Analysis Model Research Articles

Essential Number of Principal Components and Nearly Training-Free Model for Spectral Analysis.

Learning-enabled spectroscopic analysis, promising for automated real-time analysis of chemicals, is facing several challenges. First, a typical machine learning model requires a large number of training samples that physical systems can not provide. Second, it requires the testing samples to be in range with the training samples, which often is not the case in the real world. Further, a spectroscopy device is limited by its memory size, computing power, and battery capacity. That requires highly efficient learning models for on-site analysis. In this paper, by analyzing multi-gas mixtures and multi-molecule suspensions, we first show that orders of magnitude reduction of data dimension can be achieved as the number of principal components that need to be retained is the same as the independent constituents in the mixture. From this principle, we designed highly compact models in which the essential principal components can be directly extracted from the interrelations between the individual chemical properties and principal components; and only a few training samples are required. Our model can predict the constituent concentrations that have not been seen in the training dataset and provide estimations of measurement noises. This approach can be extended as an effectively standardized method for principle component extraction.

How can the natural background and ecological & environment promote the green and sustainable development of Chinese tourist attractions?

In the context of the global carbon peak and carbon neutrality initiatives and post-pandemic, studying the green and sustainable development of tourist attractions is of great significance for the sustainable utilization of tourism resources. This study focuses on tourist attractions in 30 provinces in China from 2001 to 2019, establishes an input–output indicator system for economic efficiency and eco-efficiency, and uses the Super-SBM model in Data Envelopment Analysis to calculate the economic efficiency and eco-efficiency of tourist attractions in China. To analyze the natural background and environmental driving factors that affect eco-efficiency, as well as the interaction between these factors, using a geographic detector model, and propose a green and sustainable development path for tourist attractions. The research results indicate that the eco-efficiency of Chinese tourist attractions was higher than the economic efficiency, and both showed a downward trend. The proportion of altitude and nature reserve area to the area under the jurisdiction, as well as the total investment in environmental pollution control, have a significant impact on eco-efficiency; The interaction between temperature, precipitation, and normalized difference vegetation index (NDVI), and the proportion of nature reserves in the jurisdiction and the total investment in environmental pollution control, is significantly enhanced, indirectly affecting the eco-efficiency of Chinese tourist attractions. Among the natural factors, temperature, precipitation, and NDVI all could interact with altitude to significantly the impacts on the eco-efficiency of Chinese tourist attractions. The research aims to provide a Chinese solution for developing tourist attractions in developing countries similar to China.

Protocol for the Validation of Models for Regional Risk Analysis

Protocol for the Validation of Models for Regional Risk Analysis

Combining roadkill hotspots and landscape features to guide mitigation measures on highways

Identifying highway sections with high vertebrate roadkill and associated landscape features is crucial for proposing mitigation measures to reduce the negative impacts of wildlife-vehicle collisions on biodiversity, human safety, and the economy. Focusing on highways in the State of Mato Grosso do Sul, Brazil, this study proposes a prioritization of road segments for implementing mitigation measures based on landscape characteristics and roadkill hotspots recorded between 2016 and 2018. Roadkills distribution was assessed using Hotspot Identification, detecting sections with the highest collision intensity. The effect of landscape variables, such as the area and distance of natural vegetation and anthropic land use within 1 km of the highways, on roadkill was modelled using General Linear Mixed Models, which were evaluated based on two response variables: (1) roadkill presence/absence and (2) roadkill abundance, with model selection by the Akaike information criterion. Based on roadkill hotspot identification analysis and predictive models of the influence of landscape elements on roadkill occurrence and abundance, an evaluation criterion was proposed to determine the priority sites for installing wildlife crossings, speed bumps, and signposts. Wildlife-vehicle collisions were positively associated with riparian vegetation areas and urban areas, while negatively associated with silviculture and distance to “cerradão” (forest Cerrado) patches. Eleven road sections were identified as top priority, two as high priority and 78 as medium priority for the installation of mitigation measures with varying cost-benefit ratios. Our results can potentially guide decision-making at broader scales and provide mitigating alternatives applicable to dry ecosystems such as the Cerrado (Brazilian Savanna).

