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This paper aims to propose a table tennis target detection (TD) method based on deep learning (DL) and multi-scale feature fusion (MFF) to improve the detection accuracy of the ball in table tennis competition, optimize the training process of athletes, and improve the technical level. In this paper, DL technology is used to improve the accuracy of table tennis TD through MFF guidance. Initially, based on the FAST Region-based Convolutional Neural Network (FAST R-CNN), the TD is carried out in the table tennis match. Then, through the method of MFF guidance, different levels of feature information are fused, which improves the accuracy of TD. Through the experimental verification on the test set, it is found that the mean Average Precision (mAP) value of the target detection algorithm (TDA) proposed here reaches 87.3%, which is obviously superior to other TDAs and has higher robustness. The DL TDA combined with the proposed MFF can be applied to various detection fields and can help the application of TD in real life. [ABSTRACT FROM AUTHOR]

With the rapid development of AI and big data mining technologies, computerized medical decision-making has become increasingly prominent. The aim of high-utility pattern mining (HUPM) is to discover meaningful patterns in medical databases that contribute to maximizing the utility from the perspective of diagnosis. However, HUPM pays less attention to the interpretability and explainability of these patterns in medical decision-making scenarios. This paper proposes a novel algorithm called the Improved fuzzy high-utility pattern mining (IF-HUPM) to address this problem. First, the paper applies a fuzzy preprocessing method to divide the fuzzy intervals of a medical quantitative data set, which enhances the fuzziness and interpretability of the data. Next, in the process of IF-HUPM, both fuzzy tree and list structures are employed to calculate fuzzy high-utility values. By combining the characteristics of the one-stage and two-stage algorithms of HUPM, an adaptive-phase Fuzzy HUPM hybrid frame is proposed. The experimental results demonstrate that the proposed IF-HUPM algorithm enhances both accuracy and efficiency and the mining process requires less time and space on average. [ABSTRACT FROM AUTHOR]

Environmental changes and ground subsidence along railway lines are serious concerns during high-speed railway operations. It is worth noting that AutoRegressive Integrated Moving Average (ARMA), Long Short-Term Memory (LSTM), and other prediction methods may present limitations when applied to predict InSAR time series results. To address this issue, this study proposes a prediction method that decomposes the nonlinear settlement time series of feature points obtained through InSAR technology using Ensemble Empirical Mode Decomposition (EEMD). Subsequently, multiple Intrinsic Mode Functions (IMFs) are generated, and each IMF is individually predicted using the Prophet forecasting model. Finally, we employ an equal-weight superimposition method to combine the results, resulting in the prediction of the InSAR settlement time series. The predicted values of each component are subsequently weighted equally and combined to derive the final prediction outcome. This paper selects InSAR monitoring data along a high-speed railway in inland China and uses the proposed method and ARMA and Prophet models to carry out comparative experiments. The experimental results show that compared with the ARMA and Prophet models, the method in this paper improves the root mean square error by 58.01% and 32.3%, and increases the mean absolute error by 62.69% and 33.78%, respectively. The predicted settlement values generated by our method exhibit better agreement with the actual InSAR monitoring values. [ABSTRACT FROM AUTHOR]

The improvement of enterprise ESG performance is one of the key driving forces to achieve the goal of economic and social green development. There is a gap between knowledge and practice in the ESG performance of Chinese enterprises, and digital transformation (DT) provides new ideas for ESG development. The research purpose of this paper is to explore the impact mechanism of DT on ESG and the specific path of DT to drive ESG. It provides a reference for listed enterprises to rely on DT to empower their sustainable development capability. This paper takes the panel data of A-share listed enterprises from 2011 to 2021 as the sample and measures the core indicators using the text mining method, modified Jones model, and Roy-Chowdhury model. On this basis, using a combination of econometric models and qualitative comparative analysis, we empirically analyze the impact mechanisms of DT on ESG as well as the specific grouping paths that drive ESG performance. The main conclusions are shown as follows. First, DT can significantly reduce ESG, with an impact coefficient of − 0.013, which is significant at the 5% level. It reflects that the DT of enterprises at this stage has certain deficiencies. There is a matching lag in the enterprise's internal organizational resources. The entry of digital technology will have a certain impact on traditional operations, and the high uncertainty of DT adds some hidden costs to the enterprise. Secondly, there is an indirect suppression effect of accrued earnings management (AEM) in the transmission mechanism of DT affecting ESG. It is verified that DT can reduce information asymmetry and inhibit EM activities, thus reducing the impact on ESG. Finally, a total of six configurations achieved high ESG valuations. High technology practice-low performance manipulation; digital intelligence-low performance manipulation; digital intelligence-digital resources; digital resources-inadequate digital infrastructure.; high technology practice-bottom technology deficiency; digital intelligence-high performance manipulation. Through configuration analysis, the suppression effect of EM is further verified. The lack of AEM is usually the core condition of the high-valuation group. Meanwhile, digital intelligence, digital resources, and digital technology practice can drive the improvement of enterprise ESG. The instrumental variables approach and robustness tests support these findings. [ABSTRACT FROM AUTHOR]

Coal mining machine drums are prone to damage and malfunction under extremely complex working conditions, which seriously affects the efficiency and safety of coal production. In this paper, based on the theory of coal rock cutting and virtual simulation technology, finite element models of drum cutting coal rock were established and then verified by physical experiments. Through simulation analysis, the dynamic reliability of the drum was studied from three aspects: load, stress and wear, and a mathematical model of drum load was established with respect to the traction speed and drum rotation speed; based on the orthogonal test, the optimal working parameters to improve the wear resistance of the drum were derived. The results of the study found that when the traction speed increases, the load on the drum increases, and when the drum rotation speed increases, the load on the drum decreases; when the traction speed is increased from 2 to 6 m/min, the stress on the pick body under different rotation speeds increases to different degrees, with an average increase rate of 27.394%; when the drum rotation speed is 90 r/min, the traction speed is 3 m/min, and the coal loading mode is projectile loading, the wear depth of the picks and spiral blades is relatively small. The research method and results of this paper can provide a reference for the selection of the drum working parameters. [ABSTRACT FROM AUTHOR]

Probiotics have gained a significant attention as a promising way to improve gut health and overall well-being. The increasing recognition of the potential health advantages associated with functional food products, leading to a specific emphasis on co-encapsulating probiotic bacteria and bioactive compounds within a unified matrix. To further explore this concept, a meta-analysis was performed to assess the effects of probiotics encapsulated in nanoparticles. A comprehensive meta-analysis was conducted, encompassing 10 papers published from 2017 to 2022, focusing on the encapsulation of probiotics within nanoparticles and their viability in various gastrointestinal conditions. The selection of these papers was based on their direct relevance to the research topic. Random-effect models were used to aggregate study-specific risk estimates. In the majority of studies, it was observed that nano-encapsulated nanoparticles showed improved viability over time compared to their free state counterparts. At various time intervals, the odds ratios (OR) with 95% confidence intervals (CI) were estimated using fixed and random effect models. At 0 min, the OR (95%CI) was 2.79 (2.79; 2.80) and 2.38 (2.14; 2.64) for. At 30 and 60 min observation was at similar rate of 2.23 (2.23; 2.24) and 2.05 (1.73; 2.43). However, at 90 min it was 1.39 (1.39; 1.39) and 1.66 (1.29; 2.14) and at 120 min 2.41 (2.41; 2.42) and 2.03 (1.63; 2.52). Overall evaluation of encapsulation revealed an improvement in probiotic bacterial viability in simulated the gastrointestinal environments. [ABSTRACT FROM AUTHOR]

