Management Science Research Articles

A Study on the Rational Decision-Making Process of Vessel Organization—Focusing on Cases of Vessel Accidents

Vessel organizations are exposed to the risk of accidents due to the limited experience and intuition of the top decision makers in the organization in the special environment of the sea. This study aims to provide a direction for sustainable and rational decision-making in vessel organization through decision making process theory and case studies of actual vessel accidents. The results obtained from the actual case studies show that all three vessel accidents were caused by the arbitrary decision of the decision maker based on the Garbage Can model. This can be attributed to the closed nature of the vessel’s organization, which requires a decision-making process that is a hybrid of the Carnegie Decision model and Management Science model. Implications include the introduction of a ‘My Ship’ system, the de-subjectivization of top decision makers, situational awareness, and the need to provide top decision makers with the context and information relevant to their decisions. Limitations of this study include the fact that the case study was conducted only on Korean-flagged vessels and the study was limited to vessel accidents that occurred in the waters off Korea, China, and Japan, which limits the generalizability of the findings. In order to overcome these limitations, it would be interesting to conduct a follow-up study to include vessel accidents of different nationalities or to further investigate the characteristics of the decision-making process in vessel accidents by country.

The Role of Cognitive Bias in Principal Decision Making: A Narrative Analysis of the Literature

This study aims to critically examine the role of cognitive biases in principal decision-making processes by synthesizing and analysing existing literature. It seeks to identify key cognitive biases affecting principal decisions, their impact on organizational outcomes, and potential mitigation strategies. A comprehensive narrative analysis of the literature was conducted. Relevant peer-reviewed articles, books, and book chapters published between 1970 and 2023 were systematically identified and reviewed using major academic databases such as Web of Science, JSTOR, and Google Scholar. The analysis revealed that cognitive biases, particularly confirmation bias, overconfidence, anchoring, and availability bias, significantly influence principal decision-making. These biases can lead to suboptimal decisions, affecting strategic planning, risk assessment, and overall organizational performance. The study also identified several debiasing techniques, including awareness training and structured decision-making frameworks, which show promise in mitigating the impact of cognitive biases. This study provides a comprehensive and up-to-date synthesis of research on cognitive biases in principal decision-making, offering a holistic view of the field’s current state. It uniquely combines insights from cognitive psychology, behavioural economics, and management science to provide a multidisciplinary perspective on the topic. The study contributes to both theory and practice by offering a structured understanding of how cognitive biases influence principal decision-making. It provides valuable insights for principals and organizations to improve their decision-making processes and highlights areas for future research in this critical field. The findings can inform the development of more effective principal training programs and decision support systems.

Spatiotemporal Differentiation of Land Ecological Security and Optimization Based on GeoSOS-FLUS Model: A Case Study of the Yellow River Delta in China Toward Sustainability

Land ecological security (LES) is a crucial issue pertaining to the sustainability of landscapes or land systems. Nevertheless, previous studies largely neglected the impacts of landscape structure on LES. This study, taking the Yellow River Delta (YRD) as the case area, analyzed the spatiotemporal differentiation patterns of regional LES in 19 counties (cities and districts) from 2000 to 2020 through the application of landscape metrics and employed GeoSOS-FLUS to forecast the landscape structure and corresponding land ecological security in 2030 under four different scenarios to advance sustainability. The results show that, first, landscape fragmentation and irregularity are decreasing, while aggregation and connectivity are increasing. The primary landscape change involves the transition from farmland to developed areas. Second, the LES index is rising. In 2020, about 73.68% of the research units reached level IV, while most were at level III. Third, it is projected that by 2030, the LES index will rise under all scenarios, exhibiting an eastward-declining gradient in its geographic distribution, with higher values prevailing in the eastern regions and gradually tapering off towards the west. It is expected that in the business-as-usual scenario, the proportion of LES reaching level V will be the highest. This study not only reveals the impact mechanism of the landscape structure on land ecological security but also provides an important basis for formulating scientific and rational land use planning and management strategies from the perspectives of sustainable land governance and sustainable land use.

