Fire design in safety engineering: likely fire curve for people’s safety

The present study analyses fire design settings according to Fire Safety Engineering (FSE) for the simulation of fire in civil activities and compares these simulations developed using natural and analytic fire curves. The simulated Heat Rate Release (HRR) curve, appropriately linearized, allows for the estimation of a Likely Fire Curve (LFC). The analytic curves have been introduced for the purpose of evaluating the strength and integrity of the structure, and the adoption of these curves in the fire safety engineering was made following the assumption that the phenomena of major intensity ensure the safe approach of fire design. This argument describes the method adopted for determining a likely fire model that guarantees a greater adherence of the virtualized phenomenon with respect to the potential event. The study showed that the analytic curve, adopted in order to verify the structural strength, in the beginning phases of fire produces fields of temperature and toxic concentrations lower than those obtained by simulation of the Likely Fire Curve. The assumption of the Likely Fire Curve model safeguards exposed people during self-rescue and emergency procedure. The programs used since 2011 for the simulation are FDS (Fire Dynamic Simulator v. 5.4.3) and Smokeview (5.4.8). Comparative analysis was developed using thermo-fluid dynamic parameters (temperature and heat release rate) relevant to the safety of the exposed persons; the case study focuses on children and employees of the nursery. The main result shows that the safety criterion, implicitly included in the analytical fire curves - normally used for fire resistance - doesn’t have the same applicability of a performance based approach on safety evaluation involving people. This paper shows that the Likely Fire Curve assumption involves a thermo-chemical stress more relevant to assessing the safety of exposed people

Unsupervised Machine Learning and Data Mining Procedures Reveal Short Term, Climate Driven Patterns Linking Physico-Chemical Features and Zooplankton Diversity in Small Ponds

Machine Learning (ML) is an increasingly accessible discipline in computer science that develops dynamic algorithms capable of data-driven decisions and whose use in ecology is growing. Fuzzy sets are suitable descriptors of ecological communities as compared to other standard algorithms and allow the description of decisions that include elements of uncertainty and vagueness. However, fuzzy sets are scarcely applied in ecology. In this work, an unsupervised machine learning algorithm, fuzzy c-means and association rules mining were applied to assess the factors influencing the assemblage composition and distribution patterns of 12 zooplankton taxa in 24 shallow ponds in northern Italy. The fuzzy c-means algorithm was implemented to classify the ponds in terms of taxa they support, and to identify the influence of chemical and physical environmental features on the assemblage patterns. Data retrieved during 2014 and 2015 were compared, taking into account that 2014 late spring and summer air temperatures were much lower than historical records, whereas 2015 mean monthly air temperatures were much warmer than historical averages. In both years, fuzzy c-means show a strong clustering of ponds in two groups, contrasting sites characterized by different physico-chemical and biological features. Climatic anomalies, affecting the temperature regime, together with the main water supply to shallow ponds (e.g., surface runoff vs. groundwater) represent disturbance factors producing large interannual differences in the chemistry, biology and short-term dynamic of small aquatic ecosystems. Unsupervised machine learning algorithms and fuzzy sets may help in catching such apparently erratic differences

Risperidone augmentation in treatment-resistant obsessive–compulsive disorder: a double-blind, placebo-controlled study

This double-blind, placebo-controlled trial was performed to determine the efficacy and tolerability of 8 wk of risperidone augmentation of serotonin reuptake inhibitor (SRI) treatment in adult subjects with treatment-resistant obsessive–compulsive disorder (OCD) (failure of at least two SRI trials). Sixteen adult treatment-resistant OCD patients were randomly assigned to augmentation with 8 wk of either risperidone ( n =10) (0.5–3.0 mg/d) or placebo ( n =6) following at least 12 wk of SRI treatment. Four patients on risperidone (40%) and none (0%) on placebo were responders with both a Clinical Global Impression – Improvement (CGI-I) score of 1 or 2 and a Yale–Brown Obsessive–Compulsive Scale (Y-BOCS) decrease [ges ]25%. Risperidone was generally well tolerated: there were 3 dropouts, 1 on risperidone and 2 on placebo. Better Y-BOCS insight score at baseline significantly correlated with a greater CGI-I score at endpoint on risperidone augmentation. Risperidone may be an effective and well-tolerated augmentation strategy in treatment-resistant OCD subjects, but larger sample size studies are required to demonstrate this

A CSI Dataset for Wireless Human Sensing on 80 MHz Wi-Fi Channels

In the last years, several machine learning-based techniques have been proposed to monitor human movements from Wi-Fi channel readings. However, the development of domain-adaptive algorithms that robustly work across different environments is still an open problem, whose solution requires large datasets characterized by strong domain diversity, in terms of environments, persons and Wi-Fi hardware. To date, the few public datasets available are mostly obsolete - as obtained via Wi-Fi devices operating on 20 or 40 MHz bands - and contain little or no domain diversity, thus dramatically limiting the advancements in the design of sensing algorithms. The present contribution aims to fill this gap by providing a dataset of IEEE 802.11 ac channel measurements over an 80 MHz bandwidth channel featuring notable domain diversity, through measurement campaigns that involved thirteen subjects across different environments, days, and with different hardware. Novel experimental data is provided by blocking the direct path between the transmitter and the monitor, and collecting measurements in a semi-anechoic chamber (no multi-path fading). Overall, the dataset - available on IEEE DataPort [1] - contains more than thirteen hours of channel state information readings (23.6 GB), allowing researchers to test activity/identity recognition and people counting algorithms

VREM-FL: Mobility-Aware Computation-Scheduling Co-Design for Vehicular Federated Learning

