bluuebunny/crossref_metadata_2025_split huggingface dataset:
Destructive and non-destructive measurement approaches and the application of AI models in precision agriculture: a review
Authors: Islam Maidul , Bijjahalli Suraj , Fahey Thomas , Gardi Alessandro , Sabatini Roberto , Lamb David W. 
Published: February 2024

AbstractThe estimation of pre-harvest fruit quality and maturity is essential for growers to determine the harvest timing, storage requirements and profitability of the crop yield. In-field fruit maturity indicators are highly variable and require high spatiotemporal resolution data, which can be obtained from contemporary precision agriculture systems. Such systems exploit various state-of-the-art sensors, increasingly relying on spectrometry and imaging techniques in association with advanced Artificial Intelligence (AI) and, in particular, Machine Learning (ML) algorithms. This article presents a critical review of precision agriculture techniques for fruit maturity estimation, with a focus on destructive and non-destructive measurement approaches, and the applications of ML in the domain. A critical analysis of the advantages and disadvantages of different techniques is conducted by surveying recent articles on non-destructive methods to discern trends in performance and applicability. Advanced data-fusion methods for combining information from multiple non-destructive sensors are increasingly being used to develop more accurate representations of fruit maturity for the entire field. This is achieved by incorporating AI algorithms, such as support vector machines, k-nearest neighbour, neural networks, and clustering. Based on an extensive survey of recently published research, the review also identifies the most effective fruit maturity indices, namely: sugar content, acidity and firmness. The review concludes by highlighting the outstanding technical challenges and identifies the most promising areas for future research. Hence, this research has the potential to provide a valuable resource for the growers, allowing them to familiarize themselves with contemporary Smart Agricultural methodologies currently in use. These practices can be gradually incorporated from their perspective, taking into account the availability of non-destructive techniques and the use of efficient fruit maturity indices.
UAV-based canopy monitoring: calibration of a multispectral sensor for green area index and nitrogen uptake across several crops
Authors: Bukowiecki Josephine , Rose Till , Holzhauser Katja , Rothardt Steffen , Rose Maren , Komainda Martin , Herrmann Antje , Kage Henning 
Published: February 2024

AbstractThe fast and accurate provision of within-season data of green area index (GAI) and total N uptake (total N) is the basis for crop modeling and precision agriculture. However, due to rapid advancements in multispectral sensors and the high sampling effort, there is currently no existing reference work for the calibration of one UAV (unmanned aerial vehicle)-based multispectral sensor to GAI and total N for silage maize, winter barley, winter oilseed rape, and winter wheat.In this paper, a practicable calibration framework is presented. On the basis of a multi-year dataset, crop-specific models are calibrated for the UAV-based estimation of GAI throughout the entire growing season and of total N until flowering. These models demonstrate high accuracies in an independent evaluation over multiple growing seasons and trial sites (mean absolute error of 0.19–0.48 m2 m−2 for GAI and of 0.80–1.21 g m−2 for total N). The calibration of a uniform GAI model does not provide convincing results. Near infrared-based ratios are identified as the most important component for all calibrations. To account for the significant changes in the GAI/ total N ratio during the vegetative phase of winter barley and winter oilseed rape, their calibrations for total N must include a corresponding factor. The effectiveness of the calibrations is demonstrated using three years of data from an extensive field trial. High correlation of the derived total N uptake until flowering and the whole-season radiation uptake with yield data underline the applicability of UAV-based crop monitoring for agricultural applications.
All That Glitters is Not Gold: Examining the Negative Impact of Real Estate Value on Companies' Market Competitiveness
Authors: Liang Fangzhi , Tian Gary Gang , Wei Zhihua , Zeng Aimin 
Published: February 2024

AbstractThis paper introduces and investigates the hypothesis that the crowding-out effect surpasses the collateral effect, suggesting that an increase in the value of real estate holdings by companies hinders their competitiveness in the product market within emerging economies. Through our analysis, we elucidate the underlying mechanism, demonstrating that although Chinese listed companies benefit from their real estate holdings in terms of debt financing, these financial resources are predominantly reinvested in real estate projects rather than other productive endeavors. This effect is particularly pronounced when companies face significant financial constraints, operate in highly monopolistic industries, and are situated in regions where local governments heavily rely on land finance and face substantial pressure for GDP growth. By shedding light on the adverse implications of companies' real estate holdings and uncovering the factors contributing to the crowding-out effect, our research underscores the importance of enhanced regulations in the real estate markets of developing nations during the periods of irrational housing booms.
Contributions to the Legal Semiotics of Facial Recognition Systems: Live Music, Digital Technologies, and the Display of Power
Authors: Marino Gabriele 
Published: February 2024

