Reversal Design Research Articles

The Effect of Reverse Shoulder Arthroplasty Design and Surgical Indications on Deltoid and Rotator Cuff Muscle Length

BackgroundAdvancements in surgical planning, technique, and prosthesis design have improved adaptation to patient anatomy in reverse total shoulder arthroplasty (rTSA). Postoperative changes in deltoid and rotator cuff muscle length are important and may vary based on preoperative indications and prosthesis selection. The purpose of this study is to demonstrate the changes in deltoid and rotator cuff muscle length for planned rTSA using the spectrum of prosthesis configurations in both GHOA and RCA. MethodsTen shoulder arthroplasty surgeons used preoperative planning software to plan rTSA cases for 20 subjects (10 GHOA, 10 RCA) following surgical guidelines. Each surgeon planned each case using three prosthesis configurations: (1) 8-mm lateralized glenosphere and 135° neck-shaft angle (135+8), (2) 4-mm lateralized glenosphere and 145° neck-shaft angle (145+4), and (3) 0-mm lateralized glenosphere and 155° neck-shaft angle (155+0). Pre- and postoperative deltoid and rotator cuff muscle lengths and percentage-change were calculated and compared between prosthesis configurations within each indication. Different muscle lines of action were included representing the deltoid, subscapularis, infraspinatus, and teres minor. ResultsPreoperatively, the RCA cohort had significantly shorter muscle lines of action in the posterior, lateral, and anterior deltoid (P<0.001), a longer inferior subscapularis (P=0.022), and a longer teres minor (P=0.001) than the GHOA cohort. ANOVA and post-hoc analysis showed that post-planning lengths of each deltoid action line were greater in the 155+0 configuration compared to the 135+8 configuration in the RCA cohort (P<0.001, P=0.003, P=0.032, respectively), and post-planning lengths of the anterior and middle deltoid action lines were also greater for the same comparison in the GHOA cohort (P=0.004 and P=0.017, respectively). There were no significant differences in post-planning deltoid lengths between the 135+8 and 145+4 configurations in either diagnosis cohort (P>0.05). All post-planning rotator cuff muscle lengths (subscapularis, infraspinatus, and teres minor) differed significantly (P<0.001) between all prosthesis configurations in both diagnosis cohorts, with the 135+8 configuration resulting in the longest lengths and the 155+0 configuration resulting in the shortest lengths. ConclusionAutomated preoperative planning software calculates the lengths of muscle action lines, which vary between GHOA and RCA diagnoses. Varying rTSA implant geometries result in predictable differences in deltoid lengthening and rotator cuff shortening. Shoulder prostheses with a more lateralized center of rotation show greater rotator cuff muscle length and similar deltoid muscle length when compared to medialized designs with similar deltoid lengthening. Surgeons can use this software to understand the impact of implant geometry on muscle length.

On-demand reverse design of polymers with PolyTAO

The forward screening and reverse design of drug molecules, inorganic molecules, and polymers with enhanced properties are vital for accelerating the transition from laboratory research to market application. Specifically, due to the scarcity of large-scale datasets, the discovery of polymers via materials informatics is particularly challenging. Nonetheless, scientists have developed various machine learning models for polymer structure-property relationships using only small polymer datasets, thereby advancing the forward screening process of polymers. However, the success of this approach ultimately depends on the diversity of the candidate pool, and exhaustively enumerating all possible polymer structures through human imagination is impractical. Consequently, achieving on-demand reverse design of polymers is essential. In this work, we curate an immense polymer dataset containing nearly one million polymeric structure-property pairs based on expert knowledge. Leveraging this dataset, we propose a Transformer-Assisted Oriented pretrained model for on-demand polymer generation (PolyTAO). This model generates polymers with 99.27% chemical validity in top-1 generation mode (approximately 200k generated polymers), representing the highest reported success rate among polymer generative models, and this was achieved on the largest test set. Importantly, the average R2 between the properties of the generated polymers and their expected values across 15 predefined properties is 0.96, which underscores PolyTAO’s powerful on-demand polymer generation capabilities. To further evaluate the pretrained model’s performance in generating polymers with additional user-defined properties for downstream tasks, we conduct fine-tuning experiments on three publicly available small polymer datasets using both semi-template and template-free generation paradigms. Through these extensive experiments, we demonstrate that our pretrained model and its fine-tuned versions are capable of achieving the on-demand reverse design of polymers with specified properties, whether in a semi-template generation or the more challenging template-free generation scenarios, showcasing its potential as a unified pretrained foundation model for polymer generation.