Effects of Laurus extract against cadmium-induced neurotoxicity in rats

Cadmium (Cd) is a hazardous heavy metal with widespread environmental presence, posing significant threats to human and animal health. This study investigates the neurotoxic effects of Cd exposure and explores the therapeutic efficacy of Laurus nobilis L. in counteracting these adverse effects. Cd exposure induces oxidative stress and neurotoxicity, leading to neuronal damage and histopathological alterations. Laurus, known for its antioxidant properties, is assessed for its potential in mitigating Cd-induced neurotoxicity through in vitro antioxidant assays, GC–MS analysis, HPLC profiling, and experimental animal models. Results demonstrate that Laurus ethanolic extract exhibits significant antioxidant activity, attributed to its phenolic and flavonoid constituents. GC–MS analysis reveals various bioactive compounds in the extract, including Hexadecanoic acid methyl ester and (Z)-13-Docosenamide, which posses neuroprotective properties. HPLC analysis identifies phenolic compounds such as ellagic acid and flavonoids like rutin and kaempferol in the extract. In vivo studies in rats exposed to Cd demonstrate that Laurus extract administration mitigates Cd-induced alterations in body and brain weight, hematological parameters, liver and kidney function, oxidative stress markers, and acetylcholinesterase (AchE) activity. Histopathological examination confirms the protective effects of Laurus against Cd-induced neuronal damage. The SOD model, validated with high resolution (2.05 Å) and strong R values (work: 0.192, free: 0.236, observed: 0.194), shows strong structural stability (C-score: 0.30). Docking studies reveal high binding affinities of kaempferol (−8.1 kcal/mol) and rutin (−8.7 kcal/mol) with SOD with kaempferol demonstrating superior solubility, lipophilicity, and drug-likenessSimulation analysis confirms the protein’s flexibility and adaptability, highlighting its therapeutic potential, especially with kaempferol. These findings suggest the therapeutic potential of Laurus. as a natural remedy for Cd-induced neurotoxicity, highlighting its antioxidant and neuroprotective properties.

Application of artificial intelligence in music generation: a systematic review

Our analysis explores the benefits of artificial intelligence (AI) in music generation, showcasing progress in electronic music, automatic music generation, evolution in music, contributions to music-related disciplines, specific studies, contributions to the renewal of western music, and hardware development and educational applications. The identified methods encompass neural networks, automation and simulation, neuroscience techniques, optimization algorithms, data analysis, and Bayesian models, computational algorithms, and music processing and audio analysis. These approaches signify the complexity and versatility of AI in music creation. The interdisciplinary impact is evident, extending into sound engineering, music therapy, and cognitive neuroscience. Robust frameworks for evaluation include Bayesian models, fractal metrics, and the statistical creator-evaluator. The global reach of this research underscores AI's transformative role in contemporary music, opening avenues for future interdisciplinary exploration and algorithmic enhancements.

Ferumoxytol-Enhanced Cardiac Cine MRI Reconstruction Using a Variable-Splitting Spatiotemporal Network.

Balanced steady-state free precession (bSSFP) imaging is commonly used in cardiac cine MRI but prone to image artifacts. Ferumoxytol-enhanced (FE) gradient echo (GRE) has been proposed as an alternative. Utilizing the abundance of bSSFP images to develop a computationally efficient network that is applicable to FE GRE cine would benefit future network development. To develop a variable-splitting spatiotemporal network (VSNet) for image reconstruction, trained on bSSFP cine images and applicable to FE GRE cine images. Retrospective and prospective. 41 patients (26 female, 53 ± 19 y/o) for network training, 31 patients (19 female, 49 ± 17 y/o) and 5 healthy subjects (5 female, 30 ± 7 y/o) for testing. 1.5T and 3T, bSSFP and GRE. VSNet was compared to VSNet with total variation loss, compressed sensing and low rank methods for 14× accelerated data. The GRAPPA×2/×3 images served as the reference. Peak signal-to-noise-ratio (PSNR), structural similarity index (SSIM), left ventricular (LV) and right ventricular (RV) end-diastolic volume (EDV), end-systolic volume (ESV), and ejection fraction (EF) were measured. Qualitative image ranking and scoring were independently performed by three readers. Latent scores were calculated based on scores of each method relative to the reference. Linear mixed-effects regression, Tukey method, Fleiss' Kappa, Bland-Altman analysis, and Bayesian categorical cumulative probit model. A P-value <0.05 was considered statistically significant. VSNet achieved significantly higher PSNR (32.7 ± 0.2), SSIM (0.880 ± 0.004), rank (2.14 ± 0.06), and latent scores (-1.72 ± 0.22) compared to other methods (rank >2.90, latent score < -2.63). Fleiss' Kappa was 0.52 for scoring and 0.61 for ranking. VSNet showed no significantly different LV and RV ESV (P = 0.938) and EF (P = 0.143) measurements, but statistically significant different (2.62 mL) EDV measurements compared to the reference. VSNet produced the highest image quality and the most accurate functional measurements for FE GRE cine images among the tested 14× accelerated reconstruction methods. 3 TECHNICAL EFFICACY: Stage 1.

