Important Applications Research Articles

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.

Visual Sensor with Host-Guest Specific Recognition and Light-Electrical Co-Controlled Switch.

Perception of UV radiation has important applications in medical health, industrial production, electronic communication, etc. In numerous application scenarios, there is an increasing demand for the intuitive and low-cost detection of UV radiation through colorimetric visual behavior, as well as the efficient and multi-functional utilization of UV radiation. However, photodetectors based on photoconductive modes or photosensitive colorimetric materials are not conducive to portable or multi-scene applications owing to their complex and expensive photosensitive components, potential photobleaching, and single-stimulus response behavior. Here, a multifunctional visual sensor based on the "host-guest photo-controlled permutation" strategy and the "lock and key" model is developed. The host-guest specific molecular recognition and electrochromic sensing platform is integrated at the micro-molecular scale, enabling multi-functional and multi-scene applications in the convenient and fast perception of UV radiation, military camouflage, and information erasure at the macro level of human-computer interaction through light-electrical co-controlled visual switching characteristics. This light-electrical co-controlled visual sensor based on an optoelectronic multi-mode sensing system is expected to provide new ideas and paradigms for healthcare, microelectronics manufacturing, and wearable electronic devices owing to its advantages of signal visualization, low energy consumption, low cost, and versatility.

Remarks on Solving Methods of Nonlinear Equations

Abstract: In the field of mechanical engineering, many practical problems can be converted into nonlinear problems, such as the meshing problem of mechanical transmission. So the solution of nonlinear equations has important theoretical research and practical application significance. Whether the traditional Newton iteration method or the intelligent optimization algorithm after the popularization of computers, both them have been greatly enriched and developed through the continuous in-depth research of scholars at home and abroad, and a series of improved algorithms have emerged. This paper mainly reviews the research status of solving nonlinear equations from two aspects of traditional iterative method and intelligent optimization algorithm, systematically reviews the research achievements of domestic and foreign scholars, and puts forward prospects for future research directions.

Room-Temperature Phosphorescence Materials Featuring Triplet Hybrid Local Charge Transfer Emission.

Room-temperature phosphorescence materials have found important applications in dissolved oxygen sensing, temperature monitoring, anticounterfeiting, etc., because of their prolonged phosphorescence lifetime. However, the known systems mainly utilize the triplet local excited state emission, which is generally less sensitive to microenvironment perturbation. In this work, we designed a series of 4-phenyl-1,8-naphthalimide (NMI) derivatives containing different numbers of carbazole (Cz) units (denoted as NMI-Cz, NMI-2Cz, and NMI-3Cz). Steady state and time-resolved spectroscopy studies determined that the compounds undergo intramolecular through-space charge transfer in solution, yielding a triplet hybrid local charge transfer state. Room-temperature phosphorescence emission was observed in compound-doped poly(methyl methacrylate) thin films upon ammonia treatment. Interestingly, emission from different films exhibited different persistence times. We believe a film-based, time-resolved luminescent ammonia sensor could be developed by making a device of the emissive films as fabricated.

Starlike Functions of the Miller–Ross-Type Poisson Distribution in the Janowski Domain

In this paper, considering the various important applications of Miller–Ross functions in the fields of applied sciences, we introduced a new class of analytic functions f, utilizing the concept of Miller–Ross functions in the region of the Janowski domain. Furthermore, we obtained initial coefficients of Taylor series expansion of f, coefficient inequalities for f−1 and the Fekete–Szegö problem. We also covered some key geometric properties for functions f in this newly formed class, such as the necessary and sufficient condition, convex combination, sequential subordination and partial sum findings.

