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In the field of second language pedagogy, it has become increasingly common to consider the real-world usage for language when strategizing goals and curriculum development for language instruction. Emerging from a reverse design perspective, which prioritizes desired outcomes as a starting place for curricular design, language instructors identify and define the knowledge, skills, and abilities (KSAs) they aim for their students to acquire. In cases in which existing proficiency guidelines are not well aligned with the real-world language use that a particular course is targeting, it is becoming increasingly common for instructors to design Languages for Special Purposes (LSP) courses that reflect the unique uses certain bodies of students may have for the language. This paper considers one such case, that of Yiddish for Academic Purposes. Using domain analysis, a multidimensional research framework that supports and undergirds the development of new LSP courses in an assessment-driven proficiency-oriented reverse design framework and evidence-centered design (ECD), this study presents a series of target KSAs for Yiddish for Academic Purposes, on the basis of which curriculum developers could build assessments and subsequently curricula aligned with one another and with the specific language usage unique to the real-world language use domain of graduate academic Yiddish. This process of domain analysis could be replicated for other languages when academic usage is considered as a specific purpose for which an LSP course could be developed. This study is particularly relevant to the development of LSP courses for less commonly taught languages (LCTLs), which tend to be under-resourced and under-researched. Examining academic applications of LCTLs is particularly essential for those languages for which there are fewer, or more constrained, other “real world” applications for the language outside of academic use than there are for more commonly spoken languages.

BACKGROUND: Poor incision healing and infection often occur in elderly patients with lateral malleolar fractures after traditional lateral plate fixation. With the application of engineering software in medicine, a new type of plate placed posterolateral can be designed to solve the above-described problems. OBJECTIVE: To design a new type of posterior lateral low-profile steel plate with the aid of medical bioengineering software, based on the 3D CT data of the distance between the top of the lateral ankle fracture line to the anterior starting point (ACD), the distance between the top of the fracture line to the tip of the lateral ankle (CTD), the distance between the top of the fracture line to the posterior edge of the fracture line (PCD) and the angle between the anterior and posterior lateral sides of the distal fibula (CA). METHODS: Thirty cases of unstable lateral malleolar fracture and normal ankle were taken for CT scanning and three-dimensional reconstruction. The ACD, CTD, and PCD values in patients with lateral malleolar fracture were measured by 3-matic software, and the characteristics of lateral malleolar fracture line were plotted and described. The mimics software was used to measure the value of CA in the normal ankle joint. Based on the data measured above, 3-matic software and solidworks software were used to design the low-profile steel plate and the thickness of the steel plate and the direction of the nail path were constructed. In Geomagic Studio software, fine surface, automatic surface, and fitting surface were used to generate the prototype of the low-profile steel plate, and then 3D printing was performed. After making a posterolateral incision of the lateral malleolus, the peroneus longus and brevis tendons were removed, and the prototype of the 3D-printed steel plate was placed behind the fibula to test its size and fit to the bone surface. RESULTS AND CONCLUSION: (1) The mean of ACD was (2.97±0.03) cm, and the variation was 5.23. The mean of PCD was (3.17±0.11) cm, and the variation was 17.60. The mean of CTD was (4.52±0.07) cm, and the variation was 8.60. (2) The fracture line of the lateral malleolus was drawn with an inverted “V” shape. The mean of CA between anterior and posterior lateral surfaces of the distal fibula was (103.20±1.94)°. At the midpoint section of the upper and lower vertices of the anterior edge of the distal fibula, the angle of the anterior and posterior lateral sides (CA) of the distal fibula was (78.50±1.78)°. (3) By using 3-matic, Solidworks, and Geomagic Studio software, a new type of posterior lateral low-profile steel could be successfully designed. Three to four holes were reserved for the screw holes at the proximal end of the plate with screw directions from back to front, and three screw holes were reserved on the inner and outer sides at the far end. The direction of the inner three holes could be from back to front, and the outer three screw holes needed to be biased towards the inner side, with an angle of 9.72°-13.28°. (4) It is indicated that the variability of the ACD position on the anterior lateral fracture line of the lateral malleolus is relatively small, while the variability of the posterior lateral PCD position is relatively large. The angle between the anterolateral and posterolateral sides of the lateral ankle fracture block shows a decreasing trend, with a smaller variation in the proximal angle and a larger variation in the distal angle. Based on three-dimensional CT reconstruction data of the external ankle, with the help of computer bioengineering software and the use of reverse design concept, a new type of low-profile lateral malleolus steel plate with a good fit can be quickly and conveniently designed to provide a valuable reference for the design of internal fixation devices. [ABSTRACT FROM AUTHOR]

As a two-dimensional artificial composite electromagnetic material, metasurfaces have achieved complex control of inherent properties such as amplitude, phase, polarization, and frequency of electromagnetic waves. However, the design of metasurfaces requires deep expertise and intensive iterative calculations. Deep learning, as a machine learning method, has been applied to design metasurfaces in recent years. The traditional neural network architecture solves the reverse design of nanophotonic metasurfaces as a regression problem, thereby achieving the mapping of optical responses to structural design space. This approach suffers from a lack of flexibility in the design of nanophotonic structures, as they typically limit the optimization process to a predefined set of design candidates, making it difficult to create entirely new metasurface designs. Here, we propose a deep learning method based on conditional generative adversarial networks (CGAN), which is capable of generating new graphic patterns. Compared with traditional neural networks, CGAN can achieve higher accuracy and generalization performance in reverse design. [ABSTRACT FROM AUTHOR]

The combination of thermodynamic calculation and machine learning was used to reverse design nickel-based superalloys for thermodynamic performance requirements. The results show that the thermodynamic calculation dataset of nickel-based superalloys is successfully constructed by high-throughput thermodynamic calculations， which provides the data basis for the reverse design of nickel-based superalloys with thermodynamic performance requirements by using machine learning methods. Several C2P models are established for the thermodynamic target performance， and the accuracy of the models is higher than 99%. MLDS method is used to reverse design the alloy composition， and the eight alloys are recommended to meet the performance requirements （Vγ'，1100 ℃≥60%，Vγ，1100 ℃+Vγ'，1100 ℃≥99% and Tγ′≥1300 ℃）. The experimental verification of the three alloys with the smallest prediction error of thermomechanical properties shows that the Vγ'，1100 ℃ are greater than 80%， Vγ，1100 ℃+Vγ'，1100 ℃≥99% in the microstructure after aging， and Tγ′≥1300 ℃， which meet the design requirements.

