Important Channel Research Articles

The role of ESG performance during times of COVID-19 pandemic

The outbreak of the epidemic in 2020 has caused a huge negative impact on the production and operation of firms, directly threatening their survival and development. However, some firms can make timely and effective adjustments in the face of sudden crises because of their resilience, and then turn the corner. This study selects the data of 2993 companies listed in Chinese A shares. The OLS method and event study is used to analyze the impact of ESG on the ability of corporate system crisis (corporate resilience). The research results indicate that companies with good ESG performance are more resilient in crises. The mechanism test indicates that the easing effect of corporate financing constraints and the expansion effect of corporate green innovation capabilities are important channels for ESG performance to promote the negative impact of crisis shocks on corporate value. Heterogeneity analysis indicates that ESG has a stronger ability to respond to systemic crises in small-scale firms, state-owned firms, and highly competitive market environments. Powerful CEOs can weaken ESG's ability to respond to systemic corporate crises. Further research has found that only S and G items, namely good governance level and social performance, have a significant positive promoting effect on corporate resilience. ESG performance may be more important in areas more severely affected by the epidemic. This study expands the research on ESG and the research on the decision mechanism of enterprise resilience. This study provides a new theoretical perspective for the study of corporate crisis response capabilities, and provides a certain policy reference for Chinese firms to effectively respond to public crises, which has important policy implications.

NMSCANet: stereo matching network for speckle variations in single-shot speckle projection profilometry.

In single-shot speckle projection profilometry (SSPP), the projected speckle inevitably undergoes changes in shape and size due to variations such as viewing angles, complex surface modulations of the test object and different projection ratios. These variations introduce randomness and unpredictability to the speckle features, resulting in erroneous or missing feature extraction and subsequently degrading 3D reconstruction accuracy across the tested surface. This work strives to explore the relationship between speckle size variations and feature extraction, and address the issue solely from the perspective of network design by leveraging specific variations in speckle size without expanding the training set. Based on the analysis of the relationship between speckle size variations and feature extraction, we introduce the NMSCANet, enabling the extraction of multi-scale speckle features. Multi-scale spatial attention is employed to enhance the perception of complex and varying speckle features in space, allowing comprehensive feature extraction across different scales. Channel attention is also employed to selectively highlight the most important and representative feature channels in each image, which is able to enhance the detection capability of high-frequency 3D surface profiles. Especially, a real binocular 3D measurement system and its digital twin with the same calibration parameters are established. Experimental results imply that NMSCANet can also exhibit more than 8 times the point cloud reconstruction stability (Std) on the testing set, and the smallest change range in terms of Mean~dis (0.0614 mm - 0.4066 mm) and Std (0.0768 mm - 0.7367 mm) when measuring a standard sphere and plane compared to other methods, faced with the speckle size changes, meanwhile NMSCANet boosts the disparity matching accuracy (EPE) by over 35% while reducing the matching error (N-PER) by over 62%. Ablation studies and validity experiments collectively substantiate that our proposed modules and constructed network have made significant advancements in enhancing network accuracy and robustness against speckle variations.

Excited State Vibrational Dynamics Reveals a Photocycle That Enhances the Photostability of the TagRFP-T Fluorescent Protein.

High photostability is a desirable property of fluorescent proteins (FPs) for imaging, yet its molecular basis is poorly understood. We performed ultrafast spectroscopy on TagRFP and its 9-fold more photostable variant TagRFP-T (TagRFP S158T) to characterize their initial photoreactions. We find significant differences in their electronic and vibrational dynamics, including faster excited-state proton transfer and transient changes in the frequency of the v520 mode in the excited electronic state of TagRFP-T. The frequency of v520, which is sensitive to chromophore planarity, downshifts within 0.58 ps and recovers within 0.87 ps. This vibrational mode modulates the distance from the chromophore phenoxy to the side chain of residue N143, which we suggest can trigger cis/trans photoisomerization. In TagRFP, the dynamics of v520 is missing, and this FP therefore lacks an important channel for chromophore isomerization. These dynamics are likely to be a key mechanism differentiating the photostability of the two FPs.

