Miracle-dz/UniFAR-FTUs huggingface dataset:
Distraction Detection and Mitigation Through Driver Feedback Appendices

Despite government efforts to regulate distracted driving, distraction-related fatalities and injuries continue to increase. Manufacturers are introducing real-time driver monitoring systems that detect risk from distracted driving and warn drivers; however, little is known about these systems. This report identifies evaluation techniques to characterize and assess these emerging technologies, presents results of their application, develops a framework for estimating systems’ safety benefits, and provides safety relevant information to guide technology development. A standardized language for describing and differentiating systems was created, and its application revealed key trends in the design landscape. A novel approach to detection that provides prospective indications of safety-critical vehicle state changes is described. Two evaluation protocols were developed and to provide empirical assessments of (1) detection algorithm performance and (2) the effect of mitigations on driver performance and acceptance. The protocol included driving on different types of roadways and performing secondary tasks in the high-fidelity NADS-1 driving simulator. Four progressively complex distraction detection algorithms were compared to evaluate the ability of vehicle-based systems to distinguish between distracted and non-distracted drivers. Algorithm performance varied across road types and distraction tasks. A safety benefits framework appropriate for distraction mitigation systems is proposed that expands on past benefit analyses. These are the appendices to the main report.
Systematic implications from a robust phylogenetic reconstruction of the genus Helianthemum (Cistaceae) based on genotyping-by-sequencing (GBS) data

Molecular systematics requires the establishment of a robust phylogenetic framework including extensive geographical and taxonomic sampling. In this work, we proposed systematic changes in the genus Helianthemum based on phylogenetic trees obtained by both maximum likelihood and Bayesian analyses of GBS data. The implications of these phylogenetic results for the systematics of Helianthemum entail the establishment of a new subgenus and novel re-ascriptions of sections and species along with some nomenclatural novelties. The following new combinations are proposed: Helianthemum subg. Eriocarpum (Dunal) Martín-Hernanz, Velayos, Albaladejo & Aparicio; H. oelandicum subsp. conquense (Borja & Rivas Goday ex G.López) Martín-Hernanz, Velayos, Albaladejo & Aparicio; H. nummularium subsp. cantabricum (M.Laínz) Martín-Hernanz, Velayos, Albaladejo & Aparicio; H. nummularium subsp. tinetense (M.Mayor & Fern.Benito) Martín-Hernanz, Velayos, Albaladejo & Aparicio.
Seed priming to mitigate the impact of elevated carbon dioxide associated temperature stress on germination in rice (Oryza sativa L.)

ABSTRACT Climate change has a negative impact on crop production by inducing several stresses throughout plant growth. As the germination is one of the critical stages, this study was to assess the impact of elevated carbon dioxide (eCO2) and associated temperature stress on germination of rice. The study was conducted using the rice genotype CO 51 in artificially induced eCO2 condition using open-top chamber (OTC). The efficiency of seed priming treatment with salicylic acid (SA), citric acid (CA), ascorbic acid (AsA) and distilled water (hydropriming) to alleviate stress due to eCO2 and temperature were also studied. SA 25 mg l−1 and AsA 100 mg l−1 enhanced germination, other seed quality parameters along with α- amylase activity. Also, the activity of antioxidant enzymes like catalase, peroxidase and superoxide dismutase was increased. These parameters positively affected the germination and growth by mitigating the effect of oxidative stress induced under eCO2 conditions. So, SA, CA and AsA can be effectively used for maintaining seed quality parameters and seedling growth during the stresses.
Spike Formation Is a Turning Point Determining Wheat Root Microbiome Abundance, Structures and Functions

