Related Fields Research Articles

Self-created or collaborative? The effects of virtual influencers' identity type and post type on purchase intentions

PurposeConsidering that when endorsing the same product, virtual influencers with different identity types (self-created, collaborative) can have different impacts on consumers' purchasing behaviors, this paper aims to explore how to maximize the impact effects of the VIs' respective identities. It provides companies with new perspectives on endorsement strategies.Design/methodology/approachThe interaction between VI identity type and post type (informational, storytelling) on purchase intention was analyzed in four experiments (N = 1,007), considering informational and normative social influence as intermediate mechanisms and consumer self-construal as moderators.FindingsThe findings show that self-created VI is suited to informational posts and collaborative VI to storytelling posts. This identity-content match effectively triggers the social influence mechanism: informational posts of self-created VI significantly enhance informational social influence. In contrast, storytelling posts of collaborative VI primarily stimulate normative social influence. Consumer self-construal also moderates the process of influencing mechanisms.Originality/valueBased on social influence theory and matching theory, this paper confirms the existence of an interaction between VI identity types, which influences consumers' purchase intention through informational and normative social influence. This finding fills the research gap in the field of VI endorsement strategy. It also emphasizes the importance of consumer self-construal and contributes new insights into the related field.

MFHARFNet: multi-branch feature hybrid and adaptive receptive field network for image segmentation

Abstract Accurate segmentation of medical images is crucial for disease diagnosis and understanding disease changes. Deep learning methods, utilizing encoder-decoder structures, have demonstrated cutting-edge performance in various medical image segmentation tasks. However, the pooling operation in the encoding stage results in feature loss, which makes the network lack the ability to fuse multi-scale information at different levels, hinders its effective perception of multi-scale information, and leads to poor segmentation performance. Drawing inspiration from the U-shaped network, this study introduces a multi-branch feature hybrid attention and adaptive receptive field network (MFHARFNet) for medical image segmentation. Building upon the encoder-decoder framework, we initially devise a multi-branch feature hybrid attention module (MFHAM) to seamlessly integrate feature maps of varying scales, capturing both fine-grained features and coarse-grained semantics across the entire scale. Furthermore, we redesign the skip connection to amalgamate feature information from different branches in the encoder stage and efficiently transmit it to the decoder, providing the decoder with global context feature maps at different levels. Finally, the adaptive receptive field (ARF) module is introduced in the decoder feature reconstruction stage to adapt and focus on related fields, ensuring the model’s adaptation to different segmentation target features, and achieving different weights for the output of different convolution kernels to improve segmentation performance. We comprehensively evaluate our method on medical image segmentation tasks, by using four public datasets across CT and MRI. Remarkably, MFHARFNet method consistently outperforms other state-of-the-art methods, exceeding UNet by 2.1%, 0.9%, 6.6% and 1.0% on Dice on ATLAS, LiTs, BraTs2019 and Spine and intervertebral disc datasets, respectively. In addition, MFHARFNet minimizes network parameters and computational complexity as much as possible. The source codes are in https://github.com/OneHundred99/MFHARFNet.

Fake News Detection Revisited: An Extensive Review of Theoretical Frameworks, Dataset Assessments, Model Constraints, and Forward-Looking Research Agendas

The emergence and acceptance of digital technology have caused information pollution and an infodemic on Online Social Networks (OSNs), blogs, and online websites. The malicious broadcast of illegal, objectionable and misleading content causes behavioural changes and social unrest, impacts economic growth and national security, and threatens users’ safety. The proliferation of AI-generated misleading content has further intensified the current situation. In the previous literature, state-of-the-art (SOTA) methods have been implemented for Fake News Detection (FND). However, the existing research lacks multidisciplinary considerations for FND based on theories on FN and OSN users. Theories’ analysis provides insights into effective and automated detection mechanisms for FN, and the intentions and causes behind wide-scale FN propagation. This review evaluates the available datasets, FND techniques, and approaches and their limitations. The novel contribution of this review is the analysis of the FND in linguistics, healthcare, communication, and other related fields. It also summarises the explicable methods for FN dissemination, identification and mitigation. The research identifies that the prediction performance of pre-trained transformer models provides fresh impetus for multilingual (even for resource-constrained languages), multidomain, and multimodal FND. Their limits and prediction capabilities must be harnessed further to combat FN. It is possible by large-sized, multidomain, multimodal, cross-lingual, multilingual, labelled and unlabelled dataset curation and implementation. SOTA Large Language Models (LLMs) are the innovation, and their strengths should be focused on and researched to combat FN, deepfakes, and AI-generated content on OSNs and online sources. The study highlights the significance of human cognitive abilities and the potential of AI in the domain of FND. Finally, we suggest promising future research directions for FND and mitigation.