Facing the shortage of special building materials packaging machinery with thermal insulation and low intelligence, this paper designs a set of mechanical and electrical integration packaging unit control system to reduce the risk of material transportation for different stakeholders. According to risk management tools, the system takes Mitsubishi PLC as the control core and combines with communication module, servo motor drive system and touch screen man–machine interface to realize the risk simulation and automatic control of the packaging unit. The simulation results of PID control model show that the parameters such as speed and torque can be stabilized in a relatively short period of time when the load is suddenly changed within 1.5 s. Theoretical verification of the system has small steady-state error, rapid response, and good control effect. The man–machine interface design was carried out and the actual corresponding test experiment was carried out. The experimental results showed that the overall operation rate of the packaging unit system reached 98.15%, the pass rate was 99.03%, and the production capacity was about 9600 packs/hour, which met the production requirements. The control system of the building material packaging unit designed in this paper realizes the equipment intelligence, has a high degree of automation, and shows good potential application value in the aspects of building information, reduction of construction risks and manufacturing intelligence. [ABSTRACT FROM AUTHOR]

The paper presents very rare fossil record of crane flies. Inclusions in Baltic amber have documented the second evidence of the existence of the genus Tasiocera (Diptera: Limoniidae: Chioneinae) in ancient epochs of Earth's history. Hypotheses were also introduced regarding the environmental preferences of fossil Dasymolopilus in relation to modern representatives of the subgenus. The genus is numerous in species and widespread in the modern fauna, while only one species, Tasiocera (Dasymolophilus) circumcincta, has been known from the fossil record to date. The paper presents an additional description and graphic documentation of this species based on new materials. The discovery of two other species Tasiocera (Dasymolophilus) gorskii sp. nov. and Tasiocera (Dasymolophilus) baltica sp. nov. in Eocene resins documents the occurence of Tasiocera since Eocene. Interestingly, only subgenus Dasymolophilus is represented both in extinct and extant fauna while subgenus Tasiocera is not known from the fossil record. The new discovery is very significant, and the result of the study adds a brick to our understanding of the evolution of this group of insects and their habitat. [ABSTRACT FROM AUTHOR]

This paper introduces a novel design and static optimization for a two-degrees-of-freedom transformable wheel based on a geared linkage mechanism. Overcoming obstacles, including stairs, with small wheels is a major challenge in the field of mobile robotics research. Among various robots, the transformable wheel, which can change the shape of the wheel to overcome steps and optimize the path, was presented and has undergone many improvements. Nevertheless, problems such as asymmetry and structural strength remain. Therefore, the design of this paper aims to address the structural inefficiencies identified in the previous research model, which were attributed to the asymmetric placement of the linear motion guide. Through the implementation of this mechanism, the linear motion of the lobe can be segregated, enabling each input motor to share the workload effectively. The optimization process focus on determining the optimal linkage length under static conditions, resulting in improved structural characteristics and force distribution of linkage within the designated workspace. As a result, asymmetry of motion is eliminated, required intervention angle of the driving motor and stress of linkage was reduced by 36.24% and 8.35%, respectively. [ABSTRACT FROM AUTHOR]

Automated quantity take-off from digital models in road projects has many benefits. In real project settings, however, it seems rarely to take place. This paper investigates automated quantity take-off in a real-life road project and the experiences from this automation. A literature and a document study were performed. 48 domain models serialized in IFC 2 × 3 were then analyzed before 10 project members were interviewed. The quantity take-off was automated to harvest classified quantities to a specification of work. The automated quantity take-off was reproducible at revisions for 40% of the cost items. However, the transferability of the procedures to other use cases than the specification of work is questionable because of the applied cost breakdown structure. The findings suggest three practical improvements for automated quantity take-off in road projects. The three include (1) using unambiguous classes assembled in an ontology, (2) avoiding hard coding of cost breakdown structures in authoring tools, and (3) implementing the Level of Information Need (LOIN) to improve reusability across project phases and use cases. The quantity take-off and experiences are assessed from the designer's perspective. The client's and contractor's perspectives were out of this paper's scope. [ABSTRACT FROM AUTHOR]

Recently, ConvNeXt and blueprint separable convolution (BSConv) constructed from standard ConvNet modules have demonstrated competitive performance in advanced computer vision tasks. This paper proposes an efficient model (BCRN) based on BSConv and the ConvNeXt residual structure for single image super-resolution, which achieves superior performance with very low parametric numbers. Specifically, the residual block (BCB) of the BCRN utilizes the ConvNeXt residual structure and BSConv to significantly reduce the number of parameters. Within the residual block, enhanced spatial attention and contrast-aware channel attention modules are simultaneously introduced to prioritize valuable features within the network. Multiple residual blocks are then stacked to form the backbone network, with Dense connections utilized between them to enhance feature utilization. Our model boasts extremely low parameters compared to other state-of-the-art lightweight models, while experimental results on benchmark datasets demonstrate its excellent performance. The code will be available at https://github.com/kptx666/BCRN. [ABSTRACT FROM AUTHOR]

With the urge to secure and protect digital assets, there is a need to emphasize the immediacy of taking measures to ensure robust security due to the enhancement of cyber security. Different advanced methods, like encryption schemes, are vulnerable to putting constraints on attacks. To encode the digital data and utilize the unique properties of DNA, like stability and durability, synthetic DNA sequences are offered as a promising alternative by DNA encoding schemes. This study enlightens the exploration of DNA's potential for encoding in evolving cyber security. Based on the systematic literature review, this paper provides a discussion on the challenges, pros, and directions for future work. We analyzed the current trends and new innovations in methodology, security attacks, the implementation of tools, and different metrics to measure. Various tools, such as Mathematica, MATLAB, NIST test suite, and Coludsim, were employed to evaluate the performance of the proposed method and obtain results. By identifying the strengths and limitations of proposed methods, the study highlights research challenges and offers future scope for investigation. [ABSTRACT FROM AUTHOR]

Sustainability in cotton production is inevitable because producing more cotton means more employment, economic acceleration, and industrial expansion. India, China, the United States, Brazil, and Pakistan contribute 74% of worldwide cotton production. Pakistan is contributing only 5%, despite the high potential of cotton. The average yield of cotton in Pakistan is stagnant at 570.99 kg hm−2, whereas it entails the highest cost of production among all other crops. The yield obtained in Pakistan is less than the potential, profitability is drastically lessening, and farmers are abandoning cotton for alternative kharif crops. Some traditional quantitative studies have unveiled different factors that affect cotton production. However, an in-depth qualitative study has never been conducted in Pakistan to explore the root causes of growing cotton crop failure. Following Moustakas's traditional phenomenological guidelines, this phenomenological study was conducted in the district of Rahim Yar Khan in the core cotton zone of Punjab province. A total of 10 interviews were conducted with purposively selected cotton growers based on a criterion: (i) having more than 10 years of cotton growing experience, (ii) being a cotton grower, and (iii) having at least 10 years of formal schooling. Interviews were conducted face to face on an interview guide. One interview lasted 45–50 min, and responses were recorded and analyzed using a thematic analysis approach. A total of 6 themes emerged from the collected data, including (i) climate change, (ii) varietal problems, (iii) pesticide usage, (iv) sense of institutional services, (v) attitude of farmers and (vi) soil health and environment. These six merging themes contributed to cotton crop failure and yield decline. The deep exploration further summarized that researchers, extensionists, and farmers need to seriously consider variety, sowing time, and the environment to revive cotton crops. The detailed recommendations and policy guidelines are presented in this paper, highlighting the cotton sector's research, development and investment areas. [ABSTRACT FROM AUTHOR]