Reverse design and additive manufacturing technologies are fast ways to develop customised products. In this study, furniture protective foot covers were taken as the design object. Using flexible filaments of polylactic acid (PLA) and the development process of reverse design to additive manufacturing, the protective foot covers were designed and manufactured to fit the shape of the chair feet. Furniture protective foot covers have high practical value. They have a certain buffering effect, avoiding the damage caused by the collision of furniture feet with the ground when moving furniture; secondly, they reduce the noise generated by the collision of furniture feet with the ground, creating a quiet and comfortable home environment. According to the finite element simulation results, the maximum stress value of the European-style chair installed with protective foot covers was decreased by 90.8% in the case of vertical fall, which verifies that the protective foot covers have an obvious buffering effect. Noise test results show that the noise of the European-style chair installed with protective foot covers was decreased by 51.0%, which verifies that the protective foot covers have an obvious quieting effect.

Copyright of Journal of Materials Engineering / Cailiao Gongcheng is the property of Journal of Materials Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In response to the current issues of narrow bandwidth and low design efficiency in absorbing structures, a reverse design study of broadband metamaterial absorbers is conducted. Utilizing a MATLAB-CST co-simulation approach, particle swarm optimization (PSO) is employed to search for parameter combinations within the design space that satisfy target requirements, thus achieving an efficient automated design method for a metamaterial absorber based on PSO. A parametric model of the metamaterial absorber is constructed using nested patterns of the Vicsek fractal and rings, with the use of resistive film to expand the absorption bandwidth. Combining the reverse design method based on PSO, a metamaterial absorber capable of effectively absorbing electromagnetic waves in the frequency range of 3–10 GHz is designed. Qualitative analysis of the absorbing mechanism of the designed metamaterial absorber is performed by examining the surface current and energy loss density distributions at resonance frequencies, revealing that the absorbing effect of this absorber structure is induced by electromagnetic wave-excited electric resonances. A simplified multiple reflection interference theory model is employed to quantitatively analyze the absorption mechanism of the designed metamaterial absorber. The performance of the designed metamaterial absorber sample is measured using the arch method, and the measurement results are found to be consistent with the simulation results. The designed metamaterial absorber can be applied to radar stealth design for military targets such as ships, demonstrating good generality and engineering application value in research methodology and approach. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The study of optical topological insulators (PTIs) has revealed intriguing optical properties that diversify the ways in which light can be manipulated, offering significant potential for a wide range of applications. This paper presents a machine learning (ML)-based approach for the reverse design of optical PTIs. Using finite element methods, the paper addresses the challenge of computing the band structure of a dual-band model, enabling the construction of a dataset suitable for ML training. With the goal of maximizing dual-band bandgaps, the study employs the random forest algorithm to predict target parameters and further designs topological edge states. Leveraging these boundary state patterns, two different optical PTI beam splitters are devised, and their transmission coefficients and losses are computed. The results demonstrate that optical devices designed using topological boundary states exhibit enhanced stability and robustness. This approach offers a reliable solution for applications in fields such as optical communication and optical sensing. [ABSTRACT FROM AUTHOR]

In order to rapidly and accurately evaluate the mechanical properties of a novel origami-inspired tube structure with multiple parameter inputs, this study developed a method of designing origami-inspired braces based on machine learning models. Four geometric parameters, i.e., cross-sectional side length, plate thickness, crease weakening coefficient, and plane angles, were used to establish a mapping relationship with five mechanical parameters, including elastic stiffness, yield load, yield displacement, ultimate load, and ultimate displacement, all of which were calculated from load-displacement curves. Firstly, forward prediction models were trained and compared for single and multiple mechanical outputs. The parameter ranges were extended and refined to improve the predicted results by introducing the intrinsic mechanical relationships. Secondly, certain reverse prediction models were established to obtain the optimized design parameters. Finally, the design method of this study was verified in finite element methods. The design and analysis framework proposed in this study can be used to promote the application of other novel multi-parameter structures. [ABSTRACT FROM AUTHOR]

In this paper, an intelligent prediction and design method of acoustic metamaterial beams based on deep learning is presented. A theoretical model of acoustic metamaterial beams is derived by using the spectral element method to calculate the band structure and transmission characteristic of beam-type acoustic metamaterial. A high-degree-of-freedom design space dataset of band structure and transmission characteristics is constructed. In addition, the vibration transmission characteristics of acoustic metamaterial beams are predicted and the on-demand inverse design of acoustic metamaterial beams is realized by constructing a fully connected deep learning neural network model. Furthermore, the forward prediction and reverse design network model is verified by using the autoencoder neural network model. Results show that the predicted value of the autoencoder neural network structure is in good agreement with the target value, which indicates the feasibility of the intelligent design method of acoustic metamaterials based on deep learning. The presented intelligent design method could be potentially utilized in the field of fast and efficient acoustic metamaterial design for low frequency vibration isolation in engineering structures. [ABSTRACT FROM AUTHOR]

Design of steel-reinforced concrete (SRC) beams can be a lengthy process, and understanding how design parameters as many as over 26 influence each other can be difficult. In this study, an artificial neural network was trained with large structural datasets to realize a reverse design of SRC beams based on mapping input parameters to output parameters rather than on structural mechanics or knowledge. For training, a back-substitution method was applied in two steps: reverse outputs are calculated in a reverse network of Step 1 for given reverse input parameters; then, reverse output parameters obtained in Step 1 are substituted as input parameters in forward network of Step 2 to obtain design parameters. Trained network was then implemented in determining design parameters of SRC beams for given unseen structural input parameters. Network results were ascertained through conventional structural calculations. Design charts offer predictions of multiple design parameters in any order, which can help engineers in the preliminary design of SRC beams. The developed network can be used to explore intricacies of design parameters to each other, which may be difficult with conventional design methods.

