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Human papillomavirus (HPV) is the primary cause of cervical cancer, a major global health concern affecting women worldwide. Early detection of high-risk HPV genotypes is crucial for timely intervention and reducing disease burden. However, current detection methods often lack sensitivity, speed, or cost-effectiveness, especially in resource-limited settings. This work addresses this critical need by developing an ultrasensitive electrochemical biosensing platform based on nitrogen-doped graphene paper electrodes for quantifying high-risk HPV16 DNA sequences. The N-graphene nanohybrid, fabricated via simple solvothermal treatment of biomass-derived graphene oxide, demonstrated significantly enhanced electrical and electrocatalytic properties compared to undoped graphene. DNA detection was achieved through characteristic oxidation signals of guanine bases, which displayed a wide linear dynamic range (0.5–100 μg/mL), low detection limit (75 pg/mL), and excellent reproducibility (relative standard deviation

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.

Electric bicycle datasets for lift control have played a crucial role in the development of electric bicycle lift entry detection algorithms. However, existing datasets often encounter issues, including a small dataset size, a high false detection rate, and the absence of a publicly available benchmark dataset. This paper addresses these challenges by constructing a new electric bicycle detection dataset for lift control based on the research demands of the electric bicycle detection task for lift control. XHNet_EB, a car scene image dataset covering various types of electric bicycles, was constructed based on images taken by a camera from above. Segmented annotation was employed, framing the front and rear of the electric bicycle independently. This approach effectively addressed the problems of many irrelevant features and a high false detection rate caused by the use of whole-electric-bicycle annotations in mainstream datasets. AP_50, AP_75, the mAP, and the number of false detections in the images were used for quantitative analysis of the dataset. The experimental results indicated that the object detection accuracy of the XHNet_EB dataset was excellent. Quantitative analysis and evaluation of the number of false detections in images were conducted using four mainstream lightweight detection model algorithms. The results demonstrated that segmented annotation reduced the false detection rate more effectively than entire electric bicycle annotation. This study identified drawbacks in existing datasets. The dataset proposed in this paper overcomes the shortcomings of existing commercial data and solves problems such as the high false detection rate caused by the inclusion of many irrelevant features caused by the "whole-electric-bicycle annotation" method, which could help with the development of electric bicycle detection applications in lifts. [ABSTRACT FROM AUTHOR]

Underwater target information is submerged in background scattered light due to the scattering effect of suspended particles in turbid water. In order to improve the quality of target imaging in turbid water, this paper proposes a method for achieving clear underwater target imaging using the second Lorentz depolarization index based on a dual division of focal plane Mueller matrix imaging system. This method can directly quantify the attenuation of light in water, and then solve a clear image of the target. Experimental results on underwater targets of different materials (plastic, paper, metal) show that the method proposed can effectively remove background scattering light while enhancing image contrast, ultimately improving the imaging quality of targets in turbid water. [ABSTRACT FROM AUTHOR]

It is an established and already accomplished fact that probability-based methodologies (or probability/statistical distributions) play the roles of imperative tools in illustrating and describing applied phenomena. Paying attention to these previous facts, this paper considers the evolution of a new probability distribution. Thus, the main achievement of this paper leads to a new probability distribution as a result of combining the exponentiated exponential model with a tangent-based stratagem. The new model is called the tangent exponentiated exponential (TEE) distribution. For the TEE distribution, certain distributional properties and estimators of the model parameters are derived mathematically. The appraisal of the estimators of the TEE distribution is accomplished through a simulation study. Moreover, the applicability and virtuoso of the TEE distribution are exemplified via two data sets, which are taken from the music engineering (named Data 1) and reliability sector (named Data 2). By considering the p -value and three other important statistical tests/methods as diagnostic tools, we find that the TEE distribution is the most useful and optimal model for Data 1 and Data 2 compared to certain candidate probability distributions. [ABSTRACT FROM AUTHOR]

In this paper, we presented a financial analysis of the values of financial losses for real data in light of a set of indicators for measuring financial risks. The value-at-risk (VAR), tail mean–variance (TMV), tail-VAR (TVAR), tail variance (TV), Peaks Over a Random Threshold Value-at-Risk (PORT-VAR) and the mean of order P (MOO P ) indicators are used in identifying and describing important events or outliers within US financial indemnity loss data. Some extreme financial value theory (EFVT) models are compared in view of financial indemnity loss data and according to some confidence levels. The paper provided a clear financial framework for financial institutions to help them avoid large, sudden losses. Therefore, financial data with a long tail to the right were chosen, and several financial risk measures were used that study and analyze the behavior of the long tail. [ABSTRACT FROM AUTHOR]

