Azhar Ali Laghari

Malaria remains a critical global health challenge for doctors and healthcare practitioners, particularly clinicians involved in initial treatment. Inaccurate diagnosis of malaria-infected cells often leads to delayed or inappropriate treatment, increasing the risk of severe complications or death. Traditional microscopic diagnosis is time-consuming and requires expert skills, resulting in variability and inconsistency in results. These challenges are further complicated by the complexity of malaria symptoms, which overlap with other febrile illnesses, making clinical diagnosis unreliable without laboratory confirmation. To address these challenges, this study explores deep-learning-based approaches, particularly leveraging state-of-the-art pretrained convolutional neural network (CNN) models, for automated malaria parasite detection and classification from microscopic blood smear images. Transfer learning is an effective approach to handling issues such as limited labeled data, time-consuming training, and domain-specific variations in medical image classification. By leveraging pretrained models trained on large-scale datasets like ImageNet, transfer learning enables the reuse of learned features, significantly accelerating the adaptation process for malaria detection and other medical imaging tasks. We used eight pretrained models for malaria parasite classification such as VGG16, VGG19, Inception-v3, ResNet-18, ResNet-34, ResNet-50, ResNet-101, and Xception. In particular, ResNet-50 and ResNet-101 achieved accuracies of approximately 89%, respectively, while Xception reached around 88% accuracy. In comparison, VGG-16 achieved a lower overall accuracy of about 80% due to a recall trade-off despite high precision. These metrics highlight meaningful improvements over simpler architectures and validate the efficacy of our transfer learning approach for automated malaria detection. The proposed models were fine-tuned on extensive labeled datasets comprising parasitized and uninfected cells. Quantitative and qualitative evaluations were conducted using metrics such as precision, recall, F1-score, and support. Our experimental results demonstrate that ResNet-50, ResNet-101, and Xception exhibit strong balanced performance with higher accuracy, while VGG-16 shows a trade-off of high precision but lower recall for parasitized cells.

Introduction: This study aims to provide conceptual insights on how varying levels of value congruence vary employees’ sense of belongingness within ethically led organizations. The ethical leadership effect was tested directly and indirectly through belongingness and psychological safety on creativity; the ethical climate was considered as a moderator.Methods: Study 1 data was multi-sourced and was collected from 377 participants at three different time points. Study 2 was scenario-based data collection. 208 employees participated in this study. The process and Hayes techniques were used for SPSS.Results: Findings were that ethical leadership was a significant factor in influence directly and indirectly through belongingness and psychological safety on creativity; the moderating role of ethical climate was also found significant. Belongingness varies at high and low levels of congruency with the leader.Conclusion: Findings suggest that ethical leadership is a strong predictor of belongingness and psychological safety that helps employees to be creative. Overall, the working climate, if it is ethical, also improves the impact of ethical leadership. Then, it discusses the theoretical and practical implications of ethical leadership for research and practices.

Analysing the patterns and impacts of land-use changes in the production–living–ecological space (PLES) of the Fenhe River Basin (FRB 39,721 km²), China, is necessary to support sustainable development. Based on remote sensing images from 1990 to 2020, we aimed to analyse the PLES land-use changes. Industrial production and living spaces continuously encroached on the agricultural production and ecological spaces between 1990 and 2022 owing to industrialisation and urbanisation, and the ecological land area decreased by 699.21 km², while the industrial production land area increased by 521.32 km². We used the soil and water assessment tool (SWAT) model to quantitatively analyse the impact of PLES changes on runoff in the FRB. With the continuous expansion of production and living spaces, the extensive use of concrete in cities has led to ground hardening, making it difficult for precipitation to infiltrate, with surface runoff increasing by 0.3 mm annually. The reduction in ecological space has led to a reduction in forests and grasslands, weakening the water-holding capacity of the watershed and affecting groundwater storage. This study provides a scientific basis for watershed management and the integrated development of PLES.

