bluuebunny/crossref_metadata_2025 huggingface dataset:
Contextualizing and Expanding Conversational Queries without Supervision
Authors: Krasakis Antonios Minas , Yates Andrew , Kanoulas Evangelos
Published: November 2023

Most conversational passage retrieval systems try to resolve conversational dependencies by using an intermediate query resolution step. To do so, they synthesize conversational data or assume the availability of large-scale question rewriting datasets. To relax those conditions, we propose a zero-shot unified resolution–retrieval approach, that (i) contextualizes and (ii) expands query embeddings using the conversation history and without fine-tuning on conversational data. Contextualization biases the last user question embeddings towards the conversation. Query expansion is used in two ways: (i) abstractive expansion generates embeddings based on the current question and previous history, whereas (ii) extractive expansion tries to identify history term embeddings based on attention weights from the retriever. Our experiments demonstrate the effectiveness of both contextualization and unified expansion in improving conversational retrieval. Contextualization does so mostly by resolving anaphoras to the conversation and bringing their embeddings closer to the important resolution terms that were omitted. By adding embeddings to the query, expansion targets phenomena of ellipsis more explicitly, with our analysis verifying its effectiveness on identifying and adding important resolutions to the query. By combining contextualization and expansion, we find that our zero-shot unified resolution–retrieval methods are competitive and can even outperform supervised methods.
COBRRA: COntention-aware cache Bypass with Request-Response Arbitration
Authors: Bagchi Aritra , Joshi Dinesh , Panda Preeti Ranjan
Published: November 2023

In modern multi-processor systems-on-chip (MPSoCs), requests from different processor cores, accelerators, and their responses from the lower-level memory contend for the shared cache bandwidth, making it a critical performance bottleneck. Prior research on shared cache management has considered requests from cores but has ignored crucial contributions from their responses. Prior cache bypass techniques focused on data reuse and neglected the system-level implications of shared cache contention. We propose COBRRA, a novel shared cache controller policy that mitigates the contention by aggressively bypassing selected responses from the lower-level memory and scheduling the remaining requests and responses to the cache efficiently. COBRRA is able to improve the average performance of a set of 15 SPEC workloads by 49% and 33% compared to the no-bypass baseline and the best-performing state-of-the-art bypass solution, respectively. Furthermore, COBRRA reduces the overall cache energy consumption by 38% and 31% compared to the no-bypass baseline and the most energy-efficient state-of-the-art bypass solution, respectively.
AlgBERT: Automatic Construction of Annotated Corpus for Sentiment Analysis in Algerian Dialect
Authors: Hamadouche Khaoula , Bousmaha Kheira Zineb , Bekkoucha Mohamed Abdelwaret , Hadrich-Belguith Lamia
Published: November 2023

Nowadays, sentiment analysis is one of the most crucial research fields of Natural Language Processing (NLP), and it is widely applied in a variety of applications such as marketing and politics. However, the Arabic language still lacks sufficient language resources to enable the tasks of opinion and emotion analysis comparing to other language such as English. Additionally, manual annotation requires a lot of effort and time. In this article, we address this problem and propose a novel automated annotation platform for sentiment analysis called AlgBERT by providing annotated corpus and using deep learning technology that includes many automatic natural language processing algorithms, which is the basis for text classification and opinion analysis. We suggest using BERT model as a method; it is the abbreviation of Bidirectional Encoder Representations from Transformers, as it is one of the most effective technologies in terms of results in different world languages. We used around of 54K comments collected from social networking (Twitter, YouTube) written in Arabic and Algerian dialects. Our AlgBERT system obtained excellent results with an accuracy of 91.04%, and this is considered as one of the best results for opinion analysis in Algerian dialect.
Fast Convolution Meets Low Precision: Exploring Efficient Quantized Winograd Convolution on Modern CPUs
Authors: Wang Xueying , Li Guangli , Jia Zhen , Feng Xiaobing , Wang Yida
Published: November 2023

Low-precision computation has emerged as one of the most effective techniques for accelerating convolutional neural networks and has garnered widespread support on modern hardware. Despite its effectiveness in accelerating convolutional neural networks, low-precision computation has not been commonly applied to fast convolutions, such as the Winograd algorithm, due to numerical issues. In this article, we propose an effective quantized Winograd convolution, named LoWino, which employs an in-side quantization method in the Winograd domain to reduce the precision loss caused by transformations. Meanwhile, we present an efficient implementation that integrates well-designed optimization techniques, allowing us to fully exploit the capabilities of low-precision computation on modern CPUs. We evaluate LoWino on two Intel Xeon Scalable Processor platforms with representative convolutional layers and neural network models. The experimental results demonstrate that our approach can achieve an average of 1.84× and 1.91× operator speedups over state-of-the-art implementations in the vendor library while preserving accuracy loss at a reasonable level.
Interpretable Machine Learning for Discovery: Statistical Challenges and Opportunities
Authors: Allen Genevera I. , Gan Luqin , Zheng Lili
Published: November 2023