Assisted and autonomous driving are rapidly gaining momentum, and will soon become a reality. Among their key enablers, artificial intelligence and machine learning are expected to play a prominent role, also thanks to the massive amount of data that smart vehicles will collect from their onboard sensors. In this domain, federated learning is one of the most effective and promising techniques for training global machine learning models, while preserving data privacy at the vehicles and optimizing communications resource usage. In this work, we propose VREM-FL, a computation-scheduling co-design for vehicular federated learning that leverages mobility of vehicles in conjunction with estimated 5G radio environment maps. VREM-FL jointly optimizes the global model learned at the server while wisely allocating communication resources. This is achieved by orchestrating local computations at the vehicles in conjunction with the transmission of their local model updates in an adaptive and predictive fashion, by exploiting radio channel maps. The proposed algorithm can be tuned to trade model training time for radio resource usage. Experimental results demonstrate the efficacy of utilizing radio maps. VREM-FL outperforms literature benchmarks for both a linear regression model (learning time reduced by 28%) and a deep neural network for a semantic image segmentation task (doubling the number of model updates within the same time window).Comment: This work has been submitted to IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Unsupervised machine learning and geometric morphometrics as tools for the identification of inter and intraspecific variations in the Anopheles Maculipennis complex

Geometric morphometric analysis was combined with two different unsupervised machine learning algorithms, UMAP and HDBSCAN, to visualize morphological differences in wing shape among and within four Anopheles sibling species (An. atroparvus, An. melanoon, An. maculipennis s.s. and An. daciae sp. inq.) of the Maculipennis complex in Northern Italy. Specifically, we evaluated: (1) wing shape variation among and within species; (2) the consistencies between groups of An. maculipennis s.s. and An. daciae sp. inq. identified based on COI sequences and wing shape variability; and (3) the spatial and temporal distribution of different morphotypes. UMAP detected at least 13 main patterns of variation in wing shape among the four analyzed species and mapped intraspecific morphological variations. The relationship between the most abundant COI haplotypes of An. daciae sp. inq. and shape ordination/variation was not significant. However, morphological variation within haplotypes was reported. HDBSCAN also recognized different clusters of morphotypes within An. daciae sp. inq. (12) and An. maculipennis s.s. (4). All morphotypes shared a similar pattern of variation in the subcostal vein, in the anal vein and in the radio-medial cross-vein of the wing. On the contrary, the marginal part of the wings remained unchanged in all clusters of both species. Any spatial-temporal significant difference was observed in the frequency of the identified morphotypes. Our study demonstrated that machine learning algorithms are a useful tool combined with geometric morphometrics and suggest to deepen the analysis of inter and intra specific shape variability to evaluate evolutionary constrains related to wing functionality

ChatGPT in orthopedics: a narrative review exploring the potential of artificial intelligence in orthopedic practice

The field of orthopedics faces complex challenges requiring quick and intricate decisions, with patient education and compliance playing crucial roles in treatment outcomes. Technological advancements in artificial intelligence (AI) can potentially enhance orthopedic care. ChatGPT, a natural language processing technology developed by OpenAI, has shown promise in various sectors, including healthcare. ChatGPT can facilitate patient information exchange in orthopedics, provide clinical decision support, and improve patient communication and education. It can assist in differential diagnosis, suggest appropriate imaging modalities, and optimize treatment plans based on evidence-based guidelines. However, ChatGPT has limitations, such as insufficient expertise in specialized domains and a lack of contextual understanding. The application of ChatGPT in orthopedics is still evolving, with studies exploring its potential in clinical decision-making, patient education, workflow optimization, and scientific literature. The results indicate both the benefits and limitations of ChatGPT, emphasizing the need for caution, ethical considerations, and human oversight. Addressing training data quality, biases, data privacy, and accountability challenges is crucial for responsible implementation. While ChatGPT has the potential to transform orthopedic healthcare, further research and development are necessary to ensure its reliability, accuracy, and ethical use in patient care

Unlocking cardiac motion: assessing software and machine learning for single-cell and cardioid kinematic insights

The heart coordinates its functional parameters for optimal beat-to-beat mechanical activity. Reliable detection and quantification of these parameters still represent a hot topic in cardiovascular research. Nowadays, computer vision allows the development of open-source algorithms to measure cellular kinematics. However, the analysis software can vary based on analyzed specimens. In this study, we compared different software performances in in-silico model, in-vitro mouse adult ventricular cardiomyocytes and cardioids. We acquired in-vitro high-resolution videos during suprathreshold stimulation at 0.5-1-2 Hz, adapting the protocol for the cardioids. Moreover, we exposed the samples to inotropic and depolarizing substances. We analyzed in-silico and in-vitro videos by (i) MUSCLEMOTION, the gold standard among open-source software; (ii) CONTRACTIONWAVE, a recently developed tracking software; and (iii) ViKiE, an in-house customized video kinematic evaluation software. We enriched the study with three machine-learning algorithms to test the robustness of the motion-tracking approaches. Our results revealed that all software produced comparable estimations of cardiac mechanical parameters. For instance, in cardioids, beat duration measurements at 0.5 Hz were 1053.58 ms (MUSCLEMOTION), 1043.59 ms (CONTRACTIONWAVE), and 937.11 ms (ViKiE). ViKiE exhibited higher sensitivity in exposed samples due to its localized kinematic analysis, while MUSCLEMOTION and CONTRACTIONWAVE offered temporal correlation, combining global assessment with time-efficient analysis. Finally, machine learning reveals greater accuracy when trained with MUSCLEMOTION dataset in comparison with the other software (accuracy > 83%). In conclusion, our findings provide valuable insights for the accurate selection and integration of software tools into the kinematic analysis pipeline, tailored to the experimental protocol