Important Applications Research Articles

Analysis on the Influence of Communicative Teaching Theory on English Teaching

This paper aims to analyze the influence of communication teaching theory on English teaching. Communication teaching theory is a communication-centered teaching method, which emphasizes that students use language to communicate and interact in the real context. This paper first introduces the basic principles and core concepts of communication teaching theory. Then, this paper discusses the influence of communicative teaching theory on English teaching, including teaching objectives, teaching content, teaching methods and so on. Finally, this paper summarizes the positive influence of communicative teaching theory and makes some suggestions to further promote the application and development of communicative teaching theory in English teaching. Through the study of this paper, we can better understand the importance and practical application of communicative teaching theory to English teaching, and provide a useful reference for improving the quality of English teaching.

Application of the New Importance–Performance Analysis Method to Explore the Strategies of Rural Outdoor Dining Experiences in Taiwan

Taiwan is an island where the city and nature combine to become the most beautiful open-air museum in the world, known as Formosa. With climate change and industrial development as the main changes in consumption behavior, the integration of ecology, the environment, and agriculture into food culture is gradually becoming valued in Taiwan. This study explores the quality of the rural outdoor dining experience in Taiwan; therefore, questionnaires were distributed to outdoor dining attendees from the north, central, south, and east, and we obtained 396 valid questionnaires. The rural outdoor dining satisfaction experience can be improved using the innovative New Importance–Performance Analysis (NIPA) model, which is based on the original IPA methodology but modified by the performance of the risk management judge. Additionally, we applied the zone of tolerance (ZOT) to evaluate the quality of priority and the importance–performance analysis (IPA) to make innovation decisions. The model also encourages decision-makers to consider environmental factors and customer feedback. It has not only been used to measure customer satisfaction, assess customer behavior, identify customer needs, and determine areas where quality needs to be improved, but it can also be used to measure the success of business decisions and identify potential areas for improvement. The results show that rural outdoor dining experiences in Taiwan have led to the development of a low carbon economy and a new business model for operators in order to follow the result of NIPA and develop service marketing strategies.

Enantioselective Synthesis of Heteroatom-Linked Non-Biaryl Atropisomers.

Atropisomers hold significant fascination, not only for their prevalence in natural compounds but also for their biological importance and wide-ranging applications as chiral materials, ligands, and organocatalysts. While biaryl and heterobiaryl atropisomers are commonly studied, the enantioselective synthesis of less abundant heteroatom-linked non-biaryl atropisomers presents a formidable challenge in modern organic synthesis. Unlike classical atropisomers, these molecules allow rotation around two bonds, resulting in low barriers to enantiomerization through concerted bond rotations. In recent years the discovery of new configurationally stable rare non-biaryl scaffolds such as aryl amines, aryl ethers and aryl sulfones as well as innovative methodologies to control their configuration have been disclosed in the literature and constitute the topic of this minireview.

Exploring the potential of fennel (<i>Foeniculum vulgare</i> Mill.) as a valuable medicinal plant: a comprehensive (review)

Introduction. Foeniculum vulgare Mill., commonly known as fennel or drugstore dill, is very common as one of the world's oldest spice plants, which has significant economic importance and wide application in the pharmaceutical industry. In medical practice, the fruits of this plant are mainly used, which are included in the State Pharmacopoeia of the XIV edition and are a source of essential oil. They are used as an antispasmodic and carminative agent in the form of an infusion. Fennel grass harvested before flowering is used for food, as well as fruits, stems and inflorescences of the plant as a seasoning.Text. The purpose of this study is to summarize scientific papers on the morphological characteristics, phytochemical compounds, therapeutic properties and basic mechanisms of pharmacological activity of F. vulgare. A systematic literature search was conducted using relevant keywords such as "fennel", "Foeniculum vulgare Mill.", "therapeutic" and "pharmacology" in well-known databases, including ScienceDirect, Scopus, EBSCO and Medline. The search covered articles published before April 25, 2023 in available journals. The results of the study showed that fennel has a wide range of pharmacological properties, including antioxidant, anti-cancer, anti-inflammatory, antifungal, antibacterial and estrogenic effects. These effects can be explained by the presence of aromatic compounds, in particular anethole, estragole and fenchone, which are abundantly present in fennel.Conclusion. The diverse pharmacological properties and rich chemical composition of fennel make it a promising raw material for the development of new medicines. Further study of the therapeutic potential of fennel through in vivo and in vitro studies will contribute to the establishment of mechanisms of pharmacological action of biologically active substances (BAS) of fennel.

