulation-based ones and support finely representation of nonstationary models. However, due to conditioning data weak constraints, GANs might ignore local detail features, with artifacts and noise during simulation. Some pre-processing strategies are strictly limited by the conditioning data distribution patterns, and corresponding processes should be updated frequently when new data are introduced, with relatively complicated operation. Therefore, the BicycleGAN framework was introduced in this paper. Based on bijective consistency between the latent vector and output, an image-to-image translation task with different dimensions is established, and multiple networks were coupled to realize finely representation of spatial attributes. Specifically, the mapping between conditioning data and output is established through the U-Net architecture, which not only local detail information is considered, but also reduce the impact of conditioning data distribution patterns. Meanwhile, the mapping between latent and actual test time distribution is also established by an encoding function to ensure simulation authenticities. In addition, a joint loss function combined with conditioning loss and prior loss is defined to ensure reconstruction accuracy of different facies types. Four kinds of categorical and continuous training images were selected to verify the network simulation performances. Results show that reconstruction accuracy for facies types of conditioning data is almost consistent with those of the references, keeping similarities in terms of spatial variability, connectivity, structural similarity, and facies type reproductions. Meanwhile, for the saved BicycleGAN, inputting different kinds of conditioning data once only, and a variety of simulations can be obtained rapidly, thereby realizing conditioning reconstruction of geological reservoir models.

An Ion Concentration Polarization Microplatform for Efficient Enrichment and Analysis of ctDNA.

Strategies for rapid, effective nucleic acid processing hold tremendous significance to the clinical analysis of circulating tumor DNA (ctDNA), a family of important markers indicating tumorigenesis and metastasis. However, traditional techniques remain challenging to achieve efficient DNA enrichment, further bringing about complicated operation and limited detection sensitivity. Here, we developed an ion concentration polarization microplatform that enabled highly rapid, efficient enrichment and purification of ctDNA from a variety of clinical samples, including serum, urine, and feces. The platform demonstrated efficiently separating and enriching ctDNA within 30 s, with a 100-fold improvement over traditional methods. Integrating an on-chip isothermal amplification module, the platform further achieved 100-fold enhanced sensitivity in ctDNA detection, which significantly eliminated false-negative results in the serum or urine samples due to the low abundance of ctDNA. Such a simple-designed platform offers a user-friendly yet powerful diagnosis technique with a wide applicability, ranging from early tumor diagnosis to infection screening.

Miniaturization of CRISPR/Cas12-Based DNA Sensor Array by Non-Contact Printing.

DNA microarrays have been applied for comprehensive genotyping, but remain a drawback in complicated operations. As a solution, we previously reported the solid-phase collateral cleavage (SPCC) system based on the clustered regularly interspaced short palindromic repeat/CRISPR-associated protein 12 (CRISPR/Cas12). Surface-immobilized Cas12-CRISPR RNA (crRNA) can directly hybridize target double-stranded DNA (dsDNA) and subsequently produce a signal via the cleavage of single-stranded DNA (ssDNA) reporter immobilized on the same spot. Therefore, SPCC-based multiplex dsDNA detection can be performed easily. This study reports the miniaturization of SPCC-based spots patterned by a non-contact printer and its performance in comprehensive genotyping on a massively accumulated array. Initially, printing, immobilization, and washing processes of Cas12-crRNA were established to fabricate the non-contact-patterned SPCC-based sensor array. A target dsDNA concentration response was obtained based on the developed sensor array, even with a spot diameter of 0.64 ± 0.05 mm. Also, the limit of detection was 572 pM, 531 pM, and 3.04 nM with 40, 20, and 10 nL-printing of Cas12-crRNA, respectively. Furthermore, the sensor array specifically detected three dsDNA sequences in one-pot multiplexing; therefore, the feasibility of comprehensive genotyping was confirmed. These results demonstrate that our technology can be miniaturized as a CRISPR/Cas12-based microarray by using non-contact printing. In the future, the non-contact-patterned SPCC-based sensor array can be applied as an alternative tool to DNA microarrays.

