Multiple Performance Research Articles

Design and Analysis of Optimized Approximate Multiplier Using Novel Higher-Order Compressor

The energy-efficient error-tolerant circuits have paved the way for a whole new area in low-power consumption applications with approximate computing. The approximate computing fulfills the trade-off requirement of exact computation and provides efficient performance. In this paper, a novel energy-efficient multiplier has been proposed for image processing applications. In the multiplication process, compressors are used as an important component for the reduction of partial products. Higher-order approximate 5:2 and 6:2 compressors are also designed and simulated in VIVADO using Verilog coding. The proposed higher-order compressors result in less area and low-power consumption in comparison with the existing state-of-the-art technique. These high-performance compressors are used at the multipliers’ reduction stage, resulting in an energy-efficient circuit for error-tolerant applications. All the simulations were carried out in VIVADO considering 8-bit inputs. Multiplication performance shows 37.77 % (8-bit) improvement in terms of power consumption in comparison to the conventional multiplier. The multiplication process has been done on the original, negative, and sharpened images using their masks. The proposed multiplier shows 51.36% (original image), 6.04% (negative image), and 22.44% (sharpened image) PSNR improvement in comparison to state-of-the-art work.

Machine Learning Approach to Rapidly Evaluate Curling of Concrete Pavement

This paper focuses on the methodology for evaluating the degree of total curling in concrete pavement using machine learning. Deflection induced by falling weight deflectometer (FWD) testing is known as a direct correlation to total curling including built-in and daily curling. However, deflection measurement in the in-service road is also affected by others, such as environmental conditions, pavement geometry, subgrade stiffness, and mixture design. Thus, it is challenging to determine the level of curling from FWD data due to the complexity of influencing parameters. To navigate this complexity, prominent machine learning models are exploited to identify a non-linear relationship between curling and FWD deflections. A finite-element simulation of FWD is conducted to generate a vast data set, and a robust regression model is trained to estimate the total effective temperature difference (TETD) to quantify the effects of curling. Since input parameters for testing pavements can be measurable in the field, curling from TETD can be readily obtained using the proposed methodology. Comparative simulations highlight that the proposed models, with an MAE less than 0.5 °C significantly outperform the multiple regression performance, which registers an MAE of 3.45 °C in TETD, particularly in offering cost-effective and noise-tolerant prediction.

La Ricezione Attraverso le Traduzioni del Teatro di Luigi Pirandello in Romania

Luigi Pirandello is the most translated Italian dramatist in Romania. From the first stage performances, predating his Nobel Prize in 1934, to the notoriety ensured by the prestigious award, and especially in the period after World War II, Pirandellian dramatic translations have experienced a professionalization and an increasing orientation towards performativity. Also relevant is the almost complete lack of retranslations, with a single translation being able to support multiple performances.

Comprehensive design method of controller parameters for three‐phase LCL‐type grid‐connected inverter based on D‐partition

AbstractThe LCL‐type inverter is a core component in grid‐connected renewable energy systems, with its performance heavily influenced by the controller. Conventional design methods of controller parameters generally rely on approximation or trial and error, making it difficult to optimize parameters for multiple performance indices. This paper proposes a comprehensive design method of controller parameters for a three‐phase LCL‐type grid‐connected inverter based on the D‐partition method, obtaining a multi‐objective parameter stability domain of controller parameters that simultaneously satisfies multiple performance indices, such as gain margin, phase margin, and current loop bandwidth. The stability domain can be visually presented, which can avoid repetitive adjustments. Additionally, the variation trend of the stability domain when there are deviations in the LCL filter parameters is analyzed. Finally, the accuracy and effectiveness of the proposed design method are validated through simulations and experiments, achieving precise parameter design for the controller of LCL grid‐connected inverters even in the presence of deviations in filter parameters.

Contrastive Functional Connectivity Defines Neurophysiology-informed Symptom Dimensions in Major Depression.