New technologies have led to vast troves of large and complex data sets across many scientific domains and industries. People routinely use machine learning techniques not only to process, visualize, and make predictions from these big data, but also to make data-driven discoveries. These discoveries are often made using interpretable machine learning, or machine learning models and techniques that yield human-understandable insights. In this article, we discuss and review the field of interpretable machine learning, focusing especially on the techniques, as they are often employed to generate new knowledge or make discoveries from large data sets. We outline the types of discoveries that can be made using interpretable machine learning in both supervised and unsupervised settings. Additionally, we focus on the grand challenge of how to validate these discoveries in a data-driven manner, which promotes trust in machine learning systems and reproducibility in science. We discuss validation both from a practical perspective, reviewing approaches based on data-splitting and stability, as well as from a theoretical perspective, reviewing statistical results on model selection consistency and uncertainty quantification via statistical inference. Finally, we conclude byhighlighting open challenges in using interpretable machine learning techniques to make discoveries, including gaps between theory and practice for validating data-driven discoveries.
Efficient Privacy‐Preserving Federated Deep Learning for Network Intrusion of Industrial IoT
Authors: He Ningxin , Zhang Zehui , Wang Xiaotian , Gao Tiegang
Published: November 2023

Intrusion detection systems play a very important role in industrial Internet network security. However, in the large‐scale, complex, and heterogeneous industrial Internet of Things (IoT), it is becoming more and more difficult to defend network intrusion threats due to the insufficiency of high‐quality attack samples. To solve the problem, an efficient federated network intrusion method called EFedID is proposed for industrial IoT, which can allow different industrial agents to collaboratively train a comprehensive detection model. Specifically, the adaptive gradient sparsification method is introduced to alleviate the communication and computation overheads. To protect the data privacy of the agents, a CKKS cryptosystem‐based secure communication protocol is designed to encrypt the model parameters through the federated training process. Our proposed system demonstrates exceptional detection performance on the NSL‐KDD, KDD CUP 99, and CICIDS 2017 datasets. Notably, on the NSL‐KDD dataset, the model compression rate reaches 9 times while the model accuracy reaches 84.31%. On the KDD CUP 99 dataset, the model compression rate reaches 8.9 times while the model accuracy reaches 97.3%. Lastly, on the CICIDS 2017 dataset, the model compression rate reached 6.173 times while the model accuracy reached 95.51%. The experimental results demonstrate that the proposed method is very suitable for effectively developing a high‐accuracy detection model while protecting the data information of industrial agents. Furthermore, the method can be extended to other recent deep learning networks for intrusion detection.
Heavy Metal Residue (As, Cd, Hg, and Pb) in Hen Eggs after Applying Different Cooking Methods
Authors: Hoseini Hedayat , Mohammadi-Nasrabadi Fatemeh , Abedi Abdolsamad , Rostami Negar , Bazzaz Sara , Esfarjani Fatemeh
Published: November 2023

As a nutritious food, eggs are capable of accumulating heavy metals and are a good indicator of environmental contamination. This study is aimed at determining the levels of heavy metal residues (As, Cd, Hg, and Pb) in hen eggs after applying different cooking methods. In an experimentally designed study, forty-four hen egg samples were selected from 22 best-selling brands of supermarkets in Tehran and categorized into one of four groups (raw, dry-frying, and boiling with and without eggshells). The levels of heavy metal residues were analyzed by using inductively coupled plasma mass spectrometry (ICP-MS). The average levels of heavy metals (As, Cd, Hg, and Pb) in raw samples were 0.307, 1.654, 0.121, and 6.5 (μg.kg-1), respectively. After applying cooking methods, the residue of two heavy metals in boiling without eggshells (As:

0.228
±
0.197

; Cd:

1.985
±
0.037

) was lower than boiling with eggshells (As:

0.457
±
0.918

; Cd:

2.11
±
0.223

 μg.kg-1), while the dry-frying method had the lowest level of heavy metal residue (As:

0.222
±
0.109

; Cd:

1.54
±
0.223

) (

p
<
0.05

) and could be identified as efficient in reducing the amount of these heavy metals. In addition, different cooking methods did not make a significant difference in other heavy metal residues (Hg and Pb). To explain this reduction, two hypotheses can be proposed to explain the reduction of heavy metals after applying different cooking methods. First, the eggshell is permeable; the second is the association of heavy metals bonding with sulfur groups and weight loss due to evaporation. All heavy metal residues were found to be within acceptable permissible thresholds. Therefore, policymakers should protect the health of the population by continuously monitoring heavy metals in foods and prioritizing education and research on how to reduce them in the food chain.
Design and Implementation of a Real-Time Street Light Dimming System Based on a Hybrid Control Architecture
Authors: Takruri Maen , Thulasingam K. P. , Attia Hussain , Omar Amjad , Altunaiji Ahmed , Almaeeni Sara
Published: November 2023

