Search

Spatially resolved fluid-flow modulation and its corresponding integration becomes a crucial aspect to consider when designing high-performing paper-based analytical devices. As such, high demand exists for cost-effective techniques able to selectively control flow in patternable regions of the device. In this regard, a solution composed of potassium alum and animal glue; sizing materials used in the fabrication of calligraphy Xuan paper, is successfully adapted for the first time in a revolutionary approach to efficiently modulate the capillary flow in paper microfluidic channels. Flow delays are achieved by coating the substrate systematically through the variation of parameters such as the ratio of alum to glue, the concentration of alum-glue in the solution, and the number of subsequent coating layers of alum-glue. Moreover, digitized patterning with alum-glue is developed to achieve programmable wicking profiles of accelerating, decelerating, and quasi-linear flow displacement by alternating coated and uncoated zones in the channel. Additionally, the influence of alum-glue when performing biometric assays is investigated by performing a horseradish peroxidase activity colorimetric assay and found to have negligible effects on reading, where a negligible difference of 5.2% was observed in an assay performed in a coated paper substrate, compared to an uncoated one.

Flexible inkjet-printed antenna is presented. The proposed antenna is designed on a synthetic paper substrate with an area of 35 × 40 mm2 to provide frequency bands for many popular wireless applications. Simulation and measurement are used to study the performances, including the frequency bands, radiation patterns, peak gains and efficiency, of the antenna in two severely bending conditions with a curvature radius of 20 mm in the forward and backward directions. The antenna printed in photo papers material could not be fully flexible due to cracks introduced to the coating material on the surface of the photo paper. Hence, the resin coated material was only suitable for curved devices. The synthetic paper material used in this paper does not have a resin coating, therefore, the antenna does not lose its flexibility and can be bent in both the forward and backward directions without any cracks. Results show that bending in these two conditions has insignificant effects on the performances of the antenna. With a curvature radius of 20 mm in the forward direction, the measured peak gain and efficiency of the antenna are in the ranges from −3 to +2 dBi and 40% to 60%, respectively. The results together with the low cost make the antenna most suitable for many flexible 5G communications devices.

As a reflection of the scholar's mastery of basic theories and professional knowledge, the dissertation is an important yardstick for measuring the level of scientific research. At present, the problem of academic misconduct is becoming increasingly prominent, which is not only related to personal academic ethics, but also related to the overall development of the national academic and scientific research field. In the traditional method of evaluating the quality of papers, it is mainly based on the evaluation experts' comments and scores. However, there are cases that the evaluation experts' comments and scores are inconsistent in practice. To address this problem, we proposed a paper quality consistency detection model based on the nearest neighbor analysis dimensionality reduction algorithm. Compared with other traditional models, the experimental results show that the detection accuracy of XGBoost model after dimensionality reduction using nearest neighbor analysis algorithm reaches 85.81%.

[Display omitted] The global municipal solid waste (MSW) generation rate is 2.01 billion metric tonnes annually with an average of 0.74 kg waste/person/day. Approximately 92 % of the MSW originates from organics composition (e.g., food waste; plastic; paper; garden waste/woods, and textile), where 33 % of overall MSW is improperly managed in an efficient and environmentally safe manner. One of the promising methods to solve MSW management issues is to convert MSW into refuse-derived fuel (RDF) that can be used for the clinker burning process in cement kiln to replace the usage of fossil-based solid fuel. Thus, the potential of local MSW composition in energy recovery; suitability of RDF production technology; as well as international-industry requirement on RDF in co-processing and environmental concerns are discussed. Due to heterogeneous composition and sizes in nature, high moisture, and substantial amount of chloride content in MSW, it needs to undergo pre-treatment processes to enhance the RDF's physio-chemical properties that comply with RDF ASTM/EN standards, where expected high heating value (HHV) is > 20 MJ/kg, ash (<10 %), Chloride (Cl) (<0.80 – 1.00 %), Sulphate (S) (<1.50 %), Nitrate (N) (<1.00 %). As the development of upgraded MSW to RDF is still new in the commercial phase, there is minimal information and data on the techno-economic analysis, as well as recent industrial-scale of thermos-chemical conversion technologies for RDF preparation. Hence, the present work provides a clear picture on overview of municipal solid waste (MSW) generation, MSW composition, MSW pretreatment, and application as co-processing in cement industry are discussed. Besides, recent thermochemical upgrading process (torrefaction, dry carbonization, and hydrothermal carbonization) of MSW from R&D to commercial scale was further highlighted. In summary, this review serves as basic criterion and strategies to explore the new path of upgrading the waste into RDF for the purpose of sustainable energy recovery that adopting in circular carbon economy framework. [ABSTRACT FROM AUTHOR]

