Diverse Environments Research Articles

Bridging Cultures: Javanese and Chinese Community Reconciliation in Krian

Background: Increasing interactions between local communities and newcomers highlight the importance of understanding intercultural communication and its societal impacts. Specific Background: This study examines intercultural communication between local residents and migrants, focusing on how these interactions facilitate cultural acculturation. Knowledge Gap: Despite existing literature, research on specific ethnic interactions and their social integration remains limited. Aims: The research aims to explore the dynamics of intercultural communication and acculturation processes within a particular community through qualitative analysis. Results: Findings reveal that both local residents and migrants demonstrate openness, fostering trust and social cohesion through shared experiences. Local residents adapt to migrant customs, particularly in cuisine, while migrants engage with local traditions. Novelty: This study highlights the role of interpersonal relationships in fostering acceptance and cultural blending. Implications: Results emphasize the need for community initiatives that promote intercultural dialogue to enhance social integration and cultural richness, serving as a model for regions facing cultural diversity challenges. Highlights : Openness and trust between local residents and migrants foster social cohesion. Mutual adaptation in customs, particularly in cuisine, enhances cultural exchange. Community initiatives promoting dialogue can strengthen integration in diverse environments. Keywords: intercultural communication, cultural acculturation, social integration, ethnic diversity, community initiatives

Modelling crop management and environmental effects on the development of Leptosphaeria maculans pseudothecia

AbstractThe timing of ascospore release is critical in the prediction of blackleg infection, particularly if the timing of spore release coincides with early development of canola seedlings. Historically, prediction models have used average daily temperature and an environment calibration to estimate a minimum rainfall amount to trigger development. This paper describes a different approach based on hydro-thermal time, where soil evaporation and rainfall are used as a surrogate to estimate when the stubble is wet, and temperature is accumulated on an hourly basis. Furthermore, the stubble orientation due to differing harvest management practices is considered, as stubble knocked down has greater contact with the soil compared to stubble which remains in a standing position. Pseudothecia and ascospore development in the standing and lying treatments was monitored weekly to measure the rate of development in diverse environments. The new modelling approach was used to describe the pseudothecial maturation rate and predict the timing of ascospore release. Subsequently, a range of Australian and international datasets were used to assess the robustness and accuracy of this new model’s predictions. When tested across multiple locations and seasons globally, the hydro-thermal time approach had similar performance (R2 = 0.94, RMSD = 16 days) to the existing Sporacle Ezy model (R2 = 0.90, RMSD = 22 days), but without the need to calibrate in different environments and account for the delayed rate of development in standing stubble. Integrating this new approach into early warning systems for canola growers will help in the management of control measures.

Shared reading aloud to enhance language and literacy

ABSTRACT In an era of evolving literacy challenges, an Italian based project ‘Leggere: Forte!’ (Reading Strong) is promoting shared reading aloud to spark youth interest in reading and improve language skills. Now in its third year, the project team have analysed the impact of daily shared reading on 1829 children aged 3-10. The results show significant benefits in language, comprehension, and literacy, and highlight disparities between the experimental and the control groups. Teacher feedback underscores the positive effects of this version of shared reading. The initiative demonstrates the educational value of shared reading and calls for its integration into modern education, suggesting it as a foundation for further research and the development of rich, diverse learning environments.

Diversity Audit at Starbucks: Assessment of Workforce Demography, Inclusion Initiatives, and Corporate Culture