A Tape-Wrapping Strategy towards Electrochemical Fabrication of Water-Dispersible Graphene.

Graphene has achieved mass production via various preparative routes and demonstrated its uniqueness in many application fields for its intrinsically high electron mobility and thermal conductivity. However, graphene faces limitations in assembling macroscopic structures because of its hydrophobic property. Therefore, balancing high crystal quality and good aqueous dispersibility is of great importance in practical applications. Herein, we propose a tape-wrapping strategy to electrochemically fabricate water-dispersible graphene (w-Gr) with both excellent dispersibility (~4.5 mg/mL, stable over 2 months), and well-preserved crystalline structure. A large production rate (4.5 mg/min, six times faster than previous electrochemical methods), high yield (65.4% ≤5 atomic layers) and good processability are demonstrated. A mechanism investigation indicates that the rational design of anode configuration to ensure proper oxidation, deep exfoliation and unobstructed mass transfer is responsible for the high efficiency of this strategy. This simple yet efficient electrochemical method is expected to promote the scalable preparation and applications of graphene.

Recent Progress in the Composites of Perovskite Nanocrystals and II-VI Quantum Dots: Their Synthesis, Applications, and Prospects

Abstract: The remarkable photoelectric characteristics of perovskite nanocrystals (NCs), including high fault tolerance, tunable photoluminescence (PL) emission, and high carrier mobility, contribute to making them especially attractive for photonic and optoelectronic applications. Unfortunately, the poor environmental thermal and light stability set obstacles to their industrial applications. Over the past 40 years, II-VI semiconductor quantum dots (QDs) have achieved many important photophysics findings and optoelectronic applications. Compared with perovskite NCs, II-VI semiconductor QDs still have a relatively weaker molar absorbance coefficient. Whereas, significant enhancement of both the stability and the optical performance of the composites of perovskite NCs and II-VI QDs are of interest for photovoltaic and optoelectronic devices. The composites of perovskite NCs and II-VI QDs come in two primary types: core/shell structures and heterojunction structures. To better understand the composites of perovskite NCs and II-VI QDs, the approaches of synthesis methods, their optoelectronic properties, carrier dynamics and potential applications in solar cells, light emitting diodes (LEDs) and photodetectors are summarized. Furthermore, the unmet problems and the potential applications are also presented.

From Single Metals to High‐Entropy Alloys: How Machine Learning Accelerates the Development of Metal Electrocatalysts

AbstractThe rapid advancement of high‐performance computing and artificial intelligence technology has opened up novel avenues for the development of various metal electrocatalysts. In particular, dilute and high‐entropy alloys have garnered significant attention owing to their unique electronic and spatial structures, as well as their exceptional electrocatalytic performance. Commencing with the exploration of single‐atom alloy catalysts, the latest advancements in machine learning (ML) techniques are presented for the efficient screening of a broad spectrum of metal spaces. Subsequently, the review delves into the prevailing trend in alloy research, focusing specifically on rare‐metal alloy electrocatalysts, and offers an overview of the progress and outcomes achieved through the application of ML in these domains. Finally, high‐entropy alloys are highlighted as a promising category of electrocatalysts and underscore the importance and potential applications of ML in addressing complex and challenging research issues are underscored.

