Search Results (188)

Globalization and market saturation have led Portuguese textile companies to seek international markets not only for growth but also to contribute to their country’s international image. This study aims to explore how the internationalization of the Portuguese textile sector into the Chinese market contributes to Portugal’s destination image and identify the critical success factors in this process. The research follows an inductive, qualitative methodology based on semi-structured interviews with two groups of companies: those already operating in China (n = 5) and those preparing to enter the market (n = 5). The interviews were thematically analyzed to extract key patterns and insights. The findings reveal that successful companies operate in the luxury segment, rely on prior international experience, and often use local intermediaries. Firms planning to internationalize highlight quality differentiation, brand authenticity, and innovation as strategic advantages. These insights support the role of niche positioning and cultural adaptation in building both commercial success and a refined international image of Portugal. This study contributes to the literature by linking internationalization and destination branding through industry-specific case evidence and offers practical implications for managers targeting emerging markets like China. Full article

Although carbon emissions trading systems are universally acknowledged as one of the most potent policy instruments for counteracting hazardous climate trends, and digitalization is seen as a favorable technological means to promote corporate green and low-carbon transformation, few studies have investigated the dark side of both. Using data on Chinese listed companies from 2011 to 2020 and adopting a multi-period DID methodology, this research reveals that, in response to the carbon emissions trading system, firms often adopt low-cost, strategic environmental governance behaviors—namely, carbon washing—to reduce compliance costs and maintain their reputation and image. Furthermore, the study reveals that the information advantages of digital transformation create conditions for the opportunistic manipulation of carbon disclosure. Digitalization amplifies the positive influence of the carbon trading system on corporate carbon washing behavior. Mechanism analysis confirms that the carbon emissions trading system increases the production costs of regulated firms, thereby increasing their carbon washing behavior. Economic consequence analysis confirms that firms engage in carbon washing to gain legitimacy and maintain their reputation and image, which may allow them to obtain opportunistic benefits in the capital market. Finally, this study suggests that the government should adopt supplementary policy tools, such as environmental subsidies, enhanced use of digital technologies to strengthen regulatory capacity, and increased media oversight, to mitigate the unintended consequences of the carbon trading system on corporate behavior. Full article

Predicting company bankruptcy is a critical task in financial risk assessment. This study introduces a novel approach using Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) to enhance bankruptcy prediction accuracy. By transforming financial statements into grayscale images and leveraging synthetic data generation, we analyze a dataset of 6249 companies, including 3256 active and 2993 bankrupt firms. Our methodology innovates by addressing dataset limitations through GAN-based data augmentation. CNNs are employed to take advantage of their ability to extract hierarchical patterns from financial statement images, providing a new approach to financial analysis, while GANs help mitigate dataset imbalance by generating realistic synthetic data for training. We generate synthetic financial data that closely mimics real-world patterns, expanding the training dataset and potentially improving classifier performance. The CNN model is trained on a combination of real and synthetic data, with strict separation between training/validation and testing. Full article

This study analyses the impact of agricultural greenwashing on financial performance via green innovation. To this end, it employs data from Chinese A-share agribusinesses from 2012 to 2022. The study indicates the following results: (1) the practice of greenwashing (ESG disclosure–performance gap, GW) has a significant negative impact on ROA, particularly in non-state firms; (2) green innovation (patents, GI) partially mediates this relationship, with a percentage of 9.09%, as GW diverts research and development resources toward image management. Robustness checks are employed to confirm the results obtained using ROE and lagged models. Property rights moderate the effects: non-state firms are more adversely affected by innovation dependency, while state firms are protected by policies. The “double-edged” mechanism elucidates GW’s short-term legitimacy gains in contrast to long-term innovation suppression and financial decline. The report calls for the establishment of standardised ESG metrics (for example, the disclosure of pesticide residue) and targeted green incentives (for example, SME R&D subsidies) to be aligned with UN SDGs 9.4 (green tech) and 12.6 (responsible production). The present study offers insights into the governance of environmental, social, and governance (ESG) matters within the context of agriculture in China. Full article

