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A culture of innovation that can continuously change to ongoing external market shifts will be a critical source of competitive advantage for organizations over the next decade. The lessons for leading such a culture of innovation come from humanity's earliest beginnings and are communicated through our myths. The authors discuss the use of the Hero's Journey with modern change management and innovation techniques such as foresight, customer-centered research and lean startup, to anticipate challenges along an established road of creating a culture of innovation. [ABSTRACT FROM AUTHOR]

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Games have been designed to be challenging and fun for humans to play, so we can test the AI system against the best human players in the world to quantify how good our AI systems are getting to be. We went from there to AlphaGo, which was our AI system to master the ancient and complex game of Go. DeepMind: From Games to Scientific Discovery: This paper is an edited version of Demis Hassabis' 2021 IRI Medal talk. He discussed his personal AI journey - from games to scientific discovery, some of his breakthrough results in complex games of strategy, and some of the exciting ways that lessons from the world of games are helping to accelerate scientific discovery. [Extracted from the article]

The safe and effective treatment of patients accessing multiple NHS services relies upon efficient communication between primary care, secondary care, and out of hours providers. There is a theoretical risk to patient safety from delays in these processes, to which paper communications are particularly vulnerable. When letters are received they must be reviewed and prioritised in order of clinical importance, a process that requires both time and clinical resources. This is relevant to the challenge of resource allocation to maximise patient benefit. This retrospective study investigated the impact on patient safety of 249 clinical letters reporting routine clinical encounters in secondary care and out of hours services that were delayed by an average of 18-24 months to a suburban London general practice. No clinical harm could be attributed to the delay. This small study did not suggest delays in routine communications pose a significant risk to patient safety. Conversely, it questions the efficiency and benefit to patients of prioritising clinical time to reviewing routine letters. The adoption of fully integrated, shared electronic patient records with the function to highlight clinically urgent or important communications might ease clinician workload, to the ultimate benefit of patient care. [ABSTRACT FROM AUTHOR]

Climate change is becoming a defining factor for communities in South Asia. Forming one-fifth of the world population, the region increasingly faces climate-induced disasters such as floods, droughts, heatwaves, cyclones etc. This region also has one of the world's poorest people who struggle to cope with the rapidly changing climatic conditions. Agriculture still employs many people in the region, one of the worst-hit sectors. Agriculture will become untenable in some parts of the region due to climate change. Monsoon patterns have changed, and agriculture does not guarantee sustainable income for the vast majority. Many climate change adaptations have been initiated in the region in response to the threat of climate change. Scholars and practitioners feel that these adaptations must be transformative to be effective. In this paper, we examine eight such adaptations from three South Asian Countries – Bangladesh, India, and Nepal, from the lens of transformative adaptation. We found that no single adaptation initiative meets all the criteria for sustainable socio-ecological transformations. However, there is a significant overlap between different typologies of transformation as envisaged in the paper and literature. We conclude that the concept of socio-ecological transformation is new for South Asia, so integrating it into the programmes and policies is the need of the hour. [ABSTRACT FROM AUTHOR]

Background: In order to obtain their medical degree, medical students in Morocco are required to carry out a research project and provide a thesis outlining its methodology and findings. However, little is known about the scientific output of these theses. The aim of the present study was to explore and evaluate the characteristics and publication patterns in scientific-indexed journals of medical theses written by medical students in Morocco. Methods: Data was extracted from registered theses between 2011 and 2021 in four medical schools that have an open-source documents archiving platform. Publication of these theses was assessed in 2022 using a search strategy in three indexed databases; Pubmed, Scopus and Web of science. Results: 9807 theses were registered between 2011 and 2021, 41% of them in the faculty of medicine of Rabat. 99.1% of these theses were written in French, 61.7% were reporting a retrospective case series, and 38.9% of them covered surgical disciplines. 83 (0.8%) of the registered theses were published in a scientific-indexed journal, and half of the papers (49.4%) was written in French. The graduate student was the paper's lead author in 54.2% of the papers. The articles driven from the theses were published after a mean delay of 1.49 ± 1.34 years and the targeted journals had a mean SJR score of 0.69 ± 1.21. The total number of detected citations of the 83 published papers was 216. Conclusion: The publication rate of Moroccan medical theses is very low when compared to other countries, which leads to question the real benefit of this time and resources consuming educational activity. [ABSTRACT FROM AUTHOR]