Power transformer is one of the main equipment in power transmission and distribution network. Thus, it is important to ensure optimal operation of power transformer for an efficient supply of energy to utilities. One of the main components of a power transformer is the transformer insulation system, namely, transformer insulation oil and transformer insulation paper. This review provides an in-depth discussion on the reactions that occur in the insulation system of the power transformer. These include, oxidation, hydrolysis, pyrolysis, partial discharge, and arcing. The reaction mechanisms, conditions and the relationship between these reactions are thoroughly analysed in this review. Apart from that, this review also provides an inclusive discussion on the state-of-the-art methods used to monitor the byproducts formed from the mentioned reactions. These methods were developed to overcome the limitation of conventional methods that are complex and costly. Moreover, it presents an impartial evaluation of the challenges and prospects in making the power transformer monitoring system more efficient in terms of cost and time. Information corroborated in this review is expected to provide an important roadmap for future research in monitoring the condition of power transformer.

Warehouses represent key links in domestic and international commodity flows. The increasing shortage of workers and increasing costs on the one hand, and the increasing number and stricter demands of users on the other hand lead warehouse managers to realize their operations as efficiently as possible. A proposed model has an objective of enabling companies to monitor warehouse performance in an authoritative, reliable, and simple way and define appropriate corrective measures accordingly. The proposed empirical research consists of three stages, where in the first stage a combination of Principal Component Analysis-Data Envelopment Analysis methods was applied in order to determine efficient warehouses based on 90 decision making units. In the second phase, a completely new method called Interval Fuzzy Rough Pivot Pair-wise Relative Criteria Importance Assessment method used for determining criteria weights was developed and applied, which is one of the most important novelties of this study. In the last phase, the Interval Fuzzy Rough Measurement of Alternatives and Ranking according to the Compromise Solution method was applied to rank the alternatives. Twelve criteria were observed to evaluate 21 alternatives. Based on the results, it was concluded that salary stood out as the most important criterion, while amortization stood out as the least significant criterion. On the other hand, alternatives A9 and A10 stood out as the best-ranked alternatives while A1, A2, and A3 stood out as the least efficient ones. The paper provides clear scientific contributions that are reflected in the reduction of the gap that was observed after reviewing the literature where there is a lack of papers dealing with this task. Also, the combination of methods applied in the paper has not been used so far, so it can be said that this paper represents an excellent basis for further research. The model has practical contributions as it allows decision-makers to make quality decisions regarding the operation of their warehouses in different time periods or observation periods, as well as it represents a decision support tool that can be used for better warehouse management. • New model for warehouse performance evaluation has been proposed. • An integrated PCA-DEA-IFR PIPRECIA-IFR MARCOS Model was developed. • New approach IFR PIPRECIA was developed and presented in literature for first time. • The model enables more accurate and precise decision-making in logistics. [ABSTRACT FROM AUTHOR]

In the literature, modifying the baseline distributions and proposing novel approach of the existing probability models play an important role in the analysis of real-world phenomena as well as the development of new distributions essentially stems from the need to adequately characterize environmental and lifetime events. In this paper, we introduce a new model by applying maximum compound technique to the baseline distributions for modeling more types data used in different case of studies including symmetric, asymmetric, skewed and complex data sets. The proposed model called univariate maximum Harris extended Rayleigh distribution and three parameters characterize it, and it is regarded as strong competitor for widely applied symmetrical and non symmetrical models. The paper offers varied fundamental distributional and mathematical properties of the suggested distribution, such as conditional probability and its expectation, as well as quantile and moment-generating functions. The model's parameters are estimated by applying numerous procedures including maximum likelihood estimation, Expectation–Maximization iterative method and Bayesian approach. Bayesian estimation is performed via various loss functions such as square error, Linex and general entropy functions. As well as, approximate and Bootstrap methods are used to construct the confidence interval for the unknown parameters of the proposed model. Additionally, we illustrated different extensive simulation experiments to see the potential of the suggested estimation methods which give satisfactory performance results. In the end, we applied two real data sets for illustrative purposes. In our illustration, we have compared the practicality of the recommended model with several well-known competing distributions. [ABSTRACT FROM AUTHOR]

