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Accurate and automatic clothing pattern making is very important in personalized clothing customization and virtual fitting room applications. Clothing pattern generating as well as virtual clothing simulation is an attractive research issue both in clothing industry and computer graphics.

Physics-based method is an effective way to model dynamic process and generate realistic clothing animation. Due to conceptual simplicity and computational speed, mass-spring model is frequently used to simulate deformable and soft objects follow the natural physical rules. We present a physics-based clothing pattern generating framework by using scanned human body model. After giving a scanned human body model, first, we extract feature points, planes and curves on the 3D model by geometric analysis, and then, we construct a remeshed surface which has been formatted to connected quad meshes. Second, for each clothing piece in 3D, we construct a mass-spring model with same topological structures, and conduct a typical time integration algorithm to the mass-spring model. Finally, we get the convergent clothing pieces in 2D of all clothing parts, and we reconnected parts which are adjacent on 3D model to generate the basic clothing pattern.

The results show that the presented method is a feasible way for clothing pattern generating by use of scanned human body model.

The main contribution of this work is twofold: one is the geometric algorithm to scanned human body model, which is specially conducted for clothing pattern design to extract feature points, planes and curves. This is the crucial base for suit clothing pattern generating. Another is the physics-based pattern generating algorithm which flattens the 3D shape to 2D shape of cloth pattern pieces.

Pakistan has experienced financial liberalization with rapid ups and downs in economic growth due to domestic issues during the last 2 decades. Motivated by inconclusive and conflicting time-driven findings about the performance of the business groups, this study examines the performance of business groups in Pakistan for a relatively long period from 2003 to 2018.

The study uses 3,821 firm-year observations from non-financial firms listed on the Pakistan Stock Exchange (PSX). For the estimation, pooled ordinary least squares (OLS) with industry- and year fixed effects and two-step system generalized methods of moments (GMM) are used.

The study finds that group-affiliated firms outperform independent firms in accounting performance, while underperform in market performance. The outperformance is mainly driven by medium-sized business groups, while underperformance is driven by small and large business groups. Further, the study documents that the underperformance in terms of market performance of firms affiliated with small and large groups is greater before the economic downturn, while outperformance in terms of the accounting measure of firms affiliated with medium-sized groups is greater during the economic downturn. These findings support our time-driven concerns. Overall, the authors' findings are consistent with institutional and transaction cost theories.

Business groups are important channels to reduce market inefficiencies. Business groups may enhance the affiliated firms' resources and resistance capacity through active utilization of the internal capital market, specifically when market conditions are not ideal for affiliates. However, effective utilization of internal capital markets depends on group size. Therefore, investors should deliberate on the size of business groups and diversification within business groups.

The authors extend the literature by providing fresh evidence related to the performance of business groups in the Pakistani context while accounting for the role of the size of business groups.

Although the role of returnees is critical to firm innovation, the literature offers inconsistent findings regarding returnees' effect on firms' innovation performance. To reconcile this issue, the authors argue that taking the types of innovation into account – i.e. technical innovation and commercial innovation – is necessary. Thus, the purpose of this study is to examine how firms led by returnees affect the relationship between research and development (R&D) input and above two types of innovation output, as well as the contingent role of political connections (PCs) and venture capital funding (VC funding).

This study empirically tested the hypotheses using a dataset of 54,617 firm-year observations for 18,475 Chinese firms in Zhongguancun Science Park (ZSP) from 2009 to 2014.

The results show that the positive effect of R&D input on technical innovation performance (TIP) is reinforced when firms are led by returnees, while the positive effect of R&D input on commercial innovation performance (CIP) is weakened when firms are led by returnees compared with those firms led by the local counterparts. The findings further show that returnee firms' positive effect on the relationship between R&D input and technical innovation performance is more salient for firms with more PCs but weakened for those with more VC funding.

This study enriches the research on returnee firms' advantages and disadvantages in transforming R&D input into innovation performance, and the findings highlight that firms led by returnees can increase R&D efficiency of technical innovation, but reduce R&D efficiency of commercial innovation. Moreover, this study offers a contingent view of political and economic stakeholders' roles in returnee firms' innovation, by revealing PCs help returnee firms to enhance R&D efficiency in technological innovation, while venture capital can hamper such R&D efficiency.

