Miracle-dz/UniFAR-FTUs huggingface dataset:
Beat-to-beat blood pressure variability: an early predictor of disease and cardiovascular risk

Blood pressure (BP) varies on the long, short and very-short term. Owing to the hidden physiological and pathological information present in BP time-series, increasing interest has been given to the study of continuous, beat-to-beat BP variability (BPV) using invasive and noninvasive methods. Different linear and nonlinear parameters of variability are employed in the characterization of BP signals in health and disease. Although linear parameters of beat-to-beat BPV are mainly measures of dispersion, such as standard deviation (SD), nonlinear parameters of BPV quantify the degree of complexity/irregularity- using measures of entropy or self-similarity/correlation. In this review, we summarize the value of linear and nonlinear parameters in reflecting different information about the pathophysiology of changes in beat-to-beat BPV independent of or superior to mean BP. We then provide a comparison of the relative power of linear and nonlinear parameters of beat-to-beat BPV in detecting early and subtle differences in various states. The practical advantage and utility of beat-to-beat BPV monitoring support its incorporation into routine clinical practices.
Reframing Adult Literacy: From Either/Or to Both/And.

Robert Kegan's constructive-development theory of adult growth postulates three different ways of knowing (instrumental, socializing, and self-authoring) and suggests that, although individuals develop differently, individual development can never be separated from culture. The potential of the constructive-development perspective on adult development to provide insight on how people's developmental capacities influence how they understand the concept of literacy in their roles as students was examined in a study of 41 adults in 3 adult basic education/English for speakers of other languages programs. Data were gathered through interviews, structured exercises, classroom observations, focus groups, and surveys. The way learners understood what it means to be literate was fundamentally shaped by their different ways of knowing, independent of age and country of origin. Instrumental knowers were oriented largely to the specific and concrete, externally observable behaviors and skills. Socializing knowers saw the need to learn specific skills and behaviors. Self-authoring knowers regarded themselves and other students as additional valid sources of knowledge. It was suggested that literacy practitioners expand their view of literacy instruction, incorporate principles of skills-centered and learner-centered instruction and critical literacy, and utilize the strengths of all perspectives on literacy to optimize the development of all learners. (Contains 58 references.) (MN) Reproductions supplied by EDRS are the best that can be made from the original document. 1 PERMISSION TO REPRODUCE AND DISSEMINATE THIS MATERIAL HAS BEEN GRANTED BY
A Decompositional Approach to Computer System Performance Evaluation

Introduction Throughout the history of computers, a primary design goal in all systems has been to achieve maximum performance. When digital computing was in its infancy, performance was measured in terms of throughput of scientific calculations: in the mid-1940s, the then-new ENIAC's most-touted design feature was its ability to calculate artillery trajec-tory tables over 1,000 times faster than earlier electromechanical computers [18]. Surprisingly , this traditional approach to performance evaluation and optimization remains in widespread use today. Admittedly, one does not see today's processor designers optimizing their commodity CPUs for use in anti-aircraft guns, but it is still true that the performance evaluation suites in widespread use are based either on models of scientific computation or on a systems programming or hardware design workload [23][24]. These models are becoming decreasingly valid for today's applications: modern users are focusing more and more of their attentions on applications heavy with multimedia and networking content. One need not look farther than the explosive success of the worldwide web and the plethora of multimedia extensions and protocols that it has spawned to see this trend in action. The demands that such modern applications make on the hardware and software subsystems that support them are very different than those simulated and evaluated by the traditional set of performance analysis tools. As modern applications become increasingly dependent on multimedia, graphics, and data movement, they are spending an increasing 2 fraction of their execution time in the operating system (OS) kernel, an area of the system almost completely ignored by such traditional performance-evaluation tools. As an illustrative example, consider what must undoubtedly be today's leading server application: the web server. Web servers have been shown to spend over 85% of their cycles running operating system code [4]; in contrast, the near-ubiquitous SPEC benchmarks execute less than 9% of their instructions in the OS kernel [7]. The figures in Table 1 show that the web server is not unique: the other typical multimedia, commercial, and GUI workloads listed similarly spend between 20% and 90% of their instructions in the kernel. Amdahl's
Human activity classification using radar signal and RNN networks