There are many classic highly complex optimization problems in the world, therefore, it is still necessary to find an applicable and effective algorithm to solve these problems. In this paper, self-adaptive hybrid cross mutation slime mold algorithm is proposed, which is AHCSMA, to solve these problems efficiently. Specifically, there are three innovations in this paper: (i) new self-adaptive Cauchy mutation operator is developed to improve the mutation ability of the population; (ii) the crossover rate balance mechanism is proposed to make up for the neglected relationship between individuals and crossover rates. Then the differential vector information between the dominant individual and other individuals in the population is highly utilized to increase the evolution speed of the algorithm; (iii) self-adaptive restart hybrid opposition learning is designed to alleviate the situation where the algorithm falls into local optimality. To verify the competitive of AHCSMA, UAV path planning problems, engineering problems, nonlinear parameter extraction of photovoltaic model problems and parameter identification problems of highly nonlinear infinite impulse response are used to test the ability of the AHCSMA, accumulation more than 50 algorithms are used as comparison algorithms, and results report that AHCSMA is extremely competitive and performs better when optimizing these real-life problems. [ABSTRACT FROM AUTHOR]

Sintering performance index can reflect the quality of sintered ore, and the level of sintered ore performance directly affects the stability of blast furnace production. Accurate prediction of sinter performance metrics is essential to optimise sinter quality, improve production efficiency and reduce energy consumption. Based on the sintering big data, this paper proposes the Optuna-DFNN model by combining the fuzzy neural network algorithm, the deep neural network algorithm and the Optuna framework to address the problems of difficulty in tuning the parameter, lack of self-learning ability, and poor generalisation of the model of the traditional method when faced with the input of multiple parameters. The paper predicts the four prediction indexes of yield rate, return fines ratio, drum index and RDI +3.15 respectively. Among them, the relative error of the prediction of the yield rate is within 1.3 %, the relative error of the prediction of the return fines ratio is within 2.8 %, the relative error of the prediction of the drum index is within 0.32 %, and the relative error of the prediction of the RDI +3.15 is within 1.4 %. The results indicate that the Optuna-DFNN model has better performance in predicting sintering performance metrics. Based on the Optuna-DFNN sintered ore performance prediction model, the quality of sintered ore can be accurately predicted, the sintering process parameters can be adjusted in a timely manner, the sintering process can be optimized, and the utilization coefficient of sintering can be improved. Provide a reference for the optimisation of production processes, with a positive impact on cost reduction, environmental protection and increased sustainability of production. [ABSTRACT FROM AUTHOR]

In this paper, the fractional space-time nonlinear Chen-Lee-Liu equation has been considered using various methods. The investigation of the transition from periodic to quasi-periodic behavior has been conducted using a saddle-node bifurcation approach. The paper reports the conditions of multi-dimensional bifurcations of dynamical solutions. Additionally, a direct algebraic method has been used to calculate various 2D and 3D solitonic structures of the equation, and an analysis of their accuracy and effectiveness has been conducted. Furthermore, the Galilean transformation has been used to convert the equation into a planar dynamical system, which is further utilized to obtain bifurcations and chaotic structures. Chaotic structures of perturbed dynamical system are observed and detected through chaos detecting tools such as 2D-phase portrait, 3D-phase portrait, time series analysis, multistability and Lyapunov exponents over time. Further, sensitivity behavior for a range of initial conditions, both perturbed and unperturbed. The results suggest that the investigated equation exhibits a higher degree of multi-stability. [ABSTRACT FROM AUTHOR]