Smart street LED lighting systems have received much attention driven by the need to save energy and the dramatic advances in the Internet of Things. This work proposes a new smart street lighting system that adaptively changes the street lights’ intensity based on traffic and weather conditions and provides a platform for monitoring road conditions and detecting lamp faults. The system transfers the data using the UDP protocol over NBIoT radio technology. It also maintains two-way communication between the luminaires and the central node. In order to ensure real-time response to traffic and avoid dimming delays, each light is locally controlled by a microcontroller based on the sensed traffic and weather data. The measurements of each luminaire are also sent to the central control node to locate lamp faults, detect emergency situations, and, if needed, broadcast on/off messages to the whole network’s luminaires. The system was implemented in a suburban street in Ras Al Khaimah. Evaluations proved that the system can locate and detect faulty lamps and vary the light intensity in real time based on traffic. It also resulted in energy savings of up to 55% compared to a normal LED street light network.
A Comprehensive Assessment of Indoor Air Quality and Thermal Comfort in Educational Buildings in the Mediterranean Climate
Authors: Miao S. , Gangolells M. , Tejedor B.
Published: November 2023

Maintaining good indoor air quality and thermal comfort is a challenge for naturally ventilated educational buildings, as it can be difficult to achieve both aspects simultaneously. Nonetheless, most of the existing studies only focus on one aspect. To explore the potential of balancing indoor air quality and thermal comfort, both topics must be investigated concurrently. This study assessed indoor air quality and thermal comfort in 32 naturally ventilated classrooms of 16 primary and secondary schools in the Mediterranean climate, based on a large on-site measurement campaign lasting one year that gathered over 460 hours of data. The research investigated occupants’ adaptive behaviors, analyzed the actual thermal comfort of around 600 students, and characterized the representative scenarios leading to good and poor indoor air quality and thermal comfort by clustering analysis. The results showed that poor indoor air quality was mainly due to closing windows and doors in winter, while thermal discomfort mainly occurred in summer because of the high indoor temperature. The findings suggested that a proper ventilation protocol is the key to balancing indoor air quality and thermal comfort.
A Hybrid Deep Learning Prediction Method of Remaining Useful Life for Rolling Bearings Using Multiscale Stacking Deep Residual Shrinkage Network
Authors: Song Xudong , Zhang Qi , Sun Rui , Tian Rui , Sun Jialiang , Li Changxian , Cui Yunxian
Published: November 2023

The vibration signal is easily interfered by noise due to the influence of environment and other factors, which can lead to the poor adaptability, low accuracy of remaining useful life (RUL) prediction, and other problems. To solve this problem, this paper proposes a novel RUL prediction method, which is based on multiscale stacking deep residual shrinkage network (MSDRSN). MSDRSN combines the ability of stacking in improving prediction accuracy and the advantages of deep residual shrinkage network (DRSN) in denoising. First, cumulative sum (CUSUM) from statistics is used to divide the full life cycle of the rolling bearings and discover the points of failure. Second, stacking is used for feature learning on the raw data, multiple convolutional kernels of different scales are selected as base‐learners, and fully connected neural networks are selected as meta‐learners for feature fusion and learning. Then, DRSN is used to do prediction, and the acquired results are fitted with Savitzky–Golay (SG) smoothing. Finally, the effectiveness of the proposed method is proved by the IEEE PHM 2012 data challenge dataset. Compared with the multiscale convolutional neural network with fully connected layer (MSCNN‐FC) and the bidirectional long short‐term memory (BiLSTM) for RUL prediction under the noise. Using the proposed method, the mean absolute error (MSE) of the best result is 0.002 and the mean square error (MSE) is 0.014; meanwhile, the coefficient of determination (R2) of the best prediction result can reach 97.6%. It is also compared with other machine learning methods, and all the results prove the accuracy and effectiveness of the proposed method for RUL prediction applications.
Comparing Breast Cancer and Cardiovascular Disease Risk and Use of Chemoprevention and Statins among Women with High-risk Breast Lesions
Authors: Lawal Kehinde O. , Nilan Luisa , Amenta Jacquelyn , McGuinness Julia E. , Kukafka Rita , Crew Katherine D.
Published: November 2023

Breast cancer chemoprevention with selective estrogen receptor modulators (SERM) or aromatase inhibitors (AI) remains underutilized among high-risk women. A potential barrier to chemoprevention is competing comorbidities such as atherosclerotic cardiovascular disease (ASCVD), due to concern for additional medication side effects. We conducted a retrospective cohort study among women with atypical hyperplasia (AH) or lobular carcinoma in situ (LCIS), an important target population for chemoprevention. We compared risks for breast cancer and ASCVD, as well as use of SERMs/AIs versus statins among high-risk women (defined as a 5-year invasive breast cancer risk ≥1.67% and 10-year ASCVD risk ≥7.5%, respectively). We used clinical data extracted from the electronic health record to calculate breast cancer risk according to the Breast Cancer Surveillance Consortium model and ASCVD risk according to the 2013 American College of Cardiology/American Heart Association risk calculator. Among 298 evaluable women, mean age was 58.2 years (SD, 8.34), with 33% non-Hispanic White, 41% Hispanic, 9% non-Hispanic Black, 6% Asian, and 11% other/unknown race/ethnicity. About 98% of women met high-risk criteria for breast cancer, whereas 30% were high-risk for ASCVD. Mean 10-year risk of breast cancer was higher than mean 10-year risk of ASCVD (9.14% vs. 6.69%; P < 0.001). Among women who met high-risk criteria for both diseases, use of statins was higher compared with SERMs/AIs (58% vs. 21%; P < 0.001). Among women with AH or LCIS, statin use was higher compared with breast cancer chemoprevention among eligible women, despite having a higher mean risk of breast cancer than ASCVD.