Development of a Highly Selective NanoBRET Probe to Assess MAGL Inhibition in Live Cells.

Cell-free enzymatic assays are highly useful tools in early compound profiling due to their robustness and scalability. However, their inadequacy to reflect the complexity of target engagement in a cellular environment may lead to a significantly divergent pharmacology that is eventually observed in cells. The discrepancy that emerges from properties like permeability and unspecific protein binding may largely mislead lead compound selection to undergo further chemical optimization. We report the development of a new intracellular NanoBRET assay to assess MAGL inhibition in live cells. Based on a reverse design approach, a highly potent, reversible preclinical inhibitor was conjugated to the cell-permeable BODIPY590 acceptor fluorophore while retaining its overall balanced properties. An engineered MAGL-nanoluciferase (Nluc) fusion protein provided a suitable donor counterpart for the facile interrogation of intracellular ligand activity. Validation of assay conditions using a selection of known MAGL inhibitors set the stage for the evaluation of over 1'900 MAGL drug candidates derived from our discovery program. This evaluation enabled us to select compounds for further development based not only on target engagement, but also on favorable physicochemical parameters like permeability and protein binding. This study highlights the advantages of cell-based target engagement assays for accelerating compound profiling and progress at the early stages of drug discovery programs.

Towards circularity in the wind industry: Optimal reverse supply network design under various policy scenarios

Wind energy is key to supply renewable energy. However, the increasing number of end-of-life wind turbines is still predominantly landfilled, while regulatory aspects such as waste shipment and landfilling rules hinder the development and scalable implementation of reverse supply networks.This work aims to understand how EU directives impact the structure and viability of circularity-enabling networks by investigating the optimal reverse supply network design for end-of-life wind turbine blades under different policy scenarios. Three policy scenarios were explored through a Mixed-Integer-Linear-Programming model: (i) ‘as-is’; (ii) ‘EU Proposal 2021/0367′, removing transboundary restrictions on waste shipments; (iii) ‘Landfilling Ban’ enforcing an EU-wide ban on landfilling composites. The optimal reverse supply networks with minimum costs were identified for each scenario, contextually determining location and sizing of recycling facilities and calculating landfilling quota and GHG emissions. The costs and emissions were minimum for the EU Proposal scenario, at 15,706,041€ and 2,081 tCO2e respectively. A sensitivity analysis on landfilling gate fees highlighted that they should be significantly increased to incentivise higher recycling rates and close material loops.This research is the first to evaluate the effects of policy initiatives on the shaping of optimised reverse supply chains through mathematical programming methods. The work contributes to the waste management literature by designing optimal circular supply chain networks for the management of waste from wind turbines decommissioning at the EU-level to improve sustainability of renewable energy installations.

Estimating Mediation Effects in ABAB Reversal Designs.

Single-Case Experimental Designs (SCEDs), or N-of-1 trials, are commonly used to estimate intervention effects in many disciplines including in the treatment of youth mental health problems. SCEDs consist of repeated measurements of an outcome over time for a single case (e.g., student or patient) throughout one or more baseline phases and throughout one or more intervention phases. The manipulation of the baseline and intervention phase make the SCED a type of interrupted time series design, which is considered one of the most effective experimental designs for causal inference. An important step towards understanding why interventions are effective at producing a change in the outcome is through the investigation of mediating mechanisms. Hypotheses of mediating mechanisms involve an intervention variable which is hypothesized to affect an outcome through its effect on a mediating variable. Little work has attempted to combine mediation analysis and ABAB reversal designs. Therefore, the goals of this paper are to define, estimate, and interpret mediation effects for ABAB reversal designs. An empirical example is used to demonstrate how to estimate and interpret the mediation effects. R code is provided for researchers interested in estimating mediation effects in single-case reversal designs.