Handover (HO) management in Heterogeneous Networks (HetNets) poses challenges arising from network densification and dynamic environmental behaviors. Existing HO decision algorithms struggle to efficiently utilize network resources and ensure a high-quality user experience amidst the complexity of HetNets and the burgeoning growth of mobile users. This paper introduces a robust and data-driven HO decision model designed to enhance HO performance in HetNets. Initially, a conventional HO decision algorithm is developed based on users' Reference Signal Received Power (RSRP) values in MATLAB. Various simulation cases explore different HO parameters to observe their impact on handover performance. To address these challenges, a data-driven HO decision model leveraging Long Short-Term Memory (LSTM), a deep learning technique, is proposed for the regression task. The LSTM model is trained and tested using obtained RSRP values, and the future RSRP values predicted by the model are employed to trigger HO decisions in the proposed algorithm. Results from the traditional HO decision algorithm are compared with those of the proposed machine learning-based approach across various simulation runs, considering average Signal-to-Interference-plus-Noise Ratio (SINR), RSRP, user throughput values, the number of HOs and the Radio Link Failure (RLF) ratio. Different user speeds are also considered to establish a relationship between HO frequency and mobile user speed. The proposed model achieved reducing the rate of radio link failure to levels that are deemed acceptable in order to ensure a continuous connection. • HO management in HetNets is challenging due to network densification and dynamic environmental behaviors. • Existing HO decision algorithms are unable to efficiently ensure a high-quality user experience in HetNets. • The paper introduces a robust and data-driven HO decision model aimed at improving HO performance in HetNets. • To address the limitations of the traditional approach, a data-driven HO decision model is proposed based on LSTM. • The proposed algorithm's advantages and disadvantages are assessed based on the simulation results. [ABSTRACT FROM AUTHOR]

Energy consumption optimization is crucial for improving the quality of application services in the industrial Internet of Things (IIoT) environment. Traditional optimization methods often adopt blind search strategies, which leads to a significant waste of computing resources and low efficiency. This paper proposes a Hybrid Cat Swarm Optimization and Monarch Butterfly Optimization (HCSMBO), which combines the advantages of Cat Swarm Optimization (CSO) and Monarch Butterfly Optimization (MBO) to solve this problem. By utilizing the characteristics of swarm intelligence, including strong spatial search ability, high stability, and fast convergence, HCSMBO focuses on detailed optimization of the coverage, efficiency, and energy balance coefficient of Support Vector Machine (SVM) networks. By adjusting the weight factors of the fitness function, this algorithm can effectively find the global optimal solution. The experimental results confirm that HCSMBO significantly improves the intelligence, accuracy, and stability of task scheduling in resource management configuration, while achieving lower energy consumption. The research results of this paper are of great significance for improving the service quality of IIoT applications, meeting personalized business needs, and improving resource utilization and execution efficiency. In addition, they also provide new ideas and methods for energy consumption optimization research in the future IIoT. [ABSTRACT FROM AUTHOR]

In recent years, image semantic segmentation technology has developed rapidly, but image annotation usually requires a significant amount of human and financial resources, especially for remote sensing image annotation, which can be expensive and sometimes even unaffordable. To address this issue, this paper integrates the idea of curriculum learning into the self-training method and screens reliable pseudo-labels through computing image-level confidence, significantly reducing the confirmation error problem. Furthermore, the semi-supervised model in this paper combines implicit semantic enhancement with strong data augmentation, which can reduce the coupling between the teacher model and the student model's prediction distribution and enhance the model's robustness. Finally, the proposed semi-supervised method is experimentally verified using the ISPRS competition dataset and compared with existing state-of-the-art (SOTA) methods. Experimental results show that the proposed semi-supervised segmentation method achieves higher segmentation accuracy compared to self-training methods. Moreover, despite not using iterative training to simplify the training process, the proposed method still yields satisfactory segmentation results. [ABSTRACT FROM AUTHOR]

The impact of air pollution on public health is substantial, and accurate long-term predictions of air quality are crucial for early warning systems to address this issue. Air quality prediction has drawn significant attention, bridging environmental science, statistics, and computer science. This paper presents a comprehensive review of the current research status and advances in air quality prediction methods. Deep learning, a novel machine learning approach, has demonstrated remarkable proficiency in identifying complex, nonlinear patterns in air quality data, yet its application in air quality prediction is still relatively nascent. This paper also conducts a systematic analysis and summarizes how cutting-edge deep learning models are applied in air quality prediction. Initially, the historical evolution of air quality prediction methods and datasets is presented. This is followed by an examination of conventional air quality prediction techniques. A thorough comparative analysis of progress made with both traditional and deep learning-based prediction methods is provided. This review particularly focuses on three aspects: temporal modeling, spatiotemporal modeling, and attention mechanisms. Finally, emerging trends in the field of air quality prediction are identified and discussed. [ABSTRACT FROM AUTHOR]