Exoplanet atmospheres are expected to vary significantly in thickness and chemical composition, leading to a continuum of differences in surface pressure and atmospheric density. This variability is exemplified within our Solar System, where the four rocky planets exhibit surface pressures ranging from 1 nPa on Mercury to 9.2 MPa on Venus. The direct effects and potential challenges of atmospheric pressure and density on life have rarely been discussed. For instance, atmospheric density directly affects the possibility of active flight in organisms, a critical factor since without it, dispersing across extensive and inhospitable terrains becomes a major limitation for the expansion of complex life. In this paper, we propose the existence of a critical atmospheric density threshold below which active flight is unfeasible, significantly impacting biosphere development. To qualitatively assess this threshold and differentiate it from energy availability constraints, we analyze the limits of active flight on Earth, using the common fruit fly, Drosophila melanogaster, as a model organism. We subjected Drosophila melanogaster to various atmospheric density scenarios and reviewed previous data on flight limitations. Our observations show that flies in an N2-enriched environment recover active flying abilities more efficiently than those in a helium-enriched environment, highlighting behavioral differences attributable to atmospheric density vs. oxygen deprivation. [ABSTRACT FROM AUTHOR]

The problem of cadmium pollution and its control is becoming increasingly severe issue in the world. Banana straw is an abundant bio raw material, but its burning or discarding in field not only causes pollution but also spreads fusarium wilt. The objective of this paper is to utilize biochar derived from the wilt-infected banana straw for remediation of Cd(II) pollution while to eliminate the pathogen. The activity of wilt pathogen in biochar was determined by PDA petri dish test. The Cd(II) adsorption of the biochar was determined by batch adsorption experiments. The effects of KOH concentration (0.25, 0.5 and 0.75 M) on the physicochemical characteristics of the biochar were also observed by BET, SEM, FTIR, XRD and XPS. Results showed that pristine banana straw biochar (PBBC) did not harbor any pathogen. The specific surface area (SSA) and Cd(II) adsorption capacity of 0.75 M KOH modified banana straw biochar (MBBC0.75M) were increased by 247.2% and 46.1% compared to that of PBBC, respectively. Cd(II) adsorption by MBBC0.75M was suitable to be described by the pseudo-second-order kinetic model and Freundlich isotherm. After Cd(II) adsorption, the CdCO3 were confirmed by XRD and observed through SEM. The weakness and shift of oxygen-containing functional groups in MBBC0.75M after Cd(II) adsorption implied that those groups were complexed with Cd(II). The results showed that pyrolysis could not only eliminate banana fusarium wilt, but also prepare porous biochar with the wilt-infected banana straw. The porous biochar possessed the potential to adsorb Cd(II) pollutants. [ABSTRACT FROM AUTHOR]

To solve the problem of oscillation instability in permanent magnetic synchronous generator (PMSG)-based wind power connected systems during low-voltage ride through (LVRT) process, a parameter adjustment strategy based on interaction energy path optimization is proposed in this paper. Firstly, a modular state-space model of PMSG under fault transient conditions is constructed, and the system is divided into five subsystems. Then, the dynamic energy function of subsystems reflecting the oscillation stability of the system is derived. Based on that, the dynamic energy flow path is described considering the introduction of LVRT control. On this basis, the interaction energy between LVRT control links and subsystems is analyzed, and the coupling mechanism of voltage support and damping characteristics in the LVRT process is explained. Further, aiming at the optimal change rate of the total interaction energy in the LVRT process, the adjustment strategy of LVRT control parameters is constructed to meet voltage and damping requirements. Finally, a PMSG-connected system model is built on the MATLAB/Simulink platform to verify the effectiveness of the adjustment strategy. The results show that the proposed method can effectively improve the damping level under the fault transient condition, as well as supporting system voltage. [ABSTRACT FROM AUTHOR]

This paper presents results of aerodynamic enhancement of axial compressors utilizing optimum natural aspiration through a circumferential slot made within the casing wall upstream the rotor blades row. The method of investigation is based on numerical simulation of flow field. Geometries of the slot walls were optimized to find the maximum stall margin and pressure ratio of the compressor. The optimum case is accompanied by boosting more momentum within the blades tip region through the slot. Consequently, tip leakage vortex flow weakens and compressor stability margin increases. Final results showed that in comparison to the untreated casing, the optimum geometry of the slot causes the total pressure ratio and stall margin to increase by 4.2% and 3%, respectively. This state-of-the-art technique is simple and economic which can be easily implemented in practical cases while the compressor is exposed to commencement of flow instabilities in the forms of the rotating stall or surge phenomena. [ABSTRACT FROM AUTHOR]

Indoor inspection robots operating in occupied buildings need to minimize disturbance to occupants and access high areas of a room and cramped spaces obstructed by obstacles for higher inspection coverage. However, existing indoor inspection robots are still unable to meet these requirements. This paper aims to explore the feasibility of applying wall-climbing robots to address these requirements. To this end, we propose a small-sized wall-climbing robot prototype that can move on common indoor surfaces. We extend the proposed prototype to support thermographic inspection by integrating thermal imaging technology into it. Experiment results show that the proposed robot prototype can reach more wall and floor areas for inspection than previously developed indoor inspection robots. It has also been demonstrated that the reduced size and the wall-climbing ability allow the robot to largely avoid human activity areas, thereby reducing disturbance to occupants. This study represents the first attempt to introduce wall-climbing robots into the indoor inspection domain and provides the initial validation of their advantages over existing indoor inspection robots regarding improving inspection coverage and minimizing disturbance to occupants. The findings in this study can provide valuable insights for the future design, selection and application of robotic systems for indoor inspection tasks. [ABSTRACT FROM AUTHOR]

Intelligent computing is transforming safety inspection methods and response strategies in coal mines. Due to the significant safety hazards associated with mining excavation, this study proposes a multi-source data based predictive model for assessing gas risk and implementing countermeasures. By examining the patterns of gas dispersion at the longwall face, utilizing both temporal and spatial correlation, a predictive model is crafted that incorporates safety thresholds for gas concentrations, four-level early warning method and response strategy are devised by integrating weighted predictive confidence with these correlations. Initially tested using a public dataset from Poland, this method was later verified in coal mine in China. This paper discusses the validity and correlation of multi-source monitoring data in temporal and spatial correlation and proposes a risk warning mechanism based on it, which can be applied not only for safety warning but also for regulatory management. [ABSTRACT FROM AUTHOR]

In the process of human aging, significant age-related changes occur in brain tissue. To assist individuals in assessing the degree of brain aging, screening for disease risks, and further diagnosing age-related diseases, it is crucial to develop an accurate method for predicting brain age. This paper proposes a multi-scale feature fusion method called Tri-UNet based on the U-Net network structure, as well as a brain region information fusion method based on multi-channel input networks. These methods address the issue of insufficient image feature learning in brain neuroimaging data. They can effectively utilize features at different scales of MRI and fully leverage feature information from different regions of the brain. In the end, experiments were conducted on the Cam-CAN dataset, resulting in a minimum Mean Absolute Error (MAE) of 7.46. The results demonstrate that this method provides a new approach to feature learning at different scales in brain age prediction tasks, contributing to the advancement of the field and holding significance for practical applications in the context of elderly education. [ABSTRACT FROM AUTHOR]

