An Architecture for Declarative Real-Time Scheduling on Linux

This paper proposes a novel framework and programming model for real-time applications supporting a declarative access to real-time CPU scheduling features that are available on an operating system. The core idea is to let applications declare their temporal characteristics and/or requirements on the CPU allocation, where, for example, some of them may require real-time POSIX priorities, whilst others might need resource reservations through SCHED_DEADLINE. The framework can properly handle such a set of heterogeneous requirements configuring an underlying multi-core platform so to exploit the various scheduling disciplines that are available in the kernel, matching applications requirements. The framework is realized as a modular architecture in which different plugins handle independently certain real-time scheduling features within the underlying kernel, easing the customization of its behavior to support other schedulers or operating systems by adding further plugins

Scheduling Replica Voting in Fixed-Priority Real-Time Systems

Reliability and safety are mandatory requirements for safety-critical embedded systems. The design of a fault-tolerant system is required in many fields (e.g., railway, automotive, avionics) and redundancy helps in achieving this goal. Redundant systems typically leverage voting techniques applied to the outputs produced by tasks to detect and even tolerate failures. This paper studies the integration of distributed voting protocols in fixed-priority real-time systems from a scheduling perspective. It analyzes two scheduling strategies for implementing voting. One is attractive and friendly for software developers and based on suspending the task execution until the replica provides the data to be voted. The other one is inspired by the Logical Execution Time (LET) paradigm and requires introducing additional tasks in the system to accomplish voting-related activities. Queuing and delays introduced by inter-replica communication interfaces are also analyzed. Experimental results are finally presented to compare the two strategies, showing that LET-inspired voting is much more predictable and hence more suitable than the other strategy for fixed-priority real-time systems

Early clinical predictors and correlates of long-term morbidity in bipolar disorder

OBJECTIVES: Identifying factors predictive of long-term morbidity should improve clinical planning limiting disability and mortality associated with bipolar disorder (BD). METHODS: We analyzed factors associated with total, depressive and mania-related long-term morbidity and their ratio D/M, as %-time ill between a first-lifetime major affective episode and last follow-up of 207 BD subjects. Bivariate comparisons were followed by multivariable linear regression modeling. RESULTS: Total % of months ill during follow-up was greater in 96 BD-II (40.2%) than 111 BD-I subjects (28.4%; P=0.001). Time in depression averaged 26.1% in BD-II and 14.3% in BD-I, whereas mania-related morbidity was similar in both, averaging 13.9%. Their ratio D/M was 3.7-fold greater in BD-II than BD-I (5.74 vs. 1.96; P<0.0001). Predictive factors independently associated with total %-time ill were: [a] BD-II diagnosis, [b] longer prodrome from antecedents to first affective episode, and [c] any psychiatric comorbidity. Associated with %-time depressed were: [a] BD-II diagnosis, [b] any antecedent psychiatric syndrome, [c] psychiatric comorbidity, and [d] agitated/psychotic depressive first affective episode. Associated with %-time in mania-like illness were: [a] fewer years ill and [b] (hypo)manic first affective episode. The long-term D/M morbidity ratio was associated with: [a] anxious temperament, [b] depressive first episode, and [c] BD-II diagnosis. CONCLUSIONS: Long-term depressive greatly exceeded mania-like morbidity in BD patients. BD-II subjects spent 42% more time ill overall, with a 3.7-times greater D/M morbidity ratio, than BD-I. More time depressed was predicted by agitated/psychotic initial depressive episodes, psychiatric comorbidity, and BD-II diagnosis. Longer prodrome and any antecedent psychiatric syndrome were respectively associated with total and depressive morbidity

Qualitative versus automatic evaluation of CT perfusion parameters in acute posterior circulation ischaemic stroke

Purpose To compare the diagnostic accuracy (ACC) in the detection of acute posterior circulation strokes between qualitative evaluation of software-generated colour maps and automatic assessment of CT perfusion (CTP) parameters. Methods Were retrospectively collected 50 patients suspected of acute posterior circulation stroke who underwent to CTP (GE “Lightspeed”, 64 slices) within 24 h after symptom onset between January 2016 and December 2018. The Posterior circulation-Acute Stroke Prognosis Early CT Score (pc-ASPECTS) was used for quantifying the extent of ischaemic areas on non-contrast (NC)CT and colour-coded maps generated by CTP4 (GE) and RAPID (iSchemia View) software. Final pc-ASPECTS was calculated on follow-up NCCT and/or MRI (Philips Intera 3.0 T or Philips Achieva Ingenia 1.5 T). RAPID software also elaborated automatic quantitative mismatch maps. Results By qualitative evaluation of colour-coded maps, MTT-CTP4D and Tmax-RAPID showed the highest sensitivity (SE) (88.6% and 90.9%, respectively) and ACC (84% and 88%, respectively) compared with the other perfusion parameters (CBV, CBF). Baseline NCCT and CBF provided by RAPID quantitative perfusion mismatchmaps had the lowest SE (29.6%and 6.8%, respectively) and ACC (38% and 18%, respectively). CBF and Tmax assessment provided by quantitative RAPID perfusion mismatch maps showed significant lower SE and ACC than qualitative evaluation. No significant differences were found between the pc-ASPECTSs assessed on colour-coded MTT and Tmax maps neither between the scores assessed on colourcoded CBV-CTP4D and CBF-RAPID maps. Conclusion Qualitative analysis of colour-codedmaps resultedmore sensitive and accurate in the detection of ischaemic changes than automatic quantitative analysis

