bluuebunny/crossref_metadata_2025_split huggingface dataset:
Industry 4.0 adoption challenges in lean-agile-resilient-green agri-food supply chain
Authors: Mahajan Pramod Sanjay , Raut Rakesh , Cheikhrouhou Naoufel , Yadav Vinay Surendra , Ghoshal Sudishna 
Published: December 2024

Purpose
By incorporating I4.0 technologies, the agri-food supply chain (AFSC) can become leaner, faster, more robust and greener. However, many challenges must be overcome to fully realise I4.0 in this context. Therefore, this paper aims to identify the challenges that hinder the adoption of I4.0 technologies on the development of the Lean, Agile, Resilient and Green (LARG) AFSC.

Design/methodology/approach
The approach adopted was to identify challenges addressed in the literature with expert opinion and Total Interpretive Structural Modelling (TISM) for adaptation. In addition, a Weighted Influence Non-linear Gauge Systems (WINGS) methodology has been developed that uses expert opinion to generate a power and influence matrix.

Findings
The results show that lack of commitment and understanding of top management (X12), lack of long term vision (X17) and lack of incentives and government support (15) are the most important challenges.

Research limitations/implications
This study does not explore the effectiveness of the concluded challenges of I4.0 and their strategy to overcome them. Also, the authors relied on a limited sample size for this study, which might not cover the detailed challenges within LARG AFSC. Finally, this study lacks in future advancement of I4.0, which may further affect the challenges.

Practical implications
By mentioning the key challenges, this study empowers LARG AFSC organisations to build a targeted strategy for smoother I4.0 implementation.

Originality/value
Industry 4.0 challenges remain unexplored in LARG AFSC. This improved awareness equips managers to navigate better the potential issues and complexity that may arise when adopting I4.0 in the LARG AFSC.
Lean production in family farming: application of its principles in the production of vegetables
Authors: Cuer Laiane , Scalco Andréa Rossi , Satolo Eduardo Guilherme 
Published: December 2024

Purpose
The losses of vegetables in the world are significant and are distributed throughout the production chain. In the face of this problem, the purpose of this study is to applied the principles of the Lean Production to reduce waste in the production process on a family farm.

Design/methodology/approach
A case study was conducted on a family farm producing organic vegetables, with the focus of study being on lettuce. Data collection procedures involved interviews, observations and filming of the lettuce production process. The instruments used to identify the sources of waste were the value flow mapping, vertical flowchart, Ishikawa diagram, consumer voice and spaghetti diagram.

Findings
The main sources of waste observed were the movement in the planting stage and excess processing in the fertilization stage (excess fertilization). Improvements such as adaptation and even exclusion of activities related to the production process and organization of the site were carried out and contributed to a 45% reduction in the time of the activities cycle that did not add value but which were necessary.

Practical implications
The study here presented came to innovate and contribute to the reduction of waste related to agriculture since the lean production principles were applied to reduce waste in the productive process of lettuce in a family farm. The positive results of the application of lean production prove to be replicable to other properties and cultures, given that the conduction took place in a structured way in the five lean principles and their tools. It is up to the guidance and dissemination of this knowledge to producers.

Social implications
The study here presented can estimate that lean principles, when applied in agriculture, can contribute to the sustainability of food production systems.

Originality/value
Lean Production is a model developed for application in industrial sectors, and its application in agriculture is incipient in the international literature. This paper contributes to reducing waste in a sector that has significant loss rates by adapting the lean production model to agriculture.
Human intelligence versus artificial intelligence in classifying economics research articles: exploratory evidence
Authors: Heikkilä Jussi T.S. 
Published: December 2024

PurposeWe compare human intelligence to artificial intelligence (AI) in the choice of appropriate Journal of Economic Literature (JEL) codes for research papers in economics.Design/methodology/approachWe compare the JEL code choices related to articles published in the recent issues of the Journal of Economic Literature and the American Economic Review and compare these to the original JEL code choices of the authors in earlier working paper versions and JEL codes recommended by various generative AI systems (OpenAI’s ChatGPT, Microsoft’s Copilot, Google’s Gemini) based on the abstracts of the articles.FindingsThere are significant discrepancies and often limited overlap between authors’ choices of JEL codes, editors’ choices as well as the choices by contemporary widely used AI systems. However, the observations suggest that generative AI can augment human intelligence in the micro-task of choosing the JEL codes and, thus, save researchers time.Research limitations/implicationsRapid development of AI systems makes the findings quickly obsolete.Practical implicationsAI systems may economize on classification costs and (semi-)automate the choice of JEL codes by recommending the most appropriate ones. Future studies may apply the presented approach to analyze whether the JEL code choices between authors, editors and AI systems converge and become more consistent as humans increasingly interact with AI systems.Originality/valueWe assume that the choice of JEL codes is a micro-task in which boundedly rational decision-makers rather satisfice than optimize. This exploratory experiment is among the first to compare human intelligence and generative AI in choosing and justifying the choice of optimal JEL codes.
Assemblies of leaf and root mycobiomes in a temperate grassland: Dispersal limitation overpowers selection
Authors: Bai Ren , Hu Hang‐Wei , Ge An‐Hui , Zhou Meng , Sheng Jun , Yuan Guangyuan , Zhang Wen‐Hao , Bai Wenming 
Published: December 2024