A novel 3D uniformity measurement method in mechanical stirring systems

AbstractAccurate assessment of mixing uniformity is crucial in industrial mixing processes. This study proposes an evaluation method for three‐dimensional (3D) mixing processes that combines dual‐camera positioning and point pattern density fluctuation (PD) based on disordered hyperuniformity. This study employs a positioning method using dual‐camera to achieve precise capture and reconstruction of tracer particles in 3D space. The 3D reconstruction data is then evaluated for mixing performance using the PD method. A relationship model between |k| and time I values with mixing time was established for λ = 1. The results indicate that mixing time decreases with the increase of |k| and decreases with the decrease of I values. To ensure the accuracy of the PD method, feasibility analysis was conducted using conductometry. Additionally, the superiority of the PD method was validated by comparing it with the 3D‐Q method. The impact of bottom height of stirring paddle and motor speed on mixing effect were also investigated. This study establishes a fundamental groundwork and theoretical framework for optimizing parameters of stirring systems and assessing 3D mixing uniformity. It also offers important references and insights for engineering practices and theoretical research in related fields.

A Review on the Recent Process of Lazy Wave Risers

Lazy wave risers (LWRs) are designed with equidistant buoyancy blocks attached in the lower half of the riser, allowing the riser to take on an arch shape under the buoyancy forces provided by buoyancy blocks. This arch configuration can provide flexibility to the LWR arrangement and effectively isolate the dynamic responses between the offshore floating structure and the riser’s touchdown zone (TDZ). Its design and application aim to address the issues of dynamic response and fatigue damage that traditional steel catenary risers (SCRs) face in deep water and complex marine environments. Given that research on the LWRs in the field of ocean engineering is not sufficiently abundant, the structural characteristics, hydrodynamic loads, global responses, fatigue damage assessment, and structural optimization progress of LWRs are systematically reviewed in this paper to provide references for researchers in related fields. Among these topics, the global response of LWRs is the main point of this review. This section details the theoretical analysis and numerical modeling methods employed in the study of LWRs’ global response, explores the research advancements in the vortex-induced vibration (VIV) related to LWRs, and discusses corresponding experimental studies. Finally, the installation, transfer, and repair processes of LWRs are investigated. Additionally, the importance of leveraging advanced technologies from other fields and combining them with current advanced algorithms is emphasized in efforts to assess fatigue damage and optimize the structures of LWRs, ultimately achieving complementary advantages.

Improving flexible actuating behaviors of gel-based artificial muscles through chamomile polysaccharide crosslinking