This study investigates the intricate relationship between CSR, green innovation, and environmental performance within the context of China’s manufacturing industries. Given the pressing environmental challenges faced by this sector, understanding how CSR practices correlate with sustainable innovations is critical for stakeholders aiming to enhance environmental outcomes. This was a survey-based study using a questionnaire and the five-point Likert scale; items were adopted from previous studies. Sampling was drawn through random sampling. Utilizing a sample of 327 respondents, this research employs SPSS and Structural Equation Modeling with Partial Least Squares (SEM-PLS) as analytical tools. The findings reveal a robust positive correlation between CSR practices and green innovation, as evidenced by a path coefficient of 0.704. These data support the stakeholder theory, which posits that organizations attentive to stakeholder expectations are more inclined to adopt sustainable practices. Furthermore, this study underscores the mediating role of green innovation in the relationship between CSR and environmental performance, highlighting its importance in aligning organizational strategies with sustainability-oriented stakeholder interests. This conclusion aligns with the existing literature emphasizing CSR’s significance in improving environmental performance through innovative approaches. However, an unexpected finding emerged: there exists a weak negative relationship between green innovation and organizational agility (−0.080). This suggests that, while firms strive for sustainable innovations, they may inadvertently compromise their flexibility in responding to evolving market demands. By addressing these dynamics, this research contributes valuable insights into how CSR can effectively spur green innovation and promote sustainable practices within China’s manufacturing sector. This study fills a gap in the existing literature by elucidating the mechanisms that connect CSR with enhanced environmental performance while also recognizing the potential trade-offs associated with innovation strategies. Also, the exploration of agility, which is least investigated, can also open various doors towards sustainability and the adaptation of new changes. Future research is encouraged to further explore these relationships across different industries and delve deeper into the mechanisms linking CSR to improved environmental outcomes, ultimately guiding organizations in balancing sustainability efforts with market responsiveness.

Antibiotic-resistant bacteria (ARBs) and antibiotic-resistant genes (ARGs) as new types of contaminants are discharged into the environment, increasing the risk of horizontal gene transfer (HGT). However, few researchers have examined the impacts of airborne ARB deactivation on HGT risk. The deactivation of airborne Escherichia coli 10667 (carrying sul genes) and the emission and removal of ARGs were mainly investigated in this study. Moreover, the potential mechanisms of HGT and transfer frequencies under microwave (MW) and ultraviolet (UV) irradiation were investigated using the nonresistant E. coli GMCC 13373 and E. coli DH5α with plasmid RP4 as the recipient and donor, respectively. E. coli CICC 10667 and E. coli DH5α with RP4 plasmid achieve log inactivation values as high as 5.5-log and 5.0-log, respectively, which were quite different from the antibiotic-sensitive strain E. coli CGMCC 13373 (3.4-log) subjected to MW irradiation. For UV disinfection, E. coli DH5α with the RP4 plasmid was reduced at 4.4-log, E. coli CGMCC 13373 was reduced at 2.3-log, and E. coli CICC 10667 was inactivated at 2.1-log. The removal rates of ARGs and HGT frequencies under MW irradiation were compared with those under UV irradiation. The ARGs removal efficiency (85.5%) obtained by MW was higher than that obtained by UV (48.2%). Consequently, the HGT frequency (0.008) of airborne ARGs released to the recipient (forward transfer) decreased and was lower than that under UV irradiation (0.014). Moreover, the plasmid RP4 was transferred from the donor to the surviving damaged E. coli 10667 as cell permeability (reverse transfer) was increased at a high HGT frequency (0.003) by MW, which was close to the value by UV (0.002). Additionally, sul1 and sul2 genes were confirmed to be more resistant to MW than the sul3 gene. These findings reveal the mechanism of HGT between damaged E. coli 10667 and surrounding environmental microbes. Microwave is a promising technology for disinfecting airborne microbes and preventing the spread of antibiotic resistance.