Miracle-dz/UniFAR-FTUs huggingface dataset:
Providing high-quality measurement data in analytical system of air pollution monitoring and their key importance for smart cities residents

Abstract The article presents selected, main elements of an air pollution automatic monitoring system with analytical subsystems concept in smart cities based on examples from Poland, implemented system in Pomerania, the concept of new system in Warsaw city and pilot research in Nowy Sącz city. All systems are the result of teamwork, ranging from design, development of new methodology and software to implementation in real-time air pollutants smart cities monitoring systems. Focused on the most neuralgic elements: data quality subsystems, new ideas of smart mobility measurement stations and their ability to use in future research and models. Special attention was paid to stochastic models and statistic methodology proposed and used in data diagnostics as analytical system engineering.
G Protein-Coupled Receptor Classification at the Subfamily Level with Probabilistic Suffix Tree

Classifying G protein-coupled receptors (GPCRs) is an interesting topic because of the important role of GPCRs in pharmaceutical research. GPCRs have diverse functions and are involved in many biological processes, which makes them an ideal target of novel medicine. The diverse nature of GPCRs results in the lack of overall sequence homolog among the members, making the classification of GPCRs a very challenging task. Various approaches and methods have been applied to this task, such as HMM, decision tree, and SVM. However, their performances are not completely satisfactory. In this paper, we propose a new method to classify GPCRs into different subfamilies. In the proposed method, the probabilistic suffix tree (PST) is used to construct a prediction model for each of the subfamilies. To classify a GPCR protein, we calculate its similarity score against the PST prediction model of each subfamily using the multi-domain local prediction algorithm. The protein is then classified into the subfamily which gives it the highest score. Our method only uses the primary sequence information and is also very efficient. The model construction and prediction process takes very short time. However, it reports the 98.07% and 97.35% overall accuracy on the level I and II subfamily classification in a 2-fold cross validation test respectively. Given the high accuracy and efficiency, our method is a significant improvement on previously reported ones
A Highly Efficient Ultralow Photovoltaic Power Harvesting System With MPPT for Internet of Things Smart Nodes

Implementing a monolithic highly efficient ultralow photovoltaic (PV) power harvesting system is pivotal for smart nodes of Internet of things (IOT) networks. This paper proposes a fully integrated harvesting system in 0.18-μm CMOS technology. Utilizing a small commercial solar cell of 2.5 cm2, the proposed system can provide 0-29 μW of power, which is much higher than the commonly used passive radio-frequency identification devices in IOT application. The hill-climbing maximum power point tracking algorithm is developed in an energy-efficient manner to tune the input impedance of the system and guarantee adaptive maximum power transfer under wide illumination conditions. The detailed impedance tuning approach is implemented with a capacitor value modulation to eliminate the quiescent power consumption as well as to achieve a higher efficiency than the traditional pulse-frequency modulation scheme. A supercapacitor is utilized for buffering, energy storing, and filtering purposes, which enables more functions of the IOT smart nodes such as active sensing and system-on-chip (SOC) signal processing. The output voltage ranges between 3.0 and 3.5 V for different device loads, such as sensors, SOC, or wireless transceivers. The measured results confirm that this PV harvesting system achieves both ultralow operation capability under 20 μW and a selfsustaining efficiency of 89%.
A formal classification of 3D medial axis points and their local geometry