Operational safety at airports has long been a subject of active research, with a growing focus on the safety of aircraft stands in recent years. The phase during which an aircraft taxi into its designated stand with engines still running poses safety risks to surrounding vehicles. However, there is currently a lack of effective methods for detecting potential conflicts and hazards during this taxiing phase. To address this issue, this study utilizes apron activity videos as a monitoring data source and introduces an integrated Internet of Things (IoT) conflict detection model. This system combines object detection, engine keypoint detection, coordinate conversion, and conflict warning system to provide timely alerts when vehicles incursion into the engine danger areas. Firstly, the paper streamlines the fourth-level branch and network of HRNet, resulting in the HRNet-3stage network. This network is then compared with Lite-HRNet to determine the optimal choice. Secondly, Recognizing the limitations of traditional video conflict detection based on pixel distance, a fixed monitoring camera coordinate conversion algorithm is designed to convert pixel coordinates into actual coordinates on the aircraft stand, thereby improving the accuracy of conflict detection based on distance measurement. Thirdly, considering the risks associated with engine inlet and exhaust, engine design parameters, static spacing standards, and the dynamic anti-collision process within the aircraft stand, the study proposes a method for classifying four types of aircraft engine danger areas. Corresponding conflict detection models are designed for potential scraping incidents when vehicles incursion into these danger areas. Upon detecting a vehicle entering the aircraft engine danger areas, the IoT system sends warning messages through the airport control tower monitoring system. Finally, we construct an apron sandbox to validate the conflict detection model. This validation results in an impressive F1-score exceeding 90% and a detection delay of less than 100 ms. Our innovative approach, supported by keypoint detection networks and IoT, effectively addresses the detection of critical aircraft components within stands. It comprehensively analyzes incursion issues into engine danger areas, offering a novel perspective for understanding and mitigating potential conflicts arising from aircraft taxiing into stands. • This paper provided an improved network and dataset for in-apron aircraft engine keypoint detection. • This paper designed an efficient coordinate transformation algorithm within the airport apron. • Aircraft engine danger area incursion detection model based on keypoint detection was designed to enable advanced warning and alerting of intruding vehicles. [ABSTRACT FROM AUTHOR]

Aerogels, with their exceptional properties such as high insulation performance, lightness, and eco-friendliness, present immense potential for use in the construction industry. This paper reviews the progress in research and application of various types of aerogels in construction, highlighting notable case studies demonstrating energy efficiency gains and sustainability impacts. It delves into the lifecycle of aerogels, discussing environmental implications at each stage. Despite their potential, aerogels face certain limitations including production complexity, brittleness, and high cost which hinder their widespread adoption. The paper further discusses these challenges and highlights the need for continuous research and development efforts to overcome them, with a view to achieving more scalable production, enhanced mechanical properties, and cost reduction. Looking forward, the paper discusses the expansive horizon of aerogel research, emphasizing the potential of smart aerogels, their integration with renewable energy systems, and exploring the possibility of recycling or creating bio-based aerogels to enhance their overall sustainability. While still in early stages, aerogels promise to be a major contributor to the future of construction, leading to more sustainable and efficient built environments. [ABSTRACT FROM AUTHOR]

Removal of the cationic dyes rhodamine B (RhB) and methylene blue (MB) by waste seeds Aleurites moluccana (WAM) was studied in a batch system. The adsorbent was characterized by Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), point of zero charge measurement, and the Boehm titration method. The effects of contact time and pH were investigated for the removal of cationic dyes. An increase in pH from 3 to 9 was accompanied by an approximately three-fold increase in the amount of dye adsorbed. The adsorptions equilibrium values were obtained and analyzed using the Langmuir, Freundlich, Sips, and Redlich–Peterson equations, the Sips isotherm being the one that showed the best correlation with the experimental values. The maximum adsorption capacities of the dyes were 178 mg/g for the MB and 117 mg/g for the RhB. The kinetic sorption was evaluated by the pseudo-first-order, pseudo-second-order, and intraparticle diffusion models, where it was observed that sorption follows the pseudo-second-order kinetic model. The study of thermodynamics showed that the adsorption is a spontaneous and endothermic process. The results indicate that waste seeds of A. moluccana could be used as a low cost material for the removal of cationic dyes from wastewater. [ABSTRACT FROM AUTHOR]