Radar-based human activities recognition is still an open problem and is a key to detect anomalous behaviour for security and health applications. Deep learning networks such as convolutional neural networks (CNN) have been proposed for such tasks and showed better performance than traditional supervised learning paradigm. However, it is hard to deploy CNN networks to embedded systems due to the limited computational power available. From this point of concern, the use of a recurrent neural network (RNN) is proposed in this paper for human activities classification. We also propose an innovative data argumentation method to train the neural network using a limited number of data. The experiment shows that our network can achieve a mean accuracy of 94.3% in human activity classification.
Comparison of the prognostic effects of APACHE II score and the TRISS for geriatric trauma patients in the intensive care unit

Aim: To assess the prognostic effects of Acute Physiology and Chronic Health Evaluation (APACHE) II score and the Trauma-Injury Severity Score (TRISS) for Geriatric Trauma Patients in intensive care unit (ICU). Methods: A cohort of trauma patients aged ≥ 65 years, admitted to ICU of Guangzhou General Hospital of Guangzhou Command from January 2011 to December 2015, were enrolled. Demographic data, mechanism of injury, site of injury and outcome were recorded. Glasgow Coma Score (GCS), Revised Trauma Score (RTS), Injury Severity Score (ISS), and TRISS and APACHE II scores were calculated. The abilities to predict group mortality of APACHE II score and TRISS were assessed by receiver operating characteristic curve analysis (ROC), two by two decision matrices, and calibration curve analysis. Results: There were 98 patients with 66 males. Mean age, GCS, RTS, ISS and APACHE II score were 78.05 ± 8.24, 10.08 ± 4.56, 5.80 ± 2.16, 21.08 ± 15.06, and 20.18 ± 9.33, respectively. The overall mortality was 35.7%. GCS (p < 0.001), RTS (p < 0.001) and APACHE II score (p < 0.001) were significantly different between survivors and non-survivors. The areas under the curve for APACHE II score and TRISS were 0.898 ± 0.032 and 0.824 ± 0.043 respectively. Using two by two decision matrices with a decision criterion of 0.5, the sensitivity, specificity, and correct classification rate were 57.1%, 95.2%, and 81.6% for APACHE II score, and 68.6%, 81.0%, and 76.5% for TRISS. With a decision criterion of 0.7, the sensitivity, specificity, and correct classification rates were 28.6%, 98.4%, and 73.5% for APACHE II score, and 57.1%, 87.3%, and 76.5% for TRISS. From the calibration curves, the slopes of the regression line for APACHE II score and TRISS were 1.05 (R2 = 0.74, p = 0.001) and 0.77 (R2 = 0.71, p = 0.002), respectively. Conclusions: Both APACHE II score and TRISS can predict group mortality in ICU geriatric trauma patients. However, APACHE II score may be more favorable in making clinical decisions.
Generating Innovation Scenarios using the Cross-Impact Methodology

The question why and how innovation occurs is a subject of hot debate in policy, science, and economy. We describe a judgmental approach to innovation systems research that accounts for the complexity and interdisciplinary character of innovation processes and policy impacts. An interdisciplinary expert panel developed a qualitative model of an innovation system for a set of five energy technologies. The model’s systemic implications are analyzed using cross-impact techniques. The findings offer reasons why technology characteristics influence innovation and diffusion prospects, why different technologies require different innovation policy measures, and why innovativeness is more robust for some technologies than for others.
Aspergillus fumigatus and Its Allergenic Ribotoxin Asp f I: Old Enemies but New Opportunities for Urine-Based Detection of Invasive Pulmonary Aspergillosis Using Lateral-Flow Technology