This paper proposes a novel multi-hybrid algorithm named DHPN, using the best-known properties of dwarf mongoose algorithm (DMA), honey badger algorithm (HBA), prairie dog optimizer (PDO), cuckoo search (CS), grey wolf optimizer (GWO) and naked mole rat algorithm (NMRA). It follows an iterative division for extensive exploration and incorporates major parametric enhancements for improved exploitation operation. To counter the local optima problems, a stagnation phase using CS and GWO is added. Six new inertia weight operators have been analyzed to adapt algorithmic parameters, and the best combination of these parameters has been found. An analysis of the suitability of DHPN towards population variations and higher dimensions has been performed. For performance evaluation, the CEC 2005 and CEC 2019 benchmark data sets have been used. A comparison has been performed with differential evolution with active archive (JADE), self-adaptive DE (SaDE), success history based DE (SHADE), LSHADE-SPACMA, extended GWO (GWO-E), jDE100, and others. The DHPN algorithm is also used to solve the image fusion problem for four fusion quality metrics, namely, edge-based similarity index ( Q A B / F ), sum of correlation difference (SCD), structural similarity index measure (SSIM), and artifact measure ( N A B / F ). The average Q A B / F = 0.765508 , S C D = 1.63185 , S S I M = 0.726317 , and N A B / F = 0.006617 shows the best combination of results obtained by DHPN with respect to the existing algorithms such as DCH, CBF, GTF, JSR and others. Experimental and statistical Wilcoxon's and Friedman's tests show that the proposed DHPN algorithm performs significantly better in comparison to the other algorithms under test. [ABSTRACT FROM AUTHOR]

The level of phosphorus must be carefully monitored for proper and effective utilization of coal and coal ash. The phosphorus content needs to be assessed to optimize combustion efficiency and maintenance costs of power plants, ensure quality, and minimize the environmental impact of coal and coal ash. The detection of low levels of phosphorus in coal and coal ash is a significant challenge due to its complex chemical composition and low concentration levels. Effective monitoring requires accurate and sensitive equipment for the detection of phosphorus in coal and coal ash. X-ray fluorescence (XRF) is a commonly used analytical technique for the determination of phosphorus content in coal and coal ash samples but proves challenging due to their comparatively weak fluorescence intensity. Fourier Transform Infrared spectroscopy (FTIR) emerges as a promising alternative that is simple, rapid, and cost-effective. However, research in this area has been limited. Until now, only a limited number of research studies have outlined the estimation of major elements in coal, predominantly relying on FTIR spectroscopy. In this article, we explore the potential of FTIR spectroscopy combined with machine learning models (piecewise linear regression—PLR, partial least square regression—PLSR, random forest—RF, and support vector regression—SVR) for quantifying the phosphorus content in coal and coal ash. For model development, the methodology employs the mid-infrared absorption peak intensity levels of phosphorus-specific functional groups and anionic groups of phosphate minerals at various working concentration ranges of coal and coal ash. This paper proposes a multi-model estimation (using PLR, PLSR, and RF) approach based on FTIR spectral data to detect and rapidly estimate low levels of phosphorus in coal and its ash (R 2 of 0.836, RMSE of 0.735 ppm, RMSE (%) of 34.801, MBE of − 0.077 ppm, MBE (%) of 5.499, and MAE of 0.528 ppm in coal samples and R 2 of 0.803, RMSE of 0.676 ppm, RMSE (%) of 38.050, MBE of − 0.118 ppm, MBE (%) of 4.501, and MAE of 0.474 ppm in coal ash samples). Our findings suggest that FTIR combined with the multi-model approach combining PLR, PLSR, and RF regression models is a reliable tool for rapid and near-real-time measurement of phosphorus in coal and coal ash and can be suitably modified to model phosphorus content in other natural samples such as soil, shale, etc. [ABSTRACT FROM AUTHOR]

Accurate power load forecasting is crucial for the sustainable operation of smart grids. However, the complexity and uncertainty of load, along with the large-scale and high-dimensional energy information, present challenges in handling intricate dynamic features and long-term dependencies. This paper proposes a computational approach to address these challenges in short-term power load forecasting and energy information management, with the goal of accurately predicting future load demand. The study introduces a hybrid method that combines multiple deep learning models, the Gated Recurrent Unit (GRU) is employed to capture long-term dependencies in time series data, while the Temporal Convolutional Network (TCN) efficiently learns patterns and features in load data. Additionally, the attention mechanism is incorporated to automatically focus on the input components most relevant to the load prediction task, further enhancing model performance. According to the experimental evaluation conducted on four public datasets, including GEFCom2014, the proposed algorithm outperforms the baseline models on various metrics such as prediction accuracy, efficiency, and stability. Notably, on the GEFCom2014 dataset, FLOP is reduced by over 48.8%, inference time is shortened by more than 46.7%, and MAPE is improved by 39%. The proposed method significantly enhances the reliability, stability, and cost-effectiveness of smart grids, which facilitates risk assessment optimization and operational planning under the context of information management for smart grid systems. [ABSTRACT FROM AUTHOR]

Uncaria rhynchophylla is an important traditional herbal medicine in China, and the yield and quality of Uncaria rhynchophylla can be improved by suitable soil conditioners because of changing the soil properties. In this paper, Uncaria rhynchophylla associated alkaloids and soil microbial communities were investigated. The field experiment was set up with the following control group: (M1, no soil conditioner) and different soil conditioner treatment groups (M2, biomass ash; M3, water retention agent; M4, biochar; M5, lime powder and M6, malic acid). The results showed that M2 significantly increased the fresh and dry weight and the contents of isorhynchophylline, corynoxeine, isocorynoxeine, and total alkaloids. Acidobacteria, Proteobacteria, Actinobacteria, and Chloroflexi were major bacterial phyla. Correlation analysis showed that fresh and dry weight was significantly positively correlated with Acidobacteria, while alkali-hydrolyzable nitrogen, phosphatase activity, fresh and dry weight, corynoxeine, and isocorynoxeine were significantly negatively correlated with Chloroflexi. The application of soil conditioner M2 increased the abundance of Acidobacteria and decreased the abundance of Chloroflexi, which contributed to improving the soil nutrient content, yield, and quality of Uncaria rhynchophylla. In summary, biomass ash may be a better choice of soil conditioner in Uncaria rhynchophylla growing areas. [ABSTRACT FROM AUTHOR]

Generally, the recognition performance of lightweight models is often lower than that of large models. Knowledge distillation, by teaching a student model using a teacher model, can further enhance the recognition accuracy of lightweight models. In this paper, we approach knowledge distillation from the perspective of intermediate feature-level knowledge distillation. We combine a cross-stage feature fusion symmetric framework, an attention mechanism to enhance the fused features, and a contrastive loss function for teacher and student models at the same stage to comprehensively implement a multistage feature fusion knowledge distillation method. This approach addresses the problem of significant differences in the intermediate feature distributions between teacher and student models, making it difficult to effectively learn implicit knowledge and thus improving the recognition accuracy of the student model. Compared to existing knowledge distillation methods, our method performs at a superior level. On the CIFAR100 dataset, it boosts the recognition accuracy of ResNet20 from 69.06% to 71.34%, and on the TinyImagenet dataset, it increases the recognition accuracy of ResNet18 from 66.54% to 68.03%, demonstrating the effectiveness and generalizability of our approach. Furthermore, there is room for further optimization of the overall distillation structure and feature extraction methods in this approach, which requires further research and exploration. [ABSTRACT FROM AUTHOR]