Cancer is a life-threatening ailment characterized by the uncontrolled proliferation of cells. Breast cancer (BC) represents the most highly infiltrative neoplasms and constitutes the primary cause of mortality in the female population due to cancer-related complications. Consequently, the imperative for early detection and prognosis has emerged as a means to enhance long-term survival rates and mitigate mortality. Emerging artificial intelligence (AI) technologies are being utilized to aid radiologists in the analysis of medical images, resulting in enhanced outcomes for individuals diagnosed with cancer. The purpose of this survey is to examine peer-reviewed computer-aided diagnosis (CAD) systems that have been recently developed and utilize machine learning (ML) and deep learning (DL) techniques for the diagnosis of BC. The survey aims to compare these newly developed systems with previously established methods and provide technical details, as well as the advantages and disadvantages associated with each model. In addition, this paper addresses several unresolved matters, areas of research that require further exploration, and potential avenues for future investigation in the realm of advanced computer-aided design (CAD) models utilized in the interpretation of medical images. Furthermore, the integration of Internet of Things (IoT) in BC research and treatment holds immense significance by facilitating real-time monitoring and personalized healthcare solutions. IoT devices, such as wearable sensors and smart implants, enable continuous data collection, empowering healthcare professionals to track patients' vital signs, response to treatment, and overall health trends, fostering more proactive and tailored approaches to BC management. Moreover, the advent of 5G technology in BC applications promises to revolutionize communication speeds and data transfer, enabling rapid and seamless transmission of large medical datasets. This high-speed connectivity enhances the efficiency of remote diagnostics, telemedicine, and collaborative research efforts, ultimately accelerating the pace of innovation and improving patient outcomes in BC care. The present study aims to examine various classifiers utilized in ML and DL methodologies for the purpose of diagnosing BC. Research findings have demonstrated that DL has superior performance compared to standard ML methods in the context of BC diagnosis, particularly when the dataset is extensive. The existing body of research indicates that there are significant gaps in knowledge that need to be addressed in order to enhance healthcare outcomes in the future. These gaps highlight the pressing need for both practical and scientific research in the field. Finally, IoT and 5G will be how they can be used in order to enhance BC detection, treatment and patient care. • Cancer, characterized by uncontrolled cell proliferation, poses a significant threat to human life. • Breast carcinoma is highly invasive and a leading cause of female cancer-related mortality, emphasizing the need for early detection and prognosis. • Emerging AI technologies are assisting radiologists in analyzing medical images, improving cancer diagnosis outcomes. • This survey focuses on recent computer-aided diagnosis (CAD) systems using machine learning and deep learning for breast carcinoma diagnosis. • The paper identifies unresolved research areas and future investigation prospects in advanced CAD models for medical image interpretation. [ABSTRACT FROM AUTHOR]

Diabetes is a chronic condition that is characterized by high blood glucose levels and can cause damage to multiple organs over time. Continuous monitoring of glucose levels is essential for both diabetic and non-diabetic individuals. There have been major developments in glucose monitoring technology over the past decade, which have been driven by research and industry efforts. Despite these significant advancements, the area of glucose biosensors still faces significant challenges. This paper presents a comprehensive summary of the latest glucose monitoring technologies, including invasive, minimally invasive, and non-invasive methods. Subsequently, we bring together the electronic components, wireless communication technologies, and energy harvesting opportunities, along with the limitations and challenges associated with current glucose monitoring solutions. This is followed by highlighting the potential integration of health records generated by continuous glucose monitors and artificial intelligence (AI) techniques to define precise diabetes management protocols. This integration achieves accurate results with constrained prediction horizons employing a time series of continuous glucose readings. The paper emphasizes the need for further advancements in glucose monitoring technology to improve diabetes management and address the critical need in clinical practice for improved glucose monitoring technologies with translational implications. • Comprehensive exploration of diverse glucose sensing technologies and their underlying principles. • Highlighting the crucial role of wearable biosensors in monitoring bio-molecules. • Detailed overview of essential electronic components for continuous glucose monitoring (CGM) systems. • Discussion on energy storage options, including self-powered solutions and advancements in battery technologies. • Emphasis on challenges in CGM technology, integration of AI, commercial solutions analysis, and future directions. [ABSTRACT FROM AUTHOR]