A FAST Method for Nested Estimation

Nested estimation involves estimating an expectation of a function of a conditional expectation and has many important applications in operations research and machine learning. Nested simulation is a classic approach to this estimation, and the convergence rate of the mean squared error (MSE) of nested simulation estimators is only of order [Formula: see text], where Γ is the simulation budget. To accelerate the convergence, in this paper, we establish a jackkniFe-bAsed neSted simulaTion (FAST) method for nested estimation, and a unified theoretical analysis for general functions in the nested estimation shows that the MSE of the proposed method converges at the faster rate of [Formula: see text] or even [Formula: see text]. We also provide an efficient algorithm that ensures the estimator’s MSE decays at its optimal rate in practice. In numerical experiments, we apply the proposed estimator in portfolio risk measurement and Bayesian experimental design in operations research and machine learning areas, respectively, and numerical results are consistent with the theory presented. History: Accepted by Bruno Tuffin, Area Editor for Simulation. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72031006, 72101260, and 72394375]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0118 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0118 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Stability, electronic and optical properties of buckled XO (X = Ge, Cu) graphenylene monolayers: A first-principles study

The demand for sustainable semiconducting devices that can be used in various applications necessitates the development of revolutionary materials with multifunctional properties. Using van der Waals interactions corrected first-principles density functional theory (DFT) calculations, we propose porous g-XO (X = Ge, Cu) inorganic graphenylene monolayers. These monolayers extend the family of synthesized porous organic graphenylene materials. Stability checks reveal that the g-XO monolayers are energetically, mechanically, dynamically, and thermally stable. Additionally, g-GeO monolayer is auxetic 2D material. The auxetic property of g-GeO monolayer could be advantageous for important applications in mechanics and tissue engineering, electromechanical devices, and flexible electronics. Based on the HSE06 electronic band structure results, these g-CuO and g-GeO monolayers exhibit wide and ultra-wide indirect bandgap semiconducting properties, respectively. The satisfactory absorption coefficient of 104–105 cm−1 spanning the visible to UV region implies their potential for optoelectronics and UV shields applications. Our theoretical results provide a hints for the potential of these porous g-XO monolayers in energy harvesting/conversion, nanofiltration membranes, optoelectronic, thermoelectric, and flexible electronics.

Weak measurement of the Goos–Hänchen shift for a Hermite–Gaussian laser beam

We report on an experimental investigation of the Goos–Hänchen (GH) optical beam shift in the vicinity of the critical angle of incidence at an air–glass interface using a weak value amplification (WVA) technique for two mutually orthogonal first order Hermite–Gaussian (HG) modes (HG10 and HG01) of a light beam at 633 nm generated by a phase-only reflective spatial light modulator. We have developed a mathematical approach to visualize the beam shaping due to the WVA scheme of beam shifts for the HG modes. The study reveals the angle of incidence dependency of the GH shift in the total internal reflection condition. For both modes, a detailed study of the horizontal and transverse beam shift values with varied post-selection angles is also reported. In addition, a comparison of the beam shift values for both of the selected modes with the fundamental mode (HG00) has been demonstrated. We found a significant enhancement (about two to three times) in the beam shifts for the first order HG10 and HG01 modes compared to the fundamental mode (HG00). Our results clearly demonstrate the advantages of the HG modes of the light beam-exploiting WVA technique and thus may contribute significantly to this field and open up important applications in photonic manipulation and future technologies.

Students’ Understanding of Microscopic Models of Electrical and Thermal Conductivity: Findings within the Development of a Multiple-Choice Concept Inventory

Solid-state physics has important applications in the development of modern technologies. Although solid-state concepts, such as electric and thermal conductivity, are included in the curricula at all educational levels, even university students have many incorrect ideas about them. The incorrect ideas identified in previous studies are mainly related to macroscopic aspects of solid-state physics. With the aim of gaining a more comprehensive insight into students’ understanding of solid-state physics, we have developed a multiple-choice concept inventory on microscopic models of electric and thermal conductivity of solids (METCS). The inventory has been field-tested with a sample consisting of 233 first-year medical faculty and engineering students from the University of Rijeka (Croatia). METCS proved to have good psychometric features and it helped to uncover many incorrect ideas that have not been reported in the earlier physics education literature. The findings from this study could be a good starting point for the development of evidence-based, university-level tutorials on microscopic models of electric and thermal conductivity.