Copyright of Cuadernos del Centro de Estudios de Diseño y Comunicación is the property of Cuadernos del Centro de Estudios de Diseno y Comunicacion and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, the motion characteristics of manual stirring were analyzed to achieve the goal of realizing a highquality mechanized imitation of manual stirring for oolong tea. A test bench for collecting the motion characteristics of manual stirring was designed, and the standard manual stirring motion characteristic curve was obtained. According to the requirements of the stirring motion characteristics, a variable-speed and variable-amplitude stirring mechanism was proposed, the kinematics model and reverse design model of the stirring mechanism were established, auxiliary analysis and design software were written, and the non-circular gear pitch curve and cam profile curve were obtained. A three-dimensional model of the stirring machine was established, a virtual prototype of the stirring machine was built, and the simulation experiment was completed by using the ADAMS software. A prototype of the stirring machine was developed, and the stirring experiment was conducted on the prototype. The maximum deviation of the pitch angle is 2.8772°, and the maximum deviation of the roll angle is 1.5948° according to the analysis and comparison of the actual curve data and the theoretical data curve. The experimental results showed that the simulation angle curve and the actual angle curve were consistent with the theoretical standard manual stirring motion characteristic curve, which verified the correctness of the design and modeling of the stirring mechanism. The tea was mechanically stirred by the stirring mechanism to achieve the desired stirring effect, which verified the feasibility of the stirring machine. [ABSTRACT FROM AUTHOR]

This paper explores the implementation and application of non-uniform ambient fields from two perspectives: constant heat source and abrupt heat source variation. On the one hand, the proper orthogonal decomposition (POD) method is used to find the optimum air supply parameters for different target temperatures at multiple demand points. The one-parameter, two-parameter and three-parameter cases were considered, respectively, and the parameters obtained from the search for optimisation were verified, yielding mean deviations of 0.405 K, 0.368 K and 0.380 K and mean errors of 1.48%, 1.61% and 1.68%, respectively. The accuracy of the reconstructed results of the POD method and the reliability of the POD method for finding the best results are verified step by step with the help of the experimental platform. The validation results show that the average error between the reconstructed data and the experimental data for the POD method does not exceed 5%; the average errors between the measured and set demand temperatures at the target point are 1.2% and 0.8%. On the other hand, the gappy POD method is used to accurately fill in the elements of the system with arbitrary missing data and to reconstruct the flow field in the presence of missing (gappy) data with a limited number of sensors combined with POD. The errors of the gappy POD method reconstruction are 0.54% and 1.75%. POD and gappy POD methods can better create non-uniform temperature fields in practical scenarios according to actual needs. The results of the study can provide a methodological reference for real-time reconstruction and real-time control of the indoor flow field environment. It also serves as a help and suggestion for the actual project in terms of end-regulation system and reverse design method. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study proposes an artificial neural network for a design of reinforced concrete (RC) columns for structural engineers who are interested in performing reverse designs, exploring influences of structural parameters (e.g., $$\phi {P_n},{\ }\phi {M_n}$$, and $${{\rm{\varepsilon }}_{\rm{s}}}$$) or code requirements on structural performances. The proposed networks enable both forward and reverse designs for an RC column, which is challenging to be achieved using conventional designs. An AI-based surrogate model of RC columns with sufficient training accuracy can comprehensively replace conventional design software, exhibiting excellent productivity for both forward and reverse designs. In addition, useful reverse design models based on neural networks can be established by relocating preferable control parameters, including safety factor (SF = $${\ }\phi {M_n}/{M_u}$$) and an aspect ratio of column sections, into the input region. All associated design parameters, including $$b$$, $$h$$, and $${\rho _s}$$, are computed on an output side. Design charts, such as the P–M diagram, are constructed, demonstrating design moment strength equal to the factored moment demand by specifying SF = 1. The design scenarios can be extended as further as possible to meet the requirements of engineers.

The present paper introduces a practical and convenient artificial intelligence-based design approach for doubly reinforced concrete (RC) beams. Completed designs are automatically obtained from regression models and back-substitution (BS) procedures, which satisfy the preassigned flexural strength, curvature ductility, and calculate serviceability parameters. In addition, regression algorithms are developed by training multiple Gaussian Process Regression models on structural data. Furthermore, feature selections and Chained training scheme with Revised Sequence (CRS) techniques are implemented to enhance the training accuracy, providing acceptable accuracies (less than 0.7% errors) in 91 interpolation designs. First, CRS procedures are employed, improving the regression accuracy by sequentially predicting outputs, using predictions of predecessor steps as inputs for the successor ones. In doing so, the preciseness of models is improved as training continues. Appropriate inputs and reasonable output sequences for CRS are determined using a feature selection-based procedure for obtaining optimal training. This procedure implemented three feature selection methods (F-test, Neighborhood Component Analysis (NCA), and RReliefF) in a greedy algorithm, evaluating relations among design parameters. In summary, a direct design approach of a doubly reinforced concrete beam is presented, which enables engineers to control moment capacities and curvature ductility easily, replacing ineffective iteration-based conventional design procedures.

As a kind of nature-derived bioactive materials, polyphenol-based hydrogels possess many unique and outstanding properties such as adhesion, toughness, and self-healing due to their specific crosslinking structures, which have been widely used in biomedical fields including wound healing, antitumor, treatment of motor system injury, digestive system disease, oculopathy, and bioelectronics. In this review, starting with the classification of common polyphenol-based hydrogels, the pyramid evolution process of polyphenol-based hydrogels from crosslinking structures to derived properties and then to biomedical applications is elaborated, as well as the efficient reverse design considerations of polyphenol-based hydrogel systems are proposed. Finally, the existing problems and development prospects of these hydrogel materials are discussed. It is hoped that the unique perspective of the review can promote further innovation and breakthroughs of polyphenol-based hydrogels in the future.