Abstract The flexible actuating behaviors of gel-based artificial muscles (GBAMs) are contingent upon the properties of their hydrogel actuating membranes. While the current preparation system for these membranes is deemed flawless, the electromechanical characteristics are constrained by the inherent properties of the material. The majority of raw materials used in this process are chemically synthesized; however, Chinese herbal polysaccharides offer a convenient, environmentally friendly, and non-toxic alternative, making them a prime candidate for actuating membrane preparation. The biological activities of chamomile polysaccharide (CP) include anti-inflammatory, lipid-lowering, sugar-lowering, and OH- clearance properties. Therefore, the actuating membrane of GBAM was prepared by crosslinking sodium alginate (SA) with CP. The findings indicated that at a crosslinking ratio of 4:5 for CP-SA, the electrically actuated force density and response speed reached 20.12 mN/g and 0.09 mN/g·s, respectively. Additionally, the working life extended to 781 seconds, tremor frequency decreased by 47.67%, and tremor amplitude was 19.55% of the control group. The elastic modulus was measured at 15.44 MPa, specific capacitance reached 183.99 mF/g, and internal resistance decreased by 13.44%. Charge and discharge time was 5.73 seconds, maximum energy reached 2.7 J, and specific energy was 12.66 A·J/g, representing increases of 2.3 seconds, 64.63%, and 6.47 A·J/g compared to the control group. The deflection displacement of 6.62 mm in the CP-SA group at a crosslinking ratio of 4:5 was found to be 3.06 times greater than that of the control group. In conclusion, the actuating membrane of GBAM, synthesized through the cross-linking of CP with SA at a specific ratio, demonstrated superior properties. This innovation offers a novel perspective and direction for the advancement of GBAMs and is anticipated to significantly contribute to future developments in related fields.

Vision-Based Drone Detection in Complex Environments: A Survey

The frequent illegal use of drones poses a serious threat to public security and property. Counter-drones are crucial tools. The prerequisite for an effective counter-drone is to detect drones accurately. With the rapid advancements in computer vision, vision-based drone detection methods have emerged as a hot topic of research. However, current reviews of vision-based drone detection are less focused on algorithmic summarization and analysis. For this reason, this survey aims to comprehensively review the latest methods for vision-based drone detection in complex environments, with the goal of providing a more thorough reference and guidance for related research. We first explore the imaging and drone characteristics in complex environments and summarize the main challenges of visual UAV detection. Then, we summarize the existing solutions for the main challenges. Finally, we systematically organize and introduce the commonly used datasets and evaluation metrics and conduct experiment comparisons based on the representative methods. We not only reveal the current development status of visual UAV detection but also analyze the deficiencies in current research. On this basis, we further look forward to future research directions and possible breakthroughs, with a view to providing useful insights for further research and development in related fields.

A review of multimodal deep learning methods for genomic-enabled prediction in plant breeding.

Deep learning methods have been applied when working to enhance the prediction accuracy of traditional statistical methods in the field of plant breeding. Although deep learning seems to be a promising approach for genomic prediction, it has proven to have some limitations, since its conventional methods fail to leverage all available information. Multimodal deep learning methods aim to improve the predictive power of their unimodal counterparts by introducing several modalities (sources) of input information. In this review, we introduce some theoretical basic concepts of multimodal deep learning and provide a list of the most widely used neural network architectures in deep learning, as well as the available strategies to fuse data from different modalities. We mention some of the available computational resources for the practical implementation of multimodal deep learning problems. We finally performed a review of applications of multimodal deep learning to genomic selection in plant breeding and other related fields. We present a meta-picture of the practical performance of multimodal deep learning methods to highlight how these tools can help address complex problems in the field of plant breeding. We discussed some relevant considerations that researchers should keep in mind when applying multimodal deep learning methods. Multimodal deep learning holds significant potential for various fields, including genomic selection. While multimodal deep learning displays enhanced prediction capabilities over unimodal deep learning and other machine learning methods, it demands more computational resources. Multimodal deep learning effectively captures intermodal interactions, especially when integrating data from different sources. To apply multimodal deep learning in genomic selection, suitable architectures and fusion strategies must be chosen. It is relevant to keep in mind that multimodal deep learning, like unimodal deep learning, is a powerful tool but should be carefully applied. Given its predictive edge over traditional methods, multimodal deep learning is valuable in addressing challenges in plant breeding and food security amid a growing global population.