Hyperspectral microscope imaging (HMI) was employed to evaluate the physiochemical properties of and the large intra-variability in individual fruit of ‘Xia Hui 6’ peaches during storage, which gave insights into the heterogeneity of peach fruits at the microscale. The physicochemical characteristics such as firmness (FI), soluble sugar content (SSC), and L* value of peaches showed significant changes, while the microstructure of the tissues broke down. Principal component analysis (PCA) was applied to peach tissues from the sunny side and shady side at different storage stages, which allowed us to clearly visualize the distribution of sugars, water, and pigments at the cellular scale. Single-feature variables were constructed to clarify the correlation between the characteristic bands and physicochemical parameters based on Pearson correlation analysis, with an R² of 0.99 for firmness at 588 nm, 0.98 for titratable acidity (TA) at 432 nm, 0.88 for the L* value at 430 nm and 0.83 for the b* value at 426 nm. This work demonstrated that HMI technology as an accurate and highly effective tool in evaluating the quality of ‘Xia Hui 6’ peaches and targeting, allowing us to visualize the spatial heterogeneity within peach fruit tissues. Full article

We investigate the association between voluntary assurance of a firm’s corporate social responsibility (CSR) report and earnings management. A concern with CSR reports is they are used to promote a socially responsible image without a meaningful commitment to CSR activities, referred to as “greenwashing”. To credibly signal the CSR report is reliable, a firm can incur the additional costs to voluntarily obtain assurance. Our results show that strong corporate governance plays a crucial role in limiting earnings management. The most consistent improvements in earnings quality occur when firms combine strong governance with CSR assurance from a non-accounting provider (NonACCT). The combination of strong governance and NonACCT assurance appears to be mutually reinforcing, suggesting a symbolic legitimacy strategy that is also substantively effective. Full article

The improvement of carcass traits is a key focus in pig genetic breeding programs. To identify quantitative trait loci (QTLs) and genes linked to key carcass traits, we conducted a genome-wide association study (GWAS) using whole-genome sequencing data from 1118 commercial pigs (Duroc sires and Yorkshire/Landrace F1 dams). This study focused on six phenotypes: iodine value, belly firmness, belly side fat, total side thickness (belly SThK), belly subcutaneous fat (Subq), and belly seam. Phenotypes were measured using image analysis, DEXA, and fatty acid profiling, and genotyping was performed using low-pass sequencing (SkimSeq). After quality control, 18,911,793 single nucleotide polymorphisms (SNPs) were retained for further analysis. A GWAS was conducted using a linear mixed model implemented in GCTA. Key findings include a significant QTL on SSC15 (110.83–112.23 Mb), which is associated with the iodine value, containing genes such as COX15, CHUK, SCD, and HIF1AN, which have known roles in fatty acid metabolism. Additionally, PNKD, VIL1, and PRKAG3 (120.74–121.88 Mb on SSC15) were linked to belly firmness, influencing muscle structure and fat composition. Three QTLs for belly side fat were identified on SSC1, SSC2, and SSC3, highlighting genes like SLC22A18, PHLDA2, and OSBPL5, which regulate fat deposition and lipid metabolism. The results provide novel molecular markers that can be incorporated into selective breeding programs to improve pork quality, fat distribution, and meat composition. These findings enhance our understanding of the genetic mechanisms underlying carcass belly traits while offering tools to improve pork quality, optimize fat composition, and align with consumer preferences in the meat production industry. Full article

The aim of this study is to identify the excellent goods that are required for the project of forming a moral community in a business firm. Trinitarian theology is used to reflect on these goods. Though there is a massive gap between the way the triune community expresses itself and the way human communities do, the Christian doctrine that humans are made in the image of God, and therefore imago trinitatis, suggests that Trinitarian theology offers a pattern for moral community in a firm. The Persons of the relational Trinity express love through an I–Thou–We modality. The work of Martin Buber and Karol Wojtyla (Pope John Paul II) on the I–Thou or interhuman relation is first discussed. It is then noted that Wojtyla goes further in contending that I–Thou alone does not in and of itself constitute a human community; it is only when a plurality of “I”s act together to advance the common good that we can speak of the “we” (the social dimension). It is argued that a correlational reading of social Trinitarian thought and Wojtyla’s personalist phenomenology indicates what is required in a firm aspiring to be a genuinely moral community—namely, both intersubjectivity (I–Thou relationality) and a social profile (the “we”). It is further argued that these modalities are actualized in a business firm through moral friendship, good will (I–Thou), and commitment to the common good (“we”). These are foundational stones of a moral community. Full article