Preface: 8th International Conference on Education, Management and Social Science (EMSS 2024)

At the end of 2024, we would like to present an international proceeding that containing of the selected papers from the 2024 8th International Conference on Education, Management and Social Science (EMSS 2024). This conference is an annual important event to publish of research results. We are hoping that the event will become a foothold of thinking in welcoming of global developments. In addition, publications packaged in this seminar will further strengthening existence and reputation as an educational institution that excels in scientific publication at national and international levels. EMSS 2024 is a leading international conference for researchers and practitioners to share their new ideas, original research results and practical development experiences from education research, management engineering, historical research, humanities, sociology, and social science. This conference was held in Manila, Philippines during September 21-22, 2024 with the aim to act as a forum for exchange and dissemination of knowledge and ideas on the latest developments. We hope the international proceedings will be able to encourage students, teachers, lecturers, practitioners, researchers, and higher education community in developing scientific research and publications to support the nation's developments. EMSS Organizing Committees Manila, Philippines

Preface: 2nd International Conference on Researches in Educational Technology and Psychological Sciences (RETPS 2024)

This is the proceedings for 2024 2nd International Conference on Researches in Educational Technology and Psychological Sciences (RETPS 2024). This conference was held on October 12-13, 2024 in Houston, USA, and it was a very successful joint conference by four countries namely China, United States, Canada, and New Zealand. Papers from educational technology, public management, psychology, humanities, and social science were well represented in this volume and we hope that these contributions will continue to play a large part in future RETPS conferences. RETPS 2024 also fosters cooperation among organizations and researchers involved in the merging fields and provides in-depth technical presentations with ample opportunities for one-on-one discussions with the presenters. We wish to thank each and every member of the proceeding editors and local technical committee. Many people contributed to the success of the conference, and the organisers specially would like to thank the reviewer team. The local organisers and conference office did a splendid job, and we all enjoyed an excellent conference atmosphere. They were very patient and committed in making this conference a success. Organizing Committee of RETPS Houston, USA

Wetting dynamics and heat transfer of droplet evaporation around boiling point: Facile manipulation by surface roughness

The evaporation of water–ethanol droplets on solid surfaces has vast potential for applications in thermal management, microfluidics, and biomedical sciences, while the approach of manipulating wetting dynamics and heat transfer of droplet evaporation around the boiling point remains open for investigations. The present study proposes a facile approach: fabricating solid surfaces with diverse roughness by sandpaper milling with different grit densities, which can alter the wetting and heat transfer characteristics of droplet evaporation. We discover that when the temperature of the heated surface exceeds the droplet boiling point, the existence of bubbles affects the solid–liquid wetting state; surface roughness alters the ability of bubble departure, thus determining the solid–liquid contact and the contact line retraction time. Consequently, in this case, the solid–liquid contact area and the heat absorbed from heated surfaces for droplets changes with surface roughness, altering the droplet evaporation time and the average heat flux at the solid–liquid interface. Bridged by droplet wetting dynamics, the heat transfer of droplet evaporation above the boiling point can be manipulated by adjusting surface roughness, offering a facile approach to tuning the process of droplet evaporation in the related industrial applications.

Prevalence of Stress, Sources and Coping Strategies among Nursing and Midwifery Students in Three Nursing Training Schools in the Buea Municipality, Cameroon

Background: Stress is a common human experience blamed for many ills. Nursing students experience high levels of stress and recent research on health care related fields and stress has indicated that nursing students experience higher levels of stress compared to students in the other health disciplines.Objectives: This study was carried out to determine the prevalence of stress, identify its sources and coping strategies amongst nursing and midwifery students from three nursing training schools in the Buea Municipality.Methods: A quantitative cross sectional study design was used to conduct the study for 6 months. The study population consisted of nursing and midwifery students from the Faculty of Health Sciences University of Buea, Biaka University Institute and Redemption Higher Institute of Biomedical and Management Sciences in the Buea Municipality. Purposive and a simple random sampling techniques were used to select the three nursing training schools and the participants respectively. The participants were undergraduate student nurses and midwives who had had previous clinical experience. Probability proportionate to size sample technique was used to determine the number of participants from each school. Data was collected from the participants regarding their stress levels, sources of stress, and the strategies they used to cope with stress. The data was collected using a self-administered questionnaire. Data collected was entered in to an excel spreadsheet version 2010 and analysed using SPSS Version 25. Inferential statistics was also used to describe association between variables (multivariate analysis and Pearson’s correlation); a p-value < 0.05 was considered significant.Results: A total of 342 nursing and midwifery students participated in the study. The findings revealed a very high prevalence [223 (65.2%)] of stress among participants. Age 32 years+, marital status and level of studies (3rd & 4th year) were significantly associated with stress with p values of 0.021, 0.022 and 0.001 respectively. The main sources of stress among students were from school work, from peers and personal life with mean values 2.13, and 1.96 respectively. Majority [201(58.8%)] of the students relied on problem-focused strategies as means to cope with stress, followed by the emotional-focused strategies [81(23.7%)].Conclusion: The prevalence of stress among student nurses and midwives in the Buea Municipality was very high with the main sources of stress being school work, stress from peer and personal life. It is recommended that guidance counseling services be provided in all nursing training institutions and students be encouraged to ultilise the services in order to reduce the prevalence of stress.