Measuring the Impact of Parent Brand Equity on Brand Extension: A Luxury Fashion Industry Perspective

In a diversified consumer society with extreme product homogeneity and fierce competition, many luxury fashion brands try to broaden their product offerings. Although brand extension is now highly common, it is not always met with success. Indeed, incorrect evaluations of brand equity can lead to consumer dissatisfaction with extended products, thus losing the opportunity to extend in the target market. On the other hand, previous studies have repeatedly verified the influence of the extended brand on the parent brand’s equity. Accordingly, few studies have concentrated on the influence of the parent brand’s equity on the extended brand, especially in the field of luxury fashion brands. Therefore, this study employs the brand equity model, including brand loyalty, awareness, perceived quality, and association, and measures the influence of parent brand’s equity on the extended brand from a consumer perspective. In this study, data from 151 consumers who have purchased luxury fashion products in Kuala Lumpur were collected through quantitative methods and the results were obtained by a simple linear regression analysis of SPSS. The results showed statistical significance with p<0.05 which revealed that the parent brand's equity had a significant positive impact on its extended brand while most studies focused on the impact of the extended brand on the parent brand. Simply put, this research not only makes a theoretical contribution to the parent–extended brand relationship but also fills knowledge gaps in related fields. Moreover, based on the empirical results, this study proposes a series of suggestions for correctly measuring brand equity to facilitate smoother brand extension, providing practical guidance for the luxury fashion industry.

Application of 3D printing in bacterial detection

Diseases caused by bacterial infections, especially pathogen variations and drug-resistant bacteria, are a serious threat to human health globally. Accurate and rapid identification of bacterial pathogens is essential for early diagnosis of infections and timely treatment of disease. At present, the routine diagnostic methods for identifying bacterial pathogens in clinic are bacterial culture, immunological detection, genetic analysis, etc. These methods occupy foundational position for bacterial detection that favors for clinical diagnosis of pathogen infection in daily life. Notably, 3D printing technology, together with 3D bioprinting technology, provides more options for bacterial detection due to its personalized design and manufacturing. Compared with traditional methods, 3D printing provides a more convenient, rapid and accurate bacterial detection platform, thus meeting the urgent needs of clinical and scientific research. Here, we have summarized the research progress and given a comprehensive review of 3D printing technology in bacterial detection, including bacterial detection sensors, microfluidic chips, bioprinting microarray technology, AI and spectroscopy technology, providing a scientific reference and filling in gaps in 3D printing-assistance bacterial detection. At the same time, technical advantages, challenges and future development trends will also be discussed in related healthcare fields.

A Review of Deep Learning-Based Remote Sensing Image Caption: Methods, Models, Comparisons and Future Directions

Remote sensing images contain a wealth of Earth-observation information. Efficient extraction and application of hidden knowledge from these images will greatly promote the development of resource and environment monitoring, urban planning and other related fields. Remote sensing image caption (RSIC) involves obtaining textual descriptions from remote sensing images through accurately capturing and describing the semantic-level relationships between objects and attributes in the images. However, there is currently no comprehensive review summarizing the progress in RSIC based on deep learning. After defining the scope of the papers to be discussed and summarizing them all, the paper begins by providing a comprehensive review of the recent advancements in RSIC, covering six key aspects: encoder–decoder framework, attention mechanism, reinforcement learning, learning with auxiliary task, large visual language models and few-shot learning. Subsequently a brief explanation on the datasets and evaluation metrics for RSIC is given. Furthermore, we compare and analyze the results of the latest models and the pros and cons of different deep learning methods. Lastly, future directions of RSIC are suggested. The primary objective of this review is to offer researchers a more profound understanding of RSIC.

A Cross-Attention-Based Class Alignment Network for Cross-Subject EEG Classification in a Heterogeneous Space