AbstractThe use of facial recognition systems in concerts provides a perfect pretext to semiotically discuss the role of the face in contemporary culture, identifying different strategies and axiologies (systems of values). In his visionary essay Bruits (“noises”) from 1977, the French thinker Jacques Attali establishes a close connection between music and power and locates it in the site of the collective unfolding of music: the concert hall. Following this hint, the article reconstructs the current debate on facial recognition systems in the music business, with a particular focus on concerts and festivals. The different positions (of musicians, authorities, and professionals) are outlined (some are openly in favor, others openly against), key moments are identified (Beyoncé’s concert in Cardiff, 2017; Jacky Cheung’s in Nanchang, 2018; Taylor Swift’s at the Rose Bowl in Pasadena, 2018; the “banfacialrecognition.com” campaign launched in 2019; the case of Barbara Hart at Brandi Carlile’s show at the Madison Square Garden, 2022) and, finally, the issue is framed within the broader debate about the face as an indexical marker of presence, identity, and humanity (the contrast between Italian philosopher Giorgio Agamben and American sociologist and design theorist Benjamin Bratton is addressed).
Multi-way Analysis of the Gender Dimension of the Sustainable Development Goals
Authors: Medina-Hernández Edith Johana , Fernández-Gómez María José 
Published: February 2024

AbstractThe gender dimension of the Sustainable Development Goals (SDG) is integral to the goals established to foster the development of nations, as it is intertwined with other goals and enhances their attainability. Thus, this study examines 28 indicators belonging to the goals of the sustainable development objectives, distinguishing between the gender SDGs (6 indicators) and other SDGs (22 indicators), all of which were reported by 110 countries in the year 2023. The assessment considers four regions: the African continent (28 countries), the Americas region (21 countries), Asia-Oceania (29 countries), and Europe (32 countries). The STATICO multivariate technique is used (consisting of four Co-inertia analyses and one Partial Triadic Analysis) to study the interactions among the indicators. The goal is to determine whether differences or similarities exist between these indicators within each region and to make a comparative assessment across countries. The study’s findings show the existing covariances between the various targets of the 2030 Agenda and indicate that in Europe, the gender dimension is more closely integrated with the other SDGs than in the other regions. In Africa, substantial variations between countries are observed, while Asia, Oceania, and the Americas face challenges in specific indicators and countries in terms of achieving sustainable development.
RL-CoPref: a reinforcement learning-based coordinated prefetching controller for multiple prefetchers
Authors: Yang Huijing , Fang Juan , Su Xing , Cai Zhi , Wang Yuening 
Published: February 2024

AbstractModern processors employ data prefetchers to alleviate the impact of long memory access latency. However, current prefetchers are designed for specific memory access patterns, which perform poorly on mixed applications with multiple memory access patterns. To address these issues, RL-CoPref, a reinforcement learning (RL)-based coordinated prefetching controller for multiple prefetchers, is proposed in this paper. RL-CoPref takes diverse program context information as the input, learns to maximize cumulative rewards, and evaluates prefetch quality based on prefetch hits/misses and memory bandwidth utilization. It can dynamically adjust the prefetch activation and prefetch degree, enabling multiple prefetchers to complement each other on mixed applications. Our extensive evaluation, utilizing the ChampSim simulator, demonstrates that RL-CoPref can effectively adapt to various workloads and system configurations, optimizing prefetch control. On average, RL-CoPref achieves 76.15% prefetch coverage, having 35.50% IPC improvement, outperforming state-of-the-art individual prefetchers by 5.91–16.54% and outperforming SBP, a state-of-the-art (non-RL) prefetch controller, by 4.64%.
A comparative study on the occurrence, genetic characteristics, and factors associated with the distribution of Listeria species on cattle farms and beef abattoirs in Gauteng Province, South Africa
Authors: Gana J. , Gcebe N. , Moerane R. , Ngoshe Y. B. , Tshuma T. , Moabelo K. , Adesiyun A. A. 
Published: February 2024