SAFEXPLAIN: Safe and Explainable Critical Embedded Systems Based on AI

Deep Learning (DL) techniques are at the heart of most future advanced software functions in Critical Autonomous AI-based Systems (CAIS), where they also represent a major competitive factor. Hence, the economic success of CAIS industries (e.g., automotive, space, railway) depends on their ability to design, implement, qualify, and certify DL-based software products under bounded effort/cost. However, there is a fundamental gap between Functional Safety (FUSA) requirements on CAIS and the nature of DL solutions. This gap stems from the development process of DL libraries and affects high-level safety concepts such as (1) explainability and traceability, (2) suitability for varying safety requirements, (3) FUSA-compliant implementations, and (4) real-time constraints. As a matter of fact, the data-dependent and stochastic nature of DL algorithms clashes with current FUSA practice, which instead builds on deterministic, verifiable, and pass/fail test-based software. The SAFEXPLAIN project tackles these challenges and targets by providing a flexible approach to allow the certification - hence adoption - of DL-based solutions in CAIS building on: (1) DL solutions that provide end-to-end traceability, with specific approaches to explain whether predictions can be trusted and strategies to reach (and prove) correct operation, in accordance to certification standards; (2) alternative and increasingly sophisticated design safety patterns for DL with varying criticality and fault tolerance requirements; (3) DL library implementations that adhere to safety requirements; and (4) computing platform configurations, to regain determinism, and probabilistic timing analyses, to handle the remaining non-determinism.The research leading to these results has received funding from the Horizon Europe Programme under the SAFEXPLAIN Project (www.safexplain.eu), grant agreement num. 101069595. BSC authors have also been supported by the Spanish Ministry of Science and Innovation under grant PID2019- 107255GBC21/AEI/10.13039/501100011033.Peer Reviewed"Article signat per 22 autors/es: Jaume Abella, Jon Perez, Cristofer Englund, Bahram Zonooz, Gabriele Giordana, Carlo Donzella, Francisco J. Cazorla, Enrico Mezzetti, Isabel Serra, Axel Brando, Irune Agirre, Fernando Eizaguirre, Thanh Hai Bui, Elahe Arani, Fahad Sarfraz, Ajay Balasubramaniam, Ahmed BadarIlaria Bloise, Lorenzo Feruglio, Ilaria Cinelli, Davide Brighenti, Davide Cunial"Postprint (author's final draft

Molecular analysis of grapevine Pinot gris virus and its association with grapevine leaf mottling and deformation on 'Vermentino' grapevines in Sardinia

In 2017–2018, grapevines of cultivar 'Vermentino' infected with grapevine Pinot gris virus (GPGV) in Sardinia, Italy, exhibited leaf symptoms of mosaic, chlorotic mottling, and curling, and stunted shoots. Disease incidence assessed in 2018 was greater (67%, 103 symptomatic plants out of 153 monitored) than in 2017 (26%, 40 of 153 plants). All symptomatic samples tested by RT-PCR were positive for GPGV in both years, while 70% (53 of 76) of the asymptomatic samples in 2017, and 42% (19 of 45) in 2018, were also positive for GPGV. Characterizing six GPGV isolates from 'Vermentino' by RT-PCR and sequencing of a genomic fragment covering the movement and coat protein genes showed high conservation at the nucleotide level (98.7% to 100.0%) among Sardinian isolates and isolates for which sequence information is available in GenBank. Phylogenetic analysis indicated that most Sardinian GPGV isolates grouped with other European isolates. This is the first characterization of GPGV in a Sardinian vineyard

Time-weighted lactate as a predictor of adverse outcome in acute heart failure

The role of dynamic changes in lactate concentrations on prognosis in acute heart failure has been poorly investigated. The aim of this study was to explore the predictive value of 24 h time-weighted lactate (LACTW ) in patients with acute heart failure

A Multifunctional Nanocomposite Hydrogel for Endoscopic Tracking and Manipulation

Herein, the fabrication of multi‐responsive and hierarchically organized nanomaterial using core‐shell SrF2 upconverting nanoparticles, doped with Yb3+, Tm3+, Nd3+ incorporated into gelatin methacryloyl matrix, is reported. Upon 800 nm excitation, deep monitoring of 3D‐printed constructs is demonstrated. Addition of magnetic self‐assembly of iron oxide nanoparticles within the hydrogel provides anisotropic structuration from the nano‐ to the macro‐scale and magnetic responsiveness permitting remote manipulation. The present study provides a new strategy for the fabrication of a novel highly organized multi‐responsive material using additive manufacturing, which can have important implications in biomedicine

Transcriptome characterisation and SSR markers development in the seagrass Posidonia oceanica

Posidonia oceanica is an endemic seagrass in the Mediterranean Sea, where it provides important ecosystem services and sustains a rich and diverse ecosystem. P. oceanica meadows extend from the surface to 40 meters depth. With the aim of boosting research in this iconic species, we generated a comprehensive RNA-Seq data set for P. oceanica by sequencing specimens collected at two depths and two times during the day. With this approach we attempted to capture the transcriptional diversity associated with change in light and other depth-related environmental factors. Using this extensive data set we generated gene predictions and identified an extensive catalogue of potential Simple Sequence Repeats (SSR) markers. The data generated here will open new avenues for the analysis of population genetic features and functional variation in P. oceanica. In total, 79,235 contigs were obtained by the assembly of 70,453,120 paired end reads. 43,711 contigs were successfully annotated. A total of 17,436 SSR were identified within 13,912 contigs