Background: Domain adaptation (DA) techniques have emerged as a pivotal strategy in addressing the challenges of cross-subject classification. However, traditional DA methods are inherently limited by the assumption of a homogeneous space, requiring that the source and target domains share identical feature dimensions and label sets, which is often impractical in real-world applications. Therefore, effectively addressing the challenge of EEG classification under heterogeneous spaces has emerged as a crucial research topic. Methods: We present a comprehensive framework that addresses the challenges of heterogeneous spaces by implementing a cross-domain class alignment strategy. We innovatively construct a cross-encoder to effectively capture the intricate dependencies between data across domains. We also introduce a tailored class discriminator accompanied by a corresponding loss function. By optimizing the loss function, we facilitate the aggregation of features with corresponding classes between the source and target domains, while ensuring that features from non-corresponding classes are dispersed. Results: Extensive experiments were conducted on two publicly available EEG datasets. Compared to advanced methods that combine label alignment with transfer learning, our method demonstrated superior performance across five heterogeneous space scenarios. Notably, in four heterogeneous label space scenarios, our method outperformed the advanced methods by an average of 7.8%. Moreover, in complex scenarios involving both heterogeneous label spaces and heterogeneous feature spaces, our method outperformed the state-of-the-art methods by an average of 4.1%. Conclusions: This paper presents an efficient model for cross-subject EEG classification under heterogeneous spaces, which significantly addresses the challenges of EEG classification within heterogeneous spaces, thereby opening up new perspectives and avenues for research in related fields.

Marketing Communication Strategy of Youth Works Breakfast Week (Pasar Kamu) in Increasing Local Tourist Visits in Kampoeng Lama Tourist Village, Deli Serdang Regency

This research aims to analyze the marketing communication strategy and analyze the obstacles of the marketing communication strategy of the Youth Works Breakfast Week (Pasar Kamu) in increasing local tourist visits in Kampoeng Lama Tourism Village, Deli Serdang Regency. This research uses constructivism paradigm with descriptive method and qualitative approach. The results of this study are that the marketing communication strategy of Pekan Sarapan Karya Muda (Pasar Kamu) in increasing local tourist visits at Kampoeng Lama Tourism Village, Deli Serdang Regency is by implementing marketing communication with the application of a marketing mix consisting of personal selling, advertising, as well as public relations, publicity and viral marketing. Through personal selling, managers and traders play an active and direct role in promoting to visitors who come Pasar Kamu. Advertising carried out by Pekan Sarapan Karya Anak Muda (Pasar Kamu) in collaboration with Metro Kampung as print media to promote activities that take place at Pekan Sarapan Karya Anak Muda (Pasar Kamu). Pasar Kamu's public relations role does not have a big impact because there is no public relations field. However, with the publicity carried out by Pasar Kamu to reach its target audience such as using goodie bags produced to do marketing with the public. There is viral marketing where visitors take the initiative to spread content containing information about Pasar Kamu on their Instagram and Tiktok social media accounts so that it becomes viral and succeeds in increasing tourist visits to Pasar Kamu

An Imperfect List of Eminent Sport and Exercise Psychology Researchers

The purpose of the present study was to identify eminent sport and exercise psychology researchers, based on citation count, and their most highly cited papers. Men (n = 121) and women (n = 32) from 17 different countries (e.g., the United States n = 47 to Ireland n = 1) were identified. Researchers were selected if their h-index was 60 or greater for Google Scholar and Web of Science combined. This criterion reflects Hirsch’s (2005) characterization as follows based on h-index: a successful scientist (20), an outstanding scientist (40), and truly unique (60), for one database. Our criteria of 60 for two databases are equivalent to a designation halfway between successful and outstanding. The results indicate an h-index mean of 103, median of 94, mode of 105, and range of 60–346. Top-cited publications leaned toward more sport psychology than exercise psychology topics. Prominent journals included the Journal of Sport and Exercise Psychology (n = 24) and Psychology of Sport and Exercise (n = 15). Other publications were in related fields (e.g., Psychology and Health). In summary, we have identified the most highly cited researchers in sport and exercise psychology and their single most-cited papers. We hope this information is of both heuristic and practical value to readers.