Major depressive disorder (MDD) is a prevalent psychiatric disorder characterized by substantial clinical and neurobiological heterogeneity. Conventional studies that solely focus on clinical symptoms or neuroimaging metrics often fail to capture the intricate relationship between these modalities, limiting their ability to disentangle the complexity in MDD. Moreover, patient neuroimaging data typically contains normal sources of variance shared with healthy controls, which can obscure disorder-specific variance and complicate the delineation of disease heterogeneity. We employed contrastive principal component analysis to extract disorder-specific variations in fMRI-based resting-state functional connectivity (RSFC) by contrasting MDD patients (N=233) with age-matched healthy controls (N=285). We then applied sparse canonical correlation analysis to identify latent dimensions in the disorder variations by linking the extracted contrastive connectivity features to clinical symptoms in MDD patients. Two significant and generalizable dimensions linking distinct brain circuits and clinical profiles were discovered. The first dimension, associated with an apparent "internalizing-externalizing" symptom dimension, was characterized by self-connections within the visual network and also associated with choice reaction times of cognitive tasks. The second dimension, associated with personality facets such as extraversion and conscientiousness typically inversely associated with depression symptoms, is primarily driven by self-connections within the dorsal attention network. This "depression-protective personality" dimension is also associated with multiple cognitive task performances related to psychomotor slowing and cognitive control. Our contrastive RSFC-based dimensional approach offers a new avenue to dissect clinical heterogeneity underlying MDD. By identifying two stable, neurophysiology-informed symptom dimensions in MDD patients, our findings may enhance disease mechanism insights and facilitate precision phenotyping, thus advancing the development of targeted therapeutics for precision mental health.

Unified framework for stochastic dynamic responses and system reliability analysis of long-span cable-stayed bridges under near-fault ground motions

Long-span cable-stayed bridges are complex systems with numerous components, and prone to failures of multiple components under near-fault ground motions with long-period velocity pulses. The evaluation of dynamic system reliability of cable-stayed bridges is an important challenging issue. In this work, a unified framework for simultaneously determining both the stochastic dynamic responses and system reliabilities of long-span cable-stayed bridges under near-fault stochastic ground motions is proposed based on direct probability integral method (DPIM) with adaptive strategy. Firstly, the 3-dimensional finite element model of typical cable-stayed bridge and the stochastic synthesis model of near-fault ground motions are established. Based on probability density integral equation, new formulas in input probability space of time-variant moments of response are derived. Then, DPIM with adaptive strategy is suggested to calculate the mean value and standard deviation of response via new formulas. Dynamic system reliability of cable-stayed bridge under near-fault stochastic ground motions is evaluated by establishing failure criteria of extreme value mappings of multiple performance functions. Finally, numerical results indicate that the long-period velocity pulse of near-fault motions imposes an important effect, resulting in large mean responses and failure of cables. Failure probability of cables is significantly increased with the occurrence of velocity pulses, which causes the decrease of system reliability of cable-stayed bridge. Thus, the randomness and velocity pulse effect of near-fault ground motions should be seriously considered for reliability-based design and safety assessment of cable-stayed bridges.

Sound exposure of St Paul's Cathedral choristers including unusual events

In November 2022 London South Bank University was approached by St Paul's Cathedral to undertake follow-up sound dosimetry for the music department. Rehearsals and performances were identified and measured using acoustic instrumentation to establish if the Choristers (Adults and Boys) and Organist were compliant with the Control of Noise at Work Regulations 2005. It should be noted that the boys are not employed by St Paul's Cathedral and hence would not be considered employees and are therefore exempt from the regulations. However, a duty of care should be of utmost consideration. Through a close collaboration between the organisations three unusual but representative rehearsals and performances were monitored for multiple performers, all under worst case conditions. This data was then matched to the daily and weekly work schedules to estimate annual exposure.

A Collection of Components to Design Clinical Dashboards Incorporating Patient-Reported Outcome Measures: Qualitative Study.