Financial Sentiment Analysis: Techniques and Applications

Financial Sentiment Analysis (FSA) is an important domain application of sentiment analysis that has gained increasing attention in the past decade. FSA research falls into two main streams. The first stream focuses on defining tasks and developing techniques for FSA, and its main objective is to improve the performances of various FSA tasks by advancing methods and using/curating human-annotated datasets. The second stream of research focuses on using financial sentiment, implicitly or explicitly, for downstream applications on financial markets, which has received more research efforts. The main objective is to discover appropriate market applications for existing techniques. More specifically, the application of FSA mainly includes hypothesis testing and predictive modeling in financial markets. This survey conducts a comprehensive review of FSA research in both the technique and application areas and proposes several frameworks to help understand the two areas’ interactive relationship. This article defines a clearer scope for FSA studies and conceptualizes the FSA-investor sentiment-market sentiment relationship. Major findings, challenges, and future research directions for both FSA techniques and applications have also been summarized and discussed.

Design of morphology-controlled cobalt-based spinel oxides for efficient X-band microwave absorption

The cobalt-based spinel structure oxides with synergistic effect of magnetic loss and dielectric loss have important applications in electromagnetic wave absorption. And the excess 0.5 oxygen atoms in MnCo2O4.5 provide more vacancies than traditional MnCo2O4 spinel structure and improve the electrical conductivity. In the current study, MnCo2O4.5 microsphere clusters, MnCo2O4.5 microcubic spheres and MnCo2O4.5 flowery spheres were prepared by solvothermal method and annealing process. Among the final samples with different morphologies, MnCo2O4.5 microcubic spheres showed outstanding microwave absorption capabilities, with a minimum reflection loss (RLmin) of -51.52 dB at 2.2 mm. When the thickness was further increased to 2.3 mm, the effective absorption bandwidth could be as wide as 5.36 GHz, covering most of the X-band. The excellent absorption performance of electromagnetic waves is related to the modulability of the complex dielectric parameters, the optimization of impedance matching, and the enhancement of the polarization relaxation process through the improvement of the morphologies. This work provides guidance and support for refining the electromagnetic wave absorption potential of cobalt-based spinel oxides and for designing highly efficient electromagnetic wave absorbers in the X-band.

A Review of the Research on Electro-hydraulic Load Simulator and Characteristics with Improvement Methods

Abstract: The electro-hydraulic load simulator is a semi-physical simulation system. It is used to simulate different loads on the bearing capacity object (steering gear system) under working conditions and further test the dynamic and static characteristics of the bearing capacity object. Therefore, it is widely used in important applications such as the simulation of various loads and the development of high-performance steering gear systems. By analyzing and discussing the electro-hydraulic load simulator equipment and its improvement methods, some useful conclusions have been drawn to predict the development of the electro-hydraulic load simulator and its key technologies. The current status of research on electro-hydraulic load simulator prototypes is discussed in depth. The electro-hydraulic load simulators are classified according to the loading system applying force (torque). The improvement methods of the electro-hydraulic load simulator, including the method of suppressing the extraneous force, the compensation method of nonlinear factors, and the method of establishing the mathematical model, are analyzed and summarized. By summarizing the literature and patents on electro-hydraulic load simulation technology, the principle, and characteristics of different equipment and improvement methods are analyzed and compared, and future development of electro-hydraulic load simulation equipment and technology is discussed. The research on the characteristics of the prototype, the hardware means and control strategy to suppress the extraneous force, the compensation of nonlinear factors, and the methods of establishing the mathematical model are beneficial to improve the loading accuracy of the electro-hydraulic load simulator.

Walmart sales prediction based on random forest model and application of feature importance

Sales forecasting is crucial for efficient resource allocation and inventory management in retail. This study employs Random Forest to predict weekly sales for 45 Walmart stores, leveraging a diverse dataset with store-specific sales and external factors. Through meticulous preprocessing and model application, one achieves outstanding accuracy, with a Weighted Mean Absolute Error (WMAE) as low as 1.2030 and an impressive accuracy rate of 98.8%. Additionally, integrating feature importance ranking sheds light on influential variables in sales forecasting. This study provides a blueprint for developing precise and adaptable sales forecasting models, offering profound significance for the retail industry. It underscores the effectiveness of machine learning techniques, e.g., Random Forest and insightful feature engineering in achieving highly accurate predictions. By enhancing the industry's understanding of intricate sales dynamics, this research contributes to optimizing resource allocation, inventory management, and strategic planning. Ultimately, it drives operational efficiency and success in the dynamic landscape of the retail sector.