AbstractThese cross-sectional studies reported the occurrence, genetic characteristics, and factors associated with the distribution of Listeria species on cattle farms and beef abattoirs in Gauteng Province, South Africa. A total of 328 samples (faeces, feeds, silage, and drinking water) were collected from 23 cattle farms (communal, cow-calf, and feedlot), and 262 samples (faeces, carcass swabs, and effluents) from 8 beef abattoirs (low throughput and high throughput) were processed using standard bacteriological and molecular methods to detect Listeria species. The factors associated with the prevalence of Listeria species were investigated, and multiplex polymerase chain reaction (mPCR) was used to determine Listeria species, the pathogenic serogroups, and the carriage of eight virulence-associated genes by Listeria monocytogenes. The overall prevalence of Listeria species in cattle farms was 14.6%, comprising Listeria innocua (11.3%), Listeria monocytogenes (3.4%), Listeria welshimeri (0.0%) compared with 11.1%, comprising Listeria innocua (5.7%), Listeria monocytogenes (4.6%), Listeria welshimeri (0.8%) for beef abattoirs. Of the three variables (area, type of farm/abattoir, and sample type) investigated, only the sample types at abattoirs had a significant (P < 0.001) effect on the prevalence of L. innocua and L. welshimeri. The frequency of distribution of the serogroups based on 11 L. monocytogenes isolated from farms was 72.7% and 27.3% for the serogroup 1/2a-3a and 4b-4d-4e, respectively, while for the 12 L. monocytogenes isolates recovered from abattoirs, it was 25%, 8.3%, 50% and 16.7% for the serogroup 1/2a-3a, 1/2b-3b, 1/2c-3c, and 4b-4d-4e respectively (P < 0.05). All (100%) isolates of L. monocytogenes from the farms and abattoirs were positive for seven virulence genes (hlyA, inlB, plcA, iap, inlA, inlC, and inlJ). The clinical and food safety significance of the findings cannot be ignored.
Analyzing Urban Drinking Water System Vulnerabilities and Locating Relief Points for Urban Drinking Water Emergencies
Authors: Garajeh Mohammad Kazemi , Feizizadeh Bakhtiar , Salmani Behnam , Ghasemi Mohammad 
Published: February 2024

AbstractUrban water is known as a critical sector of urban environments which significantly impacts the life quality and wellbeing of reinstates. In the context of developing sustainable urban drinking system it is critical to analyze network events and develop sufficient systems of water supply. To the best of our knowledge, fewer studies have examined the potential of automated-based approaches such as deep learning convolutional neural network (DL-CNN) for analyzing urban water network events and identifying the optimal location of urban drinking water relief posts. Therefore, the current study aims to propose an efficient approach for Geospatial based urban water network events analyze and determine the optimal location of urban drinking water relief posts in Zanjan. For this goal, first, we prepared and preprocessed various predisposing variables for analyzing the urban water network events and determining the optimal location of urban drinking water relief posts. We then applied an integrated approach of analytical network process (ANP) and DL-CNN methods to locate the best location of urban drinking water relief posts. Finally, intersection over union and accuracy assessment were employed to evaluate the performance of the results. Our findings show that the DL-CNN performed well with an accuracy of 0.942 compared to the ANP (0.895) for determining the optimal location of urban drinking water relief posts. According to the results, the best place to build a relief post is in the city center, and the surrounding areas may not be suitable, which is in accordance with field work analysis. The results of the study also reveal that areas 5 and 3 are at high risk from the number of urban water network events perspective, which requires the construction of urban water relief stations.
An analysis on the effect of body tissues and surgical tools on workflow recognition in first person surgical videos
Authors: Tomita Hisako , Ienaga Naoto , Kajita Hiroki , Hayashida Tetsu , Sugimoto Maki 
Published: February 2024

Abstract

Purpose

Analysis of operative fields is expected to aid in estimating procedural workflow and evaluating surgeons’ procedural skills by considering the temporal transitions during the progression of the surgery. This study aims to propose an automatic recognition system for the procedural workflow by employing machine learning techniques to identify and distinguish elements in the operative field, including body tissues such as fat, muscle, and dermis, along with surgical tools.

Methods

We conducted annotations on approximately 908 first-person-view images of breast surgery to facilitate segmentation. The annotated images were used to train a pixel-level classifier based on Mask R-CNN. To assess the impact on procedural workflow recognition, we annotated an additional 43,007 images. The network, structured on the Transformer architecture, was then trained with surgical images incorporating masks for body tissues and surgical tools.

Results

The instance segmentation of each body tissue in the segmentation phase provided insights into the trend of area transitions for each tissue. Simultaneously, the spatial features of the surgical tools were effectively captured. In regard to the accuracy of procedural workflow recognition, accounting for body tissues led to an average improvement of 3 % over the baseline. Furthermore, the inclusion of surgical tools yielded an additional increase in accuracy by 4 % compared to the baseline.

Conclusion

In this study, we revealed the contribution of the temporal transition of the body tissues and surgical tools spatial features to recognize procedural workflow in first-person-view surgical videos. Body tissues, especially in open surgery, can be a crucial element. This study suggests that further improvements can be achieved by accurately identifying surgical tools specific to each procedural workflow step.
Whose “place” is it? Using corpus-based techniques to sketch place-based sustainability discourses in public and academic forums
Authors: Wearne Samuel , Riedy Christopher 
Published: February 2024