A clinical dashboard is a data-driven clinical decision support tool visualizing multiple key performance indicators in a single report while minimizing time and effort for data gathering. Studies have shown that including patient-reported outcome measures (PROMs) in clinical dashboards supports the clinician's understanding of how treatments impact patients' health status, helps identify changes in health-related quality of life at an early stage, and strengthens patient-physician communication. This study aims to determine design components for clinical dashboards incorporating PROMs to inform software producers and users (ie, physicians). We conducted interviews with software producers and users to test preselected design components. Furthermore, the interviews allowed us to derive additional components that are not outlined in existing literature. Finally, we used inductive and deductive coding to derive a guide on which design components need to be considered when building a clinical dashboard incorporating PROMs. A total of 25 design components were identified, of which 16 were already surfaced during the literature search. Furthermore, 9 additional components were derived inductively during our interviews. The design components are clustered in a generic dashboard, PROM-related, adjacent information, and requirements for adoption components. Both software producers and users agreed on the primary purpose of a clinical dashboard incorporating PROMs to enhance patient communication in outpatient settings. Dashboard benefits include enhanced data visualization and improved workflow efficiency, while interoperability and data collection were named as adoption challenges. Consistency in dashboard design components is preferred across different episodes of care, with adaptations only for disease-specific PROMs. Clinical dashboards have the potential to facilitate informed treatment decisions if certain design components are followed. This study establishes a comprehensive framework of design components to guide the development of effective clinical dashboards incorporating PROMs in health care practice.

Prevalence of physiological and perceptual markers of low energy availability in male academy football players: a study protocol for a cross-sectional study

Low energy availability (LEA) is a core feature of the female athlete triad and relative energy deficiency in sport (REDs). LEA underpins multiple adverse health and performance outcomes in various...

A novel method of high-density urban block form generation based on multi-objective solar performance optimization: A case study of Nanjing

Efficiently harnessing solar radiation in high-density urban environments to optimize block forms holds significant potential for advancing urban low-carbon development. This paper introduces a novel method for generating high-density block forms based on comprehensive solar utilization. Grasshopper was used to compile an automated workflow for block form generation, solar simulation, and multi-objective optimization algorithms. Leveraging maximum solar radiation within and surrounding the block and effective photovoltaic utilization ratio are the three objectives in the multi-objective optimization process. From 60,000 generated samples, 1200 block forms were selected as the Pareto fronts. As the plot ratio of the generated blocks increases from 3 to 7, the annual mean solar radiation around the block decreases from 891.3 kWh/m2 to 863.5 kWh/m2, a reduction of only 3.1 %. When the number of blocks within the block increases from 1 to 4, the average solar radiation inside the block increases from 484.8 kWh/m2 to 516.1 kWh/m2, an increase of about 6.5 %. Additionally, as site coverage increased from 30 % to 60 %, the average solar radiation inside the block decreased from 549.2 kWh/m2 to 459.8 kWh/m2, a decrease of approximately 17 %. Notably, the photovoltaic power generation fell from 90.3 kWh/m2 to 61.2 kWh/m2, a decrease of about 32 %. The results indicate that the grid modeling method and multi-objective optimization algorithm adopted in this study can significantly improve the diversity of block morphology and achieve multiple solar performance requirements. The findings support designers in swiftly assessing building volume rationality and solar utilization potential at the early stages of urban design.

A Novel Multi-criteria Decision Making Approach Based on Bipolar Neutrosophic Set for Evaluating Financial Markets in Egypt

In the evolving landscape of financial markets, assessing company performance with precision and reliability is crucial for investors and analysts. This paper introduces an innovative framework for evaluating the financial performance of companies listed on the Egyptian Exchange (EGX) by integrating Bipolar Neutrosophic Sets (BNS) with Multi-Criteria Decision-Making (MCDM) methods. The proposed framework utilizes BNS to incorporate the inherent uncertainty and imprecision of financial data, and integrates MCDM methods to provide a comprehensive assessment of multiple performance indicators. Specifically, the evaluation focuses on seven critical criteria: Stock Returns, Volatility, Liquidity, Market Capitalization, Earnings Growth, Revenue Growth, and Analyst Ratings. By utilizing Bipolar Neutrosophic Weighted Average (BNWA) operators and similarity measures, the study effectively ranks companies through a nuanced analysis of these criteria. The results highlight the effectiveness of the proposed approach in providing a robust and accurate financial performance evaluation framework tailored to the unique dynamics of the Egyptian market. The study demonstrates that the hybrid model not only accommodates variations in criteria weights but also offers valuable insights for investment decisions in emerging markets. The approach exhibits resilience and adaptability, providing a more informed and reliable basis for evaluating financial performance. This research contributes to the advancement of financial performance assessment methodologies by showcasing the potential of combining BNS with MCDM techniques. The research also emphasizes the framework's practical applications and suggests future research directions for refining the model and exploring its use in diverse financial contexts.