Prevention Relevance:

Among women with high-risk breast lesions, mean absolute risk of breast cancer was higher compared with cardiovascular disease; however, statin use was significantly higher than chemoprevention. To address underutilization of breast cancer chemoprevention, these drugs should be placed in the context of medications used to prevent other chronic diseases.
Building a Sustainable Future: AI-Driven Economic Governance for Development
Authors: Fadli Faishal , Devia SS Vietha
Published: November 2023

The 21st century is marked by interconnected global challenges, with climate change taking center stage. In this context, economic governance plays a pivotal role in promoting sustainability. The integration of artificial intelligence (AI) offers the promise of datadriven insights and predictive capabilities to guide informed policy decisions, ultimately steering societies toward resilience and sustainable development. While these technological advancements hold great promise, they also exacerbate economic inequality. However, AIpowered economic governance has the potential to act as an equalizer by enabling targeted interventions and inclusive social safety nets. It empowers governments to design progressive tax policies and innovative job training initiatives. Furthermore, AI-driven economic governance facilitates agile policymaking, ensuring timely responses to emerging economic trends. Ethical considerations are essential in this transformative journey. Responsible and inclusive AI deployment requires transparency, fairness, and accountability. This book delves into these ethical complexities, offering insights into the delicate balance between technological progress and societal well-being. AI technologies have revolutionized various industries, but their potential in economic governance remains largely untapped. AI offers an opportunity for a paradigm shift in economic governance, providing data-informed, agile, and forward-thinking approaches. Its strength lies in its ability to swiftly and accurately analyze vast datasets, enabling proactive measures to mitigate risks and real-time policy adjustments. AI-driven governance can optimize resource allocation and tailor interventions to address specific challenges, such as economic inequality, while minimizing human biases in decision-making, promoting fairness and transparency. "Building a Sustainable Future: AI-Driven Economic Governance for Development" explores AI's role in economic governance through case studies and expert insights. Sustainable development, which seeks to harmonize economic progress, social equity, and environmental preservation, can benefit significantly from AI's data-driven approach. By embracing AI as a catalyst for equilibrium, we can work toward a future where economic growth, social inclusion, and environmental stewardship coexist. AI excels in economic governance by addressing inequalities and social exclusion, while ensuring responsible and equitable AI adoption remains a priority. This book envisions a future where AI maximizes benefits while minimizing adverse consequences, shaping a better world for future generations.
Cartas e arquivos da criação
Authors: Ferreira Ligia Fonseca , Moraes Marcos Antonio de
Published: November 2023

Editorial do dossiê 50: Cartas e arquivos da criação.
Marina de Andrada e Silva Procópio de Carvalho e seu arquivo de cartas de crianças e jovens enviadas a Monteiro Lobato: labirinto da perdição
Authors: Romano Patrícia Aparecida Beraldo
Published: November 2023

Desde que a obra de Monteiro Lobato entrou em domínio público, em 2019, muito se tem escrito novamente sobre o autor e sobre suas obras, inclusive as infantis, que ganharam até mesmo novas edições. Além de escritor, editor, adido comercial e demais atividades exercidas ao longo de sua vida, Lobato foi também um missivista contumaz, fosse para seu público leitor adulto, fosse para o infantil. Consta que nunca deixou um de seus jovens leitores sem resposta. Esse compromisso fez com que ele recebesse muitas cartas de crianças e jovens, leitores assíduos de seus textos infantis. Todo o conjunto dessas cartas que restaram está hoje no Arquivo Raul de Andrada e Silva, no Instituto de Estudos Brasileiros (IEB), na Universidade de São Paulo (USP). Elas chegaram ao Arquivo porque foram entregues, pelo escritor, a Marina de Andrada Procópio de Carvalho que, como amiga de Lobato, também com ele se correspondeu. Este trabalho pretende apresentar resultados da pesquisa que se fez nesse arquivo sobre a correspondência nele depositada.
Um curioso manuscrito de Maneco de Almeida
Authors: Ramos Daniele
Published: November 2023

Este artigo estabelece o texto, em língua portuguesa atualizada, de manuscrito inédito de Manuel Antônio de Almeida, escritor romântico elevado ao centro do cânone do romantismo nacional pelas releituras dos estudiosos dos séculos XX e XXI. Além disso, busca-se analisar o manuscrito e apontar sua importância como documento literário e histórico, ratificando a necessidade de se estudarem todos os textos legados pelos autores de relevo, inclusive material constante da correspondência pessoal ou produtos inacabados, como vem a ser o caso do manuscrito aqui analisado.
Os cosmoramas de Inês
Authors: Casarin Carolina
Published: November 2023