Parameter collaborative analysis and multi-objective optimization of a nanofluid filtered solar membrane distillation system using heat pump

The distributed solar powered membrane distillation (SPMD) system for water/electricity co-production is a valuable solution for relieving energy pressure in remote areas but it suffers from a complicated operation mechanism. This paper performs a comprehensive parameter analysis and user-oriented optimization on a nanofluid filtered solar membrane distillation system using heat pump to enhance its competitiveness. Response surface methodology is employed to examine the interactions of the influencing factors on the system performance, and the plots reveal significant interactions, particularly on equivalent specific thermal energy consumption (eSTEC). An artificial neural network is integrated with genetic algorithm to perform single and multi-objective system performance optimization. The results show that maximum system exergy efficiency, permeate flux and minimum eSTEC are 19.39 %, 35.8 kg·m−2·h−1 and 463.5 kWh·m−3, respectively. Furthermore, the technique for order preference by similarity to an ideal solution is utilized, based on performance indicator weight derived from the analytical hierarchy process, to offer a user-oriented optimal solution. It demonstrates that after optimization, the system exergy efficiency and permeate flux are improved by 9.9 % and 30.2 %, respectively, along with a reduction of eSTEC by 5.7 %. This research contributes to advancing solar-to-water/electricity efficiency and offering valuable insights for the development of SPMD systems.

Research on the Technical Model of Resource Utilization of Rural Domestic Sewage

At present, China’s rural sewage treatment technology is mostly used in municipal sewage treatment methods, with high energy consumption and complicated operation and maintenance. Through analyzing the problem of rural sewage management, we discussed the necessity and feasibility of rural sewage resource utilization, presented the principles that should be followed in the resource utilization according to local conditions, promoting the treatment, economic rationality, and ecological safety, and Summarized the technical models of irrigation utilization, ecological dissipation and decentralized on-site utilization for the resource utilization of rural domestic wastewater. The expansion of the resource utilization model will be more conducive to reducing the cost of rural sewage management and enhancing the quality of the rural environment and human settlements.

Commercially available ionic liquids enable high-performance aqueous zinc–iodine batteries: sequestered nitrogen-sites for efficient electrocatalytic iodine conversion

Commercial 1,2-dimethyl-3-propylimidazolium iodide towards high-performance zinc–iodine batteries without any complicated operations.

Recent advances in nucleic acid signal amplification-based aptasensors for sensing mycotoxins.

Mycotoxin contamination in food products may cause serious health hazards and economic losses. The effective control and accurate detection of mycotoxins have become a global concern. Even though a variety of methods have been developed for mycotoxin detection, most conventional methods suffer from complicated operation procedures, low sensitivity, high cost, and long assay time. Therefore, the development of simple and sensitive methods for mycotoxin assay is highly needed. The introduction of nucleic acid signal amplification technology (NASAT) into aptasensors significantly improves the sensitivity and facilitates the detection of mycotoxins. Herein, we give a comprehensive review of the recent advances in NASAT-based aptasensors for assaying mycotoxins and summarize the principles, features, and applications of NASAT-based aptasensors. Moreover, we highlight the challenges and prospects in the field, including the simultaneous detection of multiple mycotoxins and the development of portable devices for field detection.

A lightweight attribute-based signcryption scheme based on cloud-fog assisted in smart healthcare.