Solar energy prediction with synergistic adversarial energy forecasting system (Solar-SAFS): Harnessing advanced hybrid techniques

An effective solar forecasting is an important part of managing and enhancing the effectiveness of solar power systems. The purpose of this paper is to develop a distinct and smart energy prediction system for solar PV systems. An efficient Solar Synergistic Adversarial Energy Forecasting System (Solar-SAFS) has been developed, which greatly enhances the precision and reliability of solar power predictions. It combines various deep learning methods including Graph Convolutional Networks (GCNs), Variational Autoencoders (VAEs), Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to effectively understand spatial and time-related patterns in solar data. The strength and accuracy of this model are increased by using adversarial training, which makes sure it gives reliable predictions in different situations. For making the hyper parameters better and to increase model's effectiveness and efficiency, we are using Grey-Oystercatcher Hybrid Optimization (GOHO) technique. The Solar-SAFS is validated and tested with different data sets including Fingrid and DSK solar. It shows that this method gives better results compared to current deep learning models. Our findings suggest that Solar-SAFS improves the accuracy and stability of solar energy prediction by a large margin, offering an advanced solution for managing renewable energy resources. Moreover, it significantly helps to obtain better predictions for solar energy production, which is a crucial need in this sector. It also encourages more efficient and dependable forecasting methods for renewable energy sources like sunlight. Compared to existing deep learning models, the Solar-SAFS demonstrates significantly lower error rates across multiple performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Bias Error (MBE).

Change in Fractional Vegetation Cover and Its Prediction during the Growing Season Based on Machine Learning in Southwest China

Fractional vegetation cover (FVC) is a crucial indicator for measuring the growth of surface vegetation. The changes and predictions of FVC significantly impact biodiversity conservation, ecosystem health and stability, and climate change response and prediction. Southwest China (SWC) is characterized by complex topography, diverse climate types, and rich vegetation types. This study first analyzed the spatiotemporal variation of FVC at various timescales in SWC from 2000 to 2020 using FVC values derived from pixel dichotomy model. Next, we constructed four machine learning models—light gradient boosting machine (LightGBM), support vector regression (SVR), k-nearest neighbor (KNN), and ridge regression (RR)—along with a weighted average heterogeneous ensemble model (WAHEM) to predict growing-season FVC in SWC from 2000 to 2023. Finally, the performance of the different ML models was comprehensively evaluated using tenfold cross-validation and multiple performance metrics. The results indicated that the overall FVC in SWC predominantly increased from 2000 to 2020. Over the 21 years, the FVC spatial distribution in SWC generally showed a high east and low west pattern, with extremely low FVC in the western plateau of Tibet and higher FVC in parts of eastern Sichuan, Chongqing, Guizhou, and Yunnan. The determination coefficient R2 scores from tenfold cross-validation for the four ML models indicated that LightGBM had the strongest predictive ability whereas RR had the weakest. WAHEM and LightGBM models performed the best overall in the training, validation, and test sets, with RR performing the worst. The predicted spatial change trends were consistent with the MODIS-MOD13A3-FVC and FY3D-MERSI-FVC, although the predicted FVC values were slightly higher but closer to the MODIS-MOD13A3-FVC. The feature importance scores from the LightGBM model indicated that digital elevation model (DEM) had the most significant influence on FVC among the six input features. In contrast, soil surface water retention capacity (SSWRC) was the most influential climate factor. The results of this study provided valuable insights and references for monitoring and predicting the vegetation cover in regions with complex topography, diverse climate types, and rich vegetation. Additionally, they offered guidance for selecting remote sensing products for vegetation cover and optimizing different ML models.

An adapted Black Widow Optimization Algorithm for Financial Portfolio Optimization Problem with cardinalty and budget constraints