In the previous research, the seedling pick-up mechanism of the planetary gear train with incomplete eccentric circular gear and non-circular gears for vegetable plug seedlings still has two shortcomings. One is that not enough seedling pick-up depth leads to a low success ratio of seedling pick-up at high rotation speeds, the other is that the smaller seedling pushing angle results in poor seedling pushing effect. Therefore, the reverse design of the seedling pick-up mechanism based on its motion trajectory was carried out. The local trajectory of seedling pick-up and seedling pushing sections was adjusted to obtain the theoretical motion trajectory of the seedling pick-up mechanism. The cubic non-uniform B-spline curve was used to fit the adjusted trajectory. A novel seedling pick-up mechanism of the planetary gear train with non-circular gears was proposed, including three combined non-circular gears, four non-circular gears, one planetary carrier, and two seedling pick-up arms. The reverse design model of the mechanism was established. The analysis and design software of the mechanism was developed to obtain the mechanism parameters meeting design requirements. The virtual prototype of the mechanism was established and its physical prototype was manufactured. Through the virtual motion simulation and high-speed photographic kinematics bench tests of the mechanism, the kinematic model and results of reverse design of the mechanism were verified, with the kinematic performances of the mechanism prototype studied. The seedling pick-up tests of the mechanism were conducted in the laboratory. The success ratios of seedling pick-up were 94.2%, 95.6% and 90.2% while the seedling pick-up efficiencies of the mechanism were 60, 80 and 100 plants per minute per row, respectively. Besides, the seedling pushing effect was improved mush because of the greater seedling pushing angle. The seedling pick-up mechanism through revise design is of high value to be applied in the practical vegetable plug seedling transplanters. [ABSTRACT FROM AUTHOR]

We propose two kinds of high-efficiency free-space wave splitters in the mid-IR band using reverse design. The wavelength divider based on the abnormal reflection principle realizes a beam-splitting angle of 22.00° and 10.92° by controlling the phase distribution, and the reflection efficiency of both wavelengths exceeds 50%. The wavelength divider designed based on the concept of metalens simultaneously accomplishes the functions of focusing and beam splitting. It has a focal length of 100 μ m and a relative focal position of 100 μ m. Most importantly, the focusing efficiency for the two wavelengths reaches an impressive 48.59% and 72.51%, respectively.

Chained Training Scheme (CTS) and Chained training scheme with Revised Sequence (CRS) were proposed to train artificial neural networks (ANNs) to design doubly reinforced concrete (RC) beams. CRS and CTS performed training on large datasets based on feature selection scores determined by Neighborhood Component Analysis (NCA). Conventional training methods cannot utilize output parameters as input indexes for training. CRS-based networks can be trained on inputs and outputs at the same time, regardless of whether they belong to an input or output side. This method allows design parameters appearing on the output side to be simultaneously used as input feature indexes of the other outputs in a chained fashion, improving training accuracies. A targeted nominal moment capacity was reversely pre-assigned to the input side to design beam parameters such as beam width, both tensile and compressive rebar ratios, and cost of beam materials on the output side. This type of reverse design improving the conventional design standard is difficult to be achieved using conventional design methods. CRS- and CTS-trained ANN models substantially outperformed the parallel training method (PTM)- and training on entire dataset (TED)-trained ANN models, with the ability to accurately design doubly RC beams with multiple input and output parameters.

The Qi Tai Telescope (QTT), which has a 110 m aperture, is planned to be the largest scale steerable telescope in the world. Ideally, the telescope’s repeated pointing accuracy error should be less than 2.5 arc seconds (arcsec); thus, the telescope structure must satisfy ultra-high precision requirements. In this pursuit, the present research envisages a reverse-design method for the track surface to reduce the difficulty of the telescope’s design and manufacture. First, the distribution characteristics of the test data for the track error were verified using the skewness coefficient and kurtosis coefficient methods. According to the distribution characteristics, the azimuth track error was simulated by a two-scale model. The error of the long period and short amplitude was characterized as large-scale and described by a trigonometric function, while the short period and high amplitude error was characterized as small-scale and simulated by a fractal function. Based on the two-scale model, effect of the error on the pointing accuracy was deduced. Subsequently, the relationship between the root mean square (RMS) of the track error and the RMS of the pointing accuracy error of the telescope was deduced. Finally, the allowable RMS value of the track error was derived from the allowable pointing accuracy errors. To validate the effectiveness of the new design method, two typical radio telescopes (the Green Bank Telescope (GBT) and the Large Millimeter Telescope (LMT)) were selected as experimental examples. Through comparison, the theoretical calculated values of the pointing accuracy of the telescope were consistent with the measured values, with a maximum error of less than 10%.

The reverse design of a tunable terahertz metasurface antenna is proposed based on the deep neural network (DNN). To obtain the tunable properties of the terahertz antenna, the phase‐changed material vanadium dioxide (VO2) is introduced by controlling the voltage without changing the structures of the metasurface antenna. To improve the efficiency and accuracy of the terahertz antenna, the DNN is used to establish the relationship of amplitude and phase for the antenna unit. The prediction errors are below 10%. The radiation direction of the antenna can be changed from 0° to 52° by the VO2 with different states. The prediction error of angle is only 1° difference from the theoretical calculation. This reverse design method can be extended to other metasurface devices with improved efficiency and accuracy. [ABSTRACT FROM AUTHOR]