Invasive pulmonary aspergillosis (IPA) caused by Aspergillus fumigatus is a life-threatening lung disease of immunocompromised patients. Diagnosis currently relies on non-specific chest CT, culture of the fungus from invasive lung biopsy, and detection of the cell wall carbohydrate galactomannan (GM) in serum or in BAL fluids recovered during invasive bronchoscopy. Urine provides an ideal bodily fluid for the non-invasive detection of pathogen biomarkers, with current urine-based immunodiagnostics for IPA focused on GM. Surrogate protein biomarkers might serve to improve disease detection. Here, we report the development of a monoclonal antibody (mAb), PD7, which is specific to A. fumigatus and related species in the section Fumigati, and which binds to its 18 kDa ribotoxin Asp f I. Using PD7, we show that the protein is secreted during hyphal development, and so represents an ideal candidate for detecting invasive growth. We have developed a lateral-flow device (Afu-LFD®) incorporating the mAb which has a limit of detection of ~15 ng Asp f I/mL urine. Preliminary evidence of the test’s diagnostic potential is demonstrated with urine from a patient with acute lymphoid leukaemia with probable IPA. The Afu-LFD® therefore provides a potential novel opportunity for non-invasive urine-based detection of IPA caused by A. fumigatus.
One Engine To Serve 'em All: Inferring Taint Rules Without Architectural Semantics

Dynamic binary taint analysis has wide applications in the security analysis of commercial-off-the-shelf (COTS) binaries. One of the key challenges in dynamic binary analysis is to specify the taint rules that capture how taint information propagates for each instruction on an architecture. Most of the existing solutions specify taint rules using a deductive approach by summarizing the rules manually after analyzing the instruction semantics. Intuitively, taint propagation reflects on how an instruction input affects its output, and thus can be observed from instruction executions. In this work, we propose an inductive method for taint propagation and develop a universal taint tracking engine that is architecture-agnostic. Our taint engine, TAINTINDUCE, can learn taint rules with minimal architectural knowledge by observing the execution behavior of instructions. To measure its correctness and guide taint rule generation, we define the precise notion of soundness for bit-level taint tracking in this novel setup. In our evaluation, we show that TAINTINDUCE automatically learns rules for 4 widely used architectures: x86, x64, AArch64, and MIPS-I. It can detect vulnerabilities for 24 CVEs in 15 applications on both Linux and Windows over millions of instructions and is comparable with other mature existing tools (TEMU [51], libdft [32], Triton [42]). TAINTINDUCE can be used as a stand-alone taint engine or be used to complement existing taint engines for unhandled instructions. Further, it can be used as a cross-referencing tool to uncover bugs in taint engines, emulation implementations and ISA documentations.
Product Classification-A Hierarchical Approach ( Classifying products to compute their socio-ecological impact )

The social and environmental impact associated with consuming a product is something that is becoming increasingly important to consumers and businesses alike. This impact can in theory be computed simply by classifying a product and by mapping the classified product to the corresponding life-cycle assessments research. However, this type of mapping requires an extensive product taxonomy with a large plurality of categories, which makes classification through machine learning a non-trivial task. This thesis describes the implementation of a hierarchical product classifier using the Python library scikit-learn, that makes it possible to automatically classify products based primarily on their brands and titles into a large taxonomy. Using a data set of 3.1 million products spread over 800 categories, we trained a set of hierarchical classifiers using different learning algorithms. Evaluations of these algorithms showed that the best hierarchical classifier reached a hierarchical F1-score (hF1) of 0.85.
Comparison of Digital PCR and Quantitative PCR with Various SARS-CoV-2 Primer-Probe Sets