Misinformation surrounding the Sustainable Development Goals (SDGs) has contributed to the formation of misbeliefs among the public. The purpose of this paper is to investigate public sentiment and misbeliefs about the SDGs on the YouTube platform.

The authors extracted 8,016 comments from YouTube videos associated with SDGs. The authors used a pre-trained Python library NRC lexicon for sentiment and emotion analysis, and to extract latent topics, the authors used BERTopic for topic modeling.

The authors found eight emotions, with negativity outweighing positivity, in the comment section. In addition, the authors identified the top 20 topics discussing various SDGs and SDG-related misbeliefs.

The authors reported topics related to public misbeliefs about SDGs and associated keywords. These keywords can be used to formulate social media content moderation strategies to screen out content that creates these misbeliefs. The result of hierarchical clustering can be used to devise and optimize response strategies by governments and policymakers to counter public misbeliefs.

This study represents an initial endeavor to gain a deeper understanding of the public’s misbeliefs regarding SDGs. The authors identified novel misbeliefs about SDGs that previous literature has not studied. Furthermore, the authors introduce an algorithm BERTopic for topic modeling that leverages transformer architecture for context-aware topic modeling.

The purpose of this paper is to propose a whole set of methods for underwater target detection, because most underwater objects have small samples, low quality underwater images problems such as detail loss, low contrast and color distortion, and verify the feasibility of the proposed methods through experiments.

The improved RGHS algorithm to enhance the original underwater target image is proposed, and then the YOLOv4 deep learning network for underwater small sample targets detection is improved based on the combination of traditional data expansion method and Mosaic algorithm, expanding the feature extraction capability with SPP (Spatial Pyramid Pooling) module after each feature extraction layer to extract richer feature information.

The experimental results, using the official dataset, reveal a 3.5% increase in average detection accuracy for three types of underwater biological targets compared to the traditional YOLOv4 algorithm. In underwater robot application testing, the proposed method achieves an impressive 94.73% average detection accuracy for the three types of underwater biological targets.

Underwater target detection is an important task for underwater robot application. However, most underwater targets have the characteristics of small samples, and the detection of small sample targets is a comprehensive problem because it is affected by the quality of underwater images. This paper provides a whole set of methods to solve the problems, which is of great significance to the application of underwater robot.

There is a need for research examining how governments and firms responded to the coronavirus disease (COVID-19) pandemic. This study investigates the interdependence between governments and innovative small and medium-sized enterprises (SMEs) during the pandemic in relation to the dynamic capabilities and resource dependence theories.

We use World Bank survey data collected immediately before and after the COVID-19 outbreak and a generalized structural equation model to examine the mediating role of government support in the relationship between firm innovation, resilience and survival.

Innovative SMEs exhibited higher resilience and a better chance of survival during the pandemic, partly due to attracting more government support.

This study offers a novel understanding of the government’s role in supporting innovative SMEs during the pandemic. The findings have implications for how government support policies can limit the deadweight effect and the substitution effect.

The rapid, yet low-profit, expansion of the production capacity of state-owned enterprises (SOEs) represents a remarkable phenomenon. However, the motivation behind this key operational decision remains underexplored, especially concerning the prioritization of sociopolitical and financial goals in operations management. Drawing on the multiple-goal model in the behavioral theory of the firm (BTOF), the authors' study aims to examine how SOE capacity expansion is driven by performance feedback regarding the sociopolitical goal of employment provision and how SOEs differently prioritize sociopolitical and financial goals based on negative versus positive feedback on the sociopolitical goal.

The authors' study uses panel data on 826 Chinese SOEs in manufacturing industries from 2011 to 2019. The authors employ the fixed-effects model with Driscoll–Kraay standard errors, which are robust to heteroscedasticity, autocorrelation and cross-sectional dependence.

The authors find that SOEs increase capacity expansion as sociopolitical feedback becomes more negative, but they may not increase capacity expansion in response to positive sociopolitical feedback. Moreover, negative profitability feedback strengthens SOEs' capacity expansion in response to negative sociopolitical feedback. In contrast, negative profitability feedback weakens their response to positive sociopolitical feedback.