Helicopter gearbox support strut is one of the main research objects in the field of vibration and noise control in helicopter cabins. Aiming to further widen the vibration attenuation range of traditional Bragg periodic struts, a novel type of Local resonance (LR)/Bragg coupling periodic strut with graded parameters as well as the reverse design method is proposed. Combined with the spectral element method (SEM) and the transfer matrix method (TMM), the analytical expression of the transform relationship of longitudinal vibrations through the coupling strut is yielded. The impacts of different parameters on the boundaries of bandgaps are explored according to the results of simulation analysis. On this basis, the gradient of parameters is determined, and then all unknown structural parameters can also be determined. Compared with the traditional Bragg periodic struts and the LR/Bragg coupling periodic strut with non-graded parameters, the presented strut has an obvious advantage of widening the low-frequency bandgaps below 500 Hz. [ABSTRACT FROM AUTHOR]

Wider absorption bandwidth is usually accompanied by more complex and difficult designs and more computational costs, which have become an important challenge in the field of electromagnetic scattering. In this communication, the backend-balanced-impedance (BBI) new design concept is put forward for the first time which considers the backplane as an impedance that can be designed to compensate for the impedance of a known absorber for expanding the absorption bandwidth. Based on the design concept, an ultra-wideband absorber derived from the known absorber is proposed simply and efficiently. The matched BBI for the known absorber is obtained and developed using reverse calculation. To explain the operating principle, a simple equivalent circuit (EC) model of the ultra-wideband absorber from the BBI is established. Furthermore, the electric field and surface loss distribution are systematically analyzed to demonstrate physical pictures of the BBI. The simulated and measured results show that the designed ultra-wideband absorber from the BBI can get an absorption band in the 0.87–9.28 GHz range (165.8%) for at least 10 dB of reflection reduction at the normal incidence with a thickness of only $0.0858\lambda _{L} $ , where $\lambda _{L} $ denotes the wavelength at the lowest operating frequency. [ABSTRACT FROM AUTHOR]

In order to solve the two structural problems of trapped oil and large flow ripple in gear pump and based on transmission advantages of cycloid gear and its forming principle，tooth number and radius factor of rolling circle which can best reflect pump performance are taken as structural parameters of cycloidal tooth profile，central angle，radius factor and pressure angle of addendum arc functional parameters of gear pump，a cycloid tooth profile with a contact ratio of 1 is constructed in a reverse way and the structural problem of large flow ripple of original single gear pair is solved by the construction of double gear pairs and minimization of ripple coefficient. All results show that the reverse design method of cycloidal tooth profile is simple and accurate，and can be directly used by general engineers； the contact ratio of a single gear pair is 1 to ensure that gear pump has no trapped oil phenomenon； the axial dislocation assembly of double gear pairs with special angle of π divided by tooth number is easy to be realized，and axial comprehensive contact ratio is 2 to ensure transmission stability of cycloidal gear pair； minimum number of teeth is 6 and there is no root cutting phenomenon； compared with single gear pair，an about 75% ripple improvement rate is seen in double gear pair and the optimal tooth profile parameters based on the minimization of ripple factor can be directly applied to the production practice，etc. The aim is to provide a theoretical basis for further research and development of low flow ripple gear pump without trapped oil.

Chiral metasurfaces have been widely used in sensing, imaging and other fields because they can manipulate light through the efficient circular dichroism (CD). However, its on-demand design is still a very challenging task. In this work, we propose an on-demand multiple reverse design based on deep learning, named target-driven conditional generative network (TCGN). It can reverse design the metasurface structure that meets the required CD, and its mean square error (MAE) is 0.0089. We use this method to inversely design multiple sets of metasurfaces with different structures, and all their CD values can exceed 0.36. Both simulations and experiments prove the feasibility and effectiveness of using deep learning to reverse design metasurfaces. In addition, the designed metasurface can realize chiral wavefront control under dual frequency. This design method based on deep learning can rapidly and efficiently design the chiral metasurfaces, which provides a new way for the design of metasurfaces.

Artificial ribs interrupting the viscous sublayer beneath the absorbing plate are used to enhance SAH heat efficiency. In the present study, the CFD-adjoint reverse design method is developed to intelligently design the rib profile. Based on the zero-order CFD solution, the shape sensitivity of the rib profile is calculated by the CFD-adjoint solver, which identifies the profile characteristics to improve thermo-hydraulic performance (THPP). According to the shape sensitivity, the reverse design is conducted with geometrical constraints, optimization algorithms, and mesh deformation. To maximize THPP , five novel ribs are reversely designed under Reynolds numbers ranging from 4000 to 16000. The flow and heat transfer characteristics of the reversely designed ribs indicate the CFD-adjoint reverse design is in accord with the theory that turbulence enhances heat transfer in the boundary layer. The Nusselt number of the novel ribs is averagely 1.9 times compared with the smooth. The THPP of the novel ribs approaches the local maximum at the design Reynolds number with the average THPP of 1.51. [ABSTRACT FROM AUTHOR]

The reverse design of solid propellant grain for a performance-matching goal, one of the most challenging directions of the solid rocket motor designing work, is limited by the traditional semi-empirical parameter-driven optimization methods based on some predefined grain configurations. Grain designers call for a new method that can automatically provide brand-new grain shapes beyond the traditional ones. In this work, a shape optimization method based on the evolutionary neural network is proposed to achieve the reverse design of two-dimensional (2D) grains. Firstly, the modified ellipse-form eikonal equation is solved by using the finite element method to realize the burn-back analysis of 2D grains in any shape on a fixed unstructured mesh. Then, the neural network is introduced to determine the spatial distribution of the propellant to define the grain shape. The hyperparameters of the network are continuously evolved with the aid of the genetic algorithm. Finally, the optimal grain shape that matches the performance goal most is obtained. The method is verified in different scenarios. The result shows that the design can precisely match the given pressure-time curve of star grains and slotted-tube grains. Furthermore, the method can automatically evolve a new dendritic-shaped grain that matches the given dual-thrust pressure-time curve. Since the reverse design uses the concept of shape optimization, it does not require any pre-selection of the grain shape, and the designers shall be free from defining different kinds of geometric parameters for specific grain configurations. Consequently, the method has the potential to apply in the reconstruction of an actual grain and the conceptual design of innovative grain configurations. [ABSTRACT FROM AUTHOR]