Financial Portfolio Optimization Problem (FPOP) is a cornerstone in quantitative investing and financial engineering, focusing on optimizing assets allocation to balance risk and expected return, a concept evolving since Harry Markowitz’s 1952 Mean-Variance model. This paper introduces a novel meta-heuristic approach based on the Black Widow Algorithm for Portfolio Optimization (BWAPO) to solve the FPOP. The new method addresses three versions of the portfolio optimization problems: the unconstrained version, the equality cardinality-constrained version, and the inequality cardinality-constrained version. New features are introduced for the BWAPO to adapt better to the problem, including (1) mating attraction and (2) differential evolution mutation strategy. The proposed BWAPO is evaluated against other metaheuristic approaches used in portfolio optimization from literature, and its performance demonstrates its effectiveness through comparative studies on benchmark datasets using multiple performance metrics, particularly in the unconstrained Mean-Variance portfolio optimization version. Additionally, when encountering cardinality constraint, the proposed approach yields competitive results, especially noticeable with smaller datasets. This leads to a focused examination of the outcomes arising from equality versus inequality cardinality constraints, intending to determine which constraint type is more effective in producing portfolios with higher returns. The paper also presents a comprehensive mathematical model that integrates real-world constraints such as transaction costs, transaction lots, and a dollar-denominated budget, in addition to cardinality and bounding constraints. The model assesses both equality/inequality cardinality constraint versions of the problem, revealing that the inequality constraint tends to offer a wider range of feasible solutions with increased return potential.

Game Difficulty Prediction Based on Facial Cues and Game Performance

Current research on game difficulty prediction mainly uses heuristic functions or physiological signals. The former does not consider user data, while the latter easily causes interference to the user. This paper proposes a difficulty prediction method based on multiple facial cues and game performance. Specifically, we first utilize various computer vision methods to detect players’ facial expressions, gaze directions, and head poses. Then, we build a dataset by combining these three kinds of data and game performance as inputs, with the subjective difficulty ratings as labels. Finally, we compare the performance of several machine learning methods on this dataset using two classification tasks. The experimental results showed that the multilayer perceptron classifier (abbreviated as MLP) achieved the highest performance on these tasks, and its accuracy increased with the increase in input feature dimensions. These results demonstrate the effectiveness of our method. The proposed method could assist in improving game design and user experience.

Traditional and Integrated MCDM Approaches for Assessment and Ranking of Laser Cutting Conditions

Multiple benefits and advantages that offer laser cutting technology are difficult to achieve if the laser cutting parameter conditions are not adequately set. Determining laser cutting conditions is not trivial task considering large number of controllable inputs, existence of multiple process performances which are often mutually opposed as well as effects of noise factors. Different scientific methods and engineering approaches represent more sophisticated approaches which aid assessment and determination of the most favourable (optimized) cutting conditions. This study is focused assessment and ranking of laser cutting conditions in CO2 laser cutting of AISI 316L stainless steel. Based on kerf width and surface roughness experimental data and estimation of material removal rate (MRR) and total variable costs, four criteria in the multi-criteria decision making (MCDM) model were considered. For solving the developed model, six well-known MCDM methods were applied, and in addition, in order to overcome ranking inconsistency, robust decision-making rule was developed upon which final ranking was obtained.

A nexus of multiple integrations and business performance through supply chain agility and supply flexibility: a dynamic capability view

Purpose Manufacturing capability is a crucial component of every nation’s economy and pharmaceuticals are frequently a significant part of the manufacturing sector. Pharmaceutical supply chains are essential to health-care systems, contributing to living quality and shorter hospital stays. This study aims to examine the role of multiple integrations on business performance (BP) through supply chain flexibility (SCF) and supply chain agility (SCA). Design/methodology/approach Data was collected from 198 supply chain professionals in the pharmaceutical sector of the developing economy of Pakistan. The sample was collected based on a nonprobability purposive sampling approach. A five-point Likert-scale survey was used and analyzed with the PLS-SEM technique using SmartPLS 4. Findings This study found that process integration (PI) does not affect SCA, whereas relationship integration and measurement integration positively affect SCA. SCA positively impacts BP. In contrast, all integrations significantly influenced supply flexibility and BP except for PI. Finally, SCF significantly mediates the relationship between all integrations and BP. Originality/value This study examined the relationships of multiple integrations on BP, directly and indirectly, through SCF and agility. The theory of dynamic capabilities has been applied and extended to increase the comprehensiveness of the findings. A developing economy’s pharmaceutical industry supply chain was examined, producing empirical evidence of the results.