The authors' study offers a novel behavioral explanation of SOEs' operational decisions regarding capacity expansion. While the literature has traditionally assumed multiple goals as either hierarchical or compatible, the authors extend the BTOF's multiple-goal model to illuminate when firms pursue sociopolitical and financial goals as compatible (i.e. the activation rule) versus hierarchical (i.e. the sequential rule), thereby reconciling their tension in distinct performance situations. Practically, the authors provide fine-grained insights into how operations managers can prioritize multiple goals when making operational decisions. The authors' study also shows how policymakers can influence SOE operations to pursue sociopolitical goals for public benefit.

This paper aims to study the influence of cylindrical texture parameters on the lubrication performance of static and dynamic pressure thrust bearings (hereinafter referred to as thrust bearings) and to optimize their lubrication performance using multiobjective optimization.

The influence of texture parameters on the lubrication performance of thrust bearings was studied based on the modified Reynolds equation. The objective functions are predicted through the BP neural network, and the texture parameters were optimized using the improved multiobjective ant lion algorithm (MOALA).

Compared with smooth surface, the introduction of texture can improve the lubrication properties. Under the optimization of the improved algorithm, when the texture diameter, depth, spacing and number are approximately 0.2 mm, 0.5 mm, 5 mm and 34, respectively, the loading capacity is increased by around 27.7% and the temperature is reduced by around 1.55°C.

This paper studies the effect of texture parameters on the lubrication properties of thrust bearings based on the modified Reynolds equation and performs multiobjective optimization through an improved MOALA.

This study aims to investigate the corrosion inhibitor effect of migrating corrosion inhibitor (MCI) on Q235 steel in high alkaline environment under cathodic polarization.

This study investigated the electrochemical characteristics of Q235 steel with and without MCI by polarization curve and electrochemical impedance spectroscopy. Besides, the surface composition of Q235 steel under different environments was analyzed by X-ray photoelectron spectroscopy. In addition, the migration characteristic of MCI and the adsorption behavior of MCI under cathodic polarization were studied using Raman spectroscopy.

Diethanolamine (DEA) and N, N-dimethylethanolamine (DMEA) can inhibit the increase of Fe(II) in the oxide film of Q235 steel under cathodic polarization. The adsorption stability of DMEA film was higher under cathodic polarization potential, showing a higher corrosion inhibition ability. The corrosion inhibition mechanism of DEA and DMEA under cathodic polarization potential was proposed.

The MCI has a broad application prospect in the repair of damaged reinforced concrete due to its unique migratory characteristics. The interaction between MCIs, rebar and concrete with different compositions has been studied, but the passivation behavior of the steel interface in the presence of both the migrating electric field and corrosion inhibitors has been neglected. And it was investigated in this paper.

This study aims to verify the respective and interactive effects of subnational formal and informal institutions (i.e. legal effectiveness and social trust) on foreign firm performance, and further identify the contingent factor (i.e. institutional experience) that moderates these relationships.

Drawing on the institutional-based view, this study develops several hypotheses that are tested using a comprehensive dataset from four main data sources. The authors’ unit of analysis is foreign firms operating in China. The authors ran ordinary least squares (OLS) regression model to investigate the effects. A series of robustness tests and endogeneity tests were performed.

The results show that both legal effectiveness and social trust at subnational level positively affect foreign firm performance respectively. Legal effectiveness and social trust at subnational level have complementary effect in promoting the performance of foreign firms. Foreign firm's institutional experience in target region of emerging economies host country strengthens the positive impact of subnational legal effectiveness on performance, but weakens the positive impact of subnational social trust on performance.

It is important to fully understand the impact of heterogeneous institutional environments of subnational regions in emerging economies on foreign firm performance, which would help foreign firm make a more suitable secondary choice decision of investment destinations at the subnational regional level.

First, drawing on institutional-based view, the authors incorporate the subnational formal and informal institutional factors to investigate their impacts on foreign firm performance by switching the attention from national level to subnational level in emerging economy host countries. Second, this research furthers existing studies by bridging a missing link between both subnational formal and informal institutional environments and foreign firms' outcomes. Third, the authors prove that the model of subnational formal and informal institutions in influencing foreign firms' performance is contingent on their institutional experience in target subnational region of emerging economy host country.