bluuebunny/crossref_metadata_2025_split huggingface dataset:
Strengthening a collaborative approach to implementing surveillance systems: Lessons from the Enhanced Gonococcal Antimicrobial Surveillance Programme (EGASP) in Malawi and Zimbabwe
Authors: Vumbugwa Phiona , Maatouk Ismael , Machiha Anna , Matoga Mitch , Mitambo Collins , Nyirenda Rose , Nyasulu Ishmael , Mugabe Muchaneta , Ngwenya Mkhokheli , Mundade Yamuna , Wi Teodora , Unemo Magnus , Soge Olusegun O. 
Published: March 2024

AbstractBackgroundWith the number of antimicrobials available to effectively treat gonorrhoea rapidly diminishing, surveillance of antimicrobial–resistantNeisseria gonorrhoeae(NG) is critical for global public health security activity. Many low-and-middle-income countries (LMICs) have gaps in their existing sexually transmitted infections (STIs) surveillance systems that negatively impact global efforts geared towards achieving the United Nations (UN) Sustainable Development Goals (SDGs). This paper explains the contribution of collaborative surveillance systems to health systems strengthening (HSS) learning from integrating NG surveillance into existing Ministries of Health’s (MoH) antimicrobial resistance (AMR) surveillance services in Malawi and Zimbabwe.MethodsWe used the WHO Enhanced Gonococcal Antimicrobial Surveillance Programme (EGASP) implementation experiences in Malawi and Zimbabwe to demonstrate the collaboration in AMR and STI surveillance. We conducted qualitative interviews with purposively selected health managers directly participating in the AMR and STI programs using a standardized key informant guide to describe how to plan for a collaborative surveillance system. Qualitative thematic analysis was conducted to delineate stakeholders’ recommendations using the health systems’ building blocks.ResultsStakeholder engagement, prioritization of needs, and power to negotiate were key drivers to a successful collaborative surveillance system. Weak governance, policies, lack of accountability, and different priorities, coupled with weak collaborations, workforce, and health information systems, were challenges faced in having effective collaborative surveillance systems. Data availability, use, and negotiation power were key drivers for the prioritization of collaborative surveillance. Including collaborative surveillance in primary health services and increasing government budget allocation for surveillance were recommended.ConclusionsStrengthening collaborative surveillance systems in LMICs using a people-centered approach increases transparency and accountability and empowers national institutions, communities, and stakeholders to engage in sustainable activities that potentially strengthen health systems. EGASP implementations in Zimbabwe and Malawi serve as models for other countries planning to implement or improve collaborative surveillance systems in their context.
Untangling influences of information relevance and media richness on health anxiety and COVID-19-related stress: perspective of stimulus-organism-response
Authors: Pang Hua , Zhou Enhui , Xiao Yi 
Published: March 2024

PurposeIn light of the stimulus-organism-response (SOR) theoretical paradigm, this paper explores how information relevance and media richness affect social network exhaustion and, moreover, how social network exhaustion ultimately leads to health anxiety and COVID-19-related stress.Design/methodology/approachThe conceptual model is explicitly analyzed and estimated by using data from 309 individuals of different ages in mainland China. Confirmatory factor analysis (CFA) and structural equation modeling (SEM) were utilized to validate the proposed hypotheses through the use of online data.FindingsThe findings suggest that information relevance is negatively associated with social network exhaustion. In addition, social network exhaustion is a significant predictor of health anxiety and stress. Furthermore, information relevance and media richness can indirectly influence health anxiety and stress through the mediating effect of social network exhaustion.Research limitations/implicationsTheoretically, this paper verifies the causes and consequences of social network exhaustion during COVID-19, thus making a significant contribution to the theoretical construction and refinement of this emerging research area. Practically, the conceptual research model in this paper may provide inspiration for more investigators and scholars who are inclined to further explore the different dimensions of social network exhaustion by utilizing other variables.Originality/valueAlthough social network exhaustion and its adverse consequences have become prevalent, relatively few empirical studies have addressed the deleterious effects of social network exhaustion on mobile social media users’ psychosocial well-being and mental health during the prolonged COVID-19. These findings have important theoretical and practical implications for the rational development and construction of mobile social technologies to cultivate proper health awareness and mindset during the ongoing worldwide COVID-19 epidemic.
Deep learning algorithms for temperature prediction in two-phase immersion-cooled data centres
Authors: Suresh Pratheek , Chakravarthy Balaji 
Published: March 2024

Purpose
As data centres grow in size and complexity, traditional air-cooling methods are becoming less effective and more expensive. Immersion cooling, where servers are submerged in a dielectric fluid, has emerged as a promising alternative. Ensuring reliable operations in data centre applications requires the development of an effective control framework for immersion cooling systems, which necessitates the prediction of server temperature. While deep learning-based temperature prediction models have shown effectiveness, further enhancement is needed to improve their prediction accuracy. This study aims to develop a temperature prediction model using Long Short-Term Memory (LSTM) Networks based on recursive encoder-decoder architecture.