Object detection technology is a popular research direction which is widely used in areas such as autonomous driving and medical diagnosis. At this stage mobile devices often have limited storage resources to deploy large object detection networks and need to meet real-time requirements. This paper proposes a lightweight and efficient object detection model based on YOLOv4, first using the lightweight network GhostNet to extract image features and reduce the number of parameters and computation of the backbone structure; then combining AFmodule and Meta-ACON activation function to enhance the feature extraction capability of the backbone network, which strengthen the mode's ability to capture image spatial feature information; this paper also designs the RL-PAFPN feature fusion structure is with the Reslayer module to further improve the model's ability to extract and fuse image features. By comparing other mainstream object detection models, the YOLOv4-Ghost-AMR network in this paper has less computation and fewer parameters, and the accuracy of the model reaches 86.83%, which is suitable for deployment in mobile devices with limited storage. The model proposed in this paper can be applied to medical, traffic and fault detection fields, which changes the traditional manual detection method and saves manpower and time costs, achieving high precision real-time object detection. [ABSTRACT FROM AUTHOR]

This document is an editorial that pays tribute to the late Samir Amin, a prominent African scholar and activist. Amin was known for his Marxist perspective and his critique of capitalism and Eurocentrism. He advocated for delinking from capitalism and imperialism as a means of achieving inclusive development. The editorial highlights Amin's contributions to various fields, including development economics, political economy, and decolonization in Africa. It also introduces a special issue dedicated to Amin's legacy, which includes papers on topics such as Africa's socio-economic development, alternative development theories, and decolonization. The editorial emphasizes the continued relevance of Amin's ideas for Africa's development and the importance of implementing his recommendations. [Extracted from the article]

What are patterns of gender and authorship in urban land science? Our bibliometric analysis shows that the proportion of women shrinks among highly productive, impactful, and senior authors, akin to a pyramid shape. First, women are only one in ten researchers with an h-index above the 95th percentile. Second, women are first authors on 20% of all influential papers cited more than one hundred times. Third, women publish less frequently (1.6 papers/year) than men (2.2). Fourth, women have shorter career lengths (9.4 years) than men (11.8). Since the 2000s, citation rates for women and men have converged. For the generation starting careers since 2016, the proportion of women with an h-index above the 90th percentile increased to 25%. During the Covid-19 pandemic, there was a 51% increase in productivity for women. Despite these changes, gender disparities in urban land science are most pronounced among the most productive and impactful authors. [ABSTRACT FROM AUTHOR]

Objective: Robust scientific and clinical evidence of how to appropriately manage denture plaque is lacking. This two-part study (i) developed an in vitro model of denture plaque removal, and (ii) assessed effectiveness of these approaches in a randomised clinical trial. Method: (i) a complex denture plaque model was developed using the dominant microbial genera from a recent microbiome analyses. Biofilms formed on polymethylmethacrylate were brushed daily with a wet toothbrush, then either treated daily for 5 days or only on Days 1 and 5 with Polident® denture cleanser tablets (3 min soaking). Quantitative and qualitative microbiological assessments were performed. (ii), an examiner-blind, randomised, crossover study of complete maxillary denture wearers was performed (n = 19). Either once-daily for 7 days or on Day 7 only, participants soaked dentures for 15 min using Corega® denture cleansing tables, then brushed. Denture plaque microbiological assessment used sterilized filter paper discs. Results: The in vitro model showed daily cleaning with denture cleanser plus brushing significantly reduced microbial numbers compared to intermittent denture cleaning with daily brushing (p < 0.001). The clinical component of the study showed a statistically significant reduction in denture plaque microbial numbers in favour of daily versus weekly treatment (aerobic bacteria p = 0.0144). Both in vitro and in vivo studies showed that denture plaque biofilm composition were affected by different treatment arms. Conclusions: This study demonstrated that daily denture cleansing regimens are superior to intermittent denture cleansing, and that cleansing regimens can induce denture plaque compositional changes. Clinicaltrials.gov registration: NCT02780661. [ABSTRACT FROM AUTHOR]