This paper explores the implementation and application of non-uniform ambient fields from two perspectives: constant heat source and abrupt heat source variation. On the one hand, the proper orthogonal decomposition (POD) method is used to find the optimum air supply parameters for different target temperatures at multiple demand points. The one-parameter, two-parameter and three-parameter cases were considered, respectively, and the parameters obtained from the search for optimisation were verified, yielding mean deviations of 0.405 K, 0.368 K and 0.380 K and mean errors of 1.48%, 1.61% and 1.68%, respectively. The accuracy of the reconstructed results of the POD method and the reliability of the POD method for finding the best results are verified step by step with the help of the experimental platform. The validation results show that the average error between the reconstructed data and the experimental data for the POD method does not exceed 5%; the average errors between the measured and set demand temperatures at the target point are 1.2% and 0.8%. On the other hand, the gappy POD method is used to accurately fill in the elements of the system with arbitrary missing data and to reconstruct the flow field in the presence of missing (gappy) data with a limited number of sensors combined with POD. The errors of the gappy POD method reconstruction are 0.54% and 1.75%. POD and gappy POD methods can better create non-uniform temperature fields in practical scenarios according to actual needs. The results of the study can provide a methodological reference for real-time reconstruction and real-time control of the indoor flow field environment. It also serves as a help and suggestion for the actual project in terms of end-regulation system and reverse design method.

As crops have different agricultural requirements in pot seedling and the parameters of the planting mechanism need to be repeatedly designed, this paper proposed a reverse design method for the classic gear five-bar planting mechanism based on the agronomic requirements for pot seedling planting. The mathematical model of the planting trajectory of the geared fivebar planting mechanism was constructed through homogeneous coordinate transformation method,; the conditions for the geared five-bar planting mechanism to achieve zero-speed planting were: the gear ratio of the gear pair is -2, and the difference in length between the planetary gear rod and the sun gear rod is equal to the ratio of the planting distance to 2p. Based on the agronomic parameters of pot seedlings (planting depth, planting distance), the parameters of the geared five-bar mechanism were reversed to realize the reverse design of the geared five-bar planting mechanism. Field test results: the average planting depth of tomato pot seedlings is 47mm, the planting distance is 203mm, and the uprightness of pot seedlings is 97.5%, which meets the agronomic requirements of planting tomato pot seedlings. Therefore, the proposed reverse design method of geared five-bar planting mechanism based on agronomic requirements can solve the problem of repeated design of planting mechanism due to different agricultural parameters of most crops, which can greatly reduce the workload of designing various crop planting mechanisms. [ABSTRACT FROM AUTHOR]

Labeled chemical probes are of utmost importance to bring drugs from the laboratory through the clinic and ultimately to market. They support and impact all research and discovery phases: target verification and validation; assay development; lead optimization; and biomarker engagement in the context of preclinical studies and human trials. Probes should display high potency and selectivity as well as fulfill specific criteria in connection with absorption, distribution, metabolism, excretion and toxicology (ADMET) profile. Progress in fields such as imaging and proteomics increased the need for specialized probes to support drug discovery. Labeled probes carrying an additional reporter group are valuable tools to meet specific application requirements, but pose significant challenges in design and construction. In the reverse-design approach, small molecules previously optimized in medicinal chemistry programs form the basis for the generation of such high-quality probes. We discuss the reverse design concept for the generation of labeled probes targeting the endocannabinoid system (ECS), a complex lipid signaling network that plays a key role in many human health and disease conditions. The examples highlighted include diverse reporter units for a range of applications. In several cases the reported probes were the product of mutually rewarding and highly cross-fertilizing collaborations among academic and industry research programs, a strategy that can serve as a blueprint for future probe generation efforts.

In this paper, a 1×4 ultra-compact wavelength division multiplexing cascaded device(DMC) with an arbitrary splitting ratio based on adjoint topology optimization reverse design is proposed, which is approximately two orders of magnitude smaller than that of conventional waveguide devices. It can simultaneously perform wavelength demultiplexing, mode conversion, and arbitrary ratio power splitting. The DMC separates 1310 nm and 1550 nm wavelengths, converts the input light from fundamental transverse mode (TE0) to first-order transverse mode (TE1) and second-order transverse modes (TE2), and performs arbitrarily proportional power splitting of the converted higher-order light source. • An ultra-compact 1×4 DMC with an arbitrary splitting ratio based on adjoint method reverse design is proposed in this paper. It is located on a 220 nm thick SOI platform and simultaneously achieves wavelength demultiplexing, arbitrary proportional power division, and mode conversion. • Meanwhile, the footprint of the WDM and the arbitrary ratio splitter remains fixed, but the footprint of the bent waveguide is enlarged to 2.4 µm×2.4 µm. The widths of the input and output waveguides of the splitter are expanded to 0.9 µm. FDTD simulation results indicate that the device converts the TE0 mode to the TE1 mode. • In addition, the footprint of the WDM stays similar, with the increased footprint of the bent waveguide and arbitrary ratio splitter being 2.4 µm×3.6 µm and 4 µm×4.8 µm, respectively. Furthermore, the output waveguide width of the arbitrary ratio splitter is raised to 1.4 µm. FDTD simulation results indicate that the device will convert the TE0 mode to the TE2 mode. [ABSTRACT FROM AUTHOR]