Em 11 de fevereiro de 1912 Oswald de Andrade embarcou em Santos no navio Martha Washington para, pela primeira vez, cruzar o Oceano Atlântico a caminho da Europa. A distância ensejou a troca de cartas entre Oswald e seus pais, Inês Henriqueta e José Oswald, que estavam em São Paulo. Esta correspondência está no Fundo Oswald de Andrade sob a guarda do Centro de Documentação Alexandre Eulálio (CEDAE), na Universidade Estadual de Campinas (Unicamp). Neste artigo, publicamos pela primeira vez um dos documentos que compõem esse conjunto, a carta datada de 22 de abril de 1912, endereçada a “Seu Oswaldinho” e assinada como “Recado de Dona Inês”. Trata-se de um documento precioso pois seu conteúdo indica a importância da construção da aparência numa dimensão ampla, que passa por hábitos de consumo (o que era consumido, e como) e pela constituição de um gosto moderno.
2024 Recommendations for Validation of Noninvasive Arterial Pulse Wave Velocity Measurement Devices
Authors: Spronck Bart , Terentes-Printzios Dimitrios , Avolio Alberto P. , Boutouyrie Pierre , Guala Andrea , Jerončić Ana , Laurent Stéphane , Barbosa Eduardo C.D. , Baulmann Johannes , Chen Chen-Huan , Chirinos Julio A. , Daskalopoulou Stella S. , Hughes Alun D. , Mahmud Azra , Mayer Christopher C. , Park Jeong Bae , Pierce Gary L. , Schutte Aletta E. , Urbina Elaine M. , Wilkinson Ian B. , Segers Patrick , Sharman James E. , Tan Isabella , Vlachopoulos Charalambos , Weber Thomas , Bianchini Elisabetta , Bruno Rosa Maria ,   on behalf of the Association for Research into Arterial Structure and Physiology (ARTERY), the European Society of Hypertension Working Group on Large Arteries, European Cooperation in Science and Technology (COST) Action VascAgeNet, North American Artery Society, ARTERY LATAM, Pulse of Asia, and Society for Arterial Stiffness—Germany-Austria-Switzerland (DeGAG)
Published: November 2023

BACKGROUND:

Arterial stiffness, as measured by arterial pulse wave velocity (PWV), is an established biomarker for cardiovascular risk and target-organ damage in individuals with hypertension. With the emergence of new devices for assessing PWV, it has become evident that some of these devices yield results that display significant discrepancies compared with previous devices. This discrepancy underscores the importance of comprehensive validation procedures and the need for international recommendations.

METHODS:

A stepwise approach utilizing the modified Delphi technique, with the involvement of key scientific societies dedicated to arterial stiffness research worldwide, was adopted to formulate, through a multidisciplinary vision, a shared approach to the validation of noninvasive arterial PWV measurement devices.

RESULTS:

A set of recommendations has been developed, which aim to provide guidance to clinicians, researchers, and device manufacturers regarding the validation of new PWV measurement devices. The intention behind these recommendations is to ensure that the validation process can be conducted in a rigorous and consistent manner and to promote standardization and harmonization among PWV devices, thereby facilitating their widespread adoption in clinical practice.

CONCLUSIONS:

It is hoped that these recommendations will encourage both users and developers of PWV measurement devices to critically evaluate and validate their technologies, ultimately leading to improved consistency and comparability of results. This, in turn, will enhance the clinical utility of PWV as a valuable tool for assessing arterial stiffness and informing cardiovascular risk stratification and management in individuals with hypertension.
In-Context Retrieval-Augmented Language Models
Authors: Ram Ori , Levine Yoav , Dalmedigos Itay , Muhlgay Dor , Shashua Amnon , Leyton-Brown Kevin , Shoham Yoav
Published: November 2023

Retrieval-Augmented Language Modeling (RALM) methods, which condition a language model (LM) on relevant documents from a grounding corpus during generation, were shown to significantly improve language modeling performance. In addition, they can mitigate the problem of factually inaccurate text generation and provide natural source attribution mechanism. Existing RALM approaches focus on modifying the LM architecture in order to facilitate the incorporation of external information, significantly complicating deployment. This paper considers a simple alternative, which we dub In-Context RALM: leaving the LM architecture unchanged and prepending grounding documents to the input, without any further training of the LM. We show that In-Context RALM that builds on off-the-shelf general purpose retrievers provides surprisingly large LM gains across model sizes and diverse corpora. We also demonstrate that the document retrieval and ranking mechanism can be specialized to the RALM setting to further boost performance. We conclude that In-Context RALM has considerable potential to increase the prevalence of LM grounding, particularly in settings where a pretrained LM must be used without modification or even via API access.1
5. Information and Engineering
Authors: Ingram David
Published: November 2023

Engineering is positioned at the interface of science and society. In health care, it connects the creators, commissioners and users of information systems, shaping and navigating pathways leading to success or failure in supporting the quality and improvement of services. This chapter celebrates engineers, with stories of their focus, skill and dogged persistence. I draw first on Samuel Smiles (1812–1904) and his 1881 book, Men of Invention and Industry, a wonderful account of engineering innovation through the English Industrial Revolution, to draw parallels with innovation in the information revolution of our age.

