Miracle-dz/UniFAR-FTUs huggingface dataset:
Decision Support for Subjects Exposed to Heat Stress

The physiological and activity strain index (PASI) has been developed to improve the online decision support for workers exposed to heat stress. Fire fighters (smoke divers) which are exposed to both heat-stress and high-risk situations have been used as test case. PASI combines a modified version of the relatively well-known physiological strain index (PSI) with activity data from accelerometers. The algorithm has been developed based on tests in a laboratory, and it has been verified in two field tests performed by smoke divers exposed to heat stress. The verification demonstrates that it is possible to distinguish between high- and low-risk situations when data from accelerometers are added to the situation analysis. This indicates that PASI can contribute to an improved risk assessment and online decision support for smoke divers compared to using PSI alone.
Utilize Azure Traces Cold-Start to Predict using Machine Learning on Azure ML

A significant delay of as many as a few seconds was often noticed by researchers when initially using serverless routines. This condition, sometimes called the "cold start," is exacerbated when users use a workflow that orchestrates many serverless activities. However, users often need to launch many serverless functions at once, sometimes as part of simultaneous loops. This article introduces the spawn start and examines how three Function-as-a-Service (FaaS) providers Microsoft Azure Functions behave when concurrent loops are started both warmly and coldly. Through a series of studies, we discovered three findings that will be useful to the scientific community. unreasonable expenses to consumers or service providers. In contrast to the state-of-the-art policies with a more constrained emphasis, this study suggests a unique low-coupling, high-cohesion ensemble policy that solves the cold-start issue. The forecast of function case arrivals, between ten and fifteen minutes in the future, serves as the anchor for this ensemble strategy. Through the application of the machine-learning approach, it is possible. Benchmarking findings on a large-scale serverless cloud provider's real-world dataset demonstrate that outperforms other well-known machine learning methods for time series.
Chapter 10 CENTRINS , A NOVEL GROUP OF CA 2 +-BINDING PROTEINS IN VERTEBRATE PHOTORECEPTOR CELLS

Changes in the intracellular Ca-concentration affect the visual signal transduction cascade directly or more often indirectly through Ca-binding proteins. Here we review recent findings on centrins in photoreceptor cells of the mammalian retina. Centrins are members of a highly conserved subgroup of the EF-hand superfamily of Ca-binding proteins commonly associated with centrosome-related structures. In vertebrate photoreceptor cells, centrins are also prominent components in the connecting cilium linking the light sensitive outer segment with the biosynthetically active inner segment compartment. Recent findings demonstrate that Ca-activated centrin forms a complex with the visual G-protein transducin in photoreceptor cells. This Ca-dependent assembly of G-proteins with centrin is a novel aspect of the supply of signaling proteins in sensory cells, and a potential link between molecular translocations and signal transduction in general.
Mining Frequent Route Patterns Based on Personal Trajectory Abstraction

Frequent route pattern mining from personal trajectory data is the basis of location awareness and location services. However, because personal trajectory data is highly uncertain, most existing approaches are only capable of finding short and incomplete route patterns. In this paper, a novel approach is proposed for the discovery of frequent route patterns based on trajectory abstraction. First, trajectory partition, location extraction, data simplification, and common segment discovery are used to abstract trajectory data, convert these trajectories into common segment temporal sequences (STS) and generate 1-frequent itemsets. Then, a pattern mining algorithm is proposed based on the spatial-temporal adjacency relationship. This algorithm uses the constraint mechanism and bidirectional projected database to mine frequent route patterns from STS. Based on the real GeoLife trajectory data, the experimental results indicate that the proposed method has better performance and can find longer route patterns than other currently available methods.
Local administration of N‐acetylaspartylglutamate (NAAG) peptidase inhibitors is analgesic in peripheral pain in rats

The peptide neurotransmitter N‐acetylaspartylglutamate (NAAG) selectively activates group II metabotropic glutamate receptors (mGluRs). Systemic administration of inhibitors of the enzymes that inactivate NAAG results in decreased pain responses in rat models of inflammatory and neuropathic pain. These effects are blocked by a group II mGluR antagonist. This research tested the hypothesis that some analgesic effects of NAAG peptidase inhibition are mediated by NAAG acting on sensory neurite mGluRs at the site of inflammation. Group II mGluR agonists, SLx‐3095‐1, NAAG and APDC, or NAAG peptidase inhibitors, ZJ‐43 and 2‐PMPA, injected into the rat footpad reduced pain responses in carrageenan or formalin models. The analgesic effects of SLx‐3095‐1, APDC, ZJ‐43, 2‐PMPA and NAAG were blocked by co‐injection of LY341495, a selective group II mGluR antagonist. Injection of group II mGluR agonists, NAAG or the peptidase inhibitors into the contralateral rat footpad had no effect on pain perception in the injected paw. At 10–100 µm ZJ‐43 and 2‐PMPA demonstrated no consistent agonist activity at mGluR2 or mGluR3. Consistent with the conclusion that peripherally administered NAAG peptidase inhibitors increase the activation of mGluR3 by NAAG that is released from peripheral sensory neurites, we found that the tissue average concentration of NAAG in the unstimulated rat hind paw was about 6 µm. These data extend our understanding of the role of this peptide in sensory neurons and reveal the potential for treatment of inflammatory pain via local application of NAAG peptidase inhibitors at doses that may have little or no central nervous system effects.
The determinants of adoption of broadband access and broadband-based applications by Italian SMEs