In the food industry, the increasing use of automatic processes in the production line is contributing to the higher probability of finding contaminants inside food packages. Detecting these contaminants before sending the products to market has become a critical necessity. This paper presents a pioneering real-time system for detecting contaminants within food and beverage products by integrating microwave (MW) sensing technology with machine learning (ML) tools. Considering the prevalence of water and oil as primary components in many food and beverage items, the proposed technique is applied to both media. The approach involves a thorough examination of the MW sensing system, from selecting appropriate frequency bands to characterizing the antenna in its near-field region. The process culminates in the collection of scattering parameters to create the datasets, followed by classification using the Support Vector Machine (SVM) learning algorithm. Binary and multiclass classifications are performed on two types of datasets, including those with complex numbers and amplitude data only. High accuracy is achieved for both water-based and oil-based products. [ABSTRACT FROM AUTHOR]

This paper provides six metaheuristic algorithms, namely Fast Cuckoo Search (FCS), Salp Swarm Algorithm (SSA), Dynamic control Cuckoo search (DCCS), Gradient-Based Optimizer (GBO), Northern Goshawk Optimization (NGO), Opposition Flow Direction Algorithm (OFDA) to efficiently solve the optimal power flow (OPF) issue. Under standard and conservative operating settings, the OPF problem is modeled utilizing a range of objectives, constraints, and formulations. Five case studies have been conducted using IEEE 30-bus and IEEE 118-bus standard test systems to evaluate the effectiveness and robustness of the proposed algorithms. A performance evaluation procedure is suggested to compare the optimization techniques' strength and resilience. A fresh comparison methodology is created to compare the proposed methodologies with other well-known methodologies. Compared to previously reported optimization algorithms in the literature, the obtained results show the potential of GBO to solve various OPF problems efficiently. [ABSTRACT FROM AUTHOR]

Existing research rarely examines the subjective and objective built environment of river valley-type cities in relation to travel mode choice, particularly overlooking the heterogeneity among travelers in these cities. In this paper, based on questionnaire survey data and built environment data, terrain spatial perception (TSP) is introduced to expand the theory of planned behavior (TPB), and a Structural Equation Model (SEM) is established. Factor analysis and path analysis are conducted using SPSS and AMOS to estimate latent variables. An integrated model of SEM and random parameter Logit model (RPLM), which can not only analyze the psychological perception factors of commuters in river valley-type cities but also consider the heterogeneity of psychological perception, was constructed to analyze the impact of personal attributes, objective built environment factors, and psychological latent variables on the commuting mode choice behavior of public transport users in river valley-type cities. The results indicate that the five observation indicators corresponding to the proposed terrain spatial perception latent variables can better explain the terrain spatial perception of commuters in river valley-type cities. Different from plain cities, the subjective and objective built environment of river valley-type cities notably influence the travel behavior of commuters. Moreover, the parameters of terrain spatial perception follow a normal distribution, indicating that the sensitivity of different commuters to the terrain spatial perception of river valley-type cities is heterogeneous. The results of our study can provide a reference for alleviating traffic issues in valley cities. [ABSTRACT FROM AUTHOR]

The ionosphere can be artificially modified by employing ground-based high-power high-frequency electromagnetic waves to irradiate the ionosphere. This modification is achieved through the nonlinear interaction between the electromagnetic waves and the ionospheric plasma, leading to changes in the physical properties and structure of the ionosphere. The degree of artificial modification of the ionosphere is closely related to the heating energy density of high-frequency pump waves. Due to the high density of neutral constituents in the lower ionosphere and the high frequency of electron-neutral collisions, the energy of heating pump waves will be absorbed and attenuated during the penetration of the low ionosphere, seriously affecting the heating effect. This paper proposes a method to reduce the absorption of ionospheric heating pump waves by releasing electron attachment chemicals into low ionosphere to form a large-scale electron density hole. A model for mitigating pump waves absorption based on SF6 release is established, and the absorption at different frequencies is quantitatively calculated. The propagation characteristics of high-frequency signals in ionospheric holes are studied using a three-dimensional ray tracing method, and the results demonstrate that the chemical release method not only reduces the absorption attenuation of heating pump waves but also forms spherical electron density holes, which exhibit a focusing effect on the heating beam and enhance the heating effect. The results are of great significance for understanding the nonlinear interaction between electromagnetic wave and ionospheric plasma and improving the ionospheric heating efficiency. [ABSTRACT FROM AUTHOR]

Spectroscopic techniques generate one-dimensional spectra with distinct peaks and specific widths in the frequency domain. These features act as unique identities for material characteristics. Deep neural networks (DNNs) has recently been considered a powerful tool for automatically categorizing experimental spectra data by supervised classification to evaluate material characteristics. However, most existing work assumes balanced spectral data among various classes in the training data, contrary to actual experiments, where the spectral data is usually imbalanced. The imbalanced training data deteriorates the supervised classification performance, hindering understanding of the phase behavior, specifically, sol-gel transition (gelation) of soft materials and glycomaterials. To address this issue, this paper applies a novel data augmentation method based on a generative adversarial network (GAN) proposed by the authors in their prior work. To demonstrate the effectiveness of the proposed method, the actual imbalanced spectral data from Pluronic F-127 hydrogel and Alpha-Cyclodextrin hydrogel are used to classify the phases of data. Specifically, our approach improves 8.8%, 6.4%, and 6.2% of the performance of the existing data augmentation methods regarding the classifier's F-score, Precision, and Recall on average, respectively. Specifically, our method consists of three DNNs: the generator, discriminator, and classifier. The method generates samples that are not only authentic but emphasize the differentiation between material characteristics to provide balanced training data, improving the classification results. Based on these validated results, we expect the method's broader applications in addressing imbalanced measurement data across diverse domains in materials science and chemical engineering. [ABSTRACT FROM AUTHOR]

In this paper, based on linear matrix inequality technique, a simple controller and a compensatory controller are designed. It can track arbitrary fixed points and any periodic orbits. In addition, a synchronization control method via Chebyshev neural network with external disturbances is proposed. An adaptive controller is given. The Chebyshev neural network is used to approximate the uncertain nonlinear function and the adaptive law is used to adjust the corresponding parameters in the system. Taking a 4D memristive chaotic system as examples, the results are in consistent with the simulations. From a framework and control theoretical point of view, the proposed synchronization approach via compensation controller and Chebyshev neural network is firstly presented. From an application point of view, the proposed scheme can simplify the complexity of controller design. It is promising in many applications for mem-systems as secure communications and neural networks. [ABSTRACT FROM AUTHOR]

Correlation diagnosis in multivariate process quality management is an important and challenging issue. In this paper, a new diagnostic method based on quality component grouping is proposed. Firstly, three theorems describing the properties of the covariance matrix of multivariate process quality are established based on the statistical viewpoint of product quality, to prove the correlation decomposition theorem, which decomposes the correlation of all the quality components into a series of correlations of components pairs, and then by using the factor analysis method, all quality components are grouped in order to maximize the correlations in the same groups and minimize the ones between different groups. Finally, on the basis of correlations between different groups are ignored, T2 control charts of component pairs in the same groups are established to form the diagnostic model. Theoretical analysis and practice prove that for the multivariate process quality whose the correlations between different components vary considerably, the grouping technique enables the size of the correlation diagnostic model to be drastically reduced, thus allowing the proposed method can be used as a generalized theoretical model for the correlation diagnosis. [ABSTRACT FROM AUTHOR]