The chapter associates the kinds and groupings of data that are captured, processed, stored and retrieved with the devices and systems employed to do this. It describes how these have evolved, from the remote village life of my childhood, through school and university days, to my desktop today, in my now global village life, and the Cloud of computational resource that it immediately connects me with. It highlights how characteristics and limitations of devices and evolving computer programming paradigms have channelled both theoretical and practical developments, and determined their usefulness. It connects the discussion of models and simulations in the preceding chapter with data models, information models and knowledge models of today.

The chapter tracks the parallel evolution of software and algorithm, from early empirical methods closely aligned to the underlying machinery of the computer, to programming languages based on theory of data and algorithm, tuned to different domains of application, seeking tractable solutions for the computational challenges they pose. It concludes with a discussion of the standardization of computer systems and methods and the transformational infrastructure of the Internet and World Wide Web. The closing reflection, which concludes Part One of the book and sets the scene for Part Two and Part Three, looks towards a new interface of science and society, as the anarchic transition through the Information Age leads into a reinvention of health care supported by care information systems construed and sustained as a public utility.
8½. Halfway Houses towards openCare
Authors: Ingram David
Published: November 2023

This half chapter introduces missions and movements that have evolved from adventure of ideas, through anarchy of transition, into central components of programmes for reform of health care services, now extending across the world, at scale. It is not a pitch for their adoption or a comparison with other endeavours. It is an eyewitness account of how they came to be, and a perspective that has unfolded alongside of what the future might be created to look like. It is these aspects that seem important to record, so that progress can continue to be made.

The principal story told is of a mission to help bring coherence to electronic care records. This is the story of GEHR and openEHR—persisting along a thirty-year stretch of my songline. Its survival and continuity have rested on the enduring commitment of its pioneers and a growing, vibrant, humanly variegated (and sometimes quarrelsome!) community of creative and determined participants. It has had stalwart friends and supporters but, until quite recently, enjoyed almost negligible public funding. It is an iterative and incremental story of implementation that has embraced new perspective, approach and delivery of digital care records. I have described the three top priorities of openEHR as implementation, implementation and implementation. Only by enacting such vision can one learn how to do it. As Robert Oppenheimer wrote in his immediate post-war Reith Lectures, which I referenced in the book’s Introduction, in attempting such a mission we discover who we are. The second story, told in less detail and combining with the profile of its founding pioneer, Bill Aylward, in Chapter Eight, is of OpenEyes. This initiative has evolved and disseminated a state-of-the-art open-source eye care record, now supporting around fifty percent of ophthalmology services in the UK. It has been made possible by a public sector-led collaboration of clinicians, NHS Trusts and companies.

Care records are concerned with capturing the ‘Who did what, when, where, how and why?’ in support of the health care of individual citizens. This half chapter seeks to encompass these same attributes. It is a story of the creation of halfway houses that have been instantiated today, along a path creating common ground on which the future care information utility can grow in the coming decades. The mission to imagine, create and sustain this coherent, citizen-centred, well-governed and trusted resource will be central to future health care, as the world turns upside down in transition from Industrial Age to Information Society.

If trillion-dollar funding streams had been utilized differently, the kinds of mission described here might have saved the world much money, heartache and lost opportunity. Enacted faithfully and well, positioned at the centre of the care information utility that they can now help to create and sustain, such missions will contribute shared common ground that enables the world of health care to become a more caring, equitable and sustainable place.
Self-Supervised Cloud Classification
Authors: Geiss Andrew , Christensen Matthew W. , Varble Adam C. , Yuan Tianle , Song Hua
Published: November 2023

Low-level marine clouds play a pivotal role in Earth’s weather and climate through their interactions with radiation, heat and moisture transport, and the hydrological cycle. These interactions depend on a range of dynamical and microphysical processes that result in a broad diversity of cloud types and spatial structures, and a comprehensive understanding of cloud morphology is critical for continued improvement of our atmospheric modeling and prediction capabilities moving forward. Deep learning has recently accelerated our ability to study clouds using satellite remote sensing, and machine learning classifiers have enabled detailed studies of cloud morphology. A major limitation of deep learning approaches to this problem, however, is the large number of hand-labeled samples that are required for training. This work applies a recently developed self-supervised learning scheme to train a deep convolutional neural network (CNN) to map marine cloud imagery to vector embeddings that capture information about mesoscale cloud morphology and can be used for satellite image classification. The model is evaluated against existing cloud classification datasets and several use cases are demonstrated, including training cloud classifiers with very few labeled samples, interrogation of the CNN’s learned internal feature representations, cross-instrument application, and resilience against sensor calibration drift and changing scene brightness. The self-supervised approach learns meaningful internal representations of cloud structures and achieves comparable classification accuracy to supervised deep learning methods without the expense of creating large hand-annotated training datasets.