Why do some small and medium enterprises (SMEs) adopt Internet broadband technologies (high-speed connection and complementary applications) and others do not? This contribution aims at analyzing the issue through an econometric investigation. Relying on the (thin) previous empirical literature on the topic and focusing on a large and representative sample of Italian SMEs we analyze the determinants of broadband connection and adoption of complementary applications. Results of the econometric analysis reveal that: i) among firm-specific characteristics, size and the firm’s need to communicate are major determinants both of broadband connection and use of complementary applications, while indicators of firm efficiency and of the competitive pressure suffered from the SME positively affect only the number of used applications, while do not exert any impact on the decision to connect; ii) among location-specific characteristics, the level of telecommunications infrastructure positively influences both connection and applications use, while the presence within the local labour market of young and skilled workforce makes SMEs adopt more applications; iii) time-specific variables like those related to the actual and future price of the high-speed Internet connection affect SMEs’ decision to adopt broadband. These findings have important implications for suppliers and policy makers.
Structural Health Monitoring Using Statistical Pattern Recognition Techniques

All members of the Los Alamos Structural Health Monitoring Team contributed to this study reported herein. The team members include George Papcum and Michael L. Fugate from the CIC3 Group, and Scott Doebling from the Engineering Analysis Group. Funding for this investigation came primarily through Los Alamos National Laboratory Director’s Funded Postdoctoral Fellows Program. The authors also thank Gregg Johnson and Mike Todd of NRL for providing the experimental data and allowing the publication of the test results.
Locally Optimized Product Quantization for Approximate Nearest Neighbor Search

We present a simple vector quantizer that combines low distortion with fast search and apply it to approximate nearest neighbor (ANN) search in high dimensional spaces. Leveraging the very same data structure that is used to provide non-exhaustive search, i.e., inverted lists or a multi-index, the idea is to locally optimize an individual product quantizer (PQ) per cell and use it to encode residuals. Local optimization is over rotation and space decomposition, interestingly, we apply a parametric solution that assumes a normal distribution and is extremely fast to train. With a reasonable space and time overhead that is constant in the data size, we set a new state-of-the-art on several public datasets, including a billion-scale one.
Estimation of Activation Sequences of Heart Using Cardiac Cell Models

One of the most common causes of death globally in the past few years is cardiac arrhythmia (irregular heartbeat), which occurs when the electric signals that coordinate the heartbeat don't work appropriately. As a result, there is a need to focus on understanding the basic mechanisms of heart rhythm variations. This paper focuses on cardiac excitation and uses a model replicating the electrical behavior of heart cells to generate an action potential. The activation times of the selected sources on the heart's surface from ECGSIM were compared with those generated using an electronic circuit model. The findings indicated a good match between the simulation results with a minimal error rate of milliseconds. The computational modeling of the electrical model of the cardiac cell was performed to explore the effect of the space or length constant (λ), which measures how well a potential spreads along the heart cell as a function of distance.
Modeling Social Networks with Node Attributes using the Multiplicative Attribute Graph Model

Networks arising from social, technological and natural domains exhibit rich connectivity patterns and nodes in such networks are often labeled with attributes or features. We address the question of modeling the structure of networks where nodes have attribute information. We present a Multiplicative Attribute Graph (MAG) model that considers nodes with categorical attributes and models the probability of an edge as the product of individual attribute link formation affinities. We develop a scalable variational expectation maximization parameter estimation method. Experiments show that MAG model reliably captures network connectivity as well as provides insights into how different attributes shape the network structure.
School psychology and school-based child and family interventions in Singapore