Design/methodology/approach
This paper explores the use of deep learning algorithms to predict the temperature of a heater in a two-phase immersion-cooled system using NOVEC 7100. The performance of recursive-long short-term memory-encoder-decoder (R-LSTM-ED), recursive-convolutional neural network-LSTM (R-CNN-LSTM) and R-LSTM approaches are compared using mean absolute error, root mean square error, mean absolute percentage error and coefficient of determination (R2) as performance metrics. The impact of window size, sampling period and noise within training data on the performance of the model is investigated.

Findings
The R-LSTM-ED consistently outperforms the R-LSTM model by 6%, 15.8% and 12.5%, and R-CNN-LSTM model by 4%, 11% and 12.3% in all forecast ranges of 10, 30 and 60 s, respectively, averaged across all the workloads considered in the study. The optimum sampling period based on the study is found to be 2 s and the window size to be 60 s. The performance of the model deteriorates significantly as the noise level reaches 10%.

Research limitations/implications
The proposed models are currently trained on data collected from an experimental setup simulating data centre loads. Future research should seek to extend the applicability of the models by incorporating time series data from immersion-cooled servers.

Originality/value
The proposed multivariate-recursive-prediction models are trained and tested by using real Data Centre workload traces applied to the immersion-cooled system developed in the laboratory.
Prediction and comparative analysis of friction material properties using a GA-SVM optimization model
Authors: Zhang Jianping , Wang Leilei , Wang Guodong 
Published: March 2024

Purpose
With the rapid advancement in the automotive industry, the friction coefficient (FC), wear rate (WR) and weight loss (WL) have emerged as crucial parameters to measure the performance of automotive braking systems, so the FC, WR and WL of friction material are predicted and analyzed in this work, with an aim of achieving accurate prediction of friction material properties.

Design/methodology/approach
Genetic algorithm support vector machine (GA-SVM) model is obtained by applying GA to optimize the SVM in this work, thus establishing a prediction model for friction material properties and achieving the predictive and comparative analysis of friction material properties. The process parameters are analyzed by using response surface methodology (RSM) and GA-RSM to determine them for optimal friction performance.

Findings
The results indicate that the GA-SVM prediction model has the smallest error for FC, WR and WL, showing that it owns excellent prediction accuracy. The predicted values obtained by response surface analysis are closed to those of GA-SVM model, providing further evidence of the validity and the rationality of the established prediction model.

Originality/value
The relevant results can serve as a valuable theoretical foundation for the preparation of friction material in engineering practice.
Assessing the moderating effects of involvement on tourist attitudes and intentions through virtual reality applications
Authors: Talawar Abhishek , Suresh Sheena , Alathur Sreejith 
Published: March 2024

PurposeThis paper aims to evaluate the impact of various preview modes on tourist attitudes and intentions to visit a destination based on consumers’ level of involvement in travel decision-making.Design/methodology/approachThe study was conducted as a between-subjects one-factor [preview mode: static images vs 360-degree tour vs virtual reality (VR) mode] in a laboratory experiment setup to examine how consumers with different levels of involvement in travel decision-making respond to destination marketing toward three different preview modes.FindingsThe findings indicated that VR preview mode highly influences tourist attitudes and visit intentions toward a destination compared to static images and 360-degree tours. This effect is more significant among participants with higher levels of customer involvement. Finally, the results from the study offer empirical evidence of the effectiveness of VR in shaping user behavior compared to traditional preview modes.Research limitations/implicationsThe limitations are using a non-probability sampling method, a small sample size and affordable mobile-compatible VR headsets.Practical implicationsThis study offers empirical evidence on the effectiveness of VR in shaping tourist behavior compared to traditional preview modes. It helps destination marketers develop appropriate strategies for promoting tourist destinations.Originality/valueThe novelty of this paper lies in understanding the effectiveness of VR in shaping tourist behavior with different levels of customer involvement in travel decision-making.
Science mapping the knowledge base on microlearning: using Scopus database between 2002 and 2021
Authors: Pham Hiep-Hung , Nguyen Ngoc-Thi Nhu , Dinh Hai Luong , Nguyen Tien-Trung , Nguyen Van An Le 
Published: March 2024