Abstract

The emergence of β‐diversity of plant‐associated fungi across diverse coexisting host plant species in natural habitats is intricately linked to specific community assembly processes. Despite this, the relative contributions of various assembly processes to the observed β‐diversity patterns, as well as the influence of plant traits on these contributions, are still poorly understood.

Here, we investigated the leaf/root‐associated fungal communities across nine coexisting dominant herbaceous perennials in a temperate grassland that had undergone a 17‐year mowing treatment. We elucidated the β‐diversity components and community assembly processes of these fungal communities. Furthermore, we explored relationships between leaf/root functional trait variations and fungal community assemblies. We tested the following hypotheses: (1) both species turnover and nestedness are important components of the fungal β‐diversity, with selection predominating in the fungal community assemblies; (2) mowing enhances the contributions of nestedness/selection; (3) plant trait variations significantly affect the fungal community assembly processes.

Unexpectedly, our findings demonstrated a predominance of leaf/root fungal species turnover among coexisting plant hosts, contrasting with nestedness. Moreover, dispersal limitation emerged as the primary factor shaping fungal community assemblies, rather than selection processes. Although mowing significantly inhibited plant growth, its effects on the overall patterns of fungal assemblages were limited. We further observed that higher degrees of plant trait variations were primarily linked to stronger dispersal limitation, with a relatively weaker influence on heterogeneous selection. Additionally, the impact of plant traits on the selection process of root‐associated fungi was more pronounced compared to that of leaf‐associated fungi.

Synthesis. Our study reveals that the β‐diversity of fungi associated with coexisting plants in natural grasslands is primarily attributed to fungal species replacement rather than gain‐and‐loss dynamics among these plants. Concurrently, this observed pattern is largely governed by dispersal limitation as opposed to selection. We propose that the primary mechanism through which plant hosts and their traits influence the structures of associated fungal communities is by limiting fungal dispersal, while niche differentiation among fungal taxa plays a secondary role. These findings offer a mechanistic insight into the assemblies of plant mycobiomes and further elucidate the plant‐mycobiome relationships within complex plant communities.
Case Report: A Young Woman Diagnosed With Overlap Syndrome of Systemic Sclerosis and Ankylosing Spondylitis
Authors: Li Chen , Jin Di , Gong Yu‐Fang , Liu Hong‐Xu , Guan Yi‐Wei , Li Sheng‐Guang 
Published: December 2024

ABSTRACTThis case report describes a rare occurrence of a 25‐year‐old female diagnosed with both systemic sclerosis (SSc) and ankylosing spondylitis (AS), two distinct autoimmune diseases. The patient presented with a combination of symptoms, including progressive skin tightening, lumbosacral pain, and Raynaud's phenomenon, which complicated the diagnosis. Despite the challenges posed by the coexistence of SSc and AS, a multidisciplinary treatment approach involving corticosteroids, immunosuppressants, and supportive therapies led to significant clinical improvement. Over the course of 3 weeks, the patient's Rodnan Skin Score improved from 18 to 11, and her pain and overall disease activity were markedly reduced. Although skin sclerosis showed substantial improvement, pulmonary involvement remains a concern that requires long‐term monitoring. This case highlights the complexities in diagnosing and managing overlap syndromes, emphasizing the need for personalized treatment strategies and further investigation into the underlying genetic and immunological mechanisms.
Intraspecific competition and performance of Anagrus virlai parasitizing the corn leafhopper
Authors: Hill Jorge G. , Coll‐Aráoz María V. , Luft‐Albarracin Erica , Fernández Patricia C. , Virla Eduardo G. 
Published: December 2024