Antibiofilm and antivirulence efficacy of Pleurotus platypus methanolic extract against Staphylococcus aureus through ROS generation and cell membrane disruption

Multi-drug resistance is recognized as a significant worldwide public health concern in the current century. Biofilm formation further exacerbates bacterial resistance to antibacterial medications, host immunological responses, and phagocytosis, resulting in long-lasting chronic illnesses. Investigating natural resources is a very potent approach for developing alternative anti-infective medications to effectively control multi-drug resistant bacterial infections. In this study, a unique mushroom species namely Pleurotus platypus had been discovered from the Terai-Duars region of West Bengal, India. The myco-chemical profiling and preliminary chemical analysis of Pleurotus platypus methanolic extract determined the significant presence of metabolites belonging to several major chemical classes such as flavonoid, alkaloid, triterpenoid, polyphenol, benzoic acids, coumarin, flavone etc. Most intriguingly, the extract possessed effective antibacterial, antibiofilm and antivirulence properties against Staphylococcus aureus and Methicillin resistant Staphylococcus aureus, one of the most notable drug-resistant opportunistic and nosocomial pathogens. Mechanistically, the mushroom extract enhanced the production of Reactive Oxygen Species (ROS) inside the targeted bacteria, causing alterations in membrane potential, damage to the cellular membrane and further release of intracellular DNA, destined to cell death. Moreover, the methanolic extract reported the eradication of pre-existing biofilms from the urinary catheter surface, hinting towards its future application in the related field. To summarize, Pleurotus platypus methanolic extract could be an excellent alternative antibacterial and antibiofilm therapeutic candidate for the effective management of Staphylococcus infections with improved outcome.

Ethical considerations in surgical research conducted in African LMICs: a comprehensive narrative review

Introduction: Surgical research has revolutionized healthcare by improving the efficacy, safety, and efficiency of surgical interventions. This research has the potential to significantly impact healthcare delivery in Africa, where surgical diseases pose a major public health burden. Studies suggest surgery alone could reduce the global disease burden by 11%, with a substantial portion stemming from conditions prevalent in sub-Saharan Africa like traumatic injuries, childbirth complications, and surgical infections. However, conducting surgical research in Africa presents unique ethical challenges. The continent’s diverse socio-economic environments and rich cultural contexts necessitate careful consideration of ethical principles. Traditional research models often don’t translate well to African settings, raising concerns about informed consent, community engagement, and benefit-sharing. Aim: This review aims to shed light on various ethical dilemmas posed within surgical research, conducted in African countries. Further, standard practices and recommendations that involve a multi-pronged approach to mitigate said issues were explored. Materials and methods: A meticulous analysis of the existing literature pertaining to the ethical issues proffered in Africa was performed. Databases employed comprised PubMed/MEDLINE, Embase, Scopus, and EBSCOhost. Pertinent case studies were also reviewed to comprehend said issues, providing comprehensive recommendations. Results: In addressing the ethical challenges in the research, the importance of resource limitation, sociocultural factors, informed consent barriers, technological obsolescence, training deficits, power dynamics, vulnerable populations, regulatory oversight, and compliance was highlighted. The proposed approaches include conducting focused research lectures and practical workshops on surgery, organizing surgical boot camps for medical students, interns, and residents, promoting research in related fields such as anesthesia, radiology, and pathology, ensuring the presence of on-site surgeons as needed, and arranging outreach surgical and educational camps for patients. Conclusion: Promoting community engagement and training local researchers and surgeons are crucial for navigating the unique ethical landscape in Africa. By prioritizing ethical considerations, surgical research can contribute to improved healthcare outcomes and a more equitable healthcare system across the continent.

Study on the unique separation effect of emulsion in the stirring-induced turbulent flow field

Much attention has been gained to treat organic pollution dominated by oil pollutants in water with the frequent discharge of wastewater in industries. Liquid–liquid emulsion is a typical existence form of water and oil pollutants. The relative stable flow field is a necessary condition to separate the emulsion, while turbulence of the flow field is required for homogeneous mixing. For centuries, stirring-induced turbulent flow has been used for mixing, with a limited number of separation cases achieved by the stable part of the container at low Reynolds numbers (Re). In this study, the separation of liquid–liquid emulsions in a fully turbulent system for Reynolds number values above 10,000 was observed for the first time. Besides the turbulent flow influenced by the stirring speed, the additional dispersed phase, which was the same as the emulsified dispersed phase, played a critical role in the separation performance. The turbidity of oil-in-water emulsion could be decreased to less than 5 NTU (nephelometric turbidity unit) from 500 NTU under the optimal conditions in this study, with a separation efficiency of over 99%. This peculiar and wonderful characteristic of the turbulent flow and the study of the parameters and constraint conditions for separation may extend the application field of the turbulent flow field from conventional mixing to oil pollutants separation from water in environmental engineering.