PurposeWith the advancement of technology, microlearning has emerged as a promising method to improve the efficacy of teaching and learning. This study aims to investigate the document types, volume, growth trajectory, geographic contribution, coauthor relationships, prominent authors, research groups, influential documents and publication outlets in the microlearning literature.Design/methodology/approachWe adapt the PRISMA guidelines to assess the eligibility of 297 Scopus-indexed documents from 2002 to 2021. Each was manually labeled by educational level. Descriptive statistics and science mapping were conducted to highlight relevant objects and their patterns in the knowledge base.FindingsThis study confirms the increasing trend of microlearning publications over the last two decades, with conference papers dominating the microlearning literature (178 documents, 59.86%). Despite global contributions, a concentrated effort from scholars in 15 countries (22.39%) yielded 68.8% of all documents, while the remaining papers were dispersed across 52 other nations (77.61%). Another significant finding is that most documents pertain to three educational level categories: lifelong learning, higher education and all educational levels. In addition, this research highlights six key themes in the microlearning domain, encompassing (1) Design and evaluation of mobile learning, (2) Microlearning adaptation in MOOCs, (3) Language teaching and learning, (4) Workflow of a microlearning system, (5) Microlearning content design, (6) Health competence and health behaviors. Other aspects analyzed in this study include the most prominent authors, research groups, documents and references.Originality/valueThe finding represents all topics at various educational levels to offer a comprehensive view of the knowledge base.
Agronomic non‐native species are overrepresented across habitat types in central Canada
Authors: Murillo Raytha de Assis , Pätsch Ricarda , Wagner Viktoria 
Published: March 2024

Abstract

In response to non‐native plant invasions, many governments have established noxious weed lists to control and eradicate introduced invasive plants. However, invasive agronomic species are often unregulated, and the extent of their invasion across habitat types has never been determined.

Using vegetation surveys across 222 plots, we investigated the contribution of agronomic versus noxious non‐native species to the levels of invasion across seven habitat types in central Canada.

Although agronomic non‐native species, such as Poa pratensis and Bromus inermis, only made up 21.3% of the non‐native species pool, they accounted for 43.6% of non‐native plant species frequency and 74.4% of relative non‐native plant cover. They were also disproportionately overrepresented compared with noxious non‐native species across all habitat types, except saline marshes.

The overabundance of agronomic non‐native plants in central Canada is likely the legacy of cultivar breeding, intentional seeding of introduced forage species and a lack of incentive for the use of native species.

Synthesis and applications: Decelerating the invasion by agronomic non‐native plants will require approaches beyond noxious weed lists and initiatives in research, policy and education. These tools can include incentives to use native seed material, for example, in reclamation and forage and turf production, to reduce propagule pressure from invasive agronomic non‐native plants.
Factors controlling germination and seedling growth of an endangered Saussurea species (Asteraceae) endemic to serpentine areas in Japan
Authors: Inoue Ryusuke , Yamamoto Akihiro , Wasaki Jun , Nakatsubo Takayuki 
Published: March 2024

AbstractSaussurea ochiaiana Kadota (Asteraceae) is a perennial herb endemic to serpentine areas in western Japan. To date, only two locations are known habitats for this species of unknown ecological requirements. Therefore, this study aimed to clarify the environmental factors that limit the germination and seedling growth of this species, which is an essential requirement for designing a suitable conservation strategy. Our germination experiments, in which the achenes were subjected to a gradually increasing temperature and a gradually decreasing temperature after short (1 month) or long (4 months) storage, indicated that the achenes required a long cold stratification for germination. In an experiment where plants collected at the study site were potted individually, they did not grow under strongly shaded conditions consisting of only 10% relative photosynthetic photon flux density (RPPFD), but grew well under moderately shaded conditions (50% RPPFD). No significant differences in seedling growth were observed among the three pH conditions tested (pH 4, 6, and 8). Although average seedling shoot weight was significantly larger at low Ca:Mg molar ratios (0.6 and 0.3) than at high substrate Ca:Mg molar ratios (15 and 1.5) in pot culture experiments, the difference between treatments was not very large (<20%). Habitat conditions suitable for the regeneration of this species are decreasing because of climate change and the depopulation of rural communities (reduction in management); therefore, urgent conservation strategies, including ex situ conservation, are needed.
Impact de l'intégration financière régionale sur le commerce intra régional des produits manufacturés dans la zone CEDEAO
Authors: Issoufou Dogo Abdoul Rachid 
Published: March 2024

RésuméCet article évalue l'impact de l'intégration financière régionale sur le commerce intra régional des produits manufacturés dans les pays de la Communauté Economique des Etats de l'Afrique de l'Ouest (CEDEAO) en utilisant un modèle de gravité augmenté sur la période 2005–2019. En utilisant une mesure de régionalisation financière basée sur l'intégration des marchés de crédits et la différence des taux d'intérêts, les résultats empiriques indiquent que l'intégration financière régionale a un impact positif et significatif sur le commerce intra régional des produits manufacturés aussi bien dans les pays exportateurs que dans les pays importateurs dans la zone CEDEAO. Les résultats des différents tests de robustesse effectués corroborent cette conclusion et confirment la consistance de nos résultats. L'étude suggère aux décideurs politiques d'encourager les reformes politiques visant à renforcer davantage le processus de régionalisation financière afin de faciliter l'accès aux capitaux et de réduire les contraintes des financements pour booster les investissements et le commerce dans le secteur manufacturier au niveau régional.
In‐package cold plasma treatment to extend the shelf life of food
Authors: Alaguthevar Ramalakshmi , Packialakshmi Jeyakumar Saranya , Murugesan Balakrishnan , Rhim Jong‐Whan , Thiyagamoorthy UmaMaheshwari 
Published: March 2024