The main goal of this diversity audit is to evaluate Starbucks’ present diversity and inclusion programs for efficacy. This audit evaluates organizational policies, worker satisfaction, workforce inclusionary practices, and the results of diversity programs at two Starbucks locations. The audit uses a mixed-methods approach, combining the analysis of publicly available data and direct observational studies, in South Florida locations, to provide a comprehensive knowledge of Starbucks’ diversity policy. The findings demonstrate a noteworthy level of gender diversity and highlight areas in which minority representation in senior management roles needs to be strengthened. Creating employee resource groups, stepping up diversity training, using targeted recruitment techniques, and conducting frequent diversity audits to guarantee ongoing improvement are just a few of the recommendations. The paper provides actionable insights to help firms like Starbucks maintain a competitive edge in a diversified global market and enhance their inclusivity. The advancement of an inclusive culture that recognizes and benefits from diversity at all organizational levels depends on many measures that are tracked and improved consistently. By implementing practical suggestions, modern organizations can ensure that their diversity initiatives stay relevant and successful, upholding their standing as pioneers in corporate diversity programs. In addition to assisting Starbucks in navigating a challenging international market, a proactive commitment to diversity and inclusion will help create a more just society. In a culturally diverse work environment, a diversity audit is a crucial tool for corporations to assess and improve their workforce equity, accessibility to everyone, and inclusivity. By conducting a thorough review of existing policies, practices, and employee experiences, diversity audits help identify biases, barriers, and areas for growth before any costly lawsuits surface. This process enables companies to pinpoint disparities in hiring, promotion, and retention rates, as well as uncover hidden biases in decision-making processes. With audit findings, corporations can develop targeted strategies to foster a more inclusive culture, enhance employee engagement, and boost innovation. Ultimately, a diversity audit helps organizations ensure fairness, equity, and equal opportunities for all prospects and employees, thereby leading to increased productivity, better brand reputation, and a competitive advantage in both attracting and retaining diverse top talent for the firm.

Energy allocation theory for bacterial growth control in and out of steady state

Efficient allocation of energy resources to key physiological functions allows living organisms to grow and thrive in diverse environments and adapt to a wide range of perturbations. To quantitatively understand how unicellular organisms utilize their energy resources in response to changes in growth environment, we introduce a theory of dynamic energy allocation that describes cellular growth dynamics by partitioning metabolizable energy into key physiological functions: growth, division, cell shape regulation, energy storage and loss through dissipation. By optimizing the energy flux for growth, we develop the equations governing the time evolution of cell morphology and growth rate in diverse environments. The resulting model accurately captures experimentally observed dependencies of bacterial cell size on growth rate, superlinear scaling of metabolic rate with cell size and predicts nutrient-dependent trade-offs between energy expended for growth, division and shape maintenance. By calibrating model parameters with experimental data for the model organism Escherichia coli , our model describes bacterial growth control in dynamic conditions, particularly during nutrient shifts and osmotic shocks. Integrating both the mechanical properties of the cell and underlying biochemical regulation, our model predicts the driving factors behind a wide range of observed morphological and growth phenomena with minimal added complexity.

Wolbachia modify host cell metabolite profiles in response to short-term temperature stress.

Wolbachia are common heritable endosymbionts that influence many aspects of ecology and evolution in various insects, yet Wolbachia-mediated intracellular metabolic responses to temperature stress have been largely overlooked. Here, we introduced the Wolbachia strain wLhui from the invasive Liriomyza huidobrensis (Blanchard) into a Drosophila Schneider 2 cell line (S2) and investigated the metabolite profile of wLhui-infected (S2_wLhui) and uninfected cell lines (S2_wu) under short-term exposure to either high (37°C), moderate (27°C), or low (7 and 17°C) temperatures. We find that Wolbachia infection, temperature stress, and their interactions significantly affect cellular metabolic profiles. Most significantly, when comparing the changes in metabolites between S2_wLhui and S2_wu, glycerophospholipids, amino acids, and fatty acids associated with metabolic pathways, microbial metabolism in diverse environments, and other pathways were significantly accumulated at either low or high temperatures. Our findings suggest Wolbachia-induced cellular physiological responses to short-term temperature stress, which may in turn affect the fitness and adaptive ability of its host as an invasive species.

A Novel Approach to Node Coverage Enhancement in Wireless Sensor Networks Using Walrus Optimization Algorithm

Wireless Sensor Networks (WSNs) are crucial components of modern technology, supporting applications like healthcare, industrial automation, and environmental monitoring. This research aims to design intelligent and adaptive sensor networks by integrating metaheuristics with node coverage optimization in WSNs. By incorporating metaheuristics and optimizing node coverage, WSNs can become more resilient and robust, leading to the development of self-adapting, self-organizing networks capable of efficiently covering dynamic and diverse environments. This research introduces the Walrus Optimization Algorithm for Node Coverage Enhancement in WSNs, called the WaOA-NCEWSN technique. The primary goal of this technique is to optimize the coverage of a target region using a limited number of Sensor Nodes (SNs) and by improving their placement. The WaOA is inspired by walrus behaviours like feeding, migrating, breeding, escaping, roosting, and gathering in response to environmental signals. The WaOA-NCEWSN technique uses an objective function that defines the coverage ratio, representing the maximum probability of coverage in a 2D-WSN monitoring area. Comparative analysis with other models using 50, 75, 100, and 200 nodes shows that the WaOA-NCEWSN technique performs better. The compilation times for the WaOA-NCEWSN technique are 5.14s, 6.48s, 6.54s, and 7.47s for 50, 75, 100, and 200 nodes, respectively. Experimental results indicate that the WaOA-NCEWSN technique offers superior coverage performance compared to other models.