Naturally-occurring nematicides of plant origin: two decades of novel chemistries.

Plant-parasitic nematodes are among the most destructive plant pathogens, resulting in a global annual economic loss of about 358 billion dollars. Using synthetic nematicides to control plant-parasitic nematodes has resulted in broad-spectrum toxicity to the environment. Plant-derived secondary metabolites have recently emerged as viable options that provide effective, greener, and renewable routes for managing plant-parasitic nematodes in various cropping systems. However, limited comprehensive information on plant-derived secondary metabolites sources, chemical structures, and nematicidal activities is available. This study aims to compile and analyze data on plant-based secondary metabolites with nematicidal properties collected over the last two decades. In this review, we identified 262 plant-based metabolites with nematicidal activities that were isolated from 35 plant families and 65 plant species. Alkaloids, terpenoids, saponins, flavonoids, coumarins, thiophenes, and annonaceous acetogenins were among the most studied compounds. In addition to the structure-activity relation for specific metabolites with nematicidal potency, various techniques for their extraction and isolation from plant material are discussed. Our findings demonstrate the potential of plants as a feedstock for sourcing nematicidal compounds and discovering new chemistries that could potentially be used for developing the next generation of nematicides. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Efficacy of L-arabinose in managing cucumber Fusarium wilt and the underlying mechanism of action.

Cucumber Fusarium wilt (CFW), triggered by Fusarium oxysporum f. sp. cucumerinum, leads to substantial yield reductions in global cucumber (Cucumis sativus L.) production. Common management strategies for CFW include soil fumigation, grafting, and crop rotation. However, these methods have limitations regarding safety and efficacy stability, necessitating the development of new, cost-effective, and eco-friendly control strategies. Our prior research demonstrated that L-arabinose, an inexpensive and safe sugar commonly used in food and beverages, effectively suppressed bacterial wilt in tomatoes. This study explores the potential of L-arabinose in managing CFW and investigates its mechanism of action. Soil applications of L-arabinose, ranging from 0.00001 to 0.01%, effectively suppressed CFW. The most significant suppressive effect was observed at 0.01%, reducing the disease severity index by 67.5% compared to the control treatment. Microscopic examination of transverse root sections showed that pathogen hyphae colonized the epidermis but seldom penetrated the cortical layer of roots in L-arabinose-treated seedlings. In contrast, the entire root tissue of control seedlings was colonized by the pathogen. Quantitative real-time PCR revealed a significant increase in the expression of defense-related genes dependent on salicylic acid, jasmonic acid, and ethylene in L-arabinose-treated plants compared to control plants, 6 and 10 days post pathogen inoculation. This study demonstrated that soil application of L-arabinose can effectively suppress CFW by priming root tissues for multiple defense signaling pathways. Therefore, L-arabinose holds potential as a new fungicide for managing CFW. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

DMI fungicide resistance in Zymoseptoria tritici is unlinked to geographical origin and genetic background: a case study in Europe.