This paper proposes a novel hypergraph skeletal representation for 3D shape based on a formal derivation of the generic structure of its medial axis. By classifying each skeletal point by its order of contact, we show that, genetically, the medial axis consists of five types of points, which are then organized into sheets, curves, and points: 1) sheets (manifolds with boundary) which are the locus of bitangent spheres with regular tangency A/sub 1//sup 2/ (A/sub k//sup n/ notation means n distinct k-fold tangencies of the sphere of contact, as explained in the text); two types of curves, 2) the intersection curve of three sheets and the locus of centers of tritangent spheres, A/sub 1//sup 3/, and 3) the boundary of sheets, which are the locus of centers of spheres whose radius equals the larger principal curvature, i.e., higher order contact A/sub 3/ points; and two types of points, 4) centers of quad-tangent spheres, A/sub 1//sup 4/, and 5) centers of spheres with one regular tangency and one higher order tangency, A/sub 1/A/sub 3/. The geometry of the 3D medial axis thus consists of sheets (A/sub 1//sup 2/) bounded by one type of curve (A/sub 3/) on their free end, which corresponds to ridges on the surface, and attached to two other sheets at another type of curve (A/sub 1//sup 3/), which support a generalized cylinder description. The A/sub 3/ curves can only end in A/sub 1/ A/sub 3/ points where they must meet an A/sub 1//sup 3/ curve. The A/sub 1//sup 3/ curves meet together in fours at an A/sub 1//sup 4/ point. This formal result leads to a compact representation for 3D shape, referred to as the medial axis hypergraph representation consisting of nodes (A/sub 1//sup 4/ and A/sub 1/ A/sub 3/ points), links between pairs of nodes (A/sub 1//sup 3/ and A/sub 3/ curves) and hyperlinks between groups of links (A/sub 1//sup 2/ sheets). The description of the local geometry at nodes by itself is sufficient to capture qualitative aspects of shapes, in analogy to 2D. We derive a pointwise reconstruction formula to reconstruct a surface from this medial axis hypergraph together with the radius function. Thus, this information completely characterizes 3D shape and lays the theoretical foundation for its use in recognition, morphing, design, and manipulation of shapes.
Tranexamic Acid Reduces Blood Loss in Total Hip Replacement Surgery

Intraoperatively administered, tranexamic acid (TA) does not reduce bleeding in total hip replacement (THR). Therefore, its prophylactic use was attempted in the present study because this has been shown to be more effective in cardiac surgery. We investigated 40 patients undergoing THR in a prospective, randomized, double-blinded study. Twenty patients received TA given in two bolus doses of 10 mg/kg each, the first just before surgical incision and the second 3 h later. In addition, a continuous infusion of TA, 1.0 mg · kg−1 · h−1 for 10 h, was given after the first bolus dose. The remaining 20 patients formed a control group. Both groups used preoperative autologous blood donation and intraoperative autotransfusion. Intraoperative bleeding was significantly less (P = 0.001) in the TA group compared with the control group (630 ± 220 mL vs 850 ± 260 mL). Postoperative drainage bleeding was correspondingly less (P = 0.001) (520 ± 280 vs 920 ± 410 mL). Up to 10 h postoperatively, plasma D-dimer concentration was halved in the TA group compared with the control group. One patient in each group had an ultrasound-verified late deep vein thrombosis. In conclusion, we found TA, administrated before surgical incision, to be efficient in reducing bleeding during THR. Implications In a prospective, double-blinded study of 40 patients undergoing total hip replacement, the preoperative administration of tranexamic acid reduced bleeding by 35%, probably by decreasing induced fibrinolysis. Whether tranexamic acid therapy can replace predonation of autologous blood or intraoperative autotransfusion requires further study.
Climatic Factors Driving Invasion of the Tiger Mosquito (Aedes albopictus) into New Areas of Trentino, Northern Italy

Background The tiger mosquito (Aedes albopictus), vector of several emerging diseases, is expanding into more northerly latitudes as well as into higher altitudes in northern Italy. Changes in the pattern of distribution of the tiger mosquito may affect the potential spread of infectious diseases transmitted by this species in Europe. Therefore, predicting suitable areas of future establishment and spread is essential for planning early prevention and control strategies. Methodology/Principal Findings To identify the areas currently most suitable for the occurrence of the tiger mosquito in the Province of Trento, we combined field entomological observations with analyses of satellite temperature data (MODIS Land Surface Temperature: LST) and human population data. We determine threshold conditions for the survival of overwintering eggs and for adult survival using both January mean temperatures and annual mean temperatures. We show that the 0°C LST threshold for January mean temperatures and the 11°C threshold for annual mean temperatures provide the best predictors for identifying the areas that could potentially support populations of this mosquito. In fact, human population density and distance to human settlements appear to be less important variables affecting mosquito distribution in this area. Finally, we evaluated the future establishment and spread of this species in relation to predicted climate warming by considering the A2 scenario for 2050 statistically downscaled at regional level in which winter and annual temperatures increase by 1.5 and 1°C, respectively. Conclusions/Significance MODIS satellite LST data are useful for accurately predicting potential areas of tiger mosquito distribution and for revealing the range limits of this species in mountainous areas, predictions which could be extended to an European scale. We show that the observed trend of increasing temperatures due to climate change could facilitate further invasion of Ae. albopictus into new areas.
Low-complexity Multiclass Encryption by Compressed Sensing, Part II: Known-Plaintext Attacks