Root selection of their associated microbiome composition and activities is determined by the plant’s developmental stage and distance from the root. Total gene abundance, structure and functions of root-associated and rhizospheric microbiomes were studied throughout wheat growth season under field conditions. On the root surface, abundance of the well-known wheat colonizers Proteobacteria and Actinobacteria decreased and increased, respectively, during spike formation, whereas abundance of Bacteroidetes was independent of spike formation. Metagenomic analysis combined with functional co-occurrence networks revealed a significant impact of plant developmental stage on its microbiome during the transition from vegetative growth to spike formation. For example, gene functions related to biofilm and sensorial movement, antibiotic production and resistance and carbons and amino acids and their transporters. Genes associated with these functions were also in higher abundance in root vs. the rhizosphere microbiome. We propose that abundance of transporter-encoding genes related to carbon and amino acid, may mirror the availability and utilization of root exudates. Genes related to antibiotic resistance mechanisms were abundant during vegetative growth, while after spike formation, genes related to the biosynthesis of various antibiotics were enriched. This observation suggests that during root colonization and biofilm formation, bacteria cope with competitor’s antibiotics, whereas in the mature biofilm stage, they invest in inhibiting new colonizers. Additionally, there is higher abundance of genes related to denitrification in rhizosphere compared to root-associated microbiome during wheat growth, possibly due to competition with the plant over nitrogen in the root vicinity. We demonstrated functional and phylogenetic division in wheat root zone microbiome in both time and space: pre- and post-spike formation, and root-associated vs. rhizospheric niches. These findings shed light on the dynamics of plant–microbe and microbe–microbe interactions in the developing root zone.
Exploring the Decision Tree Method for Detecting Cognitive States of Operators

: This study aims to validate a construction methodology of a device able to estimate the cognitive state of an operator in real time. The SUaaVE project (SUpporting acceptance of automated VEhicle) studies the integration of an intelligent assistant in a level 4 autonomous car. The aim of our work is to model the cognitive state of the driver in real time and for all situations. The cognitive state is a natural state that alters or preserves the operator's ability to process information and to act. Based on a literature review we identified the cognitive functions used by the driver and the factors influencing them. Different cognitive components emerged from this synthesis: engagement (Witmer & Singer, 1998), fatigue (Marcora and al. 2009) and vigilance (Picot, 2009). Eye-tracking is a technique used to determine the orientation of the gaze in a visual scene. According to the literature the general dynamics of a visual behavior is characterized by metrics: number of fixations, duration of fixation, gaze dispersion. These dynamics are altered unconsciously due to fatigue (Faber, Maurits, & Lorist, 2012) or hypovigilance (De Gennaro et al., 2000, Bodala et al., 2016); and consciously due to engagement in driving (Freydier et al., 2014; Neboit, 1982). We carry out a phase of experimentation in a naturalistic situation (driving simulator) in order to collect data for each cognitive state. Realistic scenarios are constructed to induce cognitive states. The model’s estimation is compared to the real cognitive state of the driver measured by behavioral monitoring (eye-tracking). The model is a CARt Ihaka, decision tree: Classification And Regression Trees. The CARt aims at building a predictor. The interest is to facilitate the design of the tool as well as its future implementation in real time. We illustrate the construction methodology with an example the results obtained.
The Investigation of Metabonomic Pathways of Serum of Iranian Women with Recurrent Miscarriage Using 1H NMR

Purpose Recurrent miscarriage applies to pregnancy loss expulsion of the fetus within the first 24 weeks of pregnancy. This study is aimed at comparatively investigating the sera of women with RM with those who have no record of miscarriages to identify if there were any metabolite and metabolic pathway differences using 1H NMR spectroscopy. Methods Serum samples were collected from women with RM (n = 30) and those who had no records of RM (n = 30) to obtain metabolomics information. 1H NMR spectroscopy was carried out on the samples using Carr Purcell Meiboom Gill spin echo; also, Partial Least Squares Discriminant Analysis was performed in MATLAB software using the ProMetab program to obtain the classifying chemical shifts; the metabolites were identified by using the Human Metabolome Database (HMDB) in both the experimental and control groups. The pathway analysis option of the Metaboanalyst.ca website was used to identify the changed metabolic pathways. Results The results of the study revealed that 14 metabolites were different in the patients with RM. Moreover, the pathway analysis showed that taurine and hypotaurine metabolism along with phenylalanine, tyrosine, and tryptophan biosynthesis was significantly different in patients with RM. Conclusion The present study proposes that any alteration in the above metabolic pathways might lead to metabolic dysfunctions which may result in a higher probability of RM.
Fine-mapping from summary data with the “Sum of Single Effects” model