Assessing performance of water supply operation of water companies in China: A union dynamic multi-activity network data envelopment analysis

Urban growth and the increasing urban water demand highlight the importance of performance analysis in the water supply industry. Both water supply companies (WSCs) and water supply and sewage companies (WSSCs) produce and deliver potable water for city dwellers. Water authorities use benchmarks to monitor the two types of companies’ operations. The internal operation process and heterogeneities of observations were not considered for relative performance evaluation by conventional DEA models. To overcome these limitations, this study proposes a union dynamic multi-activity network data envelopment analysis (DMANDEA) model to extend static network DEA to a more general case for dynamic efficiency assessment of the water supply operations of water companies. For the first time, the model allows for the decomposition of the water supply operation into water production (WP) and water consumption (WC) substages and performance comparisons for water companies by considering their heterogeneity simultaneously. Specifically, by using a non-radial directional distance function (DDF), the inefficiencies for substages and the entire system can be both assessed dynamically. This model is applied to a sample of 246 water companies in China in 2016–2018. The overall, period, and substages’ group efficiencies, meta efficiency, and technology gap ratio (TGR) for 179 WSCs and 67 WSSCs are all provided. The empirical results reveal that: (1) the average group efficiencies of WSCs and WSSCs are 0.4567 and 0.6611, respectively. The meta efficiencies of WSCs and WSSCs are 0.4523 and 0.4703, respectively. The TGR of WSCs (0.994) is greater than that of WSSCs (0.7067). (2) WSSCs perform better than WSCs do, which suggests that economies of scope do exist in WSSCs. However, WSCs possess the best technology for water supply operations because of their high TGR. (3) The average meta efficiency for all the observed water companies (0.4572) is not optimistic, which correlates more with inefficiency in the WP stage. The causes stem from the natural monopoly, lack of competition, and relatively lower water tariffs. The Chinese water supply industry requires diversification of ownership and operation modes, and gradual increases in water tariffs. (4) Water companies in the eastern region outperform those in other regions. Regional cooperation, such as technology sharing, the transfer of managerial experience, and information exchange, needs to be emphasized.

Survival Analysis of Conversion Surgery in Borderline Resectable and Locally Advanced Unresectable Pancreatic Ductal Adenocarcinoma Addressing Selection and Immortal Time Bias: A Retrospective Single-Center Study.

The purpose of this study was to provide a detailed evaluation of the oncological advantages of surgery following neoadjuvant chemotherapy (NAC) for patients with borderline resectable (BR) or unresectable (UR) pancreatic ductal adenocarcinoma (PDAC), with a focus on minimizing biases. Recently, NAC has become the standard care for BR or UR locally advanced (UR-LA) PDAC, however, many studies have assessed survival benefits and favorable variables without consideration for biases, particularly immortal time bias. This study included patients diagnosed with BR or UR-LA PDAC at Juntendo University Hospital from 2019 to 2022. To mitigate bias, we applied methods such as propensity score matching (PSM), time-dependent covariate Cox proportional hazard regression analysis (TDC), landmark analysis, and multivariable Cox proportional hazards regression model. The study analyzed 124 patients, dividing them into a surgery group (n = 57) and a chemotherapy-only group (n = 67). After PSM, there were 21 matched pairs. Survival analysis using TDC analysis showed that the surgery group had significantly better overall survival compared with the chemotherapy-only group in both the entire cohort and the matched pairs. Cox regression analysis of the entire cohort also revealed a similar superiority of surgery, while the landmark analysis showed varying results depending on the landmark setting. After careful adjustment for selection and immortal time biases, surgery following NAC appears to significantly extend survival in patients with BR or UR PDAC.