In the environment of big data of the Internet of Things, smart healthcare is developed in combination with cloud computing. However, with the generation of massive data in smart healthcare systems and the need for real-time data processing, traditional cloud computing is no longer suitable for resources-constrained devices in the Internet of Things. In order to address this issue, we combine the advantages of fog computing and propose a cloud-fog assisted attribute-based signcryption for smart healthcare. In the constructed "cloud-fog-terminal" three-layer model, before the patient (data owner)signcryption, it first offloads some heavy computation burden to fog nodes and the doctor (data user) also outsources some complicated operations to fog nodes before unsigncryption by providing a blinded private key, which greatly reduces the calculation overhead of resource-constrained devices of patient and doctor, improves the calculation efficiency. Thus it implements a lightweight signcryption algorithm. Security analysis confirms that the proposed scheme achieves indistinguishability under chosen ciphertext attack and existential unforgeability under chosen message attack if the computational bilinear Diffie-Hellman problem and the decisional bilinear Diffie-Hellman problem holds. Furthermore, performance analysis demonstrates that our new scheme has less computational overhead for both doctors and patients, so it offers higher computational efficiency and is well-suited for application scenarios of smart healthcare.

Myricetin-based fluorescence probes with AIE and ESIPT properties for detection of hydrazine in the environment and fingerprinting

BackgroundHydrazine (N2H4) is a highly toxic and versatile chemical raw material, which poses a serious threat to the environment and human health when used in large quantities. However, the traditional methods for the detection of N2H4 have the disadvantages of time-consuming, complicated operation and expensive instruments. In contrast, fluorescence probes have many advantages, such as simple operation, high sensitivity, good selectivity, and fast response time. Therefore, there is an urgent need for a fluorescence probe that can rapidly and accurately detect the presence of N2H4 and monitor the changes in its concentration. ResultsFor this purpose, we designed and synthesized a series of myricetin fluorescence probes 3-(substituent group)-5,7-dimethoxy-4-oxo-2-(3,4,5-trimethoxy.phenyl)-4H-chromen-4-one (Myr-R) for N2H4 detection. In the presence of N2H4, the probe 5,7-dimethoxy-3-(2,3,4,5,6-pentafluorobenzoate)-2-(3,4,5-trimethoxyphen-yl).-4H-chr-omen-4-one (Myr-3) shows significant fluorescence changes, double emission properties and a large Stokes shift (183 nm), and exhibits high selectivity and sensitivity to N2H4 (The detection limit is 93 nM). Importantly, the qualitative and quantitative analysis of N2H4 in water, soil, and air can be accomplished using fluorescence, smartphone, and UV lamps coupled with Myr-3. In addition, Myr-3 can be used for monitoring and imaging intracellular N2H4. Meanwhile, the fluorophore 3-hydroxy-5,7-dimethoxy-2-(3,4,5-trimethoxyphenyl)-4H-benzopyran-4-one (Myr-Me) was applied to fingerprinting of different substrate materials due to the fact that it exhibits strong yellow fluorescence emission in the solid state and shows excellent contrast and high resolution. SignificanceThe probe Myr-3 is not only able to rapidly detect N2H4 in complex environments, but also can be used for imaging intracellular N2H4. In addition, the fluorophore Myr-Me can be used as an effective imaging agent for visual fingerprinting. These properties enable the probe Myr-3 and the fluorophore Myr-Me for a wide range of potential applications in related fields.

A rare case of a large tumour located under the facial nerve trunk

Aim: to introduce a technique of total parotidectomy sparing the facial nerve branches based on a clinical case. The article presents a clinical case of a large parotid tumour located under the facial nerve trunk, the stages of its surgical removal, and the postoperative course. Parotidectomy is one of the most complicated operations in maxillofacial surgery. When performing this surgical intervention, a surgeon must observe the principles of ablastics, consider individual peculiarities of the facial nerve anatomy structure and relations of nerve and tumour, and be ready to expand the scope of the operation from subtotal to total parotidectomy. Conclusions. To improve the treatment outcomes of patients with benign parotid tumours and to reduce the number of such postoperative complications as paresis and paralysis of the mimetic muscles caused by facial nerve injury, one should know and follow the technique of parotidectomy, especially in the case of a neoplasm located under the trunk or branches of facial nerve. The research was adhered to the principles of the Declaration of Helsinki. The patient informed consent was obtained for the study. No conflict of interests was declared by the authors.