Reverse design of load-bearing broadband metamaterial absorber assisted by deep learning

Abstract In response to the current challenges of narrow absorption bandwidth, weak load-bearing capacity, and low design efficiency in absorbing structures, this study focuses on the reverse design of load-bearing broadband metamaterial absorber. A parameterized model of load-bearing metamaterial absorber was developed by integrating the composite sandwich structure with the electromagnetic resonant layers. The resonant layer was constructed using the combination of Vicsek-fractal and circular rings, with resistive films employed to broaden the absorption bandwidth. A deep learning-based forward prediction model was established to accurately predict the absorbance of the metamaterial absorber. The SHapley Additive exPlanations (SHAP) framework was utilized to analyze the forward prediction network, revealing the influence of various design parameters on the absorbance at center frequencies across the L to K band spectrum. Additionally, the Group Teaching Optimization Algorithm (GTOA) was introduced into the design process, leading to the development of an automated reverse design method for metamaterial absorber that can achieve specific design objectives. Using the GTOA-based reverse design method, a metamaterial absorber capable of effectively absorbing vertically incident electromagnetic waves within the 3-20 GHz frequency range was designed. The designed absorbing structure was fabricated, and its absorption performance was measured using the arch method. The measurement results were found to be in good agreement with the simulation data. The absorbing mechanism of the designed metamaterial absorber was analyzed based on the calculation of equivalent electromagnetic parameters and the electromagnetic resonance observed at the resonant frequency. It was determined that the primary absorbing effect is induced by electric resonance triggered by electromagnetic waves. The proposed metamaterial absorber can be applied to radar stealth design for military targets such as naval vessels. The research methodology and approach demonstrate significant generalizability and engineering applicability.

Unlocking the potential of urban EV battery recycling: A dual optimization model

As electric vehicles increase, they reduce greenhouse gas emissions but pose recycling challenges for their batteries. Economic and environmental impacts are significant concerns. This study proposes a dual optimization approach to develop a city-wide recycling network benefiting vehicle owners and recycling facilities. Focusing on Xi'an City, China, we project future battery recycling volumes and analyze optimal network setups at various stages. In reverse logistics and network design research, decentralized battery processing centers and centralized energy storage centers can greatly improve consumer benefits and ensure formal recycling practices. In research on consumer engagement and behavior, it's important to design recycling subsidies wisely and enhance consumer motivation to participate in formal recycling. As the number of retired vehicle batteries grows, the government should consider shifting subsidies from consumers to centralized recycling centers to maintain a sustainable and efficient recycling system.

Reverse deformation design for bending control in welding of ring stiffeners

After Double Side Arc Welding (DSAW), the ring stiffener undergoes significant radial bending deformation due to the release of residual plastic stress, which severely impacts the dimensional quality of the welded component. This type of stress release-induced deformation is difficult to control through the application of external constraints. In this paper, a method for designing reverse deformation to compensate for bending deformation is proposed, based on the Distribution Function based Inherent Strain Method (DFISM). By leveraging the rapid calculations from DFISM and parametric modeling, welding deformation samples of ring stiffeners of different sizes were obtained in sample, and the optimal reverse deformation amount for the ring stiffener was determined using parameter evaluation criteria. The compensation effect of the designed reverse deformation was tested through DSAW experiments, where the calculation and experimental error were less than 1.0 mm (7.8 % of the total deformation), and the maximum error between the welded ring stiffener and the theoretical profile after reverse deformation design was within 1.2 mm (0.03 % of the theoretical radius). The Reverse Deformation Design Method (RDDM), based on rapid calculations and parametric modeling, effectively compensates for the bending deformation of the ring stiffener.

Reconsidering the clinical outcomes of the stemless reverse total shoulder arthroplasty design implant.