Aerial electroreception

Electroreception is the capacity of living organisms to detect the presence of electricity, usually studied in the aquatic environment. Electroreception in air, however, has received much less attention until relatively recently. Understanding how and why aerial electroreception may work requires a multidisciplinary framework, anchored in both the physics of static electricity and the ecology of sensory biology. In essence, the novel challenge arises from the fact that air is a much less conductive medium than water. Yet, recent research on terrestrial arthropods, including bees, flies, spiders, worms and caterpillars, has unveiled sensitivity to electric fields in different sensory ecological contexts. For each aerial organism considered thus far, filiform hairs and/or the antennae have been proposed to be the specialised sensory structures enabling detection based on both empirical and theoretical evidence. This newfound sensory modality reveals a previously unrecognised source of information, a new informational ecological niche integral to diverse life histories and navigational abilities, which remarkably involves animals, plants and atmospheric electricity (Figure 1). Understanding aerial electroreception in arthropods opens avenues for exploring their behaviour and ecology in diverse environments and sheds light on the evolution of sensory adaptations in terrestrial organisms. Because, as is known today, humans are not sensitive to weak electric fields, challenges arise in our comprehension of the elusive and discrete nature of aerial electric fields, and how they could be detected and used by terrestrial organisms.

Using artificial intelligence to document the hidden RNA virosphere.

Current metagenomic tools can fail to identify highly divergent RNA viruses. We developed a deep learning algorithm, termed LucaProt, to discover highly divergent RNA-dependent RNA polymerase (RdRP) sequences in 10,487 metatranscriptomes generated from diverse global ecosystems. LucaProt integrates both sequence and predicted structural information, enabling the accurate detection of RdRP sequences. Using this approach, we identified 161,979 potential RNA virus species and 180 RNA virus supergroups, including many previously poorly studied groups, as well as RNA virus genomes of exceptional length (up to 47,250 nucleotides) and genomic complexity. A subset of these novel RNA viruses was confirmed by RT-PCR and RNA/DNA sequencing. Newly discovered RNA viruses were present in diverse environments, including air, hot springs, and hydrothermal vents, with virus diversity and abundance varying substantially among ecosystems. This study advances virus discovery, highlights the scale of the virosphere, and provides computational tools to better document the global RNA virome.

Search region updating with hierarchical feature fusion for accurate thermal infrared tracking

Due to their resilience against lighting variations, thermal infrared (TIR) images demonstrate robust adaptability in diverse environments, enabling effective object tracking even in intricate scenarios. Nevertheless, TIR target tracking encounters challenges such as fast target motion and interference from visually similar objects, substantially compromising the tracking precision of TIR trackers. To surmount these challenges, we propose a method grounded in the strategy of search region updating and hierarchical feature fusion, tailored for the precise TIR target-tracking task. Specifically, to address the issue of fast motion causing the target to depart from the search region, we propose to update the current search region by leveraging historical frame information. Additionally, we employ a hierarchical feature fusion strategy to contend with interference from visually similar objects in the tracking scenario. This strategy enhances the ability to model and represent the target more accurately, thereby elevating the tracker’s capacity to discriminate between the target and similar objects. Furthermore, to tackle the challenge of inaccurate estimation of target bounding boxes, we introduce an enhanced Intersection over Union (IoU) loss function, which improvement facilitates a more precise prediction of target bounding boxes, resulting in superior target localization. Extensive experiments substantiate that our tracker exhibits a commendable level of competitiveness when compared to other trackers.