Analysis of Chinese Patents on Cathodic Protection by Applied Current Method in Marine Sector

The resource problem is the top priority of China's current development, while the ocean is an inexhaustible treasure. The development and utilization of marine resources has always been the key direction of development. The characteristics of offshore engineering operation equipment are: first, the scale of the platform is particularly large, and second, it is long-term in the harsh environment such as moisture and salinity [1]. Therefore, the anti-corrosion of offshore platforms is the research hotspot of relevant technicians, this paper systematically summarizes the patent application of external current in cathodic protection of anti-corrosion technology. It analyzes the patent type, affiliated institution, important applicant, application field and other related contents. The results show that enterprises prefer to actually invent devices, while scientific research institutions, universities and colleges are more about the creation of technical methods.

Dufour and Soret diffusions phenomena for the chemically reactive MHD viscous fluid flow across a stretching sheet with variable properties

The flow of fluid on a slandering stretchable sheet with variable thickness can have several engineering applications like design and optimization of, aerodynamic structures such as wings and airfoils, wind turbine blade design, efficient cooling systems for electronic devices, power plants and industrial processes. Keeping in view these important applications, this work explores Darcy-Forchheimer flow of magnetohydrodynamics (MHD) fluid across a slandering stretchable sheet with variable thickness in the presence of chemically reactive effects, and heat source. The flow is impacted by the combined influences of diffusive-thermo and thermo-diffusive phenomena. The fluid is set into motion along x-axis due to the stretching behavior of the sheet with y-axis as normal to it. Similarity transformations are employed to convert the modeled equations into nonlinear ordinary differential equations. It has observed as outcomes of this study that velocity distribution has augmented with expansion in viscosity factor and has declined with growth in permeability factor, inertia factor, and magnetic factor for both scenarios h1=0.0 and h1=0.5 where the impacts are more significant for the scenario h1=0.0. Thermal distribution has declined with augmentation in Prandtl number, permeability factor and Dufour number while has upsurge with escalation in thermal source factor for both scenarios h2=0.0 and h2=0.5. Concentration distribution has diminished with escalation in Schmidt number, chemical reactivity factor and has augmented with growth in Soret factor for both the scenarios h3=0.0 and h3=0.5 with more significant impacts in the scenario h3=0.0. Stream lines and contour lines for fluid flow have also sketched for the scenarios h1=0.0 and h1=0.5 as well as for M=0.0 and M=2.0. Validation of the proposed model and the methodology has ensured through comparison with fine agreement amongst current results and previously published work.

Reweighted Extreme Learning Machine-Based Clutter Suppression and Range Compensation Algorithm for Non-Side-Looking Airborne Radar

Non-side-looking airborne radar provides important applications on account of its all-round multi-angle airspace coverage. However, it suffers clutter range dependence that makes the samples fail to satisfy the condition of being independent and identically distributed (IID), and it severely degrades traditional approaches to clutter suppression and target detection. In this paper, a novel reweighted extreme learning machine (ELM)-based clutter suppression and range compensation algorithm is proposed for non-side-looking airborne radar. The proposed method involves first designing the pre-processing stage, the special reweighted complex-valued activation function containing an unknown range compensation matrix, and two new objective outputs for constructing an initial reweighted ELM-based network with its training. Then, two other objective outputs, a new loss function, and a reverse feedback framework driven by the specifically designed objectives are proposed for the unknown range compensation matrix. Finally, aiming to estimate and reconstruct the unknown compensation matrix, special processes of the complex-valued structures and the theoretical derivations are designed and analyzed in detail. Consequently, with the updated and compensated samples, further processing including space–time adaptive processing (STAP) can be performed for clutter suppression and target detection. Compared with the classic relevant methods, the proposed algorithm achieves significantly superior performance with reasonable computation time. It provides an obviously higher detection probability and better improvement factor (IF). The simulation results verify that the proposed algorithm is effective and has many advantages.

Advanced Electrospinning Technology Applied to Polymer-Based Sensors in Energy and Environmental Applications.