The hemibiotrophic fungus Zymoseptoria tritici causing Septoria tritici blotch (STB), is a devastating foliar pathogen of wheat worldwide. A common group of fungicides used to control STB are the demethylation inhibitors (DMIs). DMI fungicides restrict fungal growth by inhibiting the sterol 14-α-demethylase, a protein encoded by CYP51 gene and essential for maintaining fungal cell permeability. However, the adaptation of Z. tritici populations in response to intensive and prolonged DMI usage has resulted in a gradual shift towards reduced sensitivity to this group of fungicides. In this study, 311 isolates were collected pre-treatment from nine wheat-growing regions in Europe in 2019. These isolates were analysed by high-throughput amplicon-based sequencing of nine housekeeping genes and the CYP51 gene. Analyses based on housekeeping genes and the CYP51 gene revealed a lack of population structure in Z. tritici samples irrespective of geographical origin. Minimum spanning network (MSN) analysis showed clustering of multilocus genotypes (MLGs) based on CYP51 haplotypes, indicating an effect of selection due to DMI fungicide use. The majority of the haplotypes identified in this study have been reported previously. The diversity and frequencies of mutations varied across regions. Using a high-throughput amplicon-sequencing approach, we found several mutations in the CYP51 gene combined in different haplotypes that are likely to cause fungicide resistance. These mutations occurred irrespective of genetic background or geographical origin. Overall, these results contribute to the development of effective and sustainable risk monitoring for DMI fungicide resistance. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

NezhNPV, a new biocontrol agent for Nesodiprion zhejiangensis Zhou & Xiao (Hymenoptera: Diprionidae), an emerging forest pest.

Nesodiprion zhejiangensis, a multivoltine sawfly, is widely distributed in south China and has caused serious damage to forests. Historically, N. zhejiangensis management has relied heavily on synthetic chemicals. To reduce the reliance on chemical control, we previously isolated a nucleopolyhedrovirus, NezhNPV, from deceased N. zhejiangensis larvae. A subsequent pathogenicity assay confirmed its high virulence in a laboratory setting. In order to comprehensively examine the hypothesis that NezhNPV is an effective new biocontrol agent for N. zhejiangensis, we carried out a field test in Beijing, China, and characterized NezhNPV morphologically by electron microscopy and genetically by genome sequencing. Our field trials showed that NezhNPV was effective in controlling N. zhejiangensis in a naturally infested Himalayan blue pine forest. The occlusion bodies of NezhNPV consist of irregular polyhedra that occlude rod-shaped enveloped virions with a single nucleocapsid per virion. The NezhNPV genome is 80 637 bp in length, and contains 90 open reading frames, including 38 core, eight lepidopteran baculovirus, 34 hymenopteran baculovirus and 10 unique baculovirus genes, representing the smallest known genome among baculoviruses. The combined results based on phylogenetic analyses, Kimura-2-parameter distances and biological characteristics indicate that NezhNPV is a novel gammabaculovirus and candidate for species status with the provisional name Gammabaculovirus nezhejiangensis. NezhNPV is highly collinear with other gammabaculoviruses and contains nonsyntenic regions with an inversion and rearrangement between orf3 and orf35. The combined results from our field trials, coupled with morphological and genomic characterization clearly demonstrate the bioactivity of NezhNPV. This gammabaculovirus may be included in pest management practices against N. zhejiangensis as a novel biocontrol agent. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Monitoring, simulation and early warning of cyanobacterial harmful algal blooms: An upgraded framework for eutrophic lakes

Cyanobacterial Harmful Algal Bloom (CyanoHAB) is a global aquatic environmental issue, posing considerable eco-environmental challenges in freshwater lakes. Comprehensive monitoring and accurate prediction of CyanoHABs are essential for their scientific management. Nevertheless, traditional satellite-based monitoring and process-oriented prediction methods of CyanoHABs failed to satisfy this demand due to the limited spatiotemporal resolutions of both monitoring data and prediction results. To address this issue, this paper proposes an upgraded framework for comprehensive monitoring and accurate prediction of CyanoHABs. A collaborative CyanoHAB monitoring network was firstly constructed by integrating space, aerial, and ground-based monitoring means. As a result, CyanoHAB conditions were assessed frequently covering the entire lake, its key areas, and core positions. Furthermore, by overcoming technical limitations associated with high-precision simulation of the growth-drift-accumulation process of CyanoHABs, such as the unclear drifting process of CyanoHABs and the mechanism of its coastal accumulation, the multi-scale CyanoHAB prediction was realized interconnecting the entire lake and its nearshore areas. The implemented framework has been applied in Lake Chaohu for over three years. It provided high-frequency and high-spatial-resolution CyanoHAB monitoring, as well as its multi-scale and accurate simulation. The application of this framework in Lake Chaohu had significantly improved the accuracies of CyanoHAB monitoring, simulation, and early warning. This advancement holds significant scientific value and offers potential for CyanoHAB prevention and control in eutrophic lakes.