Genomic characterization and proteomic analysis of Bacillus amyloliquefaciens in response to lignin

This study examined the lignin degradation characteristics of Bacillus amyloliquefaciens MN-13. Specifically, whole-genome sequencing and comparative proteomic analysis were performed to investigate the responses of the MN-13 strain to lignin. A maximum lignin removal of 38.0 % was achieved after 36 h of inoculation in mineral salt medium with 0.2 g/L alkaline lignin, under the following conditions: the carbon to nitrogen ratio C/N = 1/1; inoculum size 6 %; addition of glucose as an exogenous carbon source. When the MN-13 strain was inoculated into mineral salt medium with and without lignin, respectively, 831 differentially expressed proteins were identified, 404 of which were up-regulated and 427 were down-regulated. Enrichment analysis revealed that up-regulated proteins were associated with microbial metabolism in diverse environment, biosynthesis of amino acids, and pathways related to energy production, including carbon metabolism, pyruvate metabolism, the TCA cycle etc. Genomic analysis revealed that the MN-13 strain possesses many ligninolytic enzymes and aromatics degradation pathway, including benzoate degradation and aminobenzoate degradation etc. Taken together, the proteomic and genomic analyses indicated that the meta-cleavage pathway of catechol, including benzoate degradation, etc., is the main lignin degradation pathway. These findings provide new insight into lignin degradation mediated by B. amyloliquefaciens.

Conceptual Considerations for Understanding Resilience in Healthcare Students.

Increasing attention has been given to the concept resilience in the context of healthcare especially during and post the COVID pandemic. Much of the inquiry and evidence reported has focused on promoting or enhancing resilience in healthcare for improving the quality of care and reducing medical negligence. This discursive paper aims to highlight how resilience is conceptualised and identify any potential limitations or gaps in the context of healthcare students, acknowledging considerations for further development and research into this topic. This discursive discussion draws on relevant theoretical underpinnings from the fields of adjustment and coping psychology, and research and evidence from health sciences, for facilitating an understanding of resilience in supporting healthcare students to adapt into professional practice. Investigation of resilience in healthcare students is mainly identified at an individual level as personal traits or skills for working within complex healthcare systems and clinical environments. Less attention has been given to examining resilience at the organisation or systemic level. This is primarily because of limited frameworks for investigating resilience from a multidimensional perspective recognising a wider systemic level influenced by external factors including socioecological determinants, for example, available support services for healthcare students. The link between resilience and its function to mitigate against associated neuropsychological distress and subsequent pathopsychological disorders in healthcare student cohorts is recognised; however, greater understanding of resilience as a multidimensional construct is warranted. A multidimensional investigation of resilience is critical for the preparation and readiness of healthcare structures and organisations in facilitating the needs of healthcare students entering challenging and diverse healthcare working environments. No patient or public contribution.

Solvation and its influence on the electronic structure and pharmacological activity of 2-fluoro-6-trifluromethyl acetophenone

The molecular framework and vibrational spectra of 2′-Fluoro-6′-Trifluoromethyl Acetophenone (MA3) in diverse solvent environments are being studied using quantum chemistry methods like DFT. The structure was successfully optimized using gas phase and solvent phases such as ethanol, diethyl sulfoxide, chloroform, and diethyl ether. Using FT-Raman and FT-IR techniques, both theoretical and experimental scenarios were examined. The IEFPCM model was used to explore the electronic properties, including HOMO-LUMO gaps, UV–Vis spectra, NBO, Mulliken analysis, and MEP, in various solvents. The TD-DFT approach and IEFPCM model were utilized to replicate the UV spectra. The Fukui function helped locate reactive sites and understand chemical interactions. Comprehensive topological analysis using Multiwfn – 3.8 suite integrated RDG, ALIE, ELF, and LOL to identify key binding sites and weak interactions. The molecular docking studies highlighted the compound’s binding affinities with target proteins, showcasing its potential pharmacological applications. This research underscores the critical role of solvation in determining the electronic and structural properties of MA3, paving the way for its application in drug development and other fields.

Evaluating and enhancing the applicability of thermal comfort indices in diverse outdoor environments using Bayesian and random forest regression