This article presents an overview of the range of primary, secondary, and tertiary interventions based on the Singapore Education Ministry-developed whole-school framework for pupil management and support. At the preventive level, a range of school-wide programmes are implemented to provide learning, emotional, and behavioural support for students. Where school-level programmes are inadequate to address specific student concerns, there are school counsellors or teacher-counsellors to work with those at-risk or experiencing difficulties. Students with specific learning disabilities receive in-class or pull-out support from allied educators, often in consultation with educational psychologists from the Ministry. Although parent support and collaboration are often sought, school-based family interventions are not within the purview of schools. This article further discusses issues that may be of concern to schools as they work to support students more effectively.
Relationships between Job Satisfaction and User Acceptance to IS

Information system have become exceedingly important in our daily working lives and by that tightly connected with jobs. Job satisfaction is relatively explored research field which has become very popular in the last decades. But how do information systems affect other aspects of work, and how does our perception of our working life affect our acceptance of information systems is relatively unexplored. The job characteristics are tightly connected to job satisfaction, due to what we have included them in our study. In order to find the relationships between job satisfaction, job characteristics and user acceptance of information system we have used previously established theories in those research fields, from which we have drawn our model for investigation. This was followed by collection of empirical data from online survey and interviews. We have analyzed the data by applying various quantitative and qualitative methods. The results have shown that job satisfaction, user acceptance of the information system and job characteristics are correlated. The influence on job satisfaction is also researched by performing regression analysis, but due to the small size of respondents future research is needed. Our study rises up and gives the basis of an interesting question: does higher user acceptance of the information system leads to greater job satisfaction?
Molecular Characterization of the Heparin-Dependent Transduction Domain on the Capsid of a Novel Adeno-Associated Virus Isolate, AAV(VR-942)

ABSTRACT A new adeno-associated virus (AAV), referred to as AAV(VR-942), has been isolated as a contaminant of adenovirus strain simian virus 17. The sequence of the rep gene places it in the AAV serotype 2 (AAV2) complementation group, while the capsid is only 88% identical to that of AAV2. High-level AAV(VR-942) transduction activity requires cell surface heparan sulfate proteoglycans, although AAV(VR-942) lacks residues equivalent to the AAV2 R585 and R588 amino acid residues essential for mediating the interaction of AAV2 with the heparan sulfate proteoglycan receptor. Instead, AAV(VR-942) uses a distinct transduction region. This finding shows that distinct domains on different AAV isolates can be responsible for the same activities.
George Biddell Airy and horology

Summary As Astronomer Royal from 1835 till 1881, G. B. Airy had a very important influence on nineteenth-century British astronomy. His personal qualities combined with his office to give him a position of great authority within the astronomical and general scientific communities, and his powers of organization and work on instrumentation transformed the Royal Observatory. A feature of Airy's work was an extensive interest in horology—particularly in astronomical regulators, marine chronometers and driving clocks for chronographs and equatorial telescopes. He was also concerned with building important turret clocks, and he established a public time service based at Greenwich. The enormous quantity of surviving material makes a complete review of Airy's career a daunting prospect; but the horology is a microcosm, where we can study on a manageable scale his attitudes to the Observatory, to its relation to society, and to the role of the Astronomer Royal as a public servant.
Transformative Conversational AI: Sentiment Recognition in Chatbots via Transformers

Recent years have seen a dramatic increase in the use of conversational artificial intelligence (CAI) for both academic and commercial applications, primarily in the context of chatbots and AI virtual assistants. The user’s engagement produces a human like responses. However, the capacity to discern sentiments and respond adequately is one of the major difficulties faced by conversation systems. In the present study, we propose a transformer-based framework for a sentiment-aware chatbot. The suggested transformer is a neural network architecture that is highly parallelizable and solely dependent on the self-attention mechanism. A transformer model controls variable-sized input using stacks of self-attention layers rather than deep neural networks or CNNs. In this manner, language creation is carried out using the cutting-edge pre-trained model CTRL, which can easily adapt to various pre-trained models without needing architectural adaptations. Our model was trained using the DailyDialogues dataset and was evaluated using automated metrics. Findings from experiments confirm that, in terms of content quality and emotion perception, our suggested technique works better than cutting-edge baselines.
Wireless Information Processing Module for Augmented Reality system

Augmented reality is very promising and perspective field of research, which applied in many areas of human life, from education and medical treatment to entertainment and traveling. However, nowadays modules for virtual and augmented reality are very expensive for a wide range of users. In this paper, we propose cheap and available wireless module for augmented reality system. The module is implemented on ESP32 microcontroller with GY-521 as a displacement sensor. The Madgwick filter for determining incline of the object is implemented in module firmware. The proposed system allows to integrate augmented reality to education process at an affordable price. System is modular, which makes it suitable for a wide range of applications.
Transfer Learning-Based UAV Swarm Threat Perception and Evaluation Scheme in Air-Ground-Integrated Networks