Scientific knowledge management in Brazil: an analysis of the Lattes Platform

The Lattes Platform (PL) is an essential resource for Brazil's scientific and technological landscape, centralizing researchers' curricular information and facilitating the management of scientific data. However, the lack of rigorous standardization in the data entered compromises the efficiency of retrieving and analyzing this information, limiting academic knowledge management. This study investigated the limitations of the PL and proposed a solution that integrates new technologies to optimize scientific data management. The research was conducted in two main stages: a systematic literature review on knowledge management and technologies applied to the PL, and a case study at the Federal Institute of Acre (IFAC). The review included the analysis of 79 articles and a survey of software records at the National Institute of Industrial Property (INPI). In the case study, semi-structured questionnaires were administered to IFAC staff to understand their needs and perceptions regarding the PL. The results reveal a significant demand for tools that automate data extraction and integrate the PL with other academic management systems. The analysis of software records at INPI highlighted solutions that can enhance the PL’s efficiency. The integration of Artificial Intelligence (AI), exemplified by the proposed LattesAI, emerges as a promising solution to overcome the platform's current limitations, automating processes and personalizing knowledge management. This innovation not only optimizes academic management but also strengthens Brazil's position in the global scientific landscape, promoting greater efficiency and collaboration in educational and research institutions. Future studies should focus on developing AI algorithms specifically for the PL, ensuring its continuous evolution in response to the demands of the academic environment.

Superior target genes and pathways for RNAi-mediated pest control revealed by genome-wide analysis in the beetle Tribolium castaneum.

An increasing human population, the emergence of resistances against pesticides and their potential impact on the environment call for the development of new eco-friendly pest control strategies. RNA interference (RNAi)-based pesticides have emerged as a new option with the first products entering the market. Essentially, double-stranded RNAs targeting essential genes of pests are either expressed in the plants or sprayed on their surface. Upon feeding, pests mount an RNAi response and die. However, it has remained unclear whether RNAi-based insecticides should target the same pathways as classic pesticides or whether the different mode-of-action would favor other processes. Moreover, there is no consensus on the best genes to be targeted. We performed a genome-wide screen in the red flour beetle to identify 905 RNAi target genes. Based on a validation screen and clustering, we identified the 192 most effective target genes in that species. The transfer to oral application in other beetle pests revealed a list of 34 superior target genes, which are an excellent starting point for application in other pests. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analyses of our genome-wide dataset revealed that genes with high efficacy belonged mainly to basic cellular processes such as gene expression and protein homeostasis - processes not targeted by classic insecticides. Our work revealed the best target genes and target processes for RNAi-based pest control and we propose a procedure to transfer our short list of superior target genes to other pests. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Autonomous Extraction Technology for Aquaculture Ponds in Complex Geological Environments Based on Multispectral Feature Fusion of Medium-Resolution Remote Sensing Imagery

Coastal aquaculture plays a crucial role in global food security and the economic development of coastal regions, but it also causes environmental degradation in coastal ecosystems. Therefore, the automation, accurate extraction, and monitoring of coastal aquaculture areas are crucial for the scientific management of coastal ecological zones. This study proposes a novel deep learning- and attention-based median adaptive fusion U-Net (MAFU-Net) procedure aimed at precisely extracting individually separable aquaculture ponds (ISAPs) from medium-resolution remote sensing imagery. Initially, this study analyzes the spectral differences between aquaculture ponds and interfering objects such as saltwater fields in four typical aquaculture areas along the coast of Liaoning Province, China. It innovatively introduces a difference index for saltwater field aquaculture zones (DIAS) and integrates this index as a new band into remote sensing imagery to increase the expressiveness of features. A median augmented adaptive fusion module (MEA-FM), which adaptively selects channel receptive fields at various scales, integrates the information between channels, and captures multiscale spatial information to achieve improved extraction accuracy, is subsequently designed. Experimental and comparative results reveal that the proposed MAFU-Net method achieves an F1 score of 90.67% and an intersection over union (IoU) of 83.93% on the CHN-LN4-ISAPS-9 dataset, outperforming advanced methods such as U-Net, DeepLabV3+, SegNet, PSPNet, SKNet, UPS-Net, and SegFormer. This study’s results provide accurate data support for the scientific management of aquaculture areas, and the proposed MAFU-Net method provides an effective method for semantic segmentation tasks based on medium-resolution remote sensing images.