This research aims to evaluate and improve the applicability of three commonly utilized thermal comfort indices—standard effective temperature (SET), physiological equivalent temperature (PET), and universal thermal climate index (UTCI)—in outdoor environments. The study was conducted in four typical spaces of a university in Xi’an, China, covering different environmental settings: East Lake (Place 1), tree-lined path (Place 2), campus playground (Place 3), and campus square (Place 4). A total of 322 volunteers participated by completing a subjective sensory questionnaire in randomized environments, and on-place meteorological data were collected. The study yielded three primary findings. First, the tree-lined path (Place 2) was identified as the most comfortable outdoor space on the university campus during summer, whereas East Lake (Place 1) was the least comfortable. Second, substantial residuals were observed for SET, PET, and UTCI across different places when assessing the thermal comfort variations experienced by the subjects. Third, Bayesian regression and random forest regression were employed to analyze and adjust the thermal comfort indices. The comparative study found that after correction, the model explanatory power of PET increased by up to 20%, with the standard deviation of residuals decreasing by up to 0.314 and MSE decreasing by up to 0.1912. The model explanatory power of SET increased by up to 31.2%, with the standard deviation of residuals decreasing by up to 0.3867 and MSE decreasing by up to 0.3012. For UTCI, the model explanatory power increased by up to 10.5%, the standard deviation of residuals decreased by up to 0.2215, and the MSE decreased by up to 0.1002.

Enabling efficient and low-effort decentralized federated learning with the EdgeFL framework

Context:Federated Learning (FL) has gained prominence as a solution for preserving data privacy in machine learning applications. However, existing FL frameworks pose challenges for software engineers due to implementation complexity, limited customization options, and scalability issues. These limitations prevent the practical deployment of FL, especially in dynamic and resource-constrained edge environments, preventing its widespread adoption. Objective:To address these challenges, we propose EdgeFL, an efficient and low-effort FL framework designed to overcome centralized aggregation, implementation complexity and scalability limitations. EdgeFL applies a decentralized architecture that eliminates reliance on a central server by enabling direct model training and aggregation among edge nodes, which enhances fault tolerance and adaptability to diverse edge environments. Methods:We conducted experiments and a case study to demonstrate the effectiveness of EdgeFL. Our approach focuses on reducing weight update latency and facilitating faster model evolution on edge devices. Results:Our findings indicate that EdgeFL outperforms existing FL frameworks in terms of learning efficiency and performance. By enabling quicker model evolution on edge devices, EdgeFL enhances overall efficiency and responsiveness to changing data patterns. Conclusion:EdgeFL offers a solution for software engineers and companies seeking the benefits of FL, while effectively overcoming the challenges and privacy concerns associated with traditional FL frameworks. Its decentralized approach, simplified implementation, combined with enhanced customization and fault tolerance, make it suitable for diverse applications and industries.

Gap analysis and harmonization of International Standards for Maritime Autonomous Surface Ships

Abstract This paper addresses the challenges and solutions associated with the implementation of autonomous ships, focusing on the Maritime Autonomous Surface Ships (MASS) Code developed by the International Maritime Organization (IMO). With the goal of adopting a mandatory goal-based MASS Code by 2030, this study examines the interoperability and standardization requirements necessary for the effective global operation of autonomous ships. The paper analyses the MASS Code in relation to existing international standards, identifies gaps, and proposes standards works to accommodate the emerging technologies. Additionally, it investigate on the application of Autonomous Navigation Systems (ANS) and introduces the concepts of Shore ANS and Shore Bridge to enhance remote control capabilities. The analysis emphasizes the need for comprehensive standardization to ensure the safe and efficient operation of autonomous ships in diverse maritime environments.

Multimodal Signal Retiming Projects: A Survey-Based Exploration of Traffic Signal Professionals’ Practices and Challenges

In the realm of traffic signal operations, the Signal Timing Manual second edition (STM2) serves as a foundational guide for professionals engaged in multimodal signal retiming projects. However, it is acknowledged that the STM2 has its limitations, and real-world conditions often necessitate adaptations in the established procedures. Considering this context, this research endeavors to bridge this gap by conducting a comprehensive survey aimed at traffic signal professionals. This study presents the findings of a comprehensive survey conducted among traffic signal professionals to explore the methodologies, challenges, and practices involved in multimodal signal retiming projects. The survey aimed to obtain detailed insights into the current state of signal retiming, the types of data and tools utilized, and the adaptations necessary to address the complexities of multimodal urban transportation networks. The survey highlights and summarizes responses from 36 professionals across North America, providing insight into both the common strategies and unique challenges faced by those responsible for optimizing signal timings in diverse and dynamic urban environments. The survey results reveal a reliance on diverse tools and data types for signal optimization, highlighting the complexities of accommodating different transportation needs. The findings underscore the importance of tailored approaches and advanced technologies in enhancing signal retiming processes. The insights gained from this study will inform future strategies and enhance the effectiveness of signal retiming procedures in urban areas, thereby contributing to improved traffic management and multimodal transportation efficiency.