The switching of node status on the network is a common natural phenomenon. In this paper, we study the problem of adaptive diffusion strategies with Markov jump. A simple choice for adaptive structures is the Least Mean Square (LMS) filter, which we will improve. The main work of this paper is to introduce Markov transform into diffusion adaptive structure strategies. The main result is that the Adaptive-Combine-Markov (ACM) diffusion strategy and the Combine-Adaptive-Markov (CAM) diffusion strategy are obtained by gradient descent iterative method. Furthermore, a generalized adaptive diffusion strategy based on Markov jump (ADSM) is introduced. Then, we propose several theorems about the average convergence and mean square stability of ADSM, which is enough to prove that our proposed strategies are to promote the existing conclusions. Finally, The simulation experiments also demonstrate the effectiveness of the proposed ADSM. [ABSTRACT FROM AUTHOR]

The development of financial sectors is considered as one of vital determinants of the growth of Ethiopian economy, and for secure equitable distribution of the benefits to the society. However, financial distress has an effect on the sectors. This review was conducted to assess financial distress situation of financial sectors in Ethiopia. The objective is specifically to identify the determinants of financial distress, opportunities of financial sectors, and challenges and constraints of financial distress in Ethiopia. Ethiopia is a developing country with majority of unbanked population. Liquidity, profitability, leverage, firm size, capital adequacy, management efficiency, earning ability, inflation and interest rate are major determinant of financial distress in Ethiopia. Moreover, Ethiopian financial sectors have an opportunity of trade openness, rapid economic growth, unexploited resource, population growth and encouragement of privatization. [ABSTRACT FROM AUTHOR]

The commentary of Dr. Katz and Ms. Waisman[1] misrepresents the 2017 Canadian Guideline for opioid therapy and chronic noncancer pain. The Guideline did encourage clinicians to approach patients prescribed high-dose opioid therapy to consider a voluntary trial of tapering that, in patients who elected to proceed, could be paused or discontinued based on their response. We also place a high value on patient autonomy under these circumstances (p. 1).[8] The 2017 Opioid Guideline did not introduce strict dose limits for people taking opioids. [Extracted from the article]

Nowadays, an increasing number of crises worldwide, triggered by climate extremes, natural and human-made hazards, the coronavirus pandemic, and more, pose a high pressure on crisis, emergency, and disaster management. Spatial data and Volunteered Geographic Information (VGI) are key issues in the successful and immediate response to crises. This paper aims to explore the use of VGI in crisis management, including emergency and disaster management, based on a scoping review of existing literature in English for five years (2016–2020). Specifically, the research intends to answer Scoping Review Questions (SRQ) regarding the use of VGI in crisis, emergency, and disaster management, and the verified cases' spatial distribution, the VGI sources utilized (e.g. OpenStreetMap – OSM, Crowdsourcing, Twitter), the types of hazards (e.g. natural and human-made hazards, pandemic), the specific tasks in crisis, emergency or disaster management and VGI use in the management of actual crisis events, e.g. COVID-19 pandemic, Hurricane Katrina, etc. Eligible papers on VGI use in crisis, emergency, and disaster management are geolocated based on first-author affiliation, and as a result, a spatial bibliography is provided. Thus, the term Spatial Scoping Review is introduced. Scoping Review Questions are answered, and the results are analyzed and discussed. Finally, implementing the "VGICED Atlas", a web atlas, permits the publication of the research results to a broad audience and the visualization of the analysis with several interactive maps. [ABSTRACT FROM AUTHOR]

This article is a study on the possible common theoretical approaches shared by three heterodox associations-Association for Evolutionary Economics (AFEE), Union for Radical Political Economics (URPE), and Association for Social Economics (ASE). We analyze their closeness and potentially common approaches as reported in papers published by the associations' main journals from 2010 to 2019. Using bibliometrics, the papers are analyzed in terms of convergent issues shared by these heterodox associations. Our article concludes, first, that building alternative approaches based on a criticism of both mainstream and New Institutional Economics is a possible point of convergence for AFEE, URPE, and ASE. Second, criticism of capitalism and studies on economic development could act as a convergence point for AFEE and URPE. Third, ASE seems to be more open to heterodox approaches than AFEE and URPE, and can make room for other convergence points. However, the ASE-AFEE and ASE-URPE dyads have not yet explored such opportunities for convergence. [ABSTRACT FROM AUTHOR]