Desmoid tumors are rare mesenchymal neoplasms arising from locally invasive fibroblasts. While they lack metastatic potential, they exhibit high local recurrence rates and can cause significant tissue destruction. We present the case of a 39-year-old female patient who initially presented with epigastric pain, pyrosis, and a palpable, firm, painless mass in the left upper quadrant, extending to the left hemithorax. The patient’s medical history included treated cervical neoplasia. Clinical evaluation, imaging studies, and histopathological analysis suggested aggressive fibromatosis. The patient opted for a surgical excision, which resulted in tumor recurrence one year later, with infiltration of the ribs near the sternum. Despite oncological recommendations favoring conservative management, the patient opted for a second surgical intervention, involving an en-bloc resection of the tumor and the affected sternum and ribs, followed by thoracic wall reconstruction. Full article

Energy resources, particularly oil and natural gas, are vital for global development but have significant environmental impacts, including pollution and habitat destruction. Green management has become a critical concept in today’s global industries, mostly the petroleum industry. The petroleum industry is vital not only for the world but also for Türkiye’s energy needs and economic development. However, its operations significantly impact the environment through greenhouse gas emissions, water pollution, and habitat destruction. In response to both global and national environmental concerns and regulatory pressures, the Turkish petroleum industry should adopt green management practices. Despite the lack of prior studies regarding green management approaches and practices in Turkish petroleum industry, this study examines how Türkiye’s petroleum industry should integrate green management principles to minimize environmental impacts and promote sustainable development. To evaluate the environmental protection approaches and practices of petroleum enterprises based on their operational domains, a survey was conducted, and the collected data underwent statistical analysis. The survey questions were designed by the authors to determine the attitudes, approaches, and practices of managers in crude oil production and refining companies regarding green management. According to the results of the statistical data analysis, it has been determined that companies in the Turkish petroleum industry have adopted an approach known as green management or environmentally conscious entrepreneurship. The statistical analysis of the administered survey results indicates a positive relationship between firms’ operational performance scores and their green management practices scores (r = 0.247). The survey results demonstrate an increasing adoption of environmental consciousness and green management practices among managers in the Turkish petroleum sector, with 90.2% of participants providing a positive response. The survey results also indicate that green management practices have a positive impact on business operations. In this regard, 42.4% of participating managers believe that green management practices enhance corporate image, 38.0% state that they improve efficiency, 35.0% assert that they strengthen competitive advantage, and 31.5% indicate that they contribute positively to energy savings. The survey findings further indicate that 90.2% of participants recognize the contribution of green management practices to sustainable development in businesses, while an equal proportion asserts that these practices enhance clean and safe production. Moreover, 93.5% of respondents emphasize that production and processing activities carried out without environmental considerations pose a significant threat to the future of both the planet and humanity. In conclusion, based on the responses provided by the participants, it can be inferred that business managers have adopted the green management approach and recognize the significant role of green management practices in addressing environmental challenges. In line with the objectives of this study and the statistical findings obtained, a “green management model” has been proposed for enterprises in the Turkish petroleum industry, taking into consideration global practices and aligning with the principles of environmentally responsible green entrepreneurship. In this context, the study makes a significant contribution to the literature by proposing a green management model for the Turkish petroleum industry. Full article

Predicting meat quality, especially dark, firm and dry meat, as well as muscle fat prior to slaughter, presents a challenge in practice. Medical as well as high-frequency ultrasound applications can be utilized to predict body composition and meat quality aspects. Ultrasounds are non-invasive, rapid-to-operate in vivo and show high correlations to the animal production traits being estimated. Farm animal ultrasounds are used to predict intramuscular fat content in the beef cattle industry. Challenges are identified in applying ultrasound technology to detect glycogen content in farm animals due to a wide range of fat, muscle and water composition. Other technologies and methods are reported in this literature review to overcome issues in the practicability and accuracy of ultrasound technology when estimating muscle glycogen levels in cattle. The discussion of other tools such as hyperspectral imaging, microwave sensor technology and digital infrared thermal imaging were addressed because of their superior accuracy in estimating moisture and fat components. Full article