Pioneering Prostate Cancer Diagnosis: Evaluating AI Against Traditional Methods - A Systematic Literature Review

ABSTRACT Prostate cancer is one of the most common forms of men's cancer in the world and the understanding of its epidemiology is crucial to effective prevention, early detection and management strategies. This systematic review highlights the potential of artificial intelligence-based diagnostic methods to revolutionize the early diagnosis of prostate cancer. A comprehensive literature search was carried out using databases such as PubMed, Google Scholar and Scopus. After removing duplicates, titles and abstracts were examined in 279 studies and the following full texts were examined in 128 studies. We have ten studies to review in total. Therefore, we chose to review data for all 10 identified studies that applied AI techniques to detect ca prostate. The Paige Prostate Alpha AI system significantly enhances pathologists' diagnostic capabilities, leading to more accurate and effective prostate cancer detection. These limitations indicate that further research is required to address these issues and validate the results in a more diverse and controlled environment. AI-based diagnostic methods have shown significant promise in enhancing the early detection of prostate cancer. As technology continues to advance, integrating AI with traditional diagnostic approaches could lead to more effective, efficient, and accurate prostate cancer screening and diagnosis. Future studies should focus on large-scale clinical trials and real-world applications to validate these findings and facilitate the adoption of AI in clinical settings. Keywords: Prostate Cancer, Artificial Intelligent, Deep Machine Learning, MRI

From Dis-empowerment to empowerment: Crafting a healthcare cybersecurity self-assessment

Due to the valuable and sensitive nature of its data, the Australian healthcare sector is increasingly targeted by cyberattacks. Existing cybersecurity evaluation methods often lack the specificity required to address the unique vulnerabilities within this sector, especially in terms of engaging stakeholders and fostering a proactive security culture. These evaluations often overlook psychological empowerment, which enhances individuals’ confidence in managing cybersecurity.This study aims to develop a tailored cybersecurity self-assessment index for the Australian healthcare system. It will focus on enhancing psychological empowerment alongside technical assessments to improve overall sector resilience against cyber threats.Using a design science research approach, the index was developed using expert reviews, online surveys, and in-depth interviews with key stakeholders, including healthcare providers, consumers, and government entities. This iterative process involved identifying gaps in existing cybersecurity measures and designing an index to address technical and human factors.The index’s evaluation through a pilot study revealed that it effectively raised awareness and empowered individuals within the healthcare sector to take ownership of cybersecurity practices. Participants reported increased confidence in managing cybersecurity risks and found the index’s actionable recommendations helpful in improving their security posture. However, challenges related to its applicability across diverse healthcare environments and regulatory constraints were identified.The Australian Healthcare Cybersecurity Self-Assessment Index shows promise as a tool for strengthening cybersecurity in the healthcare sector by integrating psychological empowerment with technical assessments. Further research is needed to refine the tool, incorporate quantitative data, and explore its scalability across different healthcare settings and global applications.

DNE-YOLO: A method for apple fruit detection in Diverse Natural Environments

The apple industry, recognized as a pivotal sector in agriculture, increasingly emphasizes the mechanization and intelligent advancement of picking technology. This study innovatively applies a mist simulation algorithm to apple image generation, constructing a dataset of apple images under mixed sunny, cloudy, drizzling and foggy weather conditions called DNE-APPLE. It introduces a lightweight and efficient target detection network called DNE-YOLO. Building upon the YOLOv8 base model, DNE-YOLO incorporates the CBAM attention mechanism and CARAFE up-sampling operator to enhance the focus on apples. Additionally, it utilizes GSConv and the dynamic non-monotonic focusing mechanism loss function WIOU to reduce model parameters and decrease reliance on dataset quality. Extensive experimental results underscore the efficacy of the DNE-YOLO model, which achieves a detection accuracy (precision) of 90.7%, a recall of 88.9%, a mean accuracy (mAP50) of 94.3%, a computational complexity (GFLOPs) of 25.4G, and a parameter count of 10.46M across various environmentally diverse datasets. Compared to YOLOv8, it exhibits superior detection accuracy and robustness in sunny, drizzly, cloudy, and misty environments, making it especially suitable for practical applications such as apple picking for agricultural robots. The code for this model is open source at https://github.com/wuhaitao2178827/DNE-YOLO.