Reconsidering the clinical outcomes of the stemless reverse total shoulder arthroplasty design implant.

Reverse design of molecule‐process‐process networks: A case study from HEN‐ORC system

AbstractThe integrated design of the heat exchanger network (HEN) and organic Rankine cycle (ORC) system with new working fluids is a complex optimization problem. It involves navigating a vast design space across working fluid molecules, ORC processes, and networks. In this article, a new two‐stage reverse strategy is developed. The optimal HEN‐ORC configurations and operating conditions, and the thermodynamic properties of the hypothetical working fluid are identified by an equation of state (EOS) free HEN‐ORC model in the first stage. With two developed group contribution‐artificial neural network thermodynamic property prediction models, working fluid molecules are screened out in the second stage from a database containing more than 430,000 hydrofluoroolefins (HFOs). The presented method is employed in two cases, where new working fluids are found. The total annual cost of Case 1 is 12%–22% lower than the literature, and the power output of Case 2 is 5%–8% higher than the literature.

Reverse Selection Designs for Accommodating Multiple Control Arms.

Evaluating a novel treatment in a randomized controlled trial requires comparison against existing therapies. If several existing therapies of similar benefit exist, the identification of a single control regimen may be difficult. For this situation, we propose a reverse selection design which, in its simplest form, includes a single experimental treatment arm and two control arms. Rather than carrying both control arms through the entire trial, the control arms are compared at an early interim analysis, ideally while accrual is ongoing. At this time, the worst-performing control arm is dropped and randomization continues to the remaining arms. At the end of the study, we compare the treatment to the remaining control arm. When no head-to-head comparison of the extant therapies is available or feasible, this design requires a smaller sample size than a traditional three-arm design or two sequential trials in which the extant therapies are compared and the better treatment is used in a subsequent trial as the control arm. This is because the final comparison is only between two arms and because the early interim analysis occurs prior to the end of accrual - yet with enough information such that any substantially better control arm will be selected. We evaluate the operating characteristics of a reverse selection design via simulation and show that it reduces the required sample size needed to compare the treatment against the best control, controls type I error, and likely selects the right control arm to use in the final analysis.

Reverse design and application of phononic crystals based on deep learning

Abstract This paper reverse-design phononic crystals with band gaps within a targeted frequency band using the trained conditional variational autoencoder (CVAE) and further studies the vibro-acoustic characteristics of a composite sandwich plate with a phononic crystal panel as the core layer. Firstly, a matrix composed of 0 s and 1 s, representing scatterers and substrates, is randomly generated by MATLAB to represent two-dimensional phononic crystals. The three-dimensional phononic crystals are obtained by stretching the two-dimensional phononic crystals along the average direction, and COMSOL Multiphysics is used to calculate the band gap. In order to maximize the production of phononic crystals with a band gap distribution, the convolutional neural network is trained to predict whether the generated phononic crystals have band gaps. Finally, using data on the structures of phononic crystals and their band gap distributions, the CVAE is trained to achieve the reverse design of artificial periodic structures based on the target band gap. To verify the effectiveness of the structures obtained through the reverse design method on vibration and noise reduction, the submerged vibro-acoustic characteristics of a composite sandwich plate are studied. The plate consists of a phononic crystal panel and carbon fiber panels. The model of the composite sandwich plate is fabricated, and its submerged vibro-acoustic characteristics are tested and compared with numerical results. Finally, the submerged vibro-acoustic response levels of composite sandwich plates with phononic crystal panels and honeycomb panels as core layers are compared using numerical methods. This comparison assesses the phononic crystal panel’s vibration and noise reduction effects.