Significance Statement

Marine clouds heavily influence Earth’s weather and climate, and improved understanding of marine clouds is required to improve our atmospheric modeling capabilities and physical understanding of the atmosphere. Recently, deep learning has emerged as a powerful research tool that can be used to identify and study specific marine cloud types in the vast number of images collected by Earth-observing satellites. While powerful, these approaches require hand-labeling of training data, which is prohibitively time intensive. This study evaluates a recently developed self-supervised deep learning method that does not require human-labeled training data for processing images of clouds. We show that the trained algorithm performs competitively with algorithms trained on hand-labeled data for image classification tasks. We also discuss potential downstream uses and demonstrate some exciting features of the approach including application to multiple satellite instruments, resilience against changing image brightness, and its learned internal representations of cloud types. The self-supervised technique removes one of the major hurdles for applying deep learning to very large atmospheric datasets.
The On-Orbit Performance of FY-3E in an Early Morning Orbit
Authors: Zhang Peng , Hu Xiuqing , Sun Ling , Xu Na , Chen Lin , Zhu Aijun , Lin Manyun , Lu Qifeng , Yang Zhongdong , Yang Jun , Wang Jinsong
Published: November 2023

The polar-orbiting meteorological satellites operating in an observing system composed of early morning (EM), midmorning (AM), and afternoon (PM) orbits are extremely important to global numerical weather prediction (NWP). Following the proposal of World Meteorological Organization (WMO), the China Meteorological Administration (CMA) launched Fengyun-3E (FY-3E), the world’s first EM-orbit meteorological satellite for civil use, on 5 July 2021. With 11 scientific instruments on board, FY-3E is capable of providing atmospheric sounding, low-light imaging, sea surface wind detection, and space weather monitoring. Six months after launch, we have finished the postlaunch test for all the payloads. This paper presents the FY-3E data obtained during the 6-month test period, their performance, and key geophysical products driven ready for downstream applications. Experiments have been conducted to better disseminate the sounding data within the 6-h NWP assimilation window. Further efforts have been made to benefit data application for severe weather monitoring, diurnal cycle of Earth data, quasi-continuous sun monitoring for space weather, and climate research.
First to $15: Alberta’s Minimum Wage Policy on Employment by Wages, Ages, and Places
Authors: Fossati Sebastian , Marchand Joseph
Published: November 2023

Most minimum wage studies are identified on small, plentiful, mostly expected wage changes, spread out over time. A recent set of changes have instead been large, rapid, and unexpected, following the “Fight for $15” movement. Alberta is the first North American province, state, or territory to have this $15 minimum wage, with an unexpectedly large increase (47%) occurring over a short time horizon (3 years). The employment effects of this policy are estimated using a synthetic control approach on Labour Force Survey data. Similar to the existing literature, workers moved up the wage distribution, increment by increment, but with a higher distributional reach. Employment losses occurred at similar elasticities, but with large level changes, mostly among younger workers. Newer to the literature, regional employment losses were found in four of the five non-urban economic regions, but not in Alberta’s two main cities, showing the significance and nuance of regional heterogeneity.
Motor Processing in Children With Cochlear Implants as Assessed by Functional Near-Infrared Spectroscopy
Authors: Alemi Razieh , Wolfe Jace , Neumann Sara , Manning Jacy , Hanna Lindsay , Towler Will , Wilson Caleb , Bien Alexander , Miller Sharon , Schafer Erin , Gemignani Jessica , Koirala Nabin , Gracco Vincent L. , Deroche Mickael
Published: November 2023

Auditory-motor and visual-motor networks are often coupled in daily activities, such as when listening to music and dancing; but these networks are known to be highly malleable as a function of sensory input. Thus, congenital deafness may modify neural activities within the connections between the motor, auditory, and visual cortices. Here, we investigated whether the cortical responses of children with cochlear implants (CI) to a simple and repetitive motor task would differ from that of children with typical hearing (TH) and we sought to understand whether this response related to their language development. Participants were 75 school-aged children, including 50 with CI (with varying language abilities) and 25 controls with TH. We used functional near-infrared spectroscopy (fNIRS) to record cortical responses over the whole brain, as children squeezed the back triggers of a joystick that vibrated or not with the squeeze. Motor cortex activity was reflected by an increase in oxygenated hemoglobin concentration (HbO) and a decrease in deoxygenated hemoglobin concentration (HbR) in all children, irrespective of their hearing status. Unexpectedly, the visual cortex (supposedly an irrelevant region) was deactivated in this task, particularly for children with CI who had good language skills when compared to those with CI who had language delays. Presence or absence of vibrotactile feedback made no difference in cortical activation. These findings support the potential of fNIRS to examine cognitive functions related to language in children with CI.
Introducing lifestyle medicine into family medicine: Theory and applications
Authors: Brennan Julie , Phelps Kevin , McGrady Angele , Schultz Paul
Published: November 2023