In recent work, Wang et al introduced the “Sum of Single Effects” (SuSiE) model, and showed that it provides a simple and efficient approach to fine-mapping genetic variants from individual-level data. Here we present new methods for fitting the SuSiE model to summary data, for example to single-SNP z-scores from an association study and linkage disequilibrium (LD) values estimated from a suitable reference panel. To develop these new methods, we first describe a simple, generic strategy for extending any individual-level data method to deal with summary data. The key idea is to replace the usual regression likelihood with an analogous likelihood based on summary data. We show that existing fine-mapping methods such as FINEMAP and CAVIAR also (implicitly) use this strategy, but in different ways, and so this provides a common framework for understanding different methods for fine-mapping. We investigate other common practical issues in fine-mapping with summary data, including problems caused by inconsistencies between the z-scores and LD estimates, and we develop diagnostics to identify these inconsistencies. We also present a new refinement procedure that improves model fits in some data sets, and hence improves overall reliability of the SuSiE fine-mapping results. Detailed evaluations of fine-mapping methods in a range of simulated data sets show that SuSiE applied to summary data is competitive, in both speed and accuracy, with the best available fine-mapping methods for summary data. Author summary The goal of fine-mapping is to identify the genetic variants that causally affect some trait of interest. Fine-mapping is challenging because the genetic variants can be highly correlated, due to a phenomenon called linkage disequilibrium (LD). The most successful current approaches to fine-mapping frame the problem as a variable selection problem, and here we focus on one such approach based on the “Sum of Single Effects” (SuSiE) model. The main contribution of this paper is to extend SuSiE to work with summary data, which is often accessible when the full genotype and phenotype data are not. In the process of extending SuSiE, we also developed a new mathematical framework that helps to explain existing fine-mapping methods for summary data, why they work well (or not), and under what circumstances. In simulations, we show that SuSiE applied to summary data is competitive with the best available fine-mapping methods for summary data. We also show how different factors such as accuracy of the LD estimates can affect the quality of the fine-mapping.
Searching for the Muon Decay to Three Electrons with the Mu3e Experiment

Mu3e is a dedicated experiment designed to find or exclude the charged lepton flavor violating μ→ eee decay at branching fractions above 10−16. The search is pursued in two operational phases: Phase I uses an existing beamline at the Paul Scherrer Institute (PSI), targeting a single event sensitivity of 2·10−15, while the ultimate sensitivity is reached in Phase II using a high intensity muon beamline under study at PSI. As the μ→ eee decay is heavily suppressed in the Standard Model of particle physics, the observation of such a signal would be an unambiguous indication of the existence of new physics. Achieving the desired sensitivity requires a high rate of muons (108 stopped muons per second) along with a detector with large kinematic acceptance and efficiency, able to reconstruct the low momentum of the decay electrons and positrons. To achieve this goal, the Mu3e experiment is mounted with an ultra thin tracking detector based on monolithic active pixel sensors for excellent momentum and vertex resolution, combined with scintillating fibers and tiles for precise timing measurements.
Using Camless Valvetrain for Air Hybrid Optimization

The air-hybrid engine absorbs the vehicle kinetic energy during braking, puts it into storage in the form of compressed air, and reuses it to assist in subsequent vehicle acceleration. In contrast to electric hybrid, the air hybrid does not require a second propulsion system. This approach provides a significant improvement in fuel economy without the electric hybrid complexity. The paper explores the fuel economy potential of an air hybrid engine by presenting the modeling results of a 2.5L V6 spark-ignition engine equipped with an electrohydraulic camless valvetrain and used in a 4531 kg passenger car. It describes the engine modifications, thermodynamics of various operating modes and vehicle driving cycle simulation. The air hybrid modeling projected a 64% and 12% of fuel economy improvement over the baseline vehicle in city and highway driving respectively. This is possible without reducing the vehicle weight to compensate for additional hardware and without reducing engine displacement.
IS Management Handbook