Polycyclic aromatic hydrocarbons in coastal rivers in Jiangsu Province, China: Spatial distribution, source apportionment and human impacts

The ocean is the ultimate sink for all pollutants, rivers are important channels for land-based pollutants to enter the oceans. Riverine transport of polycyclic aromatic hydrocarbons (PAHs) to coastal seas in China poses environmental threats. This study examined the spatial and temporal distribution of PAHs in coastal rivers in Yancheng City in Jiangsu Province of China, with the aim of identifying their likely sources, concentrations, and influencing factors. Surface sediments were taken from the Xinyanggang River (XYR) and the Sheyang River (SYR). The concentrations of Ʃ16PAHs in river sediments were measured on average 477.05 ng/g dry weight (dw), with values varying from 2.18 to 6351.42 ng/g, indicating a moderate pollution level, with a dominance of high molecular weight (HMW) PAHs. The XYR exhibited significantly higher PAHs concentrations compared to the SYR. The key sources of PAHs were vehicle emissions (47.87%), coal and natural gas combustion (35.07%). Geographically weighted regression and redundancy analysis linked PAHs pollution to distinct land use patterns and socioeconomic indicators, highlighting urban land as the major contributor, driven by high urbanization and industrialization (70.91%). In XYR, industrial activities and transport emissions were major contributors, while in SYR, agricultural activities predominantly influenced PAHs pollution. Urgent mitigation strategies are needed to reduce PAHs pollution in river sediments, mitigating ecological and human risks associated with these contaminants.

Inception meets Swin Transformer: A Novel Approach for Metal Defect Recognition

The detection of metal defects with high precision and efficiency is a significant challenge in modern industry. Existing machine learning methods for recognizing common metal surface defects heavily rely on expert knowledge for manual feature extraction. Conventional deep learning methods face challenges in capturing global feature information from defect images or defect detection signals.To address this issue, we proposed a metal defect recognition method based on an Inception-fused Swin Transformer model. The method combines the adaptive local feature extraction capability of the Inception structure with the advantage of the Swin Transformer in capturing global feature information from defect signals. Additionally, it utilizes the Channel-Coordinate Attention module (CoordAttention) to highlight important feature channels. Experimental results demonstrate the effectiveness of the proposed method on the Ultrasonic Defect Grayscale Image dataset (ULFSL-DET) and the publicly available Image-based Metal Defect dataset (NEU-CLS), achieving recognition accuracies of 98.1% and 99.8%, respectively. The method exhibits high effectiveness in recognizing metal defect signals in grayscale images, and it demonstrates strong generality for image-based metal defect recognition.

Moisture source differences between the 2020 and 1998 super Meiyu-flood events in the Yangtze River Valley

Two historic Meiyu events in 1998 and 2020 hit the Yangtze River Valley (YRV), causing catastrophic damage to the socio-economy. By tracking moisture supplies to the extreme precipitation events using Water Accounting Model-2Layers and ERA5 reanalysis, the moisture origins and their differences in feeding the YRV precipitation were revealed. Climatologically, the southwest monsoon channel is the most important moisture channel with the Indian Ocean contributing ∼45% and the Indo-China Peninsula contributing ∼16% of the YRV precipitation. During the two super Meiyu events, the Indian Ocean and the Indo-China Peninsula dominated the excessive moisture supply, which together contributed more than 65% of the extra precipitation. Moisture supply anomalies in 1998 and 2020 showed a robust spatial pattern of “west increase-east decrease”. When the YRV precipitation is higher than the normal, moisture mainly comes from the southwest sources, and moisture contribution from the northwestern Pacific is relatively small. We also found that the intensity of the western Pacific subtropical high is a major influencing factor that explained ∼47% of the YRV precipitation variation during 1991–2020. When it intensifies, an anomalous anticyclone is formed in the mid-lower troposphere around the tropical Northwest Pacific. In its northwestern flank, a strong southwesterly in the upwind of the YRV helps bring in more moisture through the southwest monsoon. In the downwind, it inhibits moisture supply from the northwestern Pacific Ocean. Compared with 2020, a drier condition over Indo-China Peninsula and YRV in 1998 led to a substantially less (∼29%) moisture supply to the YRV precipitation, resulting in a less strong Meiyu event in 1998.