Integrated multi-manned disassembly line balancing problem with reverse supply chain design strategies by considering lot sizing

ABSTRACT Due to strict governmental regulations, manufacturers have increasingly focused on recovering end-of-life (EoL) products through disassembly lines, aiming to achieve economic benefits while addressing the environmental aspects of supply chain sustainability. Disassembly line balancing (DLB) has become a key challenge for enterprises seeking to perform environmentally conscious manufacturing as part of a closed-loop supply chain policy. Therefore, designing a sustainable closed-loop supply chain requires analyzing interrelated problems by focusing on strategic-, tactical-, and operational-level decisions simultaneously. This study introduces an integrated problem that addresses multi-manned DLB, lot sizing, and reverse supply chain (RSC) design problems. To mathematically represent the problem, a generic optimization model is developed to minimize overall costs, including station opening, inventory holding, shortage, transportation, and collection center/facility opening costs. This study addresses several research questions to explore the impact of distribution strategies (centralized and decentralized) on multi-manned DLB and lot size decisions within the context of reverse supply chain design.

Multi-objective optimization of a bistable curved shell with controllable thickness based on machine learning

Bistable curved shells have become a promising low-cost application in energy absorption fields owing to recent advances in material and technology. Significant research has been conducted to improve their energy absorption effect through forward prediction and single-objective optimization. However, these approaches may not fully explore their functional potential. In this study, we propose a multi-objective optimization framework based on the principle of main objective optimization that combines neural networks and genetic algorithms. The energy absorption effect and backward snapping force of the bistable curved shell are improved synchronously. Meanwhile, a reverse design algorithm is developed to generate the preset load-displacement curve, which further expands the application of machine learning methods in the field of multi-objective optimization. The combination of machine learning and multi-objective optimization is highly effective for building meta-structures with specific performance requirements and has potential applications in solving complex optimization tasks in various fields.

Reliable Synchronous and Asynchronous Counter Design in QCA

Due to rapid growth in the integrated circuit (IC) industry, the demand for compact digital system design is high. However, the continued technology reductions made the feasibility of further scaling down transistor size more challenging. In response to the growing demand for ultra-compact IC designs, the revolutionary quantum-dot cellular automata (QCA) technology has emerged as a promising solution. In a digital era, the counters are widely adopted in the peer-to-peer process flow to establish a mechanism for generating unique values for each identifier/number. In this work, a unique synchronous and asynchronous counters architecture is proposed with a reliable D and T flip-flop design. The proposed QCA architecture is implemented and validated with the QCA designer tool. Furthermore, in QCA technology, unreliable QCA designs can lead to frequent errors and malfunctions in the implemented logic. To overcome this challenge, the proposed design prioritizes cell placement (the relative positions of QCA cells) to make the circuit more robust. As a result, the circuit can still produce the expected functionality even if some QCA cells malfunction. Hence, to ensure the reliability of the proposed QCA architecture, the missing cell defect analysis is carried out in comparison with existing state-of-the-art designs. Based on comparison results, the unique designs like the proposed multiplexer, D flip-flop and T flip-flop design exhibit success rates of 67.28, 77.04 and 85.15%, respectively. The experimental results demonstrate that the proposed counter-architecture outperforms existing architectures.

An exploration of teaching reform of management course based on rain class and participatory teaching under OBE concept

Management is the core course of business administration majors. In order to drive educational modernization through educational informatization, this article explores the teaching reform of management based on the concept of learning output education (OBE). It is guided by student output and follows the principle of "reverse design and positive implementation". In terms of implementation, it is specifically divided into learning goal reconstruction, course content integration The teaching process and the construction of an evaluation system are four steps. During the teaching process, the implementation of rain classroom and participatory teaching mode has broken through traditional time and space limitations, extending the interaction and communication between teachers and students from the classroom to pre class and post class, achieving a good loop of "pre class in class after class". This teaching reform practice can promote the improvement of the quality of management teaching and is conducive to cultivating high-quality management talents.

Inverse Design of Multistructured Terahertz Metamaterial Sensors Based on Improved Conditional Generative Network.