Geochemical fingerprints, evolution, and driving forces of groundwater in an alpine basin on Tibetan Plateau: Insights from unsupervised machine learning and objective weight allocation approaches

Study RegionTongde Basin, a typical alpine basin on the northeastern Tibetan Plateau Study focusResearch on the hydrochemical pattern and availability of groundwater is very limited in alpine regions, which greatly hinders the rational ulitilization and scientific management of groundwater resources. Multiple approaches integrating self-organizing map, K-means, entropy weight were employed to elucidate the distinct hydrochemical evolution patterns and controlling mechanisms of groundwater in alpine basins with dense human activities. New hydrological insights for the regionMultiple hydrochemical evolution patterns were discovered for groundwater in present alpine basin on northeastern Tibetan Plateau. Groundwater presents a gradual hydrochemical evolution from the piedmont to the riverside under the natural regulation with the increasing of TDS and major ions along the flow path. While, groundwater at some sporadic sites is featured with relatively high total alkalinity due to carbonate mineral dissolution. The riverine groundwaters are regulated by the strong hydrodynamic condition of river and demonstrate fresher hydrochemical characteristics. Groundwaters in the irrigation areas are featured by elevated nitrate levels with a maximum of 42.86 mg/L, attributed to agricultural pollution inputs. A conceptual model was established to illustrate the distinct hydrochemical fingerprints, evolution patterns and controlling mechanisms of groundwater in alpine basins that with dense human activities. This research can enhance the understanding of groundwater chemical status, genesis and availability, and is beneficial for the conservation and sustainable management of groundwater resources in ecologically fragile alpine regions worldwide.

Knowledge-oriented management systems for complex objects

The article examines the issue of improving the efficiency of complex systems management through the use of knowledge-based methods and technologies. The article emphasizes the need to revise traditional approaches to scientific research and knowledge management by introducing a transdisciplinary paradigm and artificial intelligence to achieve sustainable development and knowledge evolution. The authors try to solve the problem of management in the face of uncertainty and incomplete information, which is typical for such systems. The methods of integrating knowledge from different fields to create an integrated management system are considered. The basis of management is the holarchic structure, which organizes the system in the form of a hierarchy of holons. Each holon has a specific role and knowledge, which ensures the flexibility and adaptability of the system. The study also uses self-organization and learning methods to ensure continuous improvement of the systemʼs functioning. The article discusses the requirements for knowledge-based management systems, such as flexibility, adaptability, data and knowledge integration, learning and self-organization, transparency of decisions, and resilience to errors and failures. The findings of the study emphasize the importance of implementing knowledge-based approaches in the management system of complex facilities. This allows to increase the level of adaptability and stability of management, which is critical in todayʼs environment of dynamic development of technology and knowledge. The proposed methods can be applied in various fields, which makes them universal and useful for a wide range of scientific and practical tasks, providing a high level of adaptability, efficiency and stability of complex objects management, taking into account modern requirements and challenges.