More is better? Investigating the influence of the characteristics of training activity participation on maker innovation performance

PurposeTraining activities organized by maker-spaces have become the most important channel through which makers acquire new external knowledge related to problem solving and thus contribute significantly to makers' innovation performance. Is it true that the more training activities in which makers participate, the better their innovation performance? Following the logic of process analyses, this paper empirically verifies the effects of the scope and frequency of makers' participation in training activities on their innovation performance.Design/methodology/approachBased on a survey of 134 makers in China, this paper employed the procedure recommended by Preacher and Hayes (2008) to empirically test the total effects, direct effects and indirect effects of the scope and frequency of participation in training activities on makers' innovation performance via their ability to exploit new knowledge.FindingsThe research findings show that the scope (frequency) of participation in training activities has a positive (negative) effect on makers' innovation performance via the mediating effect of the makers' ability to exploit new knowledge. Time pressure acts as a negative moderator.Originality/valueThis paper sheds light on a considerably overlooked research area by investigating the effects of the scope and frequency of makers' participation in training activities on their innovation performance. The authors further identify and empirically test the theoretical mechanism by considering the mediating effect of makers' ability to exploit new knowledge. The research findings also provide a managerial training activity arrangement strategy for makerspaces and government.

The impact of artificial intelligence on employment: the role of virtual agglomeration

Sustainable Development Goal 8 proposes the promotion of full and productive employment for all. Intelligent production factors, such as robots, the Internet of Things, and extensive data analysis, are reshaping the dynamics of labour supply and demand. In China, which is a developing country with a large population and labour force, analysing the impact of artificial intelligence technology on the labour market is of particular importance. Based on panel data from 30 provinces in China from 2006 to 2020, a two-way fixed-effect model and the two-stage least squares method are used to analyse the impact of AI on employment and to assess its heterogeneity. The introduction and installation of artificial intelligence technology as represented by industrial robots in Chinese enterprises has increased the number of jobs. The results of some mechanism studies show that the increase of labour productivity, the deepening of capital and the refinement of the division of labour that has been introduced into industrial enterprises through the introduction of robotics have successfully mitigated the damaging impact of the adoption of robot technology on employment. Rather than the traditional perceptions of robotics crowding out labour jobs, the overall impact on the labour market has exerted a promotional effect. The positive effect of artificial intelligence on employment exhibits an inevitable heterogeneity, and it serves to relatively improves the job share of women and workers in labour-intensive industries. Mechanism research has shown that virtual agglomeration, which evolved from traditional industrial agglomeration in the era of the digital economy, is an important channel for increasing employment. The findings of this study contribute to the understanding of the impact of modern digital technologies on the well-being of people in developing countries. To give full play to the positive role of artificial intelligence technology in employment, we should improve the social security system, accelerate the process of developing high-end domestic robots and deepen the reform of the education and training system.

The innovation effect of green finance reform-based on the green innovation vitality perspective.

In order to develop green finance and realize the coordinated development of environment and economy, China set up green finance reform and innovation pilot zones in 2017. Taking the establishment of green finance reform and innovation pilot zones in China as an exogenous shock and based on the synthetic control method, this paper explores the impact of the pilot policies on green innovation vitality by taking five pilot provinces as the experimental group and 25 provinces without pilot policies as the control group. It is found that the pilot policy significantly enhances the level of green innovation dynamism in the pilot regions, but due to the different policy focuses, the innovation effect is immediate in Zhejiang, Guangdong, Guizhou, and Jiangxi, while the policy effect is significantly lagging behind in Xinjiang; government incentives are an important channel through which the policy enhances the green innovation dynamism; and the above findings are more significant in the samples of substantial green innovation dynamism and the eastern region. In addition, the extended economic consequences analysis shows that the pilot policy can significantly improve energy efficiency in the pilot region with a significant time lag. The findings of the study not only provide empirical evidence for the implementation effect of the green finance pilot policies, but also have practical implications for improving the green finance system and replicating and extending the green finance reform and innovation pilot zones.

A catchment-wide microplastic pollution investigation of the Yangtze River: The pollution and ecological risk of tributaries are non-negligible

The Yangtze River is an important global channel for plastics and microplastics (MPs) to enter the sea. However, the existing research on MPs in the Yangtze River has primarily focused on the mainstream region, without regarding the occurrence, spatial distribution, and ecological risks associated with tributaries, as well as their relationship with the mainstream. To address this knowledge gap, we conducted a large-scale catchment-wide investigation of the surface water in the Yangtze River, encompassing MPs (48 µm–5 mm) of the mainstream and 15 important tributaries. Tributaries and upstream regions exhibited relatively higher levels of MPs compared with the mainstream and different sections of the river. The distribution of MPs is primarily influenced by the emission of arable land and the pH of water. Notably, the upstream tributary areas demonstrated the highest ecological risks associated with MPs. Further analysis highlighted that the tributaries accounted for a contribution ranging from 16% to 67% in quantity and from 14% to 90% in mass of the microplastics observed in the mainstream. Our results suggest that the pollution of tributaries and their associated ecological risk migration must be effectively regulated.