ACHIEVING STRATEGIC IT ALIGNMENT Strategic IT Capabilities IT Leadership Roles Sourcing Alternatives DESIGNING AND OPERATING AN ENTERPRISE INFRASTRUCTURE Managing a Distributed Computing Environment Developing and Maintaining the Networking Infrastructure Data Warehousing Quality Assurance and Control Security and Risk Management PROVIDING APPLICATION SOLUTIONS New Tools and Applications Systems Development Approaches Project Management Software Quality Assurance LEVERAGING E-BUSINESS OPPORTUNITIES E-Business Strategy and Applications Security and Privacy Issues FACILITATING KNOWLEDGE WORK Providing Support and Controls Supporting Remote Workers Knowledge Management
Illustrating the Future Prediction of Performance Based on Computer Code, Physical Experiments, and Critical Performance Parameter Samples

ABSTRACT In this article, we present a generic example to illustrate various points about making future predictions of population performance using a biased performance computer code, physical performance data, and critical performance parameter data sampled from the population at various times. We show how the actual performance data help to correct the biased computer code and the impact of uncertainty, especially when the prediction is made far from where the available data are taken. We also demonstrate how a Bayesian approach allows both inferences about the unknown parameters and predictions to be made in a consistent framework.
A Generalized Design Principle of a Uniform Step Asymmetrical Multilevel Converter For High Power Conversion

This paper is focused on a general design principle of a uniform step multilevel converter, with K series-connected full bridges inverters per phase. A new design terminology is proposed and analytical relationships are established. The DC-voltage sources supplying partial inverters are supposed to be rationally unbalanced. The corresponding “Asymmetrical” topology provides more flexibility to the designer, and can generate a large number of levels (any odd number from 2K+1 to 3K) without increasing the number of H-bridges. Simulation results and experimental tests shown the reliability of the design approach suggested.
Managing Operations Across the Supply Chain

Part 1: Supply Chain: A Perspective for Operations Management Chapter 1: Introduction to Managing Operations Across the Supply Chain Chapter 2: Operations and Supply Chain Strategy Part 2: Foundations of Operations Management Chapter 3: Managing Processes and Capacity Chapter 3: Supplement: Process Mapping and Analysis Chapter 4: Product/Process Innovation Chapter 5: Manufacturing and Service Process Structures Chapter 6: Managing Quality Chapter 6: Supplement: Quality Improvement Tools Chapter 7: Managing Inventories Chapter 8: Lean Systems Part 3: Integrating Relationships Across the Supply Chain Chapter 9: Customer Service Management Chapter 10: Sourcing and Supply Management Chapter 11: Logistics Management Part 4: Planning for Integrated Operations Across the Supply Chain Chapter 12: Demand Planning: Forecasting and Demand Management Chapter 13: Sales and Operations Planning Chapter 14: Materials and Requirements Planning Part 5: Managing Change in Supply Chain Operations Chapter 15: Project Management Chapter 15: Supplement: Advanced Methods for Project Scheduling Chapter 16: Sustainable Operations Management - Preparing for the Future
Recent Progress on Additive Manufacturing of Piezoelectric Ceramics

: Piezoelectric ceramic is a type of functional ceramic, which is able to convert the mechanical signal and the electronic signal mutually. Composed of piezoelectric ceramics and organic phase, piezoelectric composites have different kinds of connectivities, which not only determine the diverse applications of piezoelectric devices, but also promote the development of various shaping techniques in manufacturing piezoelectric materials and devices. In comparison with the traditional shaping methods, the most distinguishable advantage of additive manufacturing lies in its ability of quickly shaping a small batch of samples into geometrically complex designs without a mould, which makes it a highly suitable technique for investigating piezoelectric ceramics and its derivative devices in different kinds of connectivities. Meanwhile, the final additively manufactured samples require only very tiny post-processing, have a high rate of utilization of the raw material and does not need cutting fluid during manufacturing. Due to the above-mentioned advantages, it attracts the widespread concerns from both academic and industrial communities. When focusing in the field of additive manufacturing ceramics, the numbers of scientific reports in additive manufacturing functional ceramics and devices prove that it is still in a growing period. In the perspective of different additive manufacturing techniques, this article discusses and compares additive manufacturing of both lead-free and lead-based piezoelectric ceramics in the aspects of their historical development of each technique, the preparation of the raw materials, the geometrical designs, the measurement of functional properties and the applications of the printed samples, and forecasts the future development based on the current benefits and drawbacks of each additive manufacturing technique.
Optical property recovery with spatially-resolved diffuse reflectance at short source-detector separations using a compact fiber-optic probe.