AbstractConventional food preservation methods such as heat treatment, irradiation, chemical treatment, refrigeration, and coating have various disadvantages, like loss of food quality, nutrition, and cost‐effectiveness. Accordingly, cold plasma is one of the new technologies for food processing and has played an important role in preventing food spoilage. Specifically, in‐package cold plasma has become a modern trend to decontaminate, process, and package food simultaneously. This strategy has proven successful in processing various fresh food ingredients, including spinach, fruits, vegetables, and meat. In particular, cold plasma treatment within the package reduces the risk of post‐processing contamination. Cryoplasm decontamination within packaging has been reported to reduce significantly the microbial load of many foods’ spoilage‐causing pathogens. However, studies are needed to focus more on the effects of in‐package treatments on endogenous enzyme activity, pest control, and removal of toxic pesticide residues. In this review, we comprehensively evaluated the efficacy of in‐package low‐temperature plasma treatment to extend the shelf life of various foods. The mechanisms by which cold plasma interacts with food were investigated, emphasizing its effects on pathogen reduction, spoilage mitigation, and surface modification. The review also critically assessed the effects of the treatments on food quality, regulatory considerations, and their potential as viable technologies to improve food safety and packaging life. In‐package cold plasma treatment could revolutionize food storage when combined with other sophisticated technologies such as nanotechnology.
From sound check to encore: A journey through high‐resolution mass spectrometry‐based food analyses and metabolomics
Authors: Díaz‐Galiano Francisco José , Murcia‐Morales María , Fernández‐Alba Amadeo Rodríguez 
Published: March 2024

AbstractThis manuscript presents a comprehensive review of high‐resolution mass spectrometry in the field of food analysis and metabolomics. We have followed the historical evolution of metabolomics, its associated techniques and technologies, and its increasing role in food science and research. The review provides a critical comparison and synthesis of tentative identification guidelines proposed for over 15 years, offering a condensed resource for researchers in the field. We have also examined a wide range of recent metabolomics studies, showcasing various methodologies and highlighting key findings as a testimony of the versatility of the field and the possibilities it offers. In doing so, we have also carefully provided a compilation of the software tools that may be employed in this type of studies. The manuscript also explores the prospects of high‐resolution mass spectrometry and metabolomics in food science. By covering the history, guidelines, applications, and tools of metabolomics, this review attempts to become a comprehensive guide for researchers in a rapidly evolving field.
Pause artificial intelligence research? Understanding AI policy challenges
Authors: Goldfarb Avi 
Published: March 2024

AbstractArtificial intelligence (AI) may be the next general purpose technology. General purpose technologies, such as the steam engine and computing, can have an outsized impact on productivity through a positive feedback loop between producing and application industries. Along with the discussion of AI's potential to improve productivity come a number of policy concerns related to AI's potential to automate jobs and to create existential risk for humanity. Because of these worries, in March 2023, a widely circulated petition called for a pause in AI research. That letter asked several questions about AI's potential impact on society. This paper examines those questions through an economic lens. It highlights reasons to be optimistic about the long‐run impact of AI, while underscoring short‐run risks. Economic models provide an understanding of where the ambiguity lies and where it does not. Our models suggest no ambiguity on whether there will be jobs and little ambiguity on long‐term productivity growth if AI diffuses widely. In contrast, there is substantial ambiguity on the implications of AI's diffusion for inequality.
Think tanks and climate obstruction: Atlas affiliates in Canada
Authors: Graham Nicolas 
Published: March 2024

AbstractThis paper provides a longitudinal social network and content analysis of Canadian think tanks affiliated with the Atlas network, analyzing their efforts to obstruct climate action over the last two decades. Network analysis reveals extensive and deepening board interlocks and joint memberships between these think tanks and the fossil fuel industry, other policy‐planning organizations within and beyond Canada, and academic institutions. Consistent with and rooted in network ties, Atlas members produce a large and growing volume of climate‐related content, including content that denies the reality and impacts of climate change, promotes and defends the fossil fuel sector, and opposes climate policy and action. Atlas affiliates are argued to be at the core of a reactionary segment of Canada's elite policy‐planning network opposed to virtually all forms of climate action, while the frames and campaigns they deploy are seen as a force obstructing progress on climate change.