In this paper, the author introduced a sentiment analysis-based customer loyalty prediction model in mobile applications using word embedding models, deep learning algorithms, and deep contextualized word representations. This method showed better performances than usual state-of-the-art methods in separate data sets along with the combined data set on both gender and region classification. Intelligent Systems can be thought of as a concept with a very broad scope. [Extracted from the article]

Economic globalization and green development have become the consensus of all countries in the world. The importance of FDI, carbon emissions and green total factor productivity is self-evident. This study analyses the impact of FDI inflows on green TFP and the role of carbon emissions in FDI inflows and green TFP. The empirical analysis employed kernel density estimation, OLS, mediation effect model, and panel quantile regression methods using China's provincial panel data spanning over 2001–2019. The facts show that China's carbon emissions have been reduced because of environmental regulations and green TFP is still rising in fluctuation. The results from OLS and mediation effect model indicate that the impact of FDI inflows on green TFP present an asymmetric inverted U-shape and FDI influences green TFP by changing carbon emissions. The panel quantile regression results also show that FDI has a stronger impact on green TFP in less developed regions. This paper believes that the introduction of FDI should be appropriate and should be accompanied by corresponding environmental regulations, in order to promote green TFP in countries around the world. [ABSTRACT FROM AUTHOR]

Stakeholders in hospitality and tourism industries are involved in many decision-making scenarios. Multi-criteria decision-making (MCDM) methods have been widely used in hospitality and tourism industries. Although some articles summarised the applications of MCDM models in hospitality and tourism industries, they ignored the fuzziness of individual cognition in an uncertain environment. In addition, these surveys lacked a comprehensive overview from the perspective of bibliometrics analysis and content analysis regarding the whole hospitality and tourism industries. To analyse the applications of fuzzy MCDM methods in hospitality and tourism industries and further explore future research directions, this article reviews 85 selected papers published from 1997 to 2022 regarding fuzzy MCDM models applied in hospitality and tourism industries. Through analysing the results of bibliometric analysis, methodologies and applications, we found that analytic hierarchy process (AHP) and TOPSIS methods are the most widely used MCDM methods, and tourism evaluation, hotel evaluation and selection, tourism destination evaluation and selection are the most attractive research issues in hospitality and tourism industries. Finally, future research directions are proposed from three aspects. This article provides insights for researchers and practitioners who have interest in fuzzy MCDM models in hospitality and tourism industries. [ABSTRACT FROM AUTHOR]

A major admissions reform was carried out in Finnish higher education at the end of the last decade. This paper focuses on three main policy papers connected to the reform and examines the use and production of evidence therein. Drawing on bibliometric research and research on educational policy, we aim to provide insight into how to utilize citation analysis when examining evidence in educational policymaking. In the three policy papers, domestic legislature was cited the most frequently, and research from higher education institutions was cited the least. Affirmational citations were the most prominent; perfunctory, assumptive, conceptual, persuasive, contrastive, and negational citations were found to a lesser extent. When cross-examined in relation to citation types, sources were mostly cited as affirmational, with the exception of research from higher education institutions, which was cited as conceptual information. We also found that the writers cited their own previously written ministry-affiliated policy brief as central information in one of the policy papers connected to the reform. Our study is in line with the earlier literature showing that certain sources, such as domestic publications, are favoured over others. It also illustrates the different citation strategies experts employ to substantiate and legitimize educational policymaking. [ABSTRACT FROM AUTHOR]