Exploring the spatial coupling relationship and interaction mechanism between green urbanization (GU) and tourism competitiveness (TC) is of great significance for promoting urban sustainable development. However, the lack of research on the interaction mechanism between GU and TC limits the formulation of effective environmental management policy and urban planning. Taking 734 counties in the Yellow River Basin (YRB) as the study area, this paper analyzes the spatial coupling relationship between GU and TC on the basis of comprehensive evaluation of GU and TC. Then, the interactive mechanism between GU and TC is systematically discussed, and the synergistic development strategy of the two is proposed. The results show that the GU level presents a multicore circle structure, with provincial capitals, prefecture-level urban districts and economically developed counties in east-central regions as high-value centers. The TC at county scale presents a multi-center spatial structure. Additionally, there is a significant positive spatial coupling between GU and TC in the YRB. The analysis further reveals that green urbanization level, social progress, population development, infrastructure construction, economic development quality, and eco-environmental protection has a observably influence on TC. Tourism competitiveness, service competitiveness, location competitiveness, resource competitiveness, market competitiveness, environmental influence, and talent competitiveness has a observably influence on GU. TC can promote GU, and the improvement of green urbanization level can support the development of tourism competitiveness. According to the spatial zoning method, 734 counties are divided into 6 categories, and the coordinated development strategy of GU and TC for each type of district is proposed. [ABSTRACT FROM AUTHOR]

Wireless charging of Electric Vehicles (EVs) has been extensively researched in the realm of electric cars, offering a convenient method. Nonetheless, there has been a scarcity of experiments conducted on low-power electric vehicles. To establish a wireless power transfer system for an electric vehicle, optimal power and transmission efficiency necessitate arranging the coils coaxially. In wireless charging systems, coils often experience angular and lateral misalignments. In this paper, a new alignment strategy is introduced to tackle the misalignment problem between the transmitter and receiver coils in the wireless charging of Electric Vehicles (EVs). The study involves the design and analysis of a coil, considering factors such as mutual inductance and efficiency. Wireless coils with angular misalignment are modelled in Ansys Maxwell simulation software. The proposed practical EV system aims to align the coils using angular motion, effectively reducing misalignment during the parking of two-wheelers. This is achieved by tilting the transmitter coil in the desired direction. Furthermore, micro sensing coils are employed to identify misalignment and facilitate automatic alignment. Additionally, adopting a power control technique becomes essential to achieve both constant current (CC) and constant voltage (CV) modes during battery charging. Integrating CC and CV modes is crucial for efficiently charging lithium-ion batteries, ensuring prolonged lifespan and optimal capacity utilization. The developed system can improve the efficiency of the wireless charging system to 90.3% with a 24 V, 16 Ah Lithium Ion Phosphate (LiFePO4) battery at a 160 mm distance between the coils. [ABSTRACT FROM AUTHOR]

Fracturing horizontal well casing deformation has become very prominent, particularly in tectonic stress-concentrated shale gas fields, limiting the efficient development progress of shale gas. The main failure mode of casing shearing deformation had been attributed to fault slip caused by multi-fracturing. The current research did not provide a clear picture of the dynamic evolution relationship between hydraulic fracturing, fault slip, and casing deformation. In this paper, the dynamic model of fault slip induced by formation pressure change is established, incorporating the effects of stress drop, physical change of friction, and casing and cement-sheath resistance loads. The discontinuous displacement approach and explicit/implicit coupling iteration methods are used to reveal the relationships between the effective normal stress, shear stress, friction coefficient, and sliding velocity during the fault slip process. Furthermore, the microscopic process of casing deformation sheared by fault slip is investigated using static equilibrium theory, and a characterization method for determining the amount casing deformation caused by real-scale fault slip is proposed. The results show that three stages exist in the process of casing deformation sheared by fault slip, including trigger activation stage, accelerated slip stage, and deceleration slip stage. Fault slip is clearly influenced by fault strike. To reduce the amount of fault slip, the fault direction with the maximum in-situ stress should be avoided as much as possible. Serious casing deformation still occurs for large-scale activated faults even though the optimization measure of wellbore structure has been well taken. To fundamentally reduce the possibility of casing shear deformation, it is necessary to prevent fault slip through optimizing the design of hydraulic fracturing. This study lays the theoretical groundwork for the casing deformation control method in shale gas wells. [ABSTRACT FROM AUTHOR]

Customer churn remains a critical concern for businesses, highlighting the significance of retaining existing customers over acquiring new ones. Effective prediction of potential churners aids in devising robust retention policies and efficient customer management strategies. This study dives into the realm of machine learning algorithms for predictive analysis in churn prediction, addressing the inherent challenge posed by diverse and imbalanced customer churn data distributions. This paper introduces a novel approach—the Ratio-based data balancing technique, which addresses data skewness as a pre-processing step, ensuring improved accuracy in predictive modelling. This study fills gaps in existing literature by highlighting the effectiveness of ensemble algorithms and the critical role of data balancing techniques in optimizing churn prediction models. While our research contributes a novel approach, there remain avenues for further exploration. This work evaluates several machine learning algorithms—Perceptron, Multi-Layer Perceptron, Naive Bayes, Logistic Regression, K-Nearest Neighbour, Decision Tree, alongside Ensemble techniques such as Gradient Boosting and Extreme Gradient Boosting (XGBoost)—on balanced datasets achieved through our proposed Ratio-based data balancing technique and the commonly used Data Resampling. Results reveal that our proposed Ratio-based data balancing technique notably outperforms traditional Over-Sampling and Under-Sampling methods in churn prediction accuracy. Additionally, using combined algorithms like Gradient Boosting and XGBoost showed better results than using single methods. Our study looked at different aspects like Accuracy, Precision, Recall, and F-Score, finding that these combined methods are better for predicting customer churn. Specifically, when we used a 75:25 ratio with the XGBoost method, we got the most promising results for our analysis which are presented in this work. [ABSTRACT FROM AUTHOR]

This paper presents an innovative control scheme designed to significantly enhance the power factor of AC/DC boost rectifiers by integrating an adaptive neuro-fuzzy inference system (ANFIS) with predictive current control. The innovative control strategy addresses key challenges in power quality and energy efficiency, demonstrating exceptional performance under diverse operating conditions. Through rigorous simulation, the proposed system achieves precise input current shaping, resulting in a remarkably low total harmonic distortion (THD) of 3.5%, which is well below the IEEE-519 standard threshold of 5%. Moreover, the power factor reaches an outstanding 0.990, indicating highly efficient energy utilization and near-unity power factor operation. To validate the theoretical findings, a 500 W laboratory prototype was implemented using the dSPACE ds1104 digital controller. Steady-state analysis reveals sinusoidal input currents with minimal THD and a power factor approaching unity, thereby enhancing grid stability and energy efficiency. Transient response tests further demonstrate the system's robustness against load and voltage fluctuations, maintaining output voltage stability within an 18 V overshoot and a 20 V undershoot during load changes, and achieving rapid response times as low as 0.2 s. Comparative evaluations against conventional methods underscore the superiority of the proposed control strategy in terms of both performance and implementation simplicity. By harnessing the strengths of ANFIS-based voltage regulation and predictive current control, this scheme offers a robust solution to power quality issues in AC/DC boost rectifiers, promising substantial energy savings and improved grid stability. The results affirm the potential of the proposed system to set new benchmarks in power factor correction technology. [ABSTRACT FROM AUTHOR]