Root spatial distribution and belowground competition in an apple/ryegrass agroforestry system

CONTEXTForestry and fruit industry is a unique and advantageous industry in Xinjiang, China, and it is an important channel for farmers to increase income. With limited land resources and a shortage of forage in southern Xinjiang, farmers began to establish a fruit-grass interplanting model to improve the comprehensive economic benefits of orchards. OBJECTIVEThe objective of this study was to explore the effects of apple/ryegrass intercropping system on root spatial distribution and competition, and to evaluate the performance of field microclimate, soil physical properties and productivity under intercropping. METHODSIn the study, three different planting patterns were designed: sole-cropped apple (The spacing of the rows of apple trees was 4.0 m, and the plant spacing was 1.0 m), sole-cropped ryegrass (Strip sowing, row spacing was 0.25 m), and apple/ryegrass intercropping (The distance from apple row to ryegrass row was 1.0 m). Two-year field experiments were conducted to measure the root length density (RLD), specific root length (SRL), interspecific competitiveness index, soil moisture content, organic matter content, air temperature, relative humidity, wind speed, yield, land equivalent ratio (LER) and aggressivity. RESULTS AND CONCLUSIONCompared with sole cropping, intercropping reduced the RLD of apple and ryegrass and increased the SRL of ryegrass. The competition space between apple and ryegrass was concentrated in the soil area of 100 cm horizontally and 0–20 cm vertically, and apple was more competitive than ryegrass. Intercropping increased the content of soil moisture and organic matter and improved the microclimate of farmland, decreased the air temperature, increased the relative humidity of an orchard, and decreased wind speed. Although intercropping reduced ryegrass and apple yield, land equivalent ratio (LER) was 1.27–1.30, indicating that intercropping improved land use efficiency. Root competition led to an uneven distribution of root length density (RLD) and specific root length (SRL). The plasticity of root morphology and root spatial distribution was key to adapting to competition and maximizing the absorption of water and nutrients in apple/ryegrass agroforestry systems. SIGNIFICANCEApple/ryegrass intercropping system maximizes resource utilization and reduces interspecies resource competition. It improved land use efficiency, improved the comprehensive benefits of fruit and grass production.

Digital transformation and corporate environmental performance: Evidence from Chinese listed companies

This paper constructs a theoretical model to explain the impact of digital technology transformation on corporate environmental performance (CEP). Then, data mining technology is used to crawl the annual report data of Shanghai and Shenzhen A-share listed companies from 2008 to 2021. With the help of text mining analysis, this study quantifies the digital transformation of enterprises and empirically analyses the impact of digital transformation on CEP. The results indicate that digital transformation has significantly improved CEP. The evidence remains valid after a series of robustness tests, including replacing core variables, eliminating the impact of external major events, improving sample quality and instrumental variable regression. Mechanism verification shows that Digital transformation (DT) enhances CEP through ‘green technology upgrading’, ‘increasing external media attention’ and ‘enhancing internal management control’ effects. Heterogeneity analysis shows that DT has a more significant positive impact on non-private, industry technology-intensive, high regional environmental regulation, high energy consumption and high emission CEP, Internet business models are less conducive to improving corporate environmental performance than other digital technologies. Further tests show that resource reallocation improves the industry’s environmental performance and is an important channel for DT to play a catalytic role.