“Lifestyle medicine (LM) is an evidence-based therapeutic intervention delivered by clinicians trained and certified in this specialty to prevent, treat, and often reverse chronic disease”. Eighty percent of the conditions primary care physicians routinely encounter in their offices, e.g., diabetes mellitus, hypertension, COPD, cardiovascular disease, have root causes in poor lifestyle choices such as smoking, insufficient sleep, being sedentary, or eating highly processed foods. Lifestyle is the foundation of most chronic disease management guidelines aimed at reducing morbidity and mortality. Studies have shown that changes in lifestyle can be achieved and link almost directly to reduction in risk for chronic illness. Primary care physicians are ideally positioned to incorporate LM into their practices. It is important to recognize and find solutions to the many barriers to implementing LM at the patient, physician, and system level. There is an urgent need to increase opportunities for practicing physicians to increase their knowledge and skills related to LM and include this in medical school and residency curricula. Many resources exist that can provide the necessary training for seasoned physicians and students/residents to become competent in practicing LM and address barriers to implementing LM. LM has the potential to revolutionize clinical practice by placing a greater emphasis on disease prevention and the role of healthy lifestyle behaviors in disease management.
Social Capital Associates With Better Cognitive Health, Oral Health and Epigenetic Age Deceleration: Findings From the Canadian Longitudinal Study on Aging
Authors: Liang Aileen , Gomaa Noha
Published: November 2023

Background: Social exposures are linked to an array of health outcomes, especially around aging. In this study, we examined the association of social capital, defined as social relationships and networks, with clinical and biological outcomes including cognitive health, oral inflammation, and epigenetic aging. Methods: We used data from the Canadian Longitudinal Study on Aging (CLSA) ( n = 1,479; aged 45–85 years), categorizing social capital as structural and cognitive capital. Oral inflammation was determined as the presence of gum bleeding. Epigenetic aging was computed as the difference between chronological age and DNA methylation age. We constructed multivariable regression models adjusted for covariates to assess the relationships of interest. Results: Higher structural social capital was associated with decelerated epigenetic aging and better cognitive health outcomes, while higher cognitive social capital was associated with better cognitive outcomes and less oral inflammation. Conclusion: Enhanced social capital may contribute to better clinical and biological outcomes around aging.
Civil Society Organizations and Harm Reduction Policy: The Mexican Case
Authors: Guerra Edgar , Zwitser Guus
Published: November 2023

The present study offers an outlook on the current situation of civil society organizations (CSOs) in the field of harm reduction in Mexico. Analyzing Mexican harm reduction civil society organizations (HRCSOs) is necessary for three reasons: because they have historically represented a sensible alternative to drug policy proposals that emanate from the logic of the State; because they have the organizational and cognitive potential and the legitimacy to protect illicit substance users (with or without problematic use) from the risks and harms that drug policies may pose to this population; and because they are in a crisis that not only threatens the viability of their healthcare projects aimed at vulnerable populations but also, potentially, their own existence. The present article seeks to answer two questions—one descriptive and one explanatory: (a) What are the characteristics of Mexican CSOs that work in the field of harm reduction? (b) How do these organizations operate in a complex and dangerous environment? To answer these questions, the study uses data on the organizations’ structures and dynamics that were obtained through 20 semistructured interviews that were conducted with members of these organizations. The article describes HRCSOs on three levels: (a) the individual that establishes a radical habitus and acquires the identity of a harm reduction activist through identity construction; (b) the collective of activists that, through the creation of trust, becomes an HRCSO with its structures, programs, and resources; and (c) the harm reduction social movement that, through cooperation and conflict, becomes a field of activism. The study seeks to have two contributions to literature: (a) providing a preliminary mapping of the anatomy of HRCSOs and the field in which they operate; and (b) sensitizing society and decision-makers on the transcendence of these organizations for social and political life.
Correlation of AC energy consumption and passive thermal comfort with floor levels in tropical high-rise residential dwellings
Authors: Chukwujindu Kosisochukwu , Kasiraman Ganapathy
Published: November 2023

The relationship between thermal comfort, building floor levels, and HVAC energy consumption in tropical high-rise residential buildings remains unclear. These buildings face heavy cooling demands due to their immense height and volume, as well as hot-humid weather found in the tropics. Architects and building developers understand that an increase in a building's floor count increases its energy requirements, but the impact on occupant thermal requirements and AC energy consumption needs further investigation. This study aimed to evaluate the relationship between passive thermal comfort, AC energy consumption, and floor levels in tropical high-rise residential buildings through survey and experimental methods. The results showed a significant relationship between thermal sensation votes and building floor levels, as well as AC energy consumption and floor levels. The study also found a strong correlation between thermal sensation votes and predicted mean votes, AC energy use, and predicted people dissatisfied. The optimal cut-off floors for thermal dissatisfaction according to (TSV) are 22nd floor and 8th floor in summer and monsoon seasons respectively. With a 90% sensitivity and 92% specificity. This research highlights the importance of addressing thermal comfort in high-rise buildings to improve energy efficiency and thermal comfort in tropical climates.