Enhanced cross-entropy framework for multiple-attribute decision-making with type-2 neutrosophic number and applications to cross-border e-commerce logistics service providers evaluation

In recent years, the lack of coordination in cross-border logistics has been one of the challenges and challenges faced by cross-border e-commerce. As the primary link in cross-border logistics, the selection of logistics service providers is an important foundation for promoting the development of cross-border e-commerce, and also a key link in improving the competitiveness of cross-border e-commerce enterprises. How to choose suitable and effective cross-border e-commerce logistics service providers has important theoretical significance and practical application value. The cross-border e-commerce logistics service providers evaluation is a multiple-attributed decision-making (MADM) problem. In this paper, the Type-2 neutrosophic number cross-entropy (T2NN-CE) technique is designed with help of cross-entropy and Type-2 neutrosophic number (T2NN). Furthermore, Then, T2NN-CE technique is built to solve the MADM. Finally, a numerical example for cross-border e-commerce logistics service providers evaluation is given and some comparisons are conducted to illustrate advantages of the designed T2NN-CE technique. The research contribution of the paper is outlined: (1) The T2NN-CE is managed under T2NNs; (2) the T2NN-CE method is implemented for MADM under T2NNs; (3) the T2NN-CE technique for cross-border e-commerce logistics service providers evaluation is constructed and were compared with some existing techniques; (4) Through the comparison, it is known that T2NN-CE technique for cross-border e-commerce logistics service providers evaluation is effective.

Influence of Quenching and Tempering on the Tribological and Corrosion Behavior of Plasma‐Nitrided Society of Automotive Engineers 52100

Society of automotive engineers (SAE) 52100 (100Cr6) steel, used in manufacturing bearings and shafts, deserves attention due to wear and corrosion problems that cause machine losses in the industry. To solve these problems, heat treatments such as quenching and tempering are adopted to reduce wear mechanisms. However, in this work, the effect of these previous heat treatments on the final properties of plasma nitrided steel at three different temperatures (400, 450, and 500 °C) is evaluated. Surface hardness, tribological behavior, and corrosion resistance are evaluated. In the results of Vickers hardness, wear, and electrochemical analysis, it is shown that performing plasma nitriding at 400 and 450 °C on previously heat‐treated SAE 52100 steel provides better mechanical resistance and chemical integrity in a corrosive environment, making the studied steel more comprehensive for important industrial applications.

Proton-fluence dependent magnetic properties of exfoliable quasi-2D van der Waals Cr2Si2Te6 magnet

The discovery of long-range magnetic ordering in atomically thin materials catapulted the van der Waals (vdW) family of compounds into an unprecedented popularity, leading to potentially important technological applications in magnetic storage and magneto-transport devices, as well as photoelectric sensors. With the potential for the use of vdW materials in space exploration technologies it is critical to understand how the properties of such materials are affected by ionizing proton irradiation. Owing to their robust intra-layer stability and sensitivity to external perturbations, these materials also provide excellent opportunities for studying proton irradiation as a non-destructive tool for controlling their magnetic properties. Specifically, the exfoliable Cr2Si2Te6 (CST) is a ferromagnetic semiconductor with the Curie temperature (T C) of ∼32 K. Here, we have investigated the magnetic properties of CST upon proton irradiation as a function of fluence (1 × 1015, 5 × 1015, 1 × 1016, 5 × 1016, and 1 × 1018 H+/cm−2) by employing variable-temperature, variable-field magnetization measurements, and detail how the magnetization, magnetic anisotropy vary as a function of proton fluence across the magnetic phase transition. While the T C remains constant as a function of proton fluence, we observed that the saturation magnetization and magnetic anisotropy diverge at the proton fluence of 5 × 1016 H+/cm−2, which is prominent in the ferromagnetic phase, in particular.This work demonstrates that proton irradiation is a feasible method for modifying the magnetic properties and local magnetic interactions of vdWs crystals, which represents a significant step forward in the design of future spintronic and magneto-electronic applications.

Mid-Long Chain Dicarboxylic Acid Production via Systems Metabolic Engineering: Progress and Prospects.