Semantic interleaving global channel attention for multilabel remote sensing image classification

ABSTRACT Multilabel remote sensing image classification (MLRSIC) has received increasing research interest. Taking the co-occurrence relationship of multiple labels as additional information helps to improve the overall performance. However, current methods only focus on using it to constrain the final feature which is output from a convolutional neural network (CNN). On the one hand, these methods need to exploit the potential of label correlation in feature representation fully. On the other hand, they increase the label noise sensitivity of the system, resulting in poor robustness. In this paper, a novel method called ‘Semantic Interleaving Global chaNnel Attention’ (SIGNA) is proposed for MLRSIC. First, the label co-occurrence graph is obtained according to the statistical information of the training set and fed into a graph neural network (GNN) to generate optimal semantic feature representations of each label. Next, the semantic features are interleaved with visual features which are extracted by CNNs to guide the overall features of the input image transform from the original feature space to the semantic feature space with embedded label relations. Then, global attention triggered by semantic interleaving is used to emphasize visual features in important channels. Finally, to make SIGNA easier to use and more optimized, multihead SIGNA-based feature adaptive weighting networks are proposed as plug-in blocks to plug into any layers of a CNN. For remote sensing images, better classification performance can be achieved by inserting the plug-in blocks into the shallow layers of CNNs. We conducted extensive experimental comparisons on three data sets: UCM, AID and DFC15. Experimental results demonstrate that the proposed SIGNA achieves superior classification performance compared to state-of-the-art (SOTA) methods. Notes that the codes of this paper will be open to the community for reproducibility research.

How do volatile and non-volatile energy factors affect energy OFDI? evidence from simultaneous equation model

Its reliance on energy from individual countries has seriously affected its energy security and foreign policy. Therefore, reducing dependence on energy from specific countries and decomposing energy import channels have become crucial goals in China’s efforts to resolve its energy crisis and ensure its national energy security. This study aims to analyze the impact of energy factors on the location determinants of China’s energy OFDI to help reduce energy dependence and improve China’s energy security. Simultaneous equation models and panel data from 162 target countries for the period 2005–2020 are used to examine the coincidental relationship between volatile and non-volatile energy and intermediary factors in investment target countries and China’s energy OFDI. The simultaneous equation model, comprising a system of equations, constitutes a multifaceted modeling approach that allows examination of two or more dependent variables. This study provides insights into energy investment in China through simultaneous equation modelling, it guides the implementation strategies for home and target countries of energy investment.

Entrepreneurship and subjective wellbeing in China: Exploring linkages and potential channels

We analyse the effect of entrepreneurship on subjective wellbeing in China. To do so, we use four waves of the nationally representative China Family Panel Studies (CFPS) longitudinal survey data. Employing a suite of quasi-experimental analytical procedures, we find that being an entrepreneur increases subjective wellbeing in China. Our estimates suggest that being an entrepreneur results in a 0.46 standard deviation higher subjective wellbeing than not being an entrepreneur. This finding is robust to different quasi-experimental methods. We also find that entrepreneurship enhances subjective wellbeing more among males and rural residents. Results on mediation analysis suggests that social and economic status are important channels through which entrepreneurship influences subjective wellbeing.

The impact of digital financial usage on resident’s income inequality in China: An empirical analysis based on CHFS data

This study investigates the relationship between digital financial use and resident’s income inequality based on 2019 CHFS data. We argue that digital finance usage impacts resident’s income inequality both directly and indirectly. To examine the impact of digital financial use on resident’s income inequality and explore transmission mechanisms, including differences across regions, occupations, work types, and education levels, we employ ordinary least squares. Our results reveal that digital payments, digital money management, digital funds, and credit card usage have a positive mitigating effect on resident’s income inequality. Digital financial services have a significant positive impact on resident’s income inequality in southern, northern, coastal, and inland regions. Among purely agricultural farmers, digital payments, digital money management, and credit cards' effects are more pronounced than purely non-agricultural farmers. The use of digital financial also reduces resident’s income inequality among different work types, including institutional, state-owned, self-employed, and private enterprise. Family entrepreneurship, entrepreneurial frequency, and working outside the home are important channels through which digital financial services mitigate resident’s income inequality. Digital payments and credit cards reduce resident’s wage income inequality, while digital money management and digital funds are effective in reducing resident’s property income inequality.