The objective of the present research work was to explore the nutritional composition of different infant formulas and determine the safety status of these products. For this purpose, different infant formula brands were procured from the local market of Faisalabad, Pakistan. After the collection of these different brands, nutritional composition, i.e. fat, protein, ash, moisture and fiber was determined whilst the microbial analysis, such as Salmonella, Yeasts and Molds Count, and Total Bacteria Count, was carried out through their methods. In addition, the amino acid profile was assessed through paper chromatography. Furthermore, an appropriate statistical design was carried out to determine the level of significance. Mean values for amino acids, i.e. lysine, valine and cysteine varied from 3.06 g/100 g to 6.63 g/100 g, 4.90 g/100 g to 6.51 g/100 g, and 2.32 g/100 g to 3.04 g/100 g, respectively, in different brands of infant formula. Moreover, moisture content, crude proteins, crude fat, crude fiber, ash content, and nitrogen-free extract (NFE) varied from 1.6% to 3.83%, 10.55% to 14.67%, 9.82% to 26.63%, 1.00% to 4.13%, 1.87% to 4.20%, and 51.03% to 69.17%, respectively. It is evident from the microbiological analyses that Salmonella was not present in any brand of infant formula. Whereas yeast and mold counts were ≤2 cfu/g to ≤10 cfu/g in different brands. Moreover, total bacteria count was exhibited in the range of 1.5 × 103cfu/g to 6.3 × 103 cfu/g among different brands. Local infant formulas contained high amounts of heavy metals, i.e. 0.99 pp, of lead content followed by cadmium and aluminum as 0.28–0.98 ppm and 1.48–2.05 ppm, respectively. Conclusively, it is evident from whole research that local brands contained higher contents of heavy metal as compared to branded. [ABSTRACT FROM AUTHOR]

The article by Zwetsloot and Woodall (2019) opportunely addresses an updated and relevant topic that has been escaping the attention of the data-centric/evidenced based communities. As data collectors become widely available across industrial processes and along the entire value chain, the issue of aggregation becomes not only a need for handling high-throughput data streams but, as I show here, an opportunity to improve the performance of classical data analytics by exploring an extra tuning dimension: data granularity (or level of aggregation). In this discussion, I begin by clarifying what I consider to be two distinct perspectives of data aggregation to be taken into account, here designated as process data streams aggregation (or the process-oriented perspective) and product samples aggregation (or the quality-oriented perspective). Next, the topic of handling variables with different levels of aggregation is addressed, as well as the potential advantages of selectively introducing aggregation, even when raw data are available at a single aggregation level. Finally, the topic of adaptability in data aggregation is commented upon within the scope of multiscale methods for statistical process monitoring. Across my exposition, I also try to convey the perspective of the process industries (PI), such as oil and chemicals, biofuels, pharmaceutical, pulp and paper, food and drink, semiconductors, and microelectronics, among others we have been involved with, and where the topic discussed in Zwetsloot and Woodall (2019) is very relevant. [ABSTRACT FROM AUTHOR]

The article discusses award-winning papers that were published in the journal during 2014, including "Preoptic activation and connectivity during thermal sweating in humans," by Michael Farrell, David Trevaks and Robin McAllen, "Responses of large mammals to climate change," by Robyn Hetem, Andrea Fuller, Shane Maloney, and Duncan Mitchell and a paper by author Boris Kingma.

Experts have described ways to improve peer review quality. Perspectives from expert reviewers are largely absent in the health professions education literature. To gather guidance from expert reviewers, to aid authors striving to publish and reviewers aiming to perform their task effectively. This study surveyed the Journal of Graduate Medical Education (JGME) 'Top Reviewers' from 2017, 2018, and 2019. 'Top Reviewers' perform four or more reviews per year, with high average ratings. Top reviewers were sent an 11-item survey in February 2020. The survey included three demographic questions and eight open-ended, free-text questions about the concepts reviewers most often target in their reviews. We calculated descriptive statistics and performed a thematic analysis of open-ended responses. Of 62 eligible top reviewers, 44 (71%) responded to the survey. Only eight (18.2%) and seven (15.9%) respondents reported having 'stock phrases' or a reviewer template used for reviewer feedback to authors, respectively. The what (research question, methods), how (presentation, writing), and why (relevance, impact) were the resulting themes summarizing how reviewers categorized and responded to common problems. For 'really good papers' reviewers found the what acceptable and focused on how and why. For 'really bad' papers, reviewers focused on big picture feedback, such as the value of the study. Top reviewers from a single health professions education journal appear to have similar approaches to conducting reviews. While most do not use stock phrases or templates, they share similar strategies to differentiate 'good' vs. 'bad' papers through the what, why, and how of a manuscript. [ABSTRACT FROM AUTHOR]