Mid-to-long-chain dicarboxylic acids (DCAi, i ≥ 6) are organic compounds in which two carboxylic acid functional groups are present at the terminal position of the carbon chain. These acids find important applications as structural components and intermediates across various industrial sectors, including organic compound synthesis, food production, pharmaceutical development, and agricultural manufacturing. However, conventional petroleum-based DCA production methods cause environmental pollution, making sustainable development challenging. Hence, the demand for eco-friendly processes and renewable raw materials for DCA production is rising. Owing to advances in systems metabolic engineering, new tools from systems biology, synthetic biology, and evolutionary engineering can now be used for the sustainable production of energy-dense biofuels. Here, we explore systems metabolic engineering strategies for DCA synthesis in various chassis via the conversion of different raw materials into mid-to-long-chain DCAs. Subsequently, we discuss the future challenges in this field and propose synthetic biology approaches for the efficient production and successful commercialization of these acids.

Fabrication and Characterization of Silica Nanoparticles from Beach Sand

Silica nanoparticles (SNPs) have many important applications including anti-reflection coating, self-cleaning surface and drug carriers. They are usually synthesized from the commercial precursor such as tetraethyl orthosilicate (TEOS). On the other hand, the natural silica can be found abundantly in organic materials such as rice husk and palm shell or in inorganic material such as beach sand and clay. Therefore, obtaining SNPs from the natural resources is very strategic for economic and technology considerations. This motivates the current study performing the synthesis and characterization of SNPs derived from the beach sand as one of natural resources available. Fort this purpose, the silica sands were mixed with sodium hydroxide (NaOH) for 2 hours at 90⁰ C, prior to filtering process for gaining the sodium silicate solution (SSS) which was further refluxed with hydrochloric acid (HCl) of 3 and 5 M until its pH reached the value of 7, and finally dried. The characterizations carried out on the resulting SNPs included UV-Vis and FTIR spectroscopies, XRD and SEM. The XRD study confirmed that the resulting samples are amorphous silica phase with the average crystallite size of 4.8 and 2.9 nm for the SNPs synthesized with 3 and 5 M chloric acid, respectively. The image analysis of SEM results revealed that the obtained SNPs have the average diameter of 11.6-12.00 nm. The formation of SNPs was further confirmed with the UV-Vis and FTIR spectroscopies. On the basis of investigation results, it was shown that the desired silica nanoparticles can be successfully derived from the beach sand.

Systematic Review of Retinal Blood Vessels Segmentation Based on AI-driven Technique.

Image segmentation is a crucial task in computer vision and image processing, with numerous segmentation algorithms being found in the literature. It has important applications in scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, image compression, among others. In light of this, the widespread popularity of deep learning (DL) and machine learning has inspired the creation of fresh methods for segmenting images using DL and ML models respectively. We offer a thorough analysis of this recent literature, encompassing the range of ground-breaking initiatives in semantic and instance segmentation, including convolutional pixel-labeling networks, encoder-decoder architectures, multi-scale and pyramid-based methods, recurrent networks, visual attention models, and generative models in adversarial settings. We study the connections, benefits, and importance of various DL- and ML-based segmentation models; look at the most popular datasets; and evaluate results in this Literature.

Machine Learning-Based Traffic Flow Prediction and Intelligent Traffic Management

With the rapid development of information technology, multiple time series forecasting, which is typical of traffic flow forecasting, has become increasingly important in big data analysis. As the cornerstone of intelligent transportation system, traffic flow forecasting has important scientific research value and practical application value for urban traffic operation scheduling, quality and efficiency improvement of logistics transportation industry and public travel planning. Traffic flow prediction is always an important task of intelligent transportation system. Due to the complex temporal and spatial dependence of traffic flow sequence, it is very challenging to construct accurate traffic flow prediction in its ring neural network, graph network and Transformer model. Much of the existing work is based on very good models. Considering the advantages of convolutional networks, such as high computational efficiency and strong feature extraction ability, a traffic flow prediction model based on multi-view spatiotemporal convolution is proposed.