Exploring the correlation between tree structure characteristics and carbon storage in historic gardens using TLS technology: a case study of Jian Xin Pavilions at Jingyi Park, Fragrant Hills Park

Historic gardens contain a greater number and variety of ancient trees, which are older, have unique forms, and larger volumes. These trees hold significant value in both natural ecosystems and cultural heritage. However, current research on the carbon sequestration value of ancient and non-ancient trees in historical gardens is relatively lacking. Based on the unique morphology and carbon storage estimation needs of ancient trees in historic gardens, this paper proposes a morphology-based point cloud single-tree segmentation method. This method can precisely extract the morphological structures of various tree species and accurately estimate their carbon storage. From the perspective of carbon sequestration, it evaluates the correlation between the structural characteristics and carbon storage of trees in historic gardens, as well as the potential changes in their carbon storage capacity.Using the Jing Yi Park’s Jian Xin Pavilions in Fragrant Hills Park as a case study, this method was applied to extract structural indicators of 116 ancient and non-ancient trees. The total carbon storage was found to be 19,171.13 kg, with an average carbon storage of 165.27 kg per tree. Among these, ancient trees accounted for 13,178.32 kg, or 68.74% of the total carbon storage. The study revealed that the correlation between tree age and carbon storage varied by species, and there were significant positive correlations between carbon storage and tree height, DBH, and canopy volume. Notably, there was a significant linear growth trend between DBH, canopy volume, and carbon storage. By 2030, the total carbon storage is projected to increase to 21,924.96 kg, with an annual average increase of 393.40 kg, representing a growth rate of 14.4%.The results indicate that studying the correlation between structural characteristics and carbon storage of aged trees in historical gardens can shed light on the important role of trees in sustainable carbon sequestration. The precise extraction of tree information through 3D digital technology and the prediction of carbon storage potential not only offer new perspectives for the conservation of cultural heritage in historical gardens, urban microclimate planning and design, and spatial management of carbon sinks and emissions but also have significant value for promoting the scientific management and protection of urban green spaces.

Wheat growth stage identification method based on multimodal data

Accurate identification of crop growth stages is a crucial basis for implementing effective cultivation management. With the development of deep learning techniques in image understanding, research on intelligent real-time recognition of crop growth stages based on RGB images has garnered significant attention. However, the small differences and high similarity in crop morphological characteristics during the transition between adjacent growth stages pose challenges for accurate identification. To address this issue, this study proposes a multi-scale convolutional neural network model, termed MultiScalNet-Wheat (MSN-W), which enhances the algorithm's ability to learn complex features by utilizing multi-scale convolution and attention mechanisms. This model extracts key information from redundant data to identify winter wheat growth stages in complex field environments. Experimental results show that the MSN-W model achieves a recognition accuracy of 97.6 %, outperforming typical convolutional neural network models such as VGG19, ResNet50, MobileNetV3, and DenseNet. To further address the difficulty in recognizing growth stages during transition periods, where canopy morphological features are highly similar and show small differences, this paper introduces an innovative approach by incorporating sequential environmental data related to wheat growth stages. By extracting these features and performing multi-modal collaborative inference, a multi-modal feature-based wheat growth stage recognition model, termed MultiModalNet-Wheat (MMN-W), is constructed on the basis of the MSN-W model. Experimental results indicate that the MMN-W model achieves a recognition accuracy of 98.5 %, improving by 0.9 % over the MSN-W model. Both the MSN-W and MMN-W models provide accurate methods for observing wheat growth stages, thereby supporting the scientific management of winter wheat at different growth stages.

Situating the science of disaster risk reduction and management (DRRM) in Nepal for policy and planning

The United Nations Framework Convention on Climate Change (UNFCCC) emphasises the necessity for “more science” in advancing Disaster Risk Reduction (DRR); however, this initiative often falters in developing countries. This paper utilises a situated knowledge framework from feminist science studies to analyse the knowledge institutionalisation pathways of two Scientific Administrative Organisations (SAOs) in Nepal: the Department of Hydrology and Meteorology (DHM) and the Department of Mines and Geology (DMG). These organisations are pivotal in generating knowledge advisories for Disaster Risk Reduction and Management (DRRM). Our findings indicate that knowledge production processes within these institutions are profoundly shaped by their contextual realities. Notably, the understanding of what constitutes “science” for DRR is influenced by the vision and mission statements of the SAOs, which are further informed by national mandates. The study also reveals entrenched knowledge hierarchies and power dynamics that elevate hazard research to “core science,” while relegating social science-based risk assessments to a secondary role. By examining these SAOs, we highlight how SAOs operational context complicates the straightforward demand for “more science”. To address these challenges, we argue for greater visibility of the operational context of these SAOs and emphasise the need to reassess their positioning within the DRR landscape to meet the evolving expectations for risk-based scientific advisories.