In recent decades, conducting polymers (CPs) like polyaniline and polypyrrole have attracted the attentions of scientists all over the world due to having important advantages such as simplicity of synthesis, excellent chemical/mechanical stability and good optical properties. Despite the existence of different shortcomings in the pristine form of CPs, hybridization with other substances can be a great solution to decrease these limitations. The synergetic influences of CPs with composites can make them a promising option for industrial-based applications in electronics, medical, biomedical and optoelectronic fields. This paper aims to present a comprehensive review for expert and non-expert readers to be acquainted with the recent advancements in the field of perception, characterization and synthesis of different types of CPs in industries. Additionally, this paper overviews the application of commonly-applied CPs in industrial activities and present the advantages/disadvantages of each of them to provide an opportunity for readers to know the efficiency and feasibility of using each CP based on their conductivity and other related parameters. [ABSTRACT FROM AUTHOR]

This paper proposes a method for dynamically controlling the working slope configuration in seasonal stripping open-pit coal mines. Constructing a bench group working slope model calculates the cyclic advancement time of the stripping working slope. A mathematical optimization model is then established to minimize the transport work of the overburden, and a Sequential Quadratic Programming (SQP) algorithm is employed to solve it. The method is applied to the Shengli West No. 2 open-pit coal mine, and the results show that the number of benches in the bench group is 5. The working bench width is 89 m, and the working slope advancement meets the stripping and mining engineering continuity requirements while minimizing the transport work of the overburden. The maximum advancement speed of the working slope under this configuration is 320 m/year. The paper also analyzes the working slope's maximum advancement speed variation with changes in the bench combination form and cyclic advancement distance. [ABSTRACT FROM AUTHOR]

The effect of target tracking is not ideal when facing various complex tracking scenarios such as non-rigid deformation of target, frequent occlusion, clutter of target background and interference of similar objects. In this paper, the feature based on deep convolutional neural network is used for target tracking in moving scenes, and a sliding window target segmentation method is proposed to study the impact of data normalization and data set expansion on the final result. In order to select more distinguishing features, principal component analysis is used to process the features of Deep Convolution Neural Network (DCNN), and the features of different network layers of DCNN are compared. The feature coding algorithm is studied, and the extracted DCNN features are encoded by Fisher Vectors algorithm, and compared with the locality-constrained linear encoding technique. Experiments show that the feature based on deep convolutional neural network in this paper can obtain higher accuracy than the traditional feature fusion method. According to the result analysis, the tracking accuracy of deep convolutional neural network algorithm is improved under the condition of illumination variation. In the case of local occlusion, the tracking accuracy is also improved. [ABSTRACT FROM AUTHOR]

The objective of this paper is to recommend an adjusted Susceptible-Exposed-Infectious-Removed (SEIR) model that characterizes the temporal patterns of various individuals affected by the omicron variant in an epidemic. This model considers factors such as vaccination, asymptomatic cases, indoor and outdoor air, hospitalizations, and deaths, as well as the impact of immunization and memory trace. While many recent studies overlook the complexities of multiple strains, including their diverse transmission rates and reaction to vaccines, this study introduces a new fractional derivative model to examine the spread of the omicron variant of COVID-19 and the implementation of a vaccination campaign. A thorough theoretical analysis is conducted, and the critical factor (R c r n z) is calculated using the model equations. It is demonstrated that when R c r n z is less than 1, the disease-free state is globally asymptotically stable, meaning that the epidemic diminishes. Moreover, the stability of both global and local equilibrium points is examined. Numerical simulations are employed to demonstrate the alignment between the numerical findings and theoretical characteristics. The model is adjusted to experimental data that reflect the progression of the omicron variant of COVID-19 in Guangzhou, exhibiting a satisfactory performance in predicting the number of infected individuals, thereby suggesting its capability to accurately estimate asymptomatic cases. Furthermore, to emphasize the benefits of employing fractional differential equations, the paper provides examples related to memory trace and hereditary characteristics. Moreover, the examined models are expected to be applied and expanded upon in order to contribute to the formulation of policies for disease control during times of limited vaccine availability. To summarize, the model appears to be a sufficient tool for researching and managing infectious diseases. It is projected that as the Omicron variant's prevalence declines, there will be a reduced need for respiratory-focused precautions. • An SVEIAOPHRD model is put forward to mathematically analyze the spread of the omicron variation of COVID-19. • Mathematical criteria for determining the stability of the equilibria are established. • An efficient nonstandard approach for resolving the continuous problem is suggested and examined. • The numerical simulations validate the analytical and numerical outcomes depicted in this study. [ABSTRACT FROM AUTHOR]