In the above paper five errors have been found. [ABSTRACT FROM AUTHOR]

Photovoltaic systems (PV) have been extensively used worldwide as a reliable and effective renewable energy resource due to their environmental and economic merits. However, PV systems are prone to several environmental and weather conditions that impact their performance. Amongst these conditions is dust accumulation, which has a significant adversative impact on the solar cells' performance, especially in hot and arid regions. This study provides a comprehensive review of 278 articles focused on the impact of dust on PV panels' performance along with other associated environmental factors, such as temperature, humidity, and wind speed. The review highlights the importance of modelling dust accumulation along with other ecological factors due to their interactive nature, and the differences between cleaning techniques and schedules effectiveness. Moreover, the study provides a review of statistical and artificial intelligence models used to predict PV performance and its prediction accuracies in terms of data size and complexity. Finally, the study draws attention to several research gaps that warrant further investigation. Among these gaps is the need for proper dynamic optimisation models for cleaning schedules and a more advanced machine and deep learning models to predict dust accumulation while considering environmental and ageing factors. [ABSTRACT FROM AUTHOR]

Although the dioxin alarm broke at the same time in Sweden and the US in the mid-1980s, Swedish pulp and paper (P&P) firms led the way towards the new market for low-chlorine and chlorine-free P&P products. This study explores the transition in the Swedish P&P industry and contrasts the Swedish case to the US experience. We highlight the importance of already established technological paths to deal with pollution, paths which were strongly formed by the different national environmental policies since the 1970s. Thus while US P&P firms were technologically locked-in when the dioxin alarm broke, the strategy of Swedish P&P firms to proactively collaborate in environmental research and development (R&D) together with a national policy that favoured process integrated abatement technology, helped Swedish firms take technological leadership. This article particularly stresses the implications of technological path-dependency and different national regulatory styles in understanding the evolution of different modes of corporate environmental strategies. [ABSTRACT FROM AUTHOR]

B Tag: Feature Article b Industry and government responses to supply chain disruptions during the COVID-19 pandemic demonstrate the importance of a coordinated and forward-thinking approach to respond at scale in any large-scale emergency. Role of R&D R&D has contributed to reducing supply chain risk at Crown in three ways: risk management, supplier qualification, and through Design For Substitution. By engaging regularly with their R&D, supply chain, and sales and operations planning (S&OP) team members, company leaders and managers can gain a deeper understanding of supply chain challenges and shortfalls. Mitigate risks. [Extracted from the article]

Recently, the company announced that its Major League Soccer stadium for the Philadelphia Union will be zero landfill, a feat that involves partnerships with venue management company Spectra, recycling data company Recycle Track Systems, and energy company Covanta (Subaru of America [4]). Subaru - Taking Ownership of Waste For the most committed organizations, setting a goal to produce zero waste is a strategy that requires embracing circularity throughout the business. Circular business practices to be more environmentally sustainable and discuss the types of innovation that are easiest to adopt for sustainability In the ethos of Denise Coogan, one of the leaders of Subaru of America's zero waste manufacturing initiative, "Waste is a material that hasn't found its use yet" (Subaru of America [15], 4:25). [Extracted from the article]

Air Liquide has mastered this technology for 50 years, including high-pressure hydrogen storage, and Air Liquide's R&D has greatly contributed to it, whether by developing specific simulation tools to design delivery systems, or by defining good practices to make sure installations are fully safe. Air Liquide investments (Air Liquide 2021) Just like energy, hydrogen presents specificities that need to be understood and addressed. Been a pioneer in hydrogen technologies. [Extracted from the article]