Due to the presence of non-line-of-sight (NLOS) obstacles, the localization accuracy in ultra-wideband (UWB) wireless indoor localization systems is typically substantially lower. To minimize the influence of these environmental factors and improve the accuracy of indoor wireless positioning, this paper proposes a density clustering with noise combined with particle swarm optimization (DCNPSO) to improve UWB positioning. Which exploits the advantages of the density-based spatial clustering algorithm with noise (DBSCAN) and particle swarm optimization (PSO) algorithm. The experimental results show that the DCNPSO algorithm achieves 45.25% and 36.14% higher average positioning accuracy than the DBSCAN and PSO algorithms, respectively. The positioning error of this algorithm remains stable within 3 cm in static positioning and can achieve high accuracy in NLOS environments. [ABSTRACT FROM AUTHOR]

Over the past decades, the software industry has expanded to include all industries. Since stakeholders tend to use it to get their work done, software houses seek to estimate the cost of the software, which includes calculating the effort, time, and resources required. Although many researchers have worked to estimate it, the prediction accuracy results are still inaccurate and unstable. Estimating it requires a lot of effort. Therefore, there is an urgent need for modern techniques that contribute to cost estimation. This paper seeks to present a model based on deep learning and machine learning techniques by combining convolutional neural networks (CNN) and the particle swarm algorithm (PSO) in the context of time series forecasting, which enables feature extraction and automatic tuning of hyperparameters, which reduces the manual effort of selecting parameters and contributes to fine-tuning. The use of PSO also enhances the robustness and generalization ability of the CNN model and its iterative nature allows for efficient discovery of hyperparameter similarity. The model was trained and tested on 13 different benchmark datasets and evaluated through six metrics: mean absolute error (MAE), mean square error (MSE), mean magnitude relative error (MMRE), root mean square error (RMSE), median magnitude relative error (MdMRE), and prediction accuracy (PRED). Comparative results reveal that the performance of the proposed model is better than other methods for all datasets and evaluation criteria. The results were very promising for predicting software cost estimation. [ABSTRACT FROM AUTHOR]

Despite the historical position of the F-expansion method as a method for acquiring exact solutions to nonlinear partial differential equations (PDEs), this study highlights its superiority over alternative auxiliary equation methods. The efficacy of this method is demonstrated through its application to solve the convective–diffusive Cahn–Hilliard (cdCH) equation, describing the dynamic of the separation phase for ternary iron alloys (Fe–Cr–Mo) and (Fe–X–Cu). Significantly, this research introduces an extensive collection of exact solutions by the auxiliary equation, comprising fifty-two distinct types. Six of these are associated with Weierstrass-elliptic function solutions, while the remaining solutions are expressed in Jacobi-elliptic functions. I think it is important to emphasize that, exercising caution regarding the statement of the term 'new,' the solutions presented in this context are not entirely unprecedented. The paper examines numerous examples to substantiate this perspective. Furthermore, the study broadens its scope to include soliton-like and trigonometric-function solutions as special cases. This underscores that the antecedently obtained outcomes through the recently specific cases encompassed within the more comprehensive scope of the present findings. [ABSTRACT FROM AUTHOR]

Permanent magnet synchronous motor (PMSM) systems have gained popularity in various fields due to their advantages such as high speed, high accuracy, low maintenance, and high reliability. This paper presents the speed tracking control of a permanent magnet synchronous motor (PMSM) using a hybrid fractional order PI and type 2 fuzzy control with fractional order PD control (FOT2F-FOPD). The SRF-PLL observes the motor speed and estimates the rotor's position by interpreting the input voltages of the motor instead of using a sensor. Then, the controller parameters (gain, μ and λ) are tuned based on a novel optimization algorithm called Incomprehensible but Intelligible-in-time (IbI) Logics algorithm (ILA). The proposed controller enhances the performance of the system and regulates the speed of the motor under parameter variations such as the speed and the load. So, the proposed ILA (FOT2F-FOPD) controller is assessed using MATLAB/Simulink simulation and compared with other controller techniques. The proposed technique reduces the settling time, steady state error and overshoot by at least 65%, 54% and 53% respectively under load conditions compared with (PSO, optimized FOPD, FOPI and PI). While at no load condition, the settling time and the error are reduced by 31% and 12.5% respectively with no overshoot in output response. The results show a significant improvement in the performance of motors used with the application of the proposed controller and the employment of the (ILA) optimization compared with FOPI and PI controllers. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive analysis of linear regression models, focusing on addressing multicollinearity challenges in breast cancer patient data. Linear regression methodologies, including GAM, Beta, GAM Beta, Ridge, and Beta Ridge, are compared using two statistical criteria. The study, conducted with R software, showcases the Beta regression model's exceptional performance, achieving a BIC of − 5520.416. Furthermore, the Ridge regression model demonstrates remarkable results with the best AIC at − 8002.647. The findings underscore the practical application of these models in real-world scenarios and emphasize the Beta regression model's superior ability to handle multicollinearity challenges. The preference for AIC over BIC in Generalized Additive Models (GAMs) is rooted in the AIC's calculation framework, highlighting its effectiveness in capturing the complexity and flexibility inherent in GAMs. [ABSTRACT FROM AUTHOR]

This paper presents an innovative control scheme designed to significantly enhance the power factor of AC/DC boost rectifiers by integrating an adaptive neuro-fuzzy inference system (ANFIS) with predictive current control. The innovative control strategy addresses key challenges in power quality and energy efficiency, demonstrating exceptional performance under diverse operating conditions. Through rigorous simulation, the proposed system achieves precise input current shaping, resulting in a remarkably low total harmonic distortion (THD) of 3.5%, which is well below the IEEE-519 standard threshold of 5%. Moreover, the power factor reaches an outstanding 0.990, indicating highly efficient energy utilization and near-unity power factor operation. To validate the theoretical findings, a 500 W laboratory prototype was implemented using the dSPACE ds1104 digital controller. Steady-state analysis reveals sinusoidal input currents with minimal THD and a power factor approaching unity, thereby enhancing grid stability and energy efficiency. Transient response tests further demonstrate the system's robustness against load and voltage fluctuations, maintaining output voltage stability within an 18 V overshoot and a 20 V undershoot during load changes, and achieving rapid response times as low as 0.2 s. Comparative evaluations against conventional methods underscore the superiority of the proposed control strategy in terms of both performance and implementation simplicity. By harnessing the strengths of ANFIS-based voltage regulation and predictive current control, this scheme offers a robust solution to power quality issues in AC/DC boost rectifiers, promising substantial energy savings and improved grid stability. The results affirm the potential of the proposed system to set new benchmarks in power factor correction technology. [ABSTRACT FROM AUTHOR]

Tabular data analysis is a critical task in various domains, enabling us to uncover valuable insights from structured datasets. While traditional machine learning methods can be used for feature engineering and dimensionality reduction, they often struggle to capture the intricate relationships and dependencies within real-world datasets. In this paper, we present Multi-representation DeepInsight (MRep-DeepInsight), a novel extension of the DeepInsight method designed to enhance the analysis of tabular data. By generating multiple representations of samples using diverse feature extraction techniques, our approach is able to capture a broader range of features and reveal deeper insights. We demonstrate the effectiveness of MRep-DeepInsight on single-cell datasets, Alzheimer's data, and artificial data, showcasing an improved accuracy over the original DeepInsight approach and machine learning methods like random forest, XGBoost, LightGBM, FT-Transformer and L2-regularized logistic regression. Our results highlight the value of incorporating multiple representations for robust and accurate tabular data analysis. By leveraging the power of diverse representations, MRep-DeepInsight offers a promising new avenue for advancing decision-making and scientific discovery across a wide range of fields. [ABSTRACT FROM AUTHOR]