In this paper, a pipeline leakage detection algorithm based on information fusion of pressure and flow is proposed, and its core work is the construction of BPA based on discount factors. The wavelet packet decomposition is carried out for the original signals, and the processed signals are used to train different SVM classifiers to achieve the first identification results. For the samples whose initial classification results are not completely consistent, BPA is calculated according to the confusion matrixes of the initial SVM classifiers. In this paper, static discount factors and dynamic discount factors are constructed using different methods, and dynamic discount factors are modified when decisions fail. Then comprehensive discount factors are constructed based on the combination of different static discount factors and dynamic discount factors. The Shafer discount rule is used to modify BPA. Finally, D-S evidence theory is used to fuse the BPA results of all classifiers under different discount combinations. Experiments show that this algorithm can effectively use the correlation of evidences to reasonably fuse the decision results of multiple sensors, overcome the problem that the accuracy of a single sensor is not high enough for leakage identification, and improve the identification accuracy of pipeline leakage. [ABSTRACT FROM AUTHOR]

In document forensics, identifying ink mismatches is crucial for detecting forgeries and determining document authenticity. However, identifying and separating specific inks from paper can be challenging. Hyperspectral imaging can capture unique spectral patterns exhibited by inks composed of different materials, even when their colors appear identical. Hyperspectral document analysis (HSDI) can be employed to authenticate documents by analyzing the ink. Earlier studies had insufficiently accurate black ink detection, so this paper proposes a novel approach using deep learning and pre-processing methods to improve black ink detection accuracy. The proposed approach uses supervised deep learning to identify ink mismatches in hyperspectral document images. The study evaluated the performance of the proposed model using a hyperspectral image dataset consisting of UWA (University of Western Australia) writing ink in both blue and black colors and different types of artificially identical color inks mixed in various ratios to find ink mismatches. The results showed that the proposed system performed better than other systems reported in the literature, improving the average accuracy by up to 0.18% for blue and 0.36% for black. The proposed method accurately detects ink mismatches and identifies various inks based on their unique spectral response, rendering it highly beneficial for applications in document forensics. [ABSTRACT FROM AUTHOR]

Due to the rapid development of confidential digital communication over international networks, security is the most significant factor. Steganography is the science of concealed communication and is used to conceal the presence of secret information among the various methods of hiding data in communications. Steganography techniques are usually implemented either in spatial or frequency domains. Some spatial domain methods fundamentally utilize the absolute difference value between neighbor pixels to encode the secret message, categorized as Pixel-Value Differencing Steganography methods (PVDS). In PVDS, the hiding capacity of the adjacent pixels depends on their difference value. Consequently, the secret data will be embedded with a high visual quality of the stego-image. Indicator-based PVDS (IPVDS) technique is derived from the PVDS method. It depends on an Indicator pixel (IP) to extract the secret data correctly. The Genetic Algorithm (GA) approach to optimization depends on the concept of survival of the fittest. This paper introduces two proposals, GA-based steganography schemes (GA-IPVD) and (GA-IPVDM). These new techniques encoded secret data using the IPVDS method. Before embedding data in the cover image, the order of the pixels corresponding to the secret data is modified and rearranged. GA controls the operation of rearranging and modifying the order of pixels and all parameters. The current paper addresses steganography as an optimization problem and seeks to determine the optimal order of the pixels that enhance the matching between the cover image and stego-image to reduce distortion despite high embedded capacity. In the first proposed technique GA-IPVD, GA concentrates on the imperceptibility of embedded data and maximizes the quality of the stego-image according to the given secret data. In contrast, the GA maximizes the capacity of embedded data while maintaining an acceptable amount of stego-image quality in the second proposed technique GA-IPVDM. The proposed systems have embedding key that increase the security of proposed schemes. The experimental results demonstrate that the proposed methods can embed a large quantity of secret data while maintaining an acceptable visual quality of the stego-images. The proposed technique has also demonstrated excellent resistance to a variety of stego-attacks, including the pixel difference histogram (PDH) as well as regular and singular (RS) analyses. Additionally, the out-of-boundary pixel issue (BI), which endures in the majority of modern data hiding algorithms, has been successfully resolved. [ABSTRACT FROM AUTHOR]