Optimizing domestic energy use and increasing the efficiency of residential power supply chains depend much on home energy management (HEM) systems. This paper examines the utilization of advanced meta-heuristics, namely the Siberian Tiger Optimization (STO) and Sand Cat Swarm Optimization (SCSO) algorithms, for developing HEM systems. The paper presents an integrated STSC algorithm, enhanced by artificial intelligence, that monitors and optimizes household energy usage. To optimize electricity distribution, this algorithm seeks a compromise between reducing costs and lowering the peak-to-average proportion of power. After extensive simulations, the STSC algorithm outperforms previous meta-heuristics in Peak Average Ratio. It demonstrates the possibility of substantial cost reductions in residential settings, reaching up to 8.5%. This improves the overall efficiency of households' power supply chains. In addition to reducing costs, the STSC algorithm contributes to sustainability objectives by utilizing AI to minimize carbon emissions, including renewable energy sources, and facilitate adaptable demand solutions. This highlights its role in promoting sustainable supply chain practices in energy efficiency. The combined use of STO and SCSO algorithms in the STSC method is a new and innovative development in HEM systems. This study highlights the capacity of AI-driven technologies to effectively and environmentally optimize household energy consumption. [ABSTRACT FROM AUTHOR]

Achieving a balance between convergence and diversity and their mutual enhancement is a complex task in the process of algorithm improvement. This is crucial because it is directly related to the effectiveness of the algorithm in obtaining accurate and uniformly distributed Pareto frontiers. Although significant progress has been made in particle swarm algorithms, exploring new approaches is necessary. In this paper, we construct a projection plane (projection line in 2D) based on the extreme values of the non-dominated solutions, select a set of uniform reference points on the projection plane, and then project the non-dominated solutions onto the constructed projection plane to form projection points. The reference points and projection points on the projection plane are thus utilized to guide the updating of the population as well as the maintenance of the external archive, a strategy that enhances the algorithm's global exploration and local exploitation capabilities. Secondly, we aggregate the target values of particles into a single scalar value and combine the idea of particle fusion to design a scheme for the particle selection of individual optimal particles. This paper further improves the algorithm's overall performance by using the information between populations to select individual optimal particles. Lastly, it is evaluated against a number of multi-objective algorithms that are currently in use and perform well on 22 test problems. The findings demonstrate that the algorithm this paper proposes performs better when solving multi-objective problems. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive bibliometricanalysis of solid-state circuit breakers, including technological developments and control methods in electric power distribution systems. By compiling and analyzing data from the Scopus database, the most cited papers in the field of protection system mechanisms can be identified. These papers, published in influential journals from various countries, covered different subject areas and case studies. Notably, 77.64 % of the articles focused on technological development and experimental control methods, while 22.35 % were review-based studies. This underscores the increasing interest among researchers in advancing the sustainability and reliability of protection devices in electric power distribution systems. The paper aims to identify and analyze the highly cited published articles on the respective field to provide future research direction on the technological development and control methods towards solid-state circuit breaker protection devices. The review also underlines numerous factors, issues, challenges, and difficulties that next-generation power semiconductor should overcome with in regards to system sustainability. Thus, this analysis will strengthen the scopes and provide context for the development of technology and control in the main protection devices in order to achieve an efficient, reliable, cost-effective, and sustainable power supply. [ABSTRACT FROM AUTHOR]