Due to its designable nanostructure and simple and inexpensive preparation process, electrospun nanofibers have important applications in energy collection, wearable sports health detection, environmental pollutant detection, pollutant filtration and degradation, and other fields. In recent years, a series of polymer-based fiber materials have been prepared using this method, and detailed research and discussion have been conducted on the material structure and performance factors. This article summarizes the effects of preparation parameters, environmental factors, a combination of other methods, and surface modification of electrospinning on the properties of composite nanofibers. Meanwhile, the effects of different collection devices and electrospinning preparation parameters on material properties were compared. Subsequently, it summarized the material structure design and specific applications in wearable device power supply, energy collection, environmental pollutant sensing, air quality detection, air pollution particle filtration, and environmental pollutant degradation. We aim to review the latest developments in electrospinning applications to inspire new energy collection, detection, and pollutant treatment equipment, and achieve the commercial promotion of polymer fibers in the fields of energy and environment. Finally, we have identified some unresolved issues in the detection and treatment of environmental issues with electrospun polymer fibers and proposed some suggestions and new ideas for these issues.

Multiplex PCR method for MinION sequencing of Bagaza virus isolated from wild caught mosquitoes in South Africa

Bagaza virus (BAGV) is a mosquito-borne orthoflavivirus known to occur in regions of southern Europe, Africa, India and the Middle East. The virus has been associated with neurological disease and fatalities in various wild bird species. Association with human disease is not confirmed although limited serological evidence has suggested human infection. Surveillance programs for screening mosquitoes for evidence of arbovirus infection play an important role in providing information regarding the circulation and spread of viruses in specific regions. BAGV was detected in a mosquito pool during surveillance of mosquitoes collected in central South Africa between November 2019 and March 2023. Homogenized mosquito pools were screened for flaviviral RNA using conventional RT-PCR and virus isolation was attempted on positive samples. BAGV was detected and subsequently isolated using cell culture. A multiplex tiling PCR method for targeted enrichment using a PCR based or amplicon sequencing approach of the complete genome of BAGV was developed and optimized. Primers were designed using alignment of complete genome sequence data retrieved from GenBank to identify suitable primer sites that would generate overlapping fragments spanning the complete genome. Six forward primers and eight reverse primers were identified that target the complete genome and amplified nine overlapping fragments, that ranged in length from 1954 to 2039 with an overlap ranging from 71 to 711 base pairs. The design strategy included multiple forward and reverse primer pairs for the 5’ and 3’ ends. Phylogenetic analysis with other isolates was performed and BAGV isolate VBD 74/23/3 was shown to share high similarity with previous BAGV isolates from all regions, with genetic distance ranging from 0.026 to 0.083. VBD 74/23/3 was most closely related to previous isolates from southern Africa, ZRU96/16/2 isolated from a post-mortem sample from a pheasant in 2016 and MP-314-NA-2018 isolated from mosquitoes in northwestern Namibia with genetic distance 0.0085 and 0.016 respectively. Currently there is limited complete genome sequence data available for many of the arboviruses circulating in Africa. The multiplex tiling method provided a simple and cost-effective method for obtaining complete genome sequence. This method can be readily applied to other viruses using sequence data from publicly available databases and would have important application facilitating genomic surveillance of arboviruses in low resource countries.

Cell-Free Protein Expression in Polymer Materials.

While synthetic biology has advanced complex capabilities such as sensing and molecular synthesis in aqueous solutions, important applications may also be pursued for biological systems in solid materials. Harsh processing conditions used to produce many synthetic materials such as plastics make the incorporation of biological functionality challenging. One technology that shows promise in circumventing these issues is cell-free protein synthesis (CFPS), where core cellular functionality is reconstituted outside the cell. CFPS enables genetic functions to be implemented without the complications of membrane transport or concerns over the cellular viability or release of genetically modified organisms. Here, we demonstrate that dried CFPS reactions have remarkable tolerance to heat and organic solvent exposure during the casting processes for polymer materials. We demonstrate the utility of this observation by creating plastics that have spatially patterned genetic functionality, produce antimicrobials in situ, and perform sensing reactions. The resulting materials unlock the potential to deliver DNA-programmable biofunctionality in a ubiquitous class of synthetic materials.