Study on influence coefficients of hydrodynamic force on dynamic response of deep-water pier under earthquake

The hydrodynamic force generated by the interaction between deep-water pier and water under earthquake exerts a significant influence on the dynamic response of the pier. At present, there is a lack of characterization methods to measure the influence degree of pier-water interaction on the dynamic response of the deep-water pier. Based on the structural dynamics theory and considering the coupling effect of pier-water interaction, the dynamic equilibrium equations for the deep-water pier under earthquake are established, and the solutions are deduced and validated by the finite element simulation results. Then the comprehensive analysis of influence mechanism is conducted. Further, the displacement influence coefficients and internal force influence coefficients are proposed to evaluate the influence degree of hydrodynamic force on dynamic response of deep-water piers. Validation shows that the proposed coefficients have high accuracy, indicating the important application of the proposed coefficients in seismic design of deep-water piers in real practice.

Highly efficient expression of Rasamsonia emersonii lipase in Pichia pastoris: characterization and gastrointestinal simulated digestion in vitro.

Acidic lipases with high catalytic activities under acidic conditions have important application values in the food, feed and pharmaceutical industries. However, the availability of acidic lipases is still the main obstacle to their industrial applications. Although a novel acidic lipase Rasamsonia emersonii (LIPR) was heterologously expressed in Escherichia coli, the expression level was unsatisfactory. To achieve the high-efficiency expression and secretion of LIPR in Pichia pastoris GS115, the combinatorial optimization strategy was adopted including gene codon preference, signal peptide, molecular chaperone co-expression and disruption of vacuolar sorting receptor VPS10. The activity of the combinatorial optimization engineered strain in a shake flask reached 1480 U mL-1, which was 8.13 times greater than the P. pastoris GS115 parental strain. After high-density fermentation in a 5-L bioreactor, the highest enzyme activity reached as high as 11 820 U mL-1. LIPR showed the highest activity at 40 °C and pH 4.0 in the presence of Ca2+ ion. LIPR exhibited strong tolerance to methanol, indicating its potential application in biodiesel biosynthesis. Moreover, the gastrointestinal digestion simulation results demonstrated that LIPR was tolerant to pepsin and trypsin, but its activity was inhibited by sodium taurodeoxycholate. This study provided an effective approach for the high expression of acidic lipase LIPR. LIPR was more appropriate for lipid digestion in the stomach than in intestine according to the gastrointestinal digestion simulation results. © 2024 Society of Chemical Industry.

Wheat Seed Detection and Counting Method Based on Improved YOLOv8 Model.

Wheat seed detection has important applications in calculating thousand-grain weight and crop breeding. In order to solve the problems of seed accumulation, adhesion, and occlusion that can lead to low counting accuracy, while ensuring fast detection speed with high accuracy, a wheat seed counting method is proposed to provide technical support for the development of the embedded platform of the seed counter. This study proposes a lightweight real-time wheat seed detection model, YOLOv8-HD, based on YOLOv8. Firstly, we introduce the concept of shared convolutional layers to improve the YOLOv8 detection head, reducing the number of parameters and achieving a lightweight design to improve runtime speed. Secondly, we incorporate the Vision Transformer with a Deformable Attention mechanism into the C2f module of the backbone network to enhance the network's feature extraction capability and improve detection accuracy. The results show that in the stacked scenes with impurities (severe seed adhesion), the YOLOv8-HD model achieves an average detection accuracy (mAP) of 77.6%, which is 9.1% higher than YOLOv8. In all scenes, the YOLOv8-HD model achieves an average detection accuracy (mAP) of 99.3%, which is 16.8% higher than YOLOv8. The memory size of the YOLOv8-HD model is 6.35 MB, approximately 4/5 of YOLOv8. The GFLOPs of YOLOv8-HD decrease by 16%. The inference time of YOLOv8-HD is 2.86 ms (on GPU), which is lower than YOLOv8. Finally, we conducted numerous experiments and the results showed that YOLOv8-HD outperforms other mainstream networks in terms of mAP, speed, and model size. Therefore, our YOLOv8-HD can efficiently detect wheat seeds in various scenarios, providing technical support for the development of seed counting instruments.