A novel mathematical modeling approach for the Triple-Diode Model (TDM) and its parameter estimation is introduced in this paper. For the first time in the literature, this paper presents two analytical solutions for calculating the current in TDM solar cells using the Lambert W function. Additionally, a new metaheuristic algorithm, the Adaptive Evaporation Rate Water Cycle Algorithm (Adaptive ERWCA), is proposed for estimating TDM solar cell parameters. The proposed algorithm is tested using both the new analytical solutions and a well-known iterative approach for the current-voltage characteristics of the TDM solar cells model. Extensive testing is conducted on three well-known solar cells/modules and a panel under various weather conditions. The results demonstrate that the proposed analytical expressions for the current-voltage characteristics are accurate and highly effective for representing TDM solar cell current. The accuracy of the parameter estimates obtained with the application of the proposed algorithm, considering both analytical solutions and the iterative approach, is compared with existing literature results. It is shown that the proposed algorithm outperforms existing algorithms in solar cell parameter estimation across both the analytical and iterative approaches for current-voltage characteristics. Remarkably, for the RTC France solar cell, the SOLAREX MSX 60 solar module, and the PHOTOWATT-PWP 201 solar module, the obtained Root Mean Square Errors (RMSE) are 0.00075141, 0.01157153, and 0.0010578, respectively. These results are currently the best reported in the literature. [ABSTRACT FROM AUTHOR]

This paper presents a single-fed Ku/Ka dual-band aperture-shared planar antenna array designed for satellite communication. The need to enhance data transmission speeds has driven the exploration of higher frequency bands, such as the Ku-band and Ka-band. Researchers have been investigating the design of multiband antennas to achieve space miniaturization and cost-effectiveness in these higher frequency bands. However, previous works faced challenges, including spatial efficiency limitations, complex radiator structures, band isolation issues, and increased complexity in the feeding network. To address the abovementioned challenges, this paper proposes a single-fed Ku/Ka dual-band shared-aperture planar antenna array. By combining a patch antenna for the Ku-band and a cavity antenna for the Ka-band, a simple and single-fed network is realized. The proposed antenna element is further extended to a linear array antenna and a sequentially rotated array antenna. The implemented sequentially rotated array antenna exhibited a measured impedance bandwidth of 17.4–19.7 GHz (12.4%) and 27.0–29.0 GHz (7.1%). Moreover, the proposed sequentially rotated array antenna demonstrated measured peak gains of 13.7 dBi for right-hand circular polarization in the Ku-band receiver and a measured peak gain of 15.6 dBi for left-hand circular polarization in the Ka-band transmitter, making it highly suitable for satellite communication. [ABSTRACT FROM AUTHOR]

As the next-generation power system, smart grid presents challenges to enterprises in managing and analyzing massive data, meeting complex operational and decision-making demands, and predicting future power demand for grid optimization. This paper aims to proposed a fusion algorithm for smart grid enterprise decision-making and economic benefit analysis, enhancing the accuracy of decision-making and predictive capability of economic benefits. The proposed method combines techniques such as Long Short-Term Memory (LSTM), Generative Adversarial Networks (GAN), and edge computing. The LSTM model is employed to model historical data of the smart grid. The GAN model generates diverse scenarios for future power demand and economic benefits. The proposed method is evaluated on four public datasets, including the ENTSO-E Dataset, and outperforms several traditional algorithms in terms of prediction accuracy, efficiency, and stability. Notably, on the ENTSO-E Dataset, the proposed algorithm achieves a reduction of over 46.6% in FLOP, and a decrease in inference time by over 48.3%, and an improvement of 38% in MAPE. The novel fusion algorithm proposed in this paper demonstrates significant advantages in accuracy and predictive capability, providing a scientific basis for smart grid enterprise decision-making and economic benefit analysis while offering practical value for real-world applications. • A novel fusion algorithm for accurate load demand prediction in smart grids. • Training were conducted on multiple datasets and validation was performed. • Significant contributions to risk assessment planning in smart grid enterprises. [ABSTRACT FROM AUTHOR]

The excavation of the pit causes displacement of the surrounding soil, and the excessive deformation causes damage to the existing support structure, which in turn affects the safety of the pit. Reasonable calculation of structural deformation and internal force is crucial for design and construction. Most of the existing theoretical methods simplify the diaphragm wall(DW) as an Euler-Bernoulli beam acting on the Winkler foundation, consider beam-soil interaction, and simplify the soil as isotropic and continuous. However, the shear effects due to the differential deformation of the structure, the unloading stresses acting on the structure due to foundation excavation, and the discontinuous nature of the multilayered soil are neglected. In this paper, an improved analysis method is proposed based on the elastic foundation beam theory. The DW is simplified as a Timoshenko beam and the foundation is simplified as a Vlasov two-parameter model, and a proposed model considering the shear effect of the DW and the interaction of adjacent springs is established, and the proposed method for the deformation and internal force of the DW is obtained by the finite-difference method. The correctness and applicability of the proposed method are verified by numerical simulation and field monitoring data. The effects of equivalent bending stiffness, equivalent shear stiffness, soil elastic modulus, and excavation depth on the deformation and internal force of the DW were further analyzed. The results show that the proposed method can accurately solve the deformation and internal force of the DW, and the maximum errors between the proposed method and the numerical simulation results are only 4.5 % and 1.3 %, respectively. The equivalent bending stiffness of the DW and the elastic modulus of the soil have more significant effects on the horizontal deformation and internal force. The excavation depth is more sensitive to the deformation of the DW, and there is an exponential decay trend between the two. When the equivalent shear and bending stiffnesses reach 6.8×107 kN·m2 and 2.9×107 kN/m, the effect on the horizontal deformation is no longer obvious. The proposed method in this paper can accurately calculate the internal force and deformation of the DW. [ABSTRACT FROM AUTHOR]