We describe a compact fiber-optic probe (2 mm outside diameter) that utilizes spatially-resolved diffuse reflectance for tissue optical property recovery. Validation was performed in phantoms containing Intralipid 20% as scatterer, and methylene blue (MB), MnTPPS, and/or India ink as absorbers. Over a range of conditions, the reduced scattering coefficient was recovered with a root mean square error (RMSE) of 0.86-2.7 cm-1 (average error = 3.8%). MB concentration was recovered with RMSE = 0.26-0.52 µM (average error = 15.0%), which did not vary with inclusion of MnTPPS (p=0.65). This system will be utilized to determine optical properties in human abscesses, in order to generate treatment plans for photodynamic therapy.
Living on the edge: trailing edge forests at risk of fire‐facilitated conversion to non‐forest

Forests are an incredibly important resource across the globe, yet they are threatened by climate change through stressors such as drought, insect outbreaks, and wildfire. Trailing edge forests—those areas expected to experience range contractions under a changing climate—are of particular concern because of the potential for abrupt conversion to non‐forest. However, due to plant‐climate disequilibrium, broad‐scale forest die‐off and range contraction in trailing edge forests are unlikely to occur over short timeframes (<~25–50 yr) without a disturbance catalyst (e.g., wildfire). This underscores that explicit attention to both climate and disturbance is necessary to understand how the distribution of forests will respond to climate change. As such, we first identify the expected location of trailing edge forests in the intermountain western United States by mid‐21st century. We then identify those trailing edge forests that have a high probability of stand‐replacing fire and consider such sites to have an elevated risk of fire‐facilitated transition to non‐forest. Results show that 18% of trailing edge forest and 6.6% of all forest are at elevated risk of fire‐facilitated conversion to non‐forest in the intermountain western United States by mid‐21st century. This estimate, however, assumes that fire burns under average weather conditions. For a subset of the study area (the southwestern United States), we were able to incorporate expected fire severity under extreme weather conditions. For this spatial subset, we found that 61% of trailing edge forest and 30% of all forest are at elevated risk of fire‐facilitated conversion to non‐forest under extreme burning conditions. However, due to compounding error in our process that results in unknowable uncertainty, we urge caution in a strict interpretation of these estimates. Nevertheless, our findings suggest the potential for transformed landscapes in the intermountain western United States that will affect ecosystem services such as watershed integrity, wildlife habitat, wood production, and recreation.
Joint carrier frequency and phase offset estimation algorithm for CPM-DSSS based secure point-to-point communication

: A point-to-point (P2P) communication system based on the CPM-DSSS scheme ensures reliability, security, and antijamming capabilities. However, for reliable detection of data carrier synchronization of CPM-DSSS based system is one of the requirements. This paper presents a joint algorithm for carrier frequency offset (CFO) and carrier phase offset (CPO) estimation for CPM-DSSS based P2P system. The results indicate that the proposed CFO estimator is unbiased and can accurately estimate a wide range of offsets. Moreover, the proposed algorithm is compared with another research work. The results show that the proposed CFO and CPO estimation algorithm outperforms its counterpart with a performance improvement of about 9 dB and above, respectively. The effectiveness of the proposed estimator in the Stanford University Interim (SUI)-1 multipath fading channel is also demonstrated. Finally, the proposed estimator’s bit-error-rate (BER) performance and computational complexity are also given for the CPM-DSSS P2P system.