AbstractCalls for a “place-based” approach to sustainability are increasingly common in the field of sustainability transitions and transformations (STT). To critically explore the agendas and politics a call towards place carries, we undertook a corpus-assisted discourse study (CADS) to examine a sample of public and academic texts from 2019 to 2020. Two distinct discourses about place were evident: an environmental discourse framing place as an assemblage of more-than-human constituents and an anthropocentric discourse framing place as a human community. These discourses present vastly different priorities about which species matter, what change entails, and what kind of future we should create. Our findings reflect the emergence of a discourse coalition that advocates for a place-based approach to STT, and we discuss how this viewpoint has continued to emerge since the compilation of our data. Our paper provides an overview of the discursive landscape we encountered, synthesises a central narrative about place-based STT based on what we observed, and provides a critical discussion of the tensions and opportunities that this narrative raises. In doing so, we suggest there remains an opportunity for fruitful dialogue amongst sustainability educators, sustainability practitioners, and researchers to refine what a place-based approach to STT looks like. By demonstrating an application of CADS, we hope to show how digital tools and techniques can be used to research discourses in sustainability. We outline specific opportunities to take this forward, including a broad opportunity to use web-derived corpora to help survey discursive landscapes, and a more specific application to explore discursive dynamics between communities, places, and at different spatial scales.
Bariatric Surgery Outcomes in Patients with Inflammatory Bowel Disease in the United States: An Analysis of the Nationwide Readmissions Database
Authors: Wilson Noah C. , Dilsaver Danielle B. , Walters Ryan W. , Nandipati Kalyana C. 
Published: February 2024

Abstract
Purpose
Bariatric surgery has been reported to produce durable weight loss in the management of obesity; sleeve gastrectomy (SG) is the most common bariatric procedure. Obesity is a common comorbidity of inflammatory bowel disease (IBD), and the impact of IBD on short-term SG outcomes has not been widely reported. This study assessed whether IBD was associated with adverse post-SG outcomes.

Materials and Methods
Hospitalizations of patients undergoing SG in the United States were identified using the 2010–2020 Nationwide Readmissions Database (NRD) and stratified by IBD diagnosis. The SG cohort was propensity-matched based on age, biological sex, body mass index (BMI), comorbid diabetes, hypertension, depression, chronic obstructive pulmonary disease, and discharge in quarter four. Primary aims were to compare in-hospital mortality, post-operative complications, and all-cause 90-day readmission between patients with and without IBD. Secondary outcomes were length of stay (LOS) and total hospital cost.

Results
A total of 2030 hospitalizations were matched. The odds of complication were 48% higher for hospitalizations of patients with IBD (11.1% vs. 7.8%; aOR 1.48, aOR 95% CI 1.10–2.00, p = .009). The most common complication was nausea (4.9% vs. 3.7%, p = .187). No statistically significant difference was observed for all-cause 90-day readmissions, LOS, or hospital cost.

Conclusion
Hospitalizations of patients with IBD who underwent SG experienced significantly higher post-operative complication rates. However, the similar lengths of stay and readmission rates compared to propensity-matched SG hospitalizations without IBD suggest many complications were minor. SG remains a safe weight loss procedure for patients suffering from IBD and obesity.

Graphical Abstract
A Systematic Review and Meta-analysis of the Socioeconomic, Lifestyle, and Environmental Factors Associated with Healthy Ageing in Low and Lower-Middle-Income Countries
Authors: Belachew Amare , Cherbuin Nicolas , Bagheri Nasser , Burns Richard 
Published: February 2024

AbstractPopulation ageing is a growing social and health issue in low and lower-middle-income countries (LLMIC). It will have an impact on rising healthcare costs, unaffordable pension liabilities, and changing healthcare demands. The health systems of many LLMICs are unprepared to meet these challenges and highlighting the modifiable factors that may help decrease these pressures is important. This review assessed the prevalence of healthy ageing and the modifiable factors that may promote/inhibit healthy ageing among older people in LLMIC. A systematic search of all articles published from 2000 to June 2022 was conducted in Scopus, PubMed (MEDLINE), and Web of Science. All observational studies reporting the prevalence of healthy ageing and its associations with socio-demographic, lifestyle, psychological, and social factors were examined. Random-effect models were used to estimate the pooled prevalence of healthy ageing, and meta-analyses were conducted to assess the risk/benefit of modifiable factors. From 3,376 records, 13 studies (n = 81,144; 53% of females; age ≥ 60 years) met the inclusion criteria. The pooled prevalence of healthy ageing ranged from 24.7% to 56.5% with lower prevalence for a multi-dimensional model and higher prevalence for single global self-rated measures. Factors positively associated with healthy ageing included education, income, and physical activity. Being underweight was negatively associated with healthy ageing. Almost half of older people in LLMIC were found to meet healthy ageing criteria, but this estimate varied substantially depending on the healthy ageing measures utilized (multi-dimensional = 24.7%; single indicator = 56.5%). The healthy ageing prevalences for both measures are lower compared to that in high-income countries. Developing health policies and educative interventions aimed at increasing physical exercise, social support, and improving socio-economic status and nutrition will be important to promote the healthy ageing of older people in LLMIC in sustainable ways.