Despite its intrinsic linearity, compressed sensing may be exploited to at least partially encrypt acquired signals from unintentional receivers: in the companion paper we have shown that the simplicity of its encoding allows the definition of a general, lightweight scheme in which transmitters distribute the same information to receivers of different classes enabled to recover it with different quality levels. In this investigation we quantify the robustness of such a scheme with respect to known-plaintext attacks. The odds of such an attack are shown by theoretical means, proving that the number of candidate encoding matrices matching a typical plaintext-ciphertext pair is astronomically large, thus making the search for the true encoding infeasible. These attacks are also simulated by applying compressed sensing to a variety of signals (speech, images and electrocardiographic traces) showing how this difficulty in extracting information on the true encoding matrix from a plaintext-ciphertext pair is reflected on the quality of the signals recovered by the attacker. The results clarify that, although not perfectly secure, CS grants a noteworthy level of security that may come at almost-zero cost and especially benefit resource-limited applications.
Critical Care Basic Ultrasound Learning Goals for American Anesthesiology Critical Care Trainees: Recommendations from an Expert Group

OBJECTIVE:In this review, we define learning goals and recommend competencies concerning focused basic critical care ultrasound (CCUS) for critical care specialists in training. DESIGN:The narrative review is, and the recommendations contained herein are, sponsored by the Society of Critical Care Anesthesiologists. Our recommendations are based on a structured literature review by an expert panel of anesthesiology intensivists and cardiologists with formal training in ultrasound. Published descriptions of learning and training routines from anesthesia–critical care and other specialties were identified and considered. Sections were written by groups with special expertise, with dissent included in the text. RESULTS:Learning goals and objectives were identified for achieving competence in the use of CCUS at a specialist level (critical care fellowship training) for diagnosis and monitoring of vital organ dysfunction in the critical care environment. The ultrasound examination was divided into vascular, abdominal, thoracic, and cardiac components. For each component, learning goals and specific skills were presented. Suggestions for teaching and training methods were described. DISCUSSION:Immediate bedside availability of ultrasound resources can dramatically improve the ability of critical care physicians to care for critically ill patients. Anesthesia--critical care medicine training should have definitive expectations and performance standards for basic CCUS interpretation by anesthesiology--critical care specialists. The learning goals in this review reflect current trends in the multispecialty critical care environment where ultrasound-based diagnostic strategies are already frequently applied. These competencies should be formally taught as part of an established anesthesiology-critical care medicine graduate medical education programs.
Persuasive Recommendations in Ubiquitous Environments

Recommender Systems have been traditionally utilized in online environments to personalize product and service offerings and can contribute towards the persuasion of a user to select or consume the recommended items. The present study examines the aforementioned systems in ubiquitous environments and focuses on the persuasive role of customer’s motivational conditions to consume a product as well as factors affecting consumers’ acceptance in recommendation systems. One of the main insights of the study demonstrates that when a consumer has low motivation to purchase an item then (s)he has the intention to purchase more garments than in the case of high motivation to purchase a particular garment. Furthermore, the present study examines and evaluates the effect of novel and serendipitous recommendations on the above motivational conditions. The results revealed that when a consumer has either high or low motivation to purchase a garment, (s)he gets persuaded by serendipitous garment recommendations while the provision of novel recommendations does not affect the acceptance of recommendations in any of the two motivational conditions.
GFAM: A Genetic Algorithm Optimization of Fuzzy ARTMAP

Fuzzy ARTMAP (FAM) is currently considered to be one of the premier neural network architectures in solving classification problems. One of the limitations of fuzzy ARTMAP that has been extensively reported in the literature is the category proliferation problem. That is fuzzy ARTMAP has the tendency