Renewable energy communities: Democratically legitimate agents in governing the energy transition?

It is crucial that the energy transition enfolds democratically, to increase its legitimacy and improve its outcomes. Although practitioners and scholars increasingly point to renewable energy communities (RECs) as legitimate agents for governing a democratic energy transition, RECs' democratic practices are poorly understood, which challenges assumptions about their legitimacy. We therefore analyzed how and to what extent democratic legitimacy is being pursued and met by RECs in their governance of renewable energy generation projects. Our comparative case study examined four different RECs in Utrecht, The Netherlands. We found that three out of the four studied RECs broadly meet the principles of energy democracy in the initial, developing and implementation phase of their energy generation projects. Significant differences between principles and RECs were found, which refutes the simplistic general assumption that RECs are either fully democratically legitimate or are not democratically legitimate at all. In reality, RECs face several trade-offs between democratic principles, which arise from the different legitimacy needs related to the REC's stage of maturity and the type of energy source it uses. To overcome these trade-offs and uphold legitimacy internally as well as with various stakeholders, RECs strategically organize participation and decision making in different governance spaces and prioritize certain specific legitimacy principles over others. Combining the concepts of energy democracy and democratic legitimacy has brought novel theoretical insights to the fore, and we recommend other researchers to further bridge these distinct but related fields of study.

A Novel Approach to Enhancing the Accuracy of Prediction in Ship Fuel Consumption

Ship fuel consumption plays a crucial role not only in understanding ships’ energy efficiency but also in gaining insights into their emissions. However, enhancing the accuracy of these predictions poses significant challenges due to data limitations and the methods employed. Due to these factors, such as data variability and equipment characteristics, ship fuel consumption exhibits certain fluctuations under specific conditions. Previous fuel consumption prediction methods primarily generate a single specific value, making it difficult to capture the volatility of and variability in fuel consumption. To overcome this limitation, this paper proposes a novel method that integrates Gaussian process prediction with quantile regression theory to perform interval predictions of ship fuel consumption, providing a range of possible outcomes. Through comparative analyses with traditional methods, the possibility of using the method is verified and its results are validated. The results indicate the following: (1) at a 95% confidence level, the proposed method achieves a prediction interval coverage probability of 0.98 and a prediction interval normalized average width of 0.123, which are significantly better than those of the existing backpropagation neural network (BPNN) and gradient boosting decision tree (GBDT) quantile regression models; (2) the prediction accuracy of the proposed method is 92% for point forecasts; and (3) the proposed method is applicable to main datasets, including both noon report and sensor datasets. These findings provide valuable insights into interval predictions of ship fuel consumption and highlight their potential applications in related fields, emphasizing the importance of accurate interval predictions in intelligent energy efficiency optimization.

Guideline for medical nutritional treatment of adult patients with gastrointestinal dysfunction (2025 edition)

Gastrointestinal dysfunction is a common clinical disease. Due to abnormal gastrointestinal digestive and absorption functions, nutrients, water, and electrolytes cannot be absorbed properly, and therefore, scientific and reasonable nutritional intervention is needed. In order to further standardize medical nutrition treatment of patients with gastrointestinal dysfunction, the Chinese Society of Parenteral and Enteral Nutrition (CSPEN) organized experts and scholars in related fields in China to elaborate on the following topics based on current evidence-based medical evidence: the standard process of medical nutrition treatment for patients with gastrointestinal dysfunction, the time and mode of enteral and parenteral nutrition application, and the nutritional intervention mode for patients with gastrointestinal dysfunction in special disease state. Finally, 29 questions and 58 recommendations were formed to provide reference for the standardized application of medical nutrition therapy for patients with gastrointestinal dysfunction.