Single-Mode Control and Individual Nanoparticle Detection in the Ultraviolet Region Based on Boron Nitride Microdisk with Whispering Gallery Mode.

Optical microcavities are known for their strongly enhanced light-matter interactions. Whispering gallery mode (WGM) microresonators have important applications in nonlinear optics, single-mode output, and biosensing. However, there are few studies on resonance modes in the ultraviolet spectrum because most materials with high absorption properties are in the ultraviolet band. In this study, the performance of a microdisk cavity based on boron nitride (BN) was simulated by using the Finite-difference time-domain (FDTD) method. The WGM characteristics of a single BN microdisk with different sizes were obtained, wherein the resonance modes could be regulated from 270 nm to 350 nm; additionally, a single-mode at 301.5 nm is achieved by cascading multiple BN microdisk cavities. Moreover, we found that a BN microdisk with a diameter of 2 μm has a position-independent precise sensitivity for the nanoparticle of 140 nm. This study provides new ideas for optical microcavities to achieve single-mode management and novel coronavirus size screening, such as SARS-CoV-2, in the ultraviolet region.

Brief Review of Recent Developments in Fiber Lasers

This review covers the recent achievements in high-power rare earth (RE)-doped fiber lasers, Raman fiber lasers, and Brillouin fiber lasers. RE-doped fiber lasers have many applications such as laser cutting, laser welding, laser cleaning, and laser precision processing. They operate in several wavelength ranges including 1050–1120 nm (ytterbium-doped fiber lasers), 1530–1590 nm (erbium- and erbium–ytterbium-doped fiber lasers), and 1900–2100 nm (thulium- and holmium-doped fiber lasers). White spaces in the wavelength spectrum, where no RE-doped fiber lasers are available, can be covered by Raman lasers. The heat power generated inside the laser active medium due to the quantum defect degrades the performance of the laser causing, for example, transverse-mode instability and thermal lensing. It can even cause catastrophic fiber damage. Different approaches permitting the mitigation of the heat generation process are considered in this review. Brillouin fiber lasers, especially multiwavelength Brillouin fiber lasers, have several important applications including optical communication, microwave generation, and temperature sensing. Recent progress in Brillouin fiber lasers is considered in this review.

Computation of prediction intervals for forest aboveground biomass predictions using generalized linear models in a large-extent boreal forest region

Abstract Remotely sensed data have an important application for estimation of forest variables, e.g. height, volume, and aboveground biomass (AGB). The increased use of remotely sensed data implemented along with model-based inference has shown improved efficiency in prediction and mapping of such forest variables. In this study, plot-level airborne laser scanning data and Swedish National Forest Inventory field reference data were used to predict AGB using generalized linear models (GLMs) assuming Gamma and Tweedie distributions for the field observed AGB. The GLMs were selected considering the convenience of not correcting transformation bias as it is required in other regression models with transformed response variable. To overcome the challenge in providing reliable uncertainty estimates for the estimated forest variable map products at individual pixel-scale, we focused on computing 95% prediction intervals (PIs) for Gamma and Tweedie GLMs with a square root link function. The relative uncertainties were computed as the ratio between the half-width of the PIs and the predicted AGBs. The AGB-airborne laser scanning models were developed with root mean square error values of 22.6 Mgha−1 (26%) and 21.7 Mgha−1 (25%), respectively, for the Gamma and Tweedie GLMs. Two methods were applied to compute PIs for the Gamma GLM, one using the R package ‘ciTools’ and another derived through asymptotic theory. It was found that the 95% PIs computed using ‘ciTools’ had the most accurate coverage probability in comparison to the other method. An extended version of these PIs was also utilized for the Tweedie GLMs. The range of PIs associated with the prediction of AGB were narrower for lower predicted AGB values compared with the length of higher predicted AGB values. Comparing the two fitted models, the Gamma GLM showed lower relative uncertainties for the lower range of predicted AGBs, whereas the Tweedie GLM showed lower relative uncertainties for the higher range of predicted AGBs. Overall, the Tweedie GLM provided a better model fit for AGB predictions.