With the advancement of 5G and artificial intelligence technologies, unmanned aerial vehicle (UAV) has emerged as crucial elements in modern warfare. However, their small size and slow speed make them difficult to detect and easily mistaken for birds or other flying objects, posing challenges for defenders to respond promptly. Moreover, existing methods for evaluating UAV threats heavily rely on subjective expert scoring, leading to inefficiency, uncertainty, and ambiguity. In this paper, we propose a comprehensive approach for UAV swarm threat perception and evaluation in integrated air-ground networks. By integrating historical observation data, we establish an indicator quantification method to evaluate the perception of UAV swarm threats. Initially, we employ convolutional neural networks for intent recognition, allowing for coarse-grained classification of enemy UAVs. Subsequently, we utilize transfer learning to in-corporate the trained intent recognition model into the threat evaluation framework, significantly enhancing the efficiency and accuracy of UAV threat evaluation. Experimental results confirm the superior performance of our UAV swarm threat perception and evaluation model compared to existing approaches.
A 1-V 225-nW 1KS/s current successive approximation ADC for pacemakers

An ultra-low-power 1KS/s 8-bit current mode successive approximation (SAR) analog-digital converter (ADC) for pacemakers is presented. The proposed system architecture is designed to achieve a small chip area and ultra low power consumption by using current mode operation. The circuit is realized in a IBM 0. 13µm CMOS technology. The simulated power consumption is 225 nW corresponding to a figure of merit of 0.657pJ/conversion-step while operating from a 1 V supply. The power consumption is one of the lowest reported.
A framework for developing ecosystem‐specific nutrient criteria: Integrating biological thresholds with predictive modeling

We present a novel ecosystem‐specific framework for developing nutrient criteria from biological thresholds and predictive modeling (BTPM) and an application of this framework to lakes in Michigan, U.S. The four main components for the BTPM framework are: (1) to predict each ecosystem's ‘expected nutrient concentration’ in the absence of human effects using a predictive model, (2) to identify important biological thresholds along a nutrient gradient (i.e., biological [BIO] benchmarks), (3) to determine each ecosystem's current nutrient concentration, and (4) to use the above information to derive a nutrient criterion for each ecosystem using the BTPM algorithm. The BTPM framework is extremely flexible in that it can be applied to any aquatic ecosystem type or nutrient and the four components can be implemented in a variety of ways. Our BTPM framework has two additional features: it recognizes that prior to human disturbance, ecosystems varied in their natural nutrient concentrations, and it incorporates risk into the decision‐making process. In the simplest scheme, a nutrient criterion is set at a BIO benchmark greater than the expected nutrient concentration. However, to protect ecosystems more conservatively, a criterion is set at current lake nutrient concentrations if current is less than the BIO benchmark. In our application of the BTPM framework, we developed total phosphorus (TP) criteria for a diverse set of 374 lakes in MI. The expected lake TP concentrations in the absence of human effects ranged from 3 µg L−1 to 24 µg L−1, suggesting that a single criterion approach would not be appropriate.We found two predominant benchmarks in the biological data along the TP gradient, one for zooplankton metrics at 8 µg L−1, and one for phytoplankton metrics at 18 µg L−1. We present the sequence of analyses and decisions that could be used to apply this approach in a management context using Michigan lakes as an example.
The relational self and the political engagements of young adults

This article explores the recent concern over young people’s apathy and disengagement from politics. It critically addresses this, first, by examining and contesting some of the literature addressing this alleged apathy and dissociation, revealing a particular, narrow and regulatory model of politics and an accompanying liberal notion of self. In contrast, a relational model of self is posited as more sociologically robust and suited to contemporary social life. Second, the article describes a qualitative Australian study of young people aged 18–30 years, recruited from across the political spectrum. In contrast to the key liberal principles, participants highlight interconnectedness, permeable public/private divides and the important role of friends and family in fostering and sustaining their political engagements. It is argued that the relational interconnected model of self presented by the participants reflects the conditions of contemporary social life.
Learning temporal correlations in biologically-inspired aVLSI

Temporally-asymmetric Hebbian learning is a class of algorithms motivated by data from recent neurophysiology experiments. While traditional Hebbian learning rules use mean bring rates to drive learning, this new form of learning involves precise bring times. Hence, such algorithms can capture temporal spike correlations. We present circuits and methods to implement temporally-asymmetric Hebbian learning in analog VLSI. We also describe a small feed-forward 2 layer network that learns spike trains correlations. A chip including a single neuron and a network of adaptive spiking neurons has been fabricated in a CMOS 0.6/spl mu/ process to validate the ideas presented.