An optimized acoustic metamaterial (OAM) railway sound barrier has been designed to address the growing noise problem in railway construction. The low-frequency broadband noise attenuation is realized by an reverse design method relying on genetic algorithm-neural network (GA-NN). Simulation results show that the OAM can perfectly cover the target frequency band from 400 Hz to 1000 Hz with a bandgap ranging from 210 Hz to 1253 Hz. Compared with the initial acoustic metamaterial (IAM), the bandgap of the OAM is widened by 1295 Hz, and the sound transmission loss is improved by 15 dB. The noise reduction performance of the OAM is verified by the experiments of the fully-enclosed impedance tube and the simulation of the acoustic field modes. The acoustic experiments of the semi-enclosed OAM sound barriers show that the noise reduction of OAM sound barriers has a low-frequency broadband noise reduction effect. Compared with the existing sound barriers, the average noise reductions of OAM sound barriers in the target frequency band and bandgap range are improved to 15.9 dB and 7.8 dB, respectively. [Display omitted] • An optimized acoustic metamaterial (OAM) railway sound barrier designed for low-frequency broadband noise reduction. • OAM sound barriers can improve the target noise reduction effect by 15.9 dB compared to the existing sound barriers. • Genetic Algorithm-Neural Network (GA-NN) based reverse design method can greatly improve the design efficiency of sound barriers. [ABSTRACT FROM AUTHOR]

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The boom of electric vehicles (EVs) has urged new designs and applications of power conversion technologies. Power converters, as one of the most critical components in EV battery systems, have seen significant developments in recent years. However, thus far forward design approaches for power converter topologies have been widely adopted, which are based on engineers’ empirical knowledge and instinct. This has been deemed having low design efficiency with high randomness and limited applicability, due to the absence of a systematic guideline. This approach, though widely adopted, can no longer meet the increasingly stringent and diverse needs of today’s research and industry, with the emerging trend of renewable energy sources. Aware of these drawbacks, in this study, we propose a novel reverse design approach through establishing general polynomial relations between the topology and voltage gain of high-step-up converters, which are found to be functions of basic boost converters’ voltage gains. With this newly formed polynomial relation, a generalized approach to devising power converters for various industrial needs can be derived, which is baptized the General Polynomial Reverse Design (GPRD) approach. Three polynomial converters are derived as examples to validate the proposed method, whereby their functionality and performances are verified and tested by prototype experiments, which substantiate the proposed design theory. [ABSTRACT FROM AUTHOR]

In order to meet the requirements of beating up technology on conjugate cam,a modified trapezoid acceleration law is used to construct motion equation of reed by segment. The reverse design program of conjugate cam is developed by MATLAB software,and precise reverse design of pitch curve on main and auxiliary cam are realized which on the basis of reconstructing law of beating-up motion. Besides,shape change of the pitch curve is analyzed by combining swing arm angle Φand center distance L. The results of reverse design show that pressure angle α at each point on the curve can meet requirements of mechanical transmission characteristics. The reasonable values of basic circle radius and roller are determined through calculating radial direction r0 and radius of curvature ρ0 of the curve point by point. The results provides a technical idea for reverse and design innovation of the conjugate cam on beating-up mechanism.

As crops have different agricultural requirements in pot seedling and the parameters of the planting mechanism need to be repeatedly designed, this paper proposed a reverse design method for the classic gear five-bar planting mechanism based on the agronomic requirements for pot seedling planting. The mathematical model of the planting trajectory of the geared five-bar planting mechanism was constructed through homogeneous coordinate transformation method,; the conditions for the geared five-bar planting mechanism to achieve zero-speed planting were: the gear ratio of the gear pair is -2, and the difference in length between the planetary gear rod and the sun gear rod is equal to the ratio of the planting distance to 2π. Based on the agronomic parameters of pot seedlings (planting depth, planting distance), the parameters of the geared five-bar mechanism were reversed to realize the reverse design of the geared five-bar planting mechanism. Field test results: the average planting depth of tomato pot seedlings is 47mm, the planting distance is 203mm, and the uprightness of pot seedlings is 97.5%, which meets the agronomic requirements of planting tomato pot seedlings. Therefore, the proposed reverse design method of geared five-bar planting mechanism based on agronomic requirements can solve the problem of repeated design of planting mechanism due to different agricultural parameters of most crops, which can greatly reduce the workload of designing various crop planting mechanisms. [ABSTRACT FROM AUTHOR]

Helicopter gearbox support strut is one of the main research objects in the field of vibration and noise control in helicopter cabins. Aiming to further widen the vibration attenuation range of traditional Bragg periodic struts, a novel type of Local resonance (LR)/Bragg coupling periodic strut with graded parameters as well as the reverse design method is proposed. Combined with the spectral element method (SEM) and the transfer matrix method (TMM), the analytical expression of the transform relationship of longitudinal vibrations through the coupling strut is yielded. The impacts of different parameters on the boundaries of bandgaps are explored according to the results of simulation analysis. On this basis, the gradient of parameters is determined, and then all unknown structural parameters can also be determined. Compared with the traditional Bragg periodic struts and the LR/Bragg coupling periodic strut with non-graded parameters, the presented strut has an obvious advantage of widening the low-frequency bandgaps below 500 Hz.

The reverse design of solid propellant grain for a performance-matching goal, one of the most challenging directions of the solid rocket motor designing work, is limited by the traditional semi-empirical parameter-driven optimization methods based on some predefined grain configurations. Grain designers call for a new method that can automatically provide brand-new grain shapes beyond the traditional ones. In this work, a shape optimization method based on the evolutionary neural network is proposed to achieve the reverse design of two-dimensional (2D) grains. Firstly, the modified ellipse-form eikonal equation is solved by using the finite element method to realize the burn-back analysis of 2D grains in any shape on a fixed unstructured mesh. Then, the neural network is introduced to determine the spatial distribution of the propellant to define the grain shape. The hyperparameters of the network are continuously evolved with the aid of the genetic algorithm. Finally, the optimal grain shape that matches the performance goal most is obtained. The method is verified in different scenarios. The result shows that the design can precisely match the given pressure-time curve of star grains and slotted-tube grains. Furthermore, the method can automatically evolve a new dendritic-shaped grain that matches the given dual-thrust pressure-time curve. Since the reverse design uses the concept of shape optimization, it does not require any pre-selection of the grain shape, and the designers shall be free from defining different kinds of geometric parameters for specific grain configurations. Consequently, the method has the potential to apply in the reconstruction of an actual grain and the conceptual design of innovative grain configurations.