AbstractIntraspecific competition and avoidance of superparasitism are critical for biological control. In this study, we conducted behavioural and biological trials to assess intraspecific competition in the egg parasitoid Anagrus virlai, targeting the corn leafhopper Dalbulus maidis. Firstly, we investigated whether A. virlai can distinguish between unparasitized and parasitized host eggs using long‐range cues in an olfactometer experiment. Secondly, we evaluated whether A. virlai parasitism and emergence under varying host densities are affected by the presence of multiple conspecific wasps foraging within the same host patch. In the olfactometer test, A. virlai did not differentiate between corn leaves with parasitized and unparasitized eggs. Regarding the parasitoid's performance, the number and percentage of parasitized eggs were influenced by the density of females foraging simultaneously in the same host patch. Significant increases in collective parasitism were observed when two or more parasitoids were present in each arena. Competitive interaction did not affect wasp emergence, and A. virlai proved to be a solitary species. Moreover, the negative relationship between per‐capita parasitism and the number of conspecific wasps per arena demonstrated mutual interference among parasitoids. These findings highlight the impact of competitive interactions on A. virlai performance and suggest a potential aggregated response of the parasitoid to high‐density patches of hosts.
Mechanisms underlying neurocognitive dysfunction following critical illness: a systematic review
Authors: Andonovic Mark , Morrison Holly , Allingham William , Adam Robert , Shaw Martin , Quasim Tara , McPeake Joanne , Quinn Terence 
Published: December 2024

SummaryIntroductionCognitive impairment is a significant healthcare problem globally and its prevalence is projected to affect over 150 million people worldwide. Survivors of critical illness are impacted frequently by long‐term neurocognitive dysfunction regardless of presenting illness, but the mechanisms are poorly understood. The goal of this review was to synthesise the existing evidence regarding potential mechanisms underlying neurocognitive dysfunction following critical illness in order to guide potential avenues for future research.MethodsWe performed a systematic search of the literature for studies published between 1 January 1974 and 15 July 2023. We included publications involving adult patients with critical illness due to any aetiology that assessed for cognitive impairment following recovery from illness, and explored or investigated potential underlying causative mechanisms. The quality and risk of bias of the individual studies was assessed using the Newcastle‐Ottawa scale.ResultsOf the 7658 reviewed references, 37 studies comprising 4344 patients were selected for inclusion. Most studies were single centre with sample sizes of < 100 patients. The proportion of patients with long‐term cognitive impairment ranged from 13% to 100%. A wide variety of theoretical mechanisms were explored, with biomarkers and neuroimaging utilised most frequently. Many studies reported associations between investigated mechanisms and reduced cognition; several of these mechanisms have been implicated in other forms of long‐term neurodegenerative conditions. Increased levels of inflammatory cytokines during acute illness and white matter hyperintensities on neuroimaging following recovery were the associations reported most commonly.DiscussionThe underlying pathophysiology of neurocognitive decline after critical illness is not yet understood fully. The mechanisms implicated in other neurodegenerative conditions suggest that this may represent an accelerated version of the same processes. Large scale studies are required to further elucidate the cause of this significant problem for survivors of critical illness.
Establishment of cancer cell radiosensitivity database linked to multi‐layer omics data
Authors: Oike Takahiro , Kambe Ryosuke , Darwis Narisa Dewi Maulany , Shibata Atsushi , Ohno Tatsuya 
Published: December 2024

AbstractPersonalized radiotherapy based on the intrinsic sensitivity of individual tumors is anticipated, however, it has yet to be realized. To explore cancer radiosensitivity, analysis in combination with omics data is important. The Cancer Cell Line Encyclopedia (CCLE) provides multi‐layer omics data for hundreds of cancer cell lines. However, the radiosensitivity counterpart is lacking. To address this issue, we aimed to establish a database of radiosensitivity, as assessed by the gold standard clonogenic assays, for the CCLE cell lines by collecting data from the literature. A deep learning‐based screen of 33,284 papers identified 926 relevant studies, from which SF2 (survival fraction after 2 Gy irradiation) data were extracted. The median SF2 (mSF2) was calculated for each cell line, generating an mSF2 database comprising 285 cell lines from 28 cancer types. The mSF2 showed a normal distribution among higher and lower cancer‐type hierarchies, demonstrating a large variation across and within cancer types. In selected cell lines, mSF2 correlated significantly with the single‐institution SF2 obtained using standardized experimental protocols or with integral survival, a radiosensitivity index that correlates with clonogenic survival. Notably, the mSF2 for blood cancer cell lines was significantly lower than that for solid cancer cell lines, which is in line with the empirical knowledge that blood cancers are radiosensitive. Furthermore, the CCLE‐derived protein levels of NFE2L2 and SQSTM1, which are involved in antioxidant damage responses that confer radioresistance, correlated significantly with mSF2. These results suggest the robustness and potential utility of the mSF2 database, linked to multi‐layer omics data, for exploring cancer radiosensitivity.
Design, Synthesis, and Biological Evaluation of New Benzimidazole‐1,2,4‐Triazole Derivatives as Potential Anticancer Agents
Authors: Kaya‐Sezginer Ecem , Oz Bedir Beyza Ecem , Terzi Emine , Ozdemir Sanci Tuba , Maryam Zahra , Acar Çevik Ulviye 
Published: December 2024