The concept, performance, and analyses of distinctive, miniaturized metamaterial (MTM) unit cell addressing the forthcoming Sub 6 GHz 5G applications are presented in this paper. Two circular split-ring resonators (CSRR) with two parallel rectangular copper elements in front of the design and a slotted square element in the background make up the suggested metamaterial. It has a line segment with tunable features that is positioned in the center of the little ring copper structure. The suggested design offers a significant operating frequency band of 220 MHz together with a resonance of transmission coefficient S21 at 3.5 GHz. Furthermore, in two (z & x) principal axes of wave propagation, wide-range achievement, single/double-negative (S/DNG) refractive index, negative permittivity, and near-zero permeability properties were demonstrated. Through varying central slotted-strip line length, resonance frequencies can be selectively altered. Moreover, the metamaterial has overall dimensions of 9 × 9 mm2 and is composed on a Rogers 5880 RT substrate. In order to create the suggested MTM's equivalent circuit, which shows similar coefficient of transmission (S21), a proposed design's numerical simulation is carried out in the CST micro-wave studio. This simulation is after that put to comparison with manufacturing of the design. [ABSTRACT FROM AUTHOR]

Virtual Reality (VR) laboratories are a new pedagogical approach to support psychomotor skills development in undergraduate programmes to achieve practical competency. VR laboratories are successfully used to carry out virtual experiments in science courses and for clinical skills training in professional courses. This paper describes the development and evaluation of a VR-based microbiology laboratory on Head-Mounted Display (HMD) for undergraduate students. Student and faculty perceptions and expectations were collected to incorporate into the laboratory design. An interactive 3-dimensional VR laboratory with a 360° view was developed simulating our physical laboratory setup. The laboratory environment was created using Unity with the (created) necessary assets and 3D models. The virtual laboratory was designed to replicate the physical laboratory environment as suggested by the students and faculty. In this VR laboratory, six microbiology experiments on Gram staining, bacterial streaking, bacterial motility, catalase test, oxidase test and biochemical tests were placed on the virtual platform. First-year biomedical science students were recruited to evaluate the VR laboratory. Students' perception of the virtual laboratory was positive and encouraging. About 70% of the students expressed they felt safe using the VR laboratory and that it was engaging. They felt that the VR laboratory provided an immersive learning experience. They appreciated that they could repeat each experiment multiple times without worrying about mistakes or mishaps. They could personalise their learning by concentrating on the specific experiments. Our in-house VR-based microbiology laboratory was later extended to other health professions programmes teaching microbiology. [ABSTRACT FROM AUTHOR]

The prevalence of cardiovascular disease (CVD) has surged in recent years, making it the foremost cause of mortality among humans. The Electrocardiogram (ECG), being one of the pivotal diagnostic tools for cardiovascular diseases, is increasingly gaining prominence in the field of machine learning. However, prevailing neural network models frequently disregard the spatial dimension features inherent in ECG signals. In this paper, we propose an ECG autoencoder network architecture incorporating low-rank attention (LRA-autoencoder). It is designed to capture potential spatial features of ECG signals by interpreting the signals from a spatial perspective and extracting correlations between different signal points. Additionally, the low-rank attention block (LRA-block) obtains spatial features of electrocardiogram signals through singular value decomposition, and then assigns these spatial features as weights to the electrocardiogram signals, thereby enhancing the differentiation of features among different categories. Finally, we utilize the ResNet-18 network classifier to assess the performance of the LRA-autoencoder on both the MIT-BIH Arrhythmia and PhysioNet Challenge 2017 datasets. The experimental results reveal that the proposed method demonstrates superior classification performance. The mean accuracy on the MIT-BIH Arrhythmia dataset is as high as 0.997, and the mean accuracy and F 1 -score on the PhysioNet Challenge 2017 dataset are 0.850 and 0.843. [ABSTRACT FROM AUTHOR]

This article investigates the impact of metal-organic frameworks (MOFs) on the performance and stability of perovskite solar cells (PSCs), specifically focusing on the type of metal and the morphology of the MOF. Two types of MOFs, copper-benzene-1,3,5-tricarboxylate (Cu-BTC MOF) with spherical morphology and cobalt-benzene-1,3,5-tricarboxylate (Co-BTC MOF) with rod morphology, are synthesized and spin-coated on TiO2 substrates to form FTO/TiO2/MOF/CH3NH3PbI3/C-paste PSCs. The morphology and size of the MOFs are characterized by scanning electron microscopy (SEM), and the crystallinity and residual PbI2 of the perovskite films are analyzed by X-ray diffraction (XRD). The results show that the Co-BTC MOF PSC exhibits the highest power conversion efficiency (PCE) of 10.4% and the best stability, retaining 82% of its initial PCE after 264 h of storage in ambient air. The improved performance and stability are attributed to the enhanced crystallinity and reduced residual PbI2 of the perovskite film after Co-BTC MOF modification. The paper showcases the immense potential of MOF-based interlayers to revolutionize PSC technology, offering a path toward next-generation solar cells with enhanced performance and longevity. [ABSTRACT FROM AUTHOR]

The initial Phase-I single centre, single dose, randomized, double-blind, cross-over study was planned to assess the pharmacokinetic and pharmacodynamic bioequivalence of the trastuzumab biosimilar (MYL-1401O) compared to the reference Herceptin®. Their respective immunomodulation profile presented in this paper involved healthy males receiving a single infusion of both monoclonals, separated by a washout period. Sixty parameters were assessed in total, including serum cytokines, peripheral mononuclear cell (PBMC) subsets, cell activation and response to recall antigens and mitogen, pre- and post- infusion, as well as a cytokine release assay (CRA) at baseline. Trastuzumab infusion induced a transient and weak peak of serum IL-6 at 6 h, and a modulation of mononuclear cell subset profile and activation level, notably CD16 + cells. Except for CD8 + T cells, there were no significant differences between Herceptin® and MYL-1401O. In CRA, PBMC stimulated with MYL-1401O or Herceptin® similarly secreted IL-6, TNF-α, IL-1β, GM-CSF, IFN-γ, and IL-10, but no or low level of IL-2. Interestingly, some observed adverse events correlated with IL-2 and IFN-γ in CRA. MYL-1401O exhibited a very similar immunomodulation profile to Herceptin®, strongly supporting its bioequivalence. This approach may thus be included in a proof-of-concept study. CRA may be used as a predictive assay for the evaluation of clinical monoclonals. [ABSTRACT FROM AUTHOR]

The concentration prediction of PM2.5 plays a vital role in controlling the air and improving the environment. This paper proposes a prediction model (namely EEMD-ALSTM) based on Ensemble Empirical Mode Decomposition (EEMD), Attention Mechanism and Long Short-Term Memory network (LSTM). Through the combination of decomposition and LSTM, attention mechanism is introduced to realize the prediction of PM2.5 concentration. The advantage of EEMD-ALSTM model is that it decomposes and combines the original data using the method of ensemble empirical mode decomposition, reduces the high nonlinearity of the original data, and Specially reintroduction the attention mechanism, which enhances the extraction and retention of data features by the model. Through experimental comparison, it was found that the EEMD-ALSTM model reduced its MAE and RMSE by about 15% while maintaining the same R2 correlation coefficient, and the stability of the model in the prediction process was also improved significantly. [ABSTRACT FROM AUTHOR]