AI based design for OLED materials are being tried in a variety of ways. An exemplary system is being developed to predict optical characteristics through machine learning (ML) with existing data. Once the performance descriptor is well defined and the quantum chemical calculation method is established, AI‐reverse design is expected to be possible. However, not all OLED emitting materials are equally capable of it. Different approaches are needed because the luminescence mechanism and its complexity of calculation are different depending on the material types. For pure fluorescence or even high efficiency phosphorescence, their luminescence mechanisms are relatively well defined and nearly irreversible and so the correlation between the calculation and performance could be better. If so, the reverse design is becoming possible and already it has begun to be tried a lot. However, in the case of TADF, the radiation and non‐radiation paths vary, ISC‐RISC is more reversible, and the controversy over luminescence mechanism remains. As a result, the calculating method of luminous efficiency has not yet been fully established. In this study, we want to report the consistency level of predicting characteristics of OLED materials using AI, and also discuss the difference between each emitting material types for reverse design. In particular, we also want to share the issues of calculating methods for TADF performance. [ABSTRACT FROM AUTHOR]

• The active learning workflows is designed to assist biomass hydrogen production. • Selecting the most informative data points to train the model and efficiently explore the search space. • Multiple machine learning models create hydrogen-biomass fuel-related models to improve model stability, even with small data sets. Biomass hydrogen production holds substantial promise in addressing critical challenges within the realms of renewable energy and environmental sustainability. Nevertheless, the labor- and time-intensive nature of conducting combinatorial experiments and relying on traditional trial-and-error methods significantly constrains the exploration of biomass fuel solutions. This paper introduces a novel framework for the reverse engineering of hydrogen production systems, which seamlessly integrates Bayesian active learning with machine learning models. In Bayesian active learning, 307 biomass fuel samples were expanded to 1354 in order to ensure the accuracy of the model prediction between the seven characteristics of biomass fuel and hydrogen production. Four machine learning algorithms including RF, XGBoost, KNNR, AdaBoost were used to build the prediction model. A stochastic search reverse design approach based on the XGBoost model with the highest coefficient of determination ( R 2 = 0.91) was innovatively used to realize the "reverse design" from "target performance" to "composition and process". Through reverse design, a two-order-of-magnitude increase in design space is achieved in hundreds of times less time than conventional design time, resulting in a significant improvement in hydrogen production efficiency. The practical application of this method for hydrogen production from biomass fuels may go far beyond linear extrapolation based on the current state of the art and can be applied to a wider range of material designs. [ABSTRACT FROM AUTHOR]

A novel direct binary search algorithm based on rotation (RDBS) is proposed in this paper. A 1*2 ultra-compact 2.4*3.6µm2 multimode wavelength demultiplexer (DEMUX) is designed in reverse, which is roughly two orders of magnitude smaller than the size of a conventional waveguide device. It can simultaneously perform wavelength demultiplexing and mode conversion. This DEMUX separates the 1310 and 1550 nm wavelengths while converting the input light from the fundamental transverse electric mode (TE0) to the first-order transverse electric mode (TE1). The simulation results using RDBS show that the insertion loss(I L) of the upper channel (wavelength 1310 nm) is −0.9644 dB, the I L of the lower channel (wavelength 1550 nm) is −0.9752 dB, and the crosstalk values(CT) are −10.079 dB and −9.261 dB, respectively. • A highly compact and efficient 1*2 multi-purpose wavelength demultiplexer based on DBS and RDBS is first designed and demonstrated in this paper. This photonic device is located on a 300nm-thick SOI platform, simultaneously achieving wavelength demultiplexing and mode conversion. Finite-difference time-domain (FDTD) simulation results show that the 1*2 WDM will output 1310nm wavelengths on the upper channel and 1550nm on the lower channel, with a footprint of 2.4*2.4µm2. • By increasing the device footprint to 2.4*3.6µm2 and increasing the output waveguide width to 1.4µm, the device simultaneously achieves wavelength demultiplexing and mode conversion. The FDTD simulation results show that the device converts TE0 modes to TE1 modes and that the structural parameters of the waveguide, the aperture size, and the silicon thickness have good dimensional tolerances. • These results provide a good foundation for the design of ultra-compact and complex photonic devices in low-mode optical communication, advanced photonic information processing, and many other fields. Secondly, the direct binary search algorithm is improved in the paper, and a new RDBS algorithm is proposed. By comparing two optoelectronic devices with the same design function, it is found that the device designed using RDBS has better performance. [ABSTRACT FROM AUTHOR]

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study introduces an evidence‐centered approach to reverse design for languages for specific purposes (LSP) courses called domain analysis. The article describes the research framework and its potential to provide consistent empirical rigor to the necessarily decentralized field of LSP by reporting on one study using domain analysis in the legal Spanish context. The research questions examine the parameters of the domain under study—legal Spanish for law students at one university—and the target functional outcomes for those students. The results included parameters that were limited to public interest law in the U.S. context. Eight topic areas emerged as central to that context and functional outcomes were identified for reading, listening, and speaking. Though sight translation was a necessary skill, writing was not needed in the domain. The Challenge: The field of languages for specific purposes (LSP) is necessarily decentralized; courses are developed locally to meet local needs, but does that mean anything goes? How can LSP practitioners engage in evidence‐centered reverse design? This article describes a study that uses one research framework—domain analysis—that can provide a common empirical foundation for reverse design of LSP courses. [ABSTRACT FROM AUTHOR]

To meet the diversified demands of shoe last design, the author proposes a reverse reconstruction model by automatic surface creation. By comparing the reconstructed model and the original point cloud data with Geomagic Control software, the causes of the error and the solutions were found. The results show that this method is both economical and efficient. [ABSTRACT FROM AUTHOR]

Copyright of New Architecture is the property of New Architecture Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)