Supervised learning of spatial features with STDP and homeostasis using Spiking Neural Networks on SpiNNaker

Artificial Neural Networks (ANN) have gained significant popularity thanks to their ability to learn using the well-known backpropagation algorithm. Conversely, Spiking Neural Networks (SNNs), despite having broader capabilities than ANNs, have always posed challenges in the training phase. This paper shows a new method to perform supervised learning on SNNs, using Spike Timing Dependent Plasticity (STDP) and homeostasis, aiming at training the network to identify spatial patterns. Spatial patterns refer to spike patterns without a time component, where all spike events occur simultaneously. The method is tested using the SpiNNaker digital architecture. A SNN is trained to recognise one or multiple patterns and performance metrics are extracted to measure the performance of the network. Some considerations are drawn from the results showing that, in the case of a single trained pattern, the network behaves as the ideal detector, with 100% accuracy in detecting the trained pattern. However, as the number of trained patterns on a single network increases, the accuracy of identification is linked to the similarities between these patterns. This method of training an SNN to detect spatial patterns may be applied to pattern recognition in static images or traffic analysis in computer networks, where each network packet represents a spatial pattern. It will be stipulated that the homeostatic factor may enable the network to detect patterns with some degree of similarity, rather than only perfectly matching patterns. The principles outlined in this article serve as the fundamental building blocks for more complex systems that utilise both spatial and temporal patterns by converting specific features of input signals into spikes. One example of such a system is a computer network packet classifier, tasked with real-time identification of packet streams based on features within the packet content.

Design of multiple anti-fouling and honeycomb-like NH2-AgBiS2 @g-C3N4 hydrogel layer onto PAN fiber membrane for multicomponent pollutant-oil-water emulsion treatment

Nano-structured hydrogel with unique anti-oil fouling property exhibits big advantage in oil/water separation, but its application in complex oily wastewater (contain oils, organic matter, bacteria, etc.) cleanup is hampered by the insufficient capabilities in multi-antifouling and synergistic treatment. Herein, we constructed the amino-rich NH2-AgBiS2/PANI (polyaniline)-g-C3N4 based multi-functional hydrogel functional layer onto the polyacrylonitrile (PAN) fiber membrane via polyphenol-mediated chitosan gelation and vacuum-assisted self-assembly techniques. The unique honeycomb-like structure and super-wetting feature synergistically contributed to the powerful oil resistance and flux breakthrough of composite membrane. Such membrane achieved superior permeability (up to 3558 L−1 m−2 h−1) for various SDS-stabilized oil-in-water emulsions and remarkable synergistic treatment efficiency of multicomponent pollutant-oil-water emulsion. The rational design of hydrogel layer on membrane surface intensified the photo-response ability and multiple electron transport channels, which offered the favorable photocatalytic self-cleaning performance towards degradation of organic dyes. According to the free radical quenching and EPR experiments, the photocatalytic mechanism was proposed. In addition, the inhibition rate of E. coli could reach 100 % under illumination of 24 h. Therefore, the integration of ultra-low oil adhesion, photocatalytic self-cleaning, and antibacterial features endows membrane with exceptional multiple anti-fouling performance, exhibiting unique advantages over traditional membranes in handling complex membrane fouling issues.

A 3D Printing Platform for Design and Manufacturing of Multi-Functional Cementitious Construction Components and Its Validation for a Post-Tensioned Beam.

Three-dimensional printing of cementitious materials for construction has been extensively investigated in recent years, with several demonstration projects successfully carried out. These efforts aim to leverage the printing process to achieve more efficient production of components compared to conventional concrete technologies. This includes both the process itself (eliminating the formwork stage) and the flexibility in producing complexly shaped elements. To maximize the potential of 3D printing in the construction industry, additional steps must be taken, grounded in a holistic view of the entire process. This involves integration of the production chain, including design, materials, and manufacturing of components, to create elements with optimal performance, encompassing structural, environmental, and architectural aspects. Such multi-functionality requires the viewing of 3D printing not just as a production technology but as a platform enabling the integration of all these components. To advance this approach, quantitative tools are developed to optimize the following three key components: material composition; manufacturing parameters to ensure buildability; and design tools to optimize multiple performance criteria, particularly structural and architectural shape. A demonstration component, namely a post-tensioned beam, featuring two multi-functional characteristics-structural and architectural-is designed, produced, and evaluated. The scientific concepts and research tools used to develop these quantitative design tools are multidisciplinary, including rheological characterization, control of the internal structure and composition of granular materials, simulation of the mechanical behavior of green material during printing, and the hardened properties of the components, all utilizing structural optimization to enhance performance.