Spatio-temporal analysis of pulmonary tuberculosis in Astambul District, South Kalimantan, Indonesia 2020-2021

Respiratory tuberculosis (TB) remains a significant global health concern and ranks among the top 10 leading causes of death worldwide, following human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS). Globally, it is estimated that 1.2 billion people are at risk of being infected with tuberculosis, and Indonesia is the country that contributes the third-highest number of TB cases worldwide, behind China and India. Tuberculosis is still a significant issue in South Kalimantan, notably in Banjar Regency. This study is a descriptive study that intends to identify respiratory TB clusters spatially and temporally in Astambul District during 2020–2021 with the SatScan application, which is visualized as a map of respiratory TB clusters using the Quantum GIS 3.28 application. Data on respiratory TB cases collected from the Banjar District Health Office were examined using retrospective space-time scan statistics, employing a Poisson probability model for analysis. This study found 4 respiratory TB clusters, and 2 of them had significant results, namely in 2020 (RR=6.90, p-value=0.000030) and 2021 (RR=5.27, p-value=0.00003). Factors that affect these clusters are population density, the physical condition of houses, humidity levels, and the availability of health facilities.

Acoustic black hole immersed sonoreactor for high-efficiency cavitation treatment

Developing innovative sonoreactors to enhance acoustic processing efficiency holds immense importance in the field of sonochemistry. Traditional immersed sonoreactors (TISs) mainly produce cavitation at the probe tip, with a relatively weak cavitation around the probe, resulting in posing challenges for high-efficiency cavitation treatment. Here we propose an acoustic black hole immersed sonoreactor (ABHIS) in longitudinal-flexural coupled vibration, enabling high-efficiency cavitation treatment by unleashing the cavitation potential of the probe. The symmetrical structure of the probe is altered to introduce a coupling of flexural vibration mode, and an acoustic black hole (ABH) profile is integrated to further enhance both flexural wave number and amplitude. In this paper, we present a systematic theoretical design method for ABHIS and compare its performance with TIS using finite element method (FEM). An ABHIS prototype is fabricated and subjected to experimental tests and cavitation observation. The results demonstrate that our theoretical analysis model accurately predicts the frequency characteristics of ABHIS. The proposed ABHIS exhibits satisfactory dynamic characteristics, with significantly increased vibration displacement and acoustic radiation ability compared to TIS. Importantly, the ABH design significantly expands ultrasonic cavitation regions and enhances acoustic radiation intensity of ABHIS resulting in a substantial improvement in acoustic processing efficiency.

Risk stratification models incorporating oxidative stress factors to predict survival and recurrence in patients with gastric cancer after radical gastrectomy: a real-world multicenter study

BackgroundOxidative stress significantly influences the development and progression of gastric cancer (GC). It remains unreported whether incorporating oxidative stress factors into nomograms can improve the predictive accuracy for survival and recurrence risk in GC patients. Methods3498 GC patients who underwent radical gastrectomy between 2009 and 2017 were enrolled and randomly divided into training cohort (TC) and internal validation cohort (IVC). Cox regression analysis model was used to evaluate six preoperative oxidative stress indicators to formulate the Systemic oxidative stress Score (SOSS). Two nomograms based on SOSS was constructed by multivariate Cox regression and validated using 322 patients from another two hospitals. ResultsA total of 3820 patients were included. The SOSS, composed of three preoperative indicators—fibrinogen, albumin, and cholesterol—was an independent prognostic factor for both overall survival (OS) and disease-free survival (DFS). The two nomograms based on SOSS showed a significantly higher AUC than the pTNM stage (OS: 0.830 vs. 0.778, DFS: 0.824 vs. 0.775, all P < 0.001) and were validated in the IVC and EVC (all P<0.001). The local recurrence rate, peritoneal recurrence rate, distant recurrence rate and multiple recurrence rate in high-risk group were significantly higher than those in low-risk group (P < 0.05). ConclusionsThe two novel nomograms based on SOSS which was a combination score of three preoperative blood indicators, demonstrated outstanding predictive abilities for both survival and recurrence in GC patients with different risk groups, which may potentially improve survival through perioperatively active intervention strategies and individualized postoperatively close surveillance.