This review offers an extensive examination of the evolving landscape of Long Range (LoRa) Microstrip Patch Antennas (MPAs), highlighting their crucial role in optimizing LoRa systems within the Internet of Things (IoT). As the demand for energy-efficient standards like LoRa grows with the expanding IoT market, this research becomes increasingly relevant. This comprehensive review, the first of its kind, serves as a foundational resource for researchers seeking to optimize LoRa systems within the IoT. The study has categorized these LoRa MPAs – including monopole, Planar Inverted F Antenna (PIFA), dipole, yagi-uda, and array – into single band, dual-band, multiband, and wearable antennas, thus providing substantial viewpoints on their diverse design architectures and performance characteristics. Through systematic tabulation, the review facilitates a thorough comparison of antenna advancements. Notably, the review addresses inherent challenges in LoRa MPAs, emphasizing critical aspects that necessitate attention, including the need for miniaturization and integration, advancements in substrate materials and fabrication techniques, and the imperative for reconfigurable and adaptive antennas. Various approaches to enhance antenna performance are explored, including the metamaterial incorporation, slot-based enhancements, Electromagnetic Band Gap (EBG), dielectric resonator antennas (DRAs), substrate material considerations, and corrugation techniques. Looking ahead, the paper explores the future trends and subtle considerations that are poised to shape the trajectory of LoRa MPAs. To the best of our knowledge, this paper represents the first comprehensive review on the multifaceted topic of LoRa MPAs, serving as a foundational resource for researchers in the field. [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]

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]

Vibration is inevitable in operation of mechanical equipment, but severe vibrations will cause serious harm to wind turbines (WTs). This paper takes the drivetrain of 8 MW WT as the object, established a multi-body electromechanical coupling model of WT and verified the accuracy of the established model through the WT drivetrain in the laboratory. Subsequently, based on this model, time-frequency domain analysis was conducted on the vibration response of the WT drivetrain under different control strategies of the generator, and the influence of the generator control strategy on the vibration of the drivetrain was studied. The result shows that the generator control strategy has a significant impact on the vibration response amplitude of the drivetrain, but has a relatively small impact on the frequency component. Meanwhile, the sliding mode controller designed in this paper has good anti-interference ability, which can make the system quickly reach dynamic stability, and effectively suppress the vibration of the main components in the drivetrain. Under the new controller's control, the transverse vibration displacement of some parts of the WT drivetrain can be reduced by up to nearly 60 %. This paper provides new ideas and new methods for the active vibration reduction control of WT drivetrain. [ABSTRACT FROM AUTHOR]

Statistical analysis of the time-to-event scenarios commonly involves the use of the exponential and Rayleigh distributions. However, the exponential distribution is constrained by its constant failure rate, while the Rayleigh distribution exhibits a growing (or increasing) failure rate. The linear failure rate distribution provides a broader perspective by encompassing both of these distributions, including the possibility of a non-increasing hazard function. In this paper, a new distribution is introduced, which could be considered as one of the major modifications to the linear failure rate distribution, known as the weighted linear failure rate distribution. The model proposed illustrates both monotonic and a known non-monotonic failure rate. This paper extensively explores the characteristics of the proposed distribution. It presents the maximum likelihood estimates for the unknown parameters and includes a simulation study. The analysis focuses on three real data sets obtained from the fields of musical education and engineering. By employing three decisive tools, it is observed that the current distribution provides a superior fit in comparison to several other widely recognized 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]

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]

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]