The World Health Organization (WHO) has declared the coronavirus disease 2019 (COVID-19) as an international health emergency. Current diagnostic tests are based on the reverse transcription-quantitative polymerase chain reaction (RT-qPCR) method, which is the gold standard test that involves the amplification of viral RNA. However, the RT-qPCR assay has limitations in terms of sensitivity and quantification. In this study, we tested both qPCR and droplet digital PCR (ddPCR) to detect low amounts of viral RNA. The cycle threshold (CT) of the viral RNA by RT-PCR significantly varied according to the sequences of the primer and probe sets with in vitro transcript (IVT) RNA or viral RNA as templates, whereas the copy number of the viral RNA by ddPCR was effectively quantified with IVT RNA, cultured viral RNA, and RNA from clinical samples. Furthermore, the clinical samples were assayed via both methods, and the sensitivity of the ddPCR was determined to be equal to or more than that of the RT-qPCR. However, the ddPCR assay is more suitable for determining the copy number of reference materials. These findings suggest that the qPCR assay with the ddPCR defined reference materials could be used as a highly sensitive and compatible diagnostic method for viral RNA detection.
Spatial Analysis of Social Vulnerability to Floods Based on the MOVE Framework and Information Entropy Method: Case Study of Katsushika Ward, Tokyo

Tokyo is located in a lowland area that is vulnerable to flooding. Due to global climate change, the scalability and frequency of flooding is increasing. On the other hand, population aging and family structural changes, as well as the lack of adaptation measures, would accelerate flooding vulnerability. The key factors involved in social vulnerability must be studied to reduce the risk of flooding. In this study, we refer to the MOVE framework (a disaster vulnerability assessment framework) and analyze it from three perspectives: Exposure to social vulnerability, susceptibility, and resilience. We subsequently develop an index system to complete the evaluation using 11 indicators. The collected data will help reveal social vulnerability to floods in the Katsushika Ward, Tokyo, using the information entropy method and GIS. We found that the western region of the Katsushika Ward is at more risk than the eastern region during flooding. Additionally, the possibility of a serious crisis erupting is greater in the southwestern region than in the northwestern region. Consequently, we conclude that the spatial distribution of flooding varies in the region. The results of this study will help in understanding social vulnerability, in selecting and combining adaptation measures suited to the characteristics of the area, and in the effective and efficient implementation of these measures by the local government’s disaster department.
Living Arrangements and Time Allocation: Evidence from the United Kingdom ⇤

This paper studies the labour supply of cohabiting and married couples, using the British Household Panel Survey from 1992 to 2008. Estimating labour supply equations, I find that women work about two hours more on the market per week when cohabiting than when married, controlling for a wide set of demographic variables, individual effects, predicted wages, and selection into the labour force, as well as partner’s characteristics. The results suggest no significant difference in men’s labour supply by living arrangement. At the same time, men do half an hour more household work when cohabiting than when married. I propose a simple two-period model of family labour supply that can account for the difference in labour supply by marital status. Cohabiting couples are able to sustain less risk sharing in the face of wage risk, because of a lower cost, thus a higher probability of separation. In turn, less intra-household insurance provides incentives for the less productive partner to work more on the market.
Archaeology, climate change and environmental ethics: diachronic perspectives on human:non-human:environment worldviews, activism and care

ABSTRACT This paper calls for archaeological engagement with the ethical dimension of past:present:future global environmental discourse and Anthropocene studies. In contrast to the recent chronological focus of archaeology’s engagement with Anthropocene studies, and its often rather generalised call for recognising the relevance of historically attested adaptive responses to climate change to current challenges, it highlights the need to examine the individual contributing and resulting factors of climate change and extreme environmental events. It advocates an approach that combines archaeology’s traditional focus on the practical and material elements of disaster management, with one that explores historical epistemologies of human:non-human care and entanglement, and socio-religious and collective ideological movements as driving forces behind historically specific environmental ethics. In relation to the ‘non-human’ element of the human:non-human:environment configuration there is special emphasis not only on non-human animals, but also conceptualisations of divine, ‘supra-human’, and numinous entities and spheres such as gods, spirits, and sacred places which are essential for attaining fully syncretic perspectives on diachronic environmental ethics. A key argument is that recognition of the multi-directional dynamics of human:environment entanglement, drawing on developments within religious studies, the environmental and medical humanities, as well as environmental health discourse, is crutical for achieving more widespread engagement with environmental activism, and movement towards long term behavioural changes that ultimately reduce global suffering and increase environmental, economic and human wellbeing.
Early Intervention in the Real World Evaluating an individualized lifestyle and life skills intervention to prevent antipsychotic-induced weight gain in first-episode psychosis