An interlinked dynamic model of timber and carbon stocks in Japan's wooden houses and plantation forests

Carbon absorption in growing trees is an important element of a carbon-neutral society, and the long-term storage of carbon stocks is a crucial sustainability challenge. Previous studies have focused on either live-biomass carbon stocks in plantation forests or anthropogenic carbon stocks in man-made objects. For a comprehensive nature-based climate solution, an analytical framework, dataset, and scenario setup for modeling the interrelationship between timber supply and demand are required. This study developed an interlinked material flow analysis model in which the timber demand for wooden houses is connected with timber supply from managed plantation forestry. We demonstrate the model by quantifying both live-biomass and anthropogenic carbon stocks and their potentials in Japan. We compared multiple scenario-runs of the model for wooden house demands estimated by population change with varying combinations of house types, structures, and lifespans. Our results show that carbon stocks will reach a maximum amount of 1.1 billion t-C by 2050 in a scenario of high demand for wooden detached houses with lifespan extensions. On the other hand, we also found that the aging of plantation forests and their reduced carbon-stocking capacities appear inevitable in any scenario owing to the limited demand for timber. Notably, despite the widely different settings of the various scenarios, our results exhibited narrow variances in future potential carbon storage in Japan. This can be explained by the unique population characteristics and building demographics of Japan. These counterintuitive findings highlight the need for interrelated modeling of the forestry and construction sectors. Our model and its scope are versatile and applicable to other case study areas, estimation periods, and target materials.

Dynamic behavior of piled jacket offshore wind turbines based on integrated aero-servo-hydro-SSI-OWT model

Jacket foundation is typically the preferred choice for Offshore Wind Turbines (OWTs) erected in water depth varying from 40 m to 80 m. In this paper, an integrated dynamic analysis model is designed to study the coupling between aerodynamics, servodynamics, hydrodynamics, soil-structure interaction for piled jacket OWTs. The performances of the AeroDyn and ServoDyn modules are verified by FAST, showcasing their applicability under deterministic and stochastic environmental conditions. The OWT dynamic responses, especially for t-z modeling, stress-transfer mechanism and structural fatigue damage, are subsequently studied. The overall deformation of the jacket calculated by the nonlinear elastic t-z curve in the API guideline, is overwhelmed by the t-z curve formulated using bounding surface plasticity framework, due to the ignorance of the loading history effect. Accompanied by a “compressed-released-recompressed” stress-transfer process, the downwind tube would experience high stress level, hence necessitating more attention in the ultimate limit state design of piled jacket structure. Otherwise, the upwind tube seems to be more decisive to the fatigue limit state design of piled jacket structure, owing to severe fluctuation in structural stress caused by a “tensed-released-re-tensed” stress-transfer tendency.

Effect of steel fibers on the stress–strain behaviour of aligned steel fiber ultra-high performance concrete under uniaxial compression

The mechanical properties of ultra-high performance concrete (UHPC) can be significantly enhanced by aligning the fibers. Aligned steel fiber UHPC (ASF-UHPC) was fabricated using a uniform magnetic field, yet the corresponding stress-strain relationship has not been extensively studied, hindering accurate calculations. To address this gap, ASF-UHPC was prepared and subsequently subjected to CT scans and uniaxial compression tests to investigate its stress-strain behavior. The analysis of failure mode and stress-strain curves revealed distinct anisotropic characteristics in compressive stresses and fiber distributions. Various steel fiber parameters, including the alignment orientation, volume fraction, and length, were systematically analysed to assess their impact on the material properties. A unit-cell model was developed to elucidate the influence of the fiber alignment orientation on UHPC performance. The results demonstrate that a parallel fiber alignment significantly enhances the elastic modulus, albeit with a Poisson ratio similar to that of the matrix, owing to the limited lateral confinement. Conversely, perpendicular fiber alignment restrains lateral deformation, enhancing the material strength and toughness. Randomly distributed fibers offered a degree of horizontal confinement, whereas inclined fibers induced horizontal thrust, leading to excessive lateral deformation and consequent elevation of the Poisson ratio. Finally, predictive equations for UHPC elastic modulus and compressive strength, along with an analysis model for stress-strain characteristics, were refined taking into account the direction of fiber arrangement.