Poverty has always been a global concern that has restricted human development. The first goal (SDG 1) of the United Nations Sustainable Development Goals (SDGs) is to eliminate all forms of poverty all over the world. The establishment of a scientific and effective localized SDG 1 evaluation and monitoring method is the key to achieving SDG 1. This paper proposes SDG 1 China district and county-level localization evaluation method based on multi-source remote sensing data for the United Nations Sustainable Development Framework. The temporal and spatial distribution characteristics of China's poverty areas and their SDG 1 evaluation values in 2012, 2014, 2016, and 2018 have been analyzed. Based on the SDGs global indicator framework, this paper first constructed SDG 1 China's district and county localization indicator system and then extracted multidimensional feature factors from nighttime light images, land cover data, and digital elevation model data. Secondly, we establish SDG 1 China's localized partial least squares estimation model and SDG 1 China's localized machine learning estimation model. Finally, we analyze and verify the spatiotemporal distribution characteristics of China's poverty areas and counties and their SDG 1 evaluation values. The results show that SDG 1 China's district and county localization indicator system proposed in this study and SDG 1 China's localized partial least squares estimation model can better reflect the poverty level of China's districts and counties. The estimated model R2 is 0.65, which can identify 72.77% of China's national poverty counties. From 2012 to 2018, the spatial distribution pattern of SDG evaluation values in China's districts and counties is that the SDG evaluation values gradually increase from western China to eastern China. In addition, the average SDG 1 evaluation value of China's districts and counties increased by 23% from 2012 to 2018. This paper is oriented to the United Nations SDGs framework, explores the SDG 1 localized evaluation method of China's districts and counties based on multisource remote sensing data, and provides a scientific and rapid regional poverty monitoring and evaluation program for the implementation of the 2030 agenda poverty alleviation goals. [ABSTRACT FROM AUTHOR]

The launch of large language models (LLMs) like ChatGPT in late 2022 and the anticipated arrival of future GPT-x iterations have marked the beginning of the generative artificial intelligence (GAI) era. We conducted a systematic review of how to integrate LLMs including GPT and other GAI models into geospatial science, based on 293 papers obtained from four databases of academic publications -- Web of Science (WoS), Scopus, SSRN and arXiv -- 26 papers were eventually included for analysis. We statistically outlined the share of domains where LLMs and other GAI models, the type of data that have been used for these models, and the modelling tasks and roles that they play. We also pointed out the challenges and future directions for the next research agenda -- along with which we could better position ourselves in the mainstream of science and the cutting-edge research paradigm as others leverage insights from the growing data deluge. [ABSTRACT FROM AUTHOR]

Human error estimating food intake is a major source of bias in nutrition research. Artificial intelligence (AI) methods may reduce bias, but the overall accuracy of AI estimates is unknown. This study was a systematic review of peer-reviewed journal articles comparing fully automated AI-based (e.g. deep learning) methods of dietary assessment from digital images to human assessors and ground truth (e.g. doubly labelled water). Literature was searched through May 2023 in four electronic databases plus reference mining. Eligible articles reported AI estimated volume, energy, or nutrients. Independent investigators screened articles and extracted data. Potential sources of bias were documented in absence of an applicable risk of bias assessment tool. Database and hand searches identified 14,059 unique publications; fifty-two papers (studies) published from 2010 to 2023 were retained. For food detection and classification, 79% of papers used a convolutional neural network. Common ground truth sources were calculation using nutrient tables (51%) and weighed food (27%). Included papers varied widely in food image databases and results reported, so meta-analytic synthesis could not be conducted. Relative errors were extracted or calculated from 69% of papers. Average overall relative errors (AI vs. ground truth) ranged from 0.10% to 38.3% for calories and 0.09% to 33% for volume, suggesting similar performance. Ranges of relative error were lower when images had single/simple foods. Relative errors for volume and calorie estimations suggest that AI methods align with – and have the potential to exceed – accuracy of human estimations. However, variability in food image databases and results reported prevented meta-analytic synthesis. The field can advance by testing AI architectures on a limited number of large-scale food image and nutrition databases that the field determines to be adequate for training and testing and by reporting accuracy of at least absolute and relative error for volume or calorie estimations. These results suggest that AI methods are in line with – and have the potential to exceed – accuracy of human estimations of nutrient content based on digital food images. Variability in food image databases used and results reported prevented meta-analytic synthesis. The field can advance by testing AI architectures on a limited number of large-scale food image and nutrition databases that the field determines to be accurate and by reporting accuracy of at least absolute and relative error for volume or calorie estimations. Overall, the tools currently available need more development before deployment as stand-alone dietary assessment methods in nutrition research or clinical practice. [ABSTRACT FROM AUTHOR]