CTCNet: A CNN Transformer capsule network for sleep stage classification

In this paper, we propose a novel neural network architecture called CTCNet. First, we adopt a multi-scale convolutional neural network (MSCNN) to extract low and high-frequency features, adaptive channel feature recalibration (ACFR) to enhance the model’s sensitivity to important channel features in the feature maps and reduce dependence on irrelevant or redundant features, a multi-scale dilated convolutional block (MSDCB) to capture characteristics of different types among feature channels. Second, we use Transformer to extract global temporal context features. Third, we employ capsule network to capture spatial location relationships among EEG features and refine these features. Besides, the capsule network module is used as our model’s classifier to classify the final results. It is worth noting that our model better solves the problem that previous researches failed to take into account the simultaneous extraction of local features and global temporal context characteristics of EEG signals, and ignored the spatial location relationships between these features. Eventually, we assess our model on three datasets and it achieves better or comparable performance than most state-of-the-art methods.

Assessing the potential of ICT to increase land and labour productivity in agriculture: Global and regional perspectives

AbstractIncreasing agricultural productivity in low‐ and middle‐income countries (LMICs) is an important channel for reducing poverty and food insecurity. Information and communication technologies (ICT) have the potential to boost agricultural productivity by lowering transaction costs and enhancing access to information. Even though there are several micro‐level studies analysing the effects of ICT on agricultural productivity, there is scant research addressing the role of ICT in agricultural productivity at the global and regional levels. Using data from 86 countries for the period 2000 to 2019 and utilising a fixed effect panel regression with a feasible generalised least square approach, we find that globally there is a positive and significant association between ICT uptake and both land and labour productivity in agriculture. In each case, however, the magnitude of the effect is much smaller than other important determinants, such as human capital, access to inputs or environmental factors. At the regional level, the relationship between ICT uptake and land productivity is not significant in Africa and Asia, while we find a significant effect on labour productivity. This finding indicates that while ICT can provide valuable information and tools for land management, the effect on land productivity might be less immediate in these regions. Finally, we revisit the question of whether ICT expansion increases agricultural productivity gaps between high‐income nations and LMICs. In contrast to previous research, this study does not find significant differences in the effects of ICT on land and labour productivity between higher‐income and lower‐income countries.

Religiosity as a buffer of the harmful effects of workplace loneliness on negative work rumination and job performance

PurposeThis study investigates how employees' experience of resource-depleting workplace loneliness may steer them away from performance-enhancing work efforts as informed by their propensity to engage in negative work rumination. It also addresses whether and how religiosity might serve as a buffer of this harmful dynamic.Design/methodology/approachThe hypotheses tests rely on three-round survey data collected among employees who work in various organizations in Pakistan – a relevant country context, considering the importance of people's religious faith for their professional functioning and its high-uncertainty avoidance and collectivism, which likely make workplace loneliness a particularly upsetting experience.FindingsAn important channel through which a sense of being abandoned at work compromises job performance is that employees cannot “switch off” and stop thinking about work, even after hours. The role of this explanatory mechanism is mitigated, however, when employees can draw from their religious beliefs.Practical implicationsFor human resource (HR) managers, this study pinpoints a notable intrusion into the personal realm, namely, repetitive thinking about work-related issues, through which perceptions of work-related loneliness translate into a reluctance to contribute to organizational effectiveness with productive work activities. It also showcases how this translation can be subdued with personal resources that enable employees to contain the hardships they have experienced.Originality/valueThis study helps unpack the connection between workplace loneliness and job performance by detailing the unexplored roles of two important factors (negative work rumination and religiosity) in this connection.

A dynamic modeling method using channel-selection convolutional neural network: A case study of NOx emission

A novel channel-selection convolutional neural network (CS-CNN) is proposed to predict NOx emission from coal-fired boilers under steady-state and transient load conditions. First, a new channel-selection convolutional layer (CS-CL) is presented to replace regular convolutional layer (RCL). The CS-CL evaluates the channel importance of the input variables, selects the Top-C important channels and releases the hyperparameters of the remaining low-importance channels, thus contributing to maximize the utilization of the parameter resources of the model. The advantages of using CS-CLs are the preservation of the great majority of manipulated variables involved in combustion control among the input variables and the prevention of the model overfitting problem due to the redundancy of input variables. Second, a sliding window-based preprocessing method is applied to the historical data of the boiler which is divided into four-dimensional (4D) tensors. Then, comparative tests are performed on long short-term memory (LSTM) model, baseline CNN and CS-CNN using the historical data of a 670 MW boiler. The results of tests showed that CS-CNN has higher prediction performance. Finally, in order to increase the interpretability of the deep learning black box model, this study analyzes the working mechanism of the CS-CNN through ablation analysis and visualization of model parameters.