The firmness of meningiomas is a critical factor that impacts the surgical approach recommended for patients. The conventional approaches that couple image processing techniques with radiologists’ visual assessments of magnetic resonance imaging (MRI) proved to be time-consuming and subjective to the physician’s judgment. Recently, machine learning-based methods have emerged to classify MRI instances into firm or soft categories. Typically, such solutions rely on hand-crafted attributes and/or feature engineering techniques to encode the visual content of patient MRIs. This research introduces a novel adversarial feature learning approach to tackle meningioma firmness classification. Specifically, we present two key contributions: (i) an unsupervised feature extraction approach utilizing the Bidirectional Generative Adversarial Network (BiGAN) and (ii) a depth-wise separable deep learning model were designed to map the relevant MRI features with the predefined meningioma firmness classes. The experiments demonstrated that associating the BiGAN encoder, for unsupervised feature extraction, with a depth-wise separable deep learning model enhances the classification performance. Moreover, the proposed pre-trained BiGAN encoder-based model outperformed relevant state-of-the-art methods in meningioma firmness classification. It achieved an accuracy of 94.7% and a weighted F1-score of 95.0%. This showcases the proposed model’s ability to extract discriminative features and accurately classify meningioma consistency. Full article

This case study highlights the use of cinematic rendering (CR) in preoperative planning for the excision of a cyst in the oral and maxillofacial region of a 60-year-old man. The patient presented with a firm, non-tender mass in the right cheek, clinically suspected to be an epidermoid cyst. Conventional imaging, including dental magnetic resonance imaging (MRI) protocols, confirmed the lesion’s size, location, and benign nature. CR reconstructions, combining advanced algorithms and novel skin presets, allow for the generation of highly realistic, three-dimensional visualizations from conventional imaging datasets. CR provided an enhanced, detailed depiction of the lesion within its anatomical context, significantly improving spatial understanding for surgical planning. The surgical excision was performed without complications, and histological analysis confirmed the diagnosis of a benign epidermoid cyst with no evidence of dysplasia or malignancy. This case demonstrates the potential of CR to refine preoperative planning, especially in complex anatomical regions such as the face and jaw, by offering superior visualization of superficial and deep structures. Thus, the integration of CR into clinical workflows has the potential to lead to improved diagnostic accuracy and better surgical outcomes. Full article

The texture of bakery products is considered the most important trait for consumers, which needs to be explored in new products developed, such as high-in-fiber bread. This study aimed to develop models for texture attributes and starch retrogradation (based on initial enthalpy) as a function of porosity measured in non-complicated experimental processes—using Digital Imaging Analysis (DIA) of bread with various levels of dietary fiber (DF) content. Bread with high fiber content was prepared as the sample matrix by replacing part of the wheat flour with an oat fiber powder. The models for firmness, springiness, chewiness, cohesiveness, and enthalpy were developed using Response Surface Methodology (RSM), a robust statistical technique. Results indicated that the bread crumb’s firmness depended more on porosity than DF content. Models for each texture attribute were established and verified, and it was found that no significant difference (p < 0.05) existed between the predicted and measured values, confirming the validation of the models developed. Full article

The non-destructive detection of fruit quality is indispensable in the agricultural and food industries. This study aimed to explore the application of hyperspectral imaging (HSI) technology, combined with machine learning, for a quality assessment of pears, so as to provide an efficient technical method. Six varieties of pears were used for inspection, including ‘Sucui No.1’, ‘Zaojinxiang’, ‘Huangguan’, ‘Akizuki’, ‘Yali’, and ‘Hongli No.1’. Spectral data within the 398~1004 nm wavelength range were analyzed to compare the predictive performance of the Least Squares Support Vector Machine (LS-SVM) models on various quality parameters, using different preprocessing methods and the selected feature wavelengths. The results indicated that the combination of Fast Detrend-Standard Normal Variate (FD-SNV) preprocessing and Competitive Adaptive Reweighted Sampling (CARS)-selected feature wavelengths yielded the best improvement in model predictive ability for forecasting key quality parameters such as firmness, soluble solids content (SSC), pH, color, and maturity degree. They could enhance the predictive capability and reduce computational complexity. Furthermore, in order to construct a quality prediction model, integrating hyperspectral data from six pear varieties resulted in an RPD (Ratio of Performance to Deviation) exceeding 2.0 for all the quality parameters, indicating that increasing the fruit sample size and variety number further strengthened the robustness of the model. The Backpropagation Neural Network (BPNN) model could accurately distinguish six distinct pear varieties, achieving prediction accuracies of above 99% for both the calibration and test sets. In summary, the combination of HSI and machine learning models enabled an efficient, rapid, and non-destructive detection of pear quality and provided a practical value for quality control and the commercial processing of pears. Full article