ABSTRACTNew series of benzimidazole‐1,2,4‐triazole derivatives were designed, synthesized, and characterized using 1H‐NMR, 13C‐NMR, and HRMS. These compounds were evaluated for anticancer activity toward HTB‐9 bladder and HT‐29 colorectal cancer cell lines. Compounds 7h and 7ı were found to be the most active against HTB‐9 cell line, with IC50 6.27 and 6.44 μM, respectively, comparable to positive control cisplatin (IC50 = 11.40 μM). Additionally, in HT‐29 cell line, compounds 7a and 7ı exhibited the lowest IC50 values (20.37 and 22.71 μM, respectively), which was higher than those of cisplatin (19.79 μM). All active compounds induced apoptosis and caspase 3/7 activity and reduced the migration ability in both cell lines. Particularly, HT‐29 cells treated with compound 7ı exerted a higher apoptotic index than cisplatin‐treated cells. Furthermore, compounds 7h and 7ı led to G1 cell cycle arrest of HTB‐9, and compounds 7a and 7ı against HT‐29 induced S and G1 cell cycle arrest, respectively. In conclusion, the antiproliferative effect of active compounds is associated with the induction of apoptosis through caspase 3/7 activation and cell cycle arrest at different phases in HTB‐9 and HT‐29 cell lines.
A Chemical Reprogramming Approach Efficiently Producing Human Retinal Pigment Epithelium Cells for Retinal Disease Therapies
Authors: Zhang Ke , Wang Yanqiu , An Qi , Ji Hengjing , Wu Defu , Li Xuri , Suo Lingge , Zhang Chun , Dong Xuran 
Published: December 2024

ABSTRACTHuman induced pluripotent stem cells (hiPSCs) represent a promising cell source for generating functional cells suitable for clinical therapeutic applications, particularly in the context of autologous cell therapies. However, the production of hiPSCs through genetic manipulation, especially involving oncogenes, may raise safety concerns. Furthermore, the complexity and high costs associated with hiPSCs generation have hindered their broad clinical use. In this study, we utilised a recently developed chemical reprogramming method in conjunction with a guided differentiation protocol, introducing a chemically defined strategy for generating functional human retinal pigment epithelium (RPE) cells from adipose tissue, bypassing conventional hiPSCs generation challenges. By utilising small molecule‐based chemical cocktails, we reprogrammed somatic adipose cells into human chemically induced pluripotent stem cells (hCiPSCs) in a safer and more streamlined manner, entirely free from gene manipulation. Subsequent differentiation of hCiPSCs into functional RPE cells demonstrated their capability for secretion and phagocytosis, emphasising their vital role in maintaining retinal homeostasis and underscoring their therapeutic potential. Our findings highlight the transformative potential of hCiPSCs as a safer, more efficient option for personalised cell therapies, with applications extending beyond ocular disease to a wide range of medical conditions.
Predictive Analysis of Global Terrorist Attacks Using Lexical Patterns Across Multiple Datasets
Authors: Atoum Mohammed Salem , Alarood Ala Abdulsalam , Alsolmi Eesa Abdullah , Obeidat Areej , Alazab Moutaz 
Published: December 2024

ABSTRACTWorldwide terrorist activities continue to pose a significant threat to global security and stability. The unpredictable nature of these acts necessitates advanced analytical approaches to enhance prevention and response strategies. This study examines undetectable word extensions across multiple datasets, using terrorism‐related datasets as a case study. This research aims to overcome constraints in current predictive models associated with terrorist attack prediction. While many studies have used the GTD for predicting global terrorist attacks, this study expands beyond GTD by evaluating a corpus of terrorism incidents to enhance predictive analysis through lexical usage. The study employs several machine learning algorithms including Decision Tree (DT), Bootstrap Aggregating (BA), Random Forest (RF), Extra Trees (ET) and XGBoost (XG) algorithms for evaluation. Our approach integrates multiple datasets to reduce dependence on GTD alone. Findings indicate that RF performs best on the GTD database, with 90.20% accuracy in predicting worldwide terrorist attacks. DT achieves 90.40% accuracy when applied to the TF–IDF dataset. XG demonstrates superior performance across various aggregation settings and feature sets, achieving 95.77% accuracy in forecasting worldwide terrorist acts. XG's consistent and effective performance across various contexts highlights its versatility. Its high adaptability and robust performance position it as the preferred algorithm for conducting predictive research on global terrorist acts using the available datasets. Our research findings underscore the importance of incorporating diverse datasets to enhance understanding of terrorist activities and improve predictive capabilities.