The terahertz (THz) metamaterial sensor design is typically complex and requires substantial expertise in physics. To simplify this process, we propose a novel reverse design model based on an improved conditional generative adversarial network that integrates self-attention generative adversarial network and Wasserstein generative adversarial network (WGAN) networks, and is referred to as the self-attention conditional Wasserstein GAN (SACW-GAN) model. By using the target response of the sensor as the input to the generator network, and incorporating labeling information, an attention mechanism, and the Wasserstein distance, we achieve effective reverse design of THz metamaterial sensors. The simulation results demonstrate the model's high performance, with spectral and image accuracies of 95% and 97%, respectively. This deep learning approach offers new perspectives and methodologies for the reverse design and application of THz metamaterial sensors, significantly advancing the field.

Atomically dispersed recognition unit for selective in vivo photoelectrochemical medicine detection

Continuous and long-term therapeutic monitoring of medicine molecules in biological systems will revolutionize healthcare by offering personalized pharmacokinetic reports. However, the extremely complex biological environment brings great challenges for in vivo molecule detection in living organisms. Here we introduce an in vivo photoelectrochemical biosensor following a reverse design strategy with single atoms as molecular recognition units. Atomic dispersion of Cu single atoms on TiO2-x substrate create synergistic anchoring triple-site for efficiently and selectively capturing of dual-carbonyl group and neighboring dual-hydroxyl group of tetracycline molecules. The photoelectrode is encapsulated with antibiofouling layer and implanted into the vein of living mouse to enable long-term in vivo monitoring of tetracycline in real biological environments. It is important to note that our approach was exclusively tested in male mice, and therefore, the findings may not be generalizable to female mice or other species without further research. The rationally designed biological-components-free in vivo biosensor with excellent selectivity, robustness, and stability endows possibility for enabling personalized medicine guidance through real-time feedbacking information and providing direct and authentic medicine molecular analysis.

Mechanical performance and prediction of a novel reinforced octagonal honeycomb

Honeycomb structures are widely used in various engineering applications due to their lightweight and excellent energy absorption capabilities. Materials with two plateau stress regions exhibit unique advantages in multi-stage energy dissipation and multi-task applications. This paper presents a reinforced octagonal honeycomb structure (ROHC) inspired by the topology of an octagonal tensegrity structures. The paper demonstrates the phenomenon of two plateau deformation stages through the 3D printing of ROHC specimens and finite element simulation. By adjusting three geometric parameters of ROHC (angle α, length ratio r, and thickness t of internal reinforcement), the paper obtains the influence laws on the first and second plateau stress and strain, and proves the controllability of two-stage mechanical performance of ROHC. Based on deep learning technology, a performance prediction model for ROHC's two-stage mechanical performance is proposed, with the MSE and R values confirming the accuracy of the prediction model. Based on the prediction model, a rapid reverse design method is proposed, capable of designing structures with expected two-stage mechanical performance, with errors <7.25 %. The proposed honeycomb structure with predictable and reversible design has significant research value in fields with multi-stage energy absorption and crash protection requirements.

Lattice metamaterials with controllable mechanical properties inspired by projection of four-dimensional hypercubes

There has been an increasing interest among the material research community in the pursuit of enhancing the designability of mechanical properties. The existing approaches usually resorted to sophisticated algorithms (such as machine learning) for the reverse design of materials with specific properties. Different from these existing approaches, here we propose a new approach to create lattice metamaterials with continuously controllable mechanical properties by continuously adjusting the geometric parameters of a unique cell topology originated from the projection of four-dimensional hypercubes. The cells contain an inner region and an outer region, each with different deformation characteristics. For example, the inner region is a stretching-dominated simple cubic (SC) unit cell, while the outer region is a bending-dominated body-centered cubic (BCC) unit cell. Specifically, both stiffness and strength isotropy can be simultaneously realized. The proposed lattice metamaterial exhibits intriguing feature of dual stress plateaus. These plateaus can be effectively controlled by adjusting the geometric parameters of inner and outer regions, which enables these lattice metamaterials to hold promising application prospects in the energy absorption scenarios, such as vehicle and pedestrian protection. Such lattice metamaterial design can be used to realize the gradient distribution of mechanical properties through continuous transition of cell topology without introduction of inefficient interfaces, providing a new approach for the design of heterogeneous metamaterials used in the scenarios involving non-uniform stress distribution.