Convolutional Neural Networks for Sentiment Analysis

Dedicated Identifying aimed at discerning subjective information by analyzing the polarity of opinions conveyed in text. Traditionally, recurrent neural networks (RNNs) have been the dominant approach for this type of analysis because of their ability to process sequential data. However, sentiment analysis has experienced a significant transformation with the introduction of convolutional neural networks (CNNs). Originally developed for image analysis, processing text due to their efficient mechanisms for extracting local features.. This paper explores the role of CNNs in sentiment analysis, evaluating their architecture, methodology, and comparative effectiveness against RNN-based models. We propose a comprehensive CNN-based model for sentiment analysis and examine its potential for sentiment classification tasks across multiple datasets.

Digitalization and Dynamic Criticality Analysis for Railway Asset Management

The primary aim of this paper is to support the optimization of asset management in railway infrastructure through digitalization and criticality analysis. It addresses the current challenges in railway infrastructure management, where data-driven decision making and automation are key for effective resource allocation. The paper presents a methodology that emphasizes the development of a robust data model for criticality analysis, along with the advantages of integrating advanced digital tools. A master table is designed to rank assets and automatically calculate criticality through a novel asset attribute characterization (AAC) process. Digitalization facilitates dynamic, on-demand criticality assessments, which are essential in managing complex networks. The study also underscores the importance of combining digital technology adoption with organizational change management. The data process and structure proposed can be viewed as an ontological framework adaptable to various contexts, enabling more informed and efficient asset ranking decisions. This methodology is derived from its application to a metropolitan railway network, where thousands of assets were evaluated, providing a practical approach for conducting criticality assessments in a digitized environment.

North Atlantic and the Barents Sea variability contribute to the 2023 extreme fire season in Canada

In the late spring to summer season of 2023, Canada witnessed unprecedented wildfires, with an extensive burning area and smoke spreading as far as the East Coast of the United States and Europe. Here, using multisource data analysis and climate model simulations, we show that an abnormally warm North Atlantic, as well as an abnormally low Barents Sea ice concentration (SIC), are likely key climate drivers of this Canadian fire season, contributing to ~80% of the fire weather anomaly over Canada from June to August 2023. Specifically, the warm North Atlantic forms an anomalous regional zonal cell with ascending air over the Atlantic and descending air encircling Canada, creating hot and dry local conditions. Meanwhile, reduced Barents SIC leads to a high-pressure center and reinforces the dry northern winds in Canada through Rossby wave dynamics. These exacerbated dry and hot conditions create a favorable environment for the ignition and spread of fires, thus contributing to the prolonged and extreme fire season in Canada. These teleconnections can extend to decadal scales and have important implications for understanding and predicting decadal fire activity in Canada and the surrounding regions.

Weight Restriction Approach in a Two-stage Network Structure: A DEA-based Approach

The standard data envelopment analysis model aimed to maximize the efficiency scores using the most favourable input/output weights. In two-stage network structures, the presence of intermediate undesirable measures leads to a distinct domain of weight restriction study. In the presence of undesirable intermediate measures and equipped with non-cooperative game theory, this study developes a joint weight restriction approach to derive a common set of weights. Concurrently, the overall efficiency of the system can be evaluated. Numerical instances of 34 OECD countries in 2012 have been presented to elucidate the details and applicability of the proposed method.

Optimal Design of Water Distribution System Using Improved Life Cycle Energy Analysis: Development of Optimal Improvement Period and Unit Energy Formula

Water distribution systems (WDSs) are crucial for providing clean drinking water, requiring an efficient design to minimize costs and energy usage. This study introduces an enhanced life cycle energy analysis (LCEA) model for an optimal WDS design, incorporating novel criteria for pipe maintenance and a new resilience index based on nodal pressure. The improved LCEA model features a revised unit energy formula and sets standards for pipe rehabilitation and replacement based on regional regulations. Applied to South Korea’s Goyang network, the model reduces energy expenditure by approximately 35% compared to the cost-based design. Unlike the cost-based design, the energy-based design achieves results that can relatively reduce energy when designing water distribution networks by considering recovered energy. This allows designers to propose designs that consume relatively less energy. Analysis using the new resilience index shows that the energy-based design outperforms the cost-based design in terms of pressure and service under most pipe failure scenarios. The implementation of the improved LCEA in real-world pipe networks, including Goyang, promises a practical life cycle-based optimal design.