1Early Psychosis Programme, The Bondi Centre, South Eastern Sydney Local Health District, 2School of Psychiatry, University of New South Wales, 3Faculty of Health, University of Technology Sydney, 4Musculoskeletal Division, The George Institute for Global Health, School of Public Health, Sydney University, Sydney, 5Department of Endocrinology, St. Vincent’s Hospital, 6Diabetes and Obesity Program, Garvan Institute of Medical Research, Darlinghurst, and 7Schizophrenia Research Unit, South Western Sydney Local Health District, Liverpool, New South Wales, Australia
Short-Term Response of the Soil Microbial Abundances and Enzyme Activities to Experimental Warming in a Boreal Peatland in Northeast China

Global warming is likely to influence the soil microorganisms and enzyme activity and alter the carbon and nitrogen balance of peatland ecosystems. To investigate the difference in sensitivities of carbon and nitrogen cycling microorganisms and enzyme activity to warming, we conducted three-year warming experiments in a boreal peatland. Our findings demonstrated that both mcrA and nirS gene abundance in shallow soil and deep soil exhibited insensitivity to warming, while shallow soil archaea 16S rRNA gene and amoA gene abundance in both shallow soil and deep soil increased under warming. Soil pmoA gene abundance of both layers, bacterial 16S rRNA gene abundance in shallow soil, and nirK gene abundance in deep soil decreased due to warming. The decreases of these gene abundances would be a result of losing labile substrates because of the competitive interactions between aboveground plants and underground soil microorganisms. Experimental warming inhibited β-glucosidase activity in two soil layers and invertase activity in deep soil, while it stimulated acid phosphatase activity in shallow soil. Both temperature and labile substrates regulate the responses of soil microbial abundances and enzyme activities to warming and affect the coupling relationships of carbon and nitrogen. This study provides a potential microbial mechanism controlling carbon and nitrogen cycling in peatland under climate warming.
A New Paradigm To Help Accelerate The Transfor- mation of Resource-Rich Nations into Sustainable Knowledge Economies

The discovery of hydrocarbons in a number of countries especially in the Middle -East has enabled these nations to develop and generate significant wealth. However, long- term sustainability within these countries' transforming economies has not been addressed in full, with oil revenues still accounting for the vast part of the countries' income. In this paper we introduce a novel open innovation concept, the Companion Direct Investment (CDI). By using the Emirate of Abu Dhabi as a case study, we discuss CDI's potential as a complimen- tary approach to enable value exchange leading to the location and nurturing of knowledge enterprise as a means to accelerating the transformation of these nations into knowledge- based economies.
Enhancing B2B supply chain traceability using smart contracts and IoT

Purpose: The management of B2B supply chains that involve many stakeholders requires traceability processes. Those processes need to be secured. Furthermore, quality traceability data has to be transparently shared among the stakeholders. In order to improve the traceability process, we propose to enhance blockchain based traceability architectures with the capability to detect and record well-qualified incidents. Methodology: To achieve this goal, we propose a generic smart contract for B2B traceability data management, including transport constraints such as temperature, delay and allowing automatic incident detection and recording. We propose an architecture where data are collected by connected objects and verified and qualified before being sent to the smart contract. This proposition has been validated with medical equipment transport use cases. Findings: As results, the proposed generic template contract can be used in various traceability use cases, well qualified incidents are transparently shared among stakeholders, and secured, qualified and verified traceability data can be used in case of claims or litigation and can facilitate also the automation of invoicing process. Originality: The originality of this work arises from the automated B2B traceability management system based on qualified IoT data, contractual milestones and process coded in a generic smart contract, and also from the fact that traceability related data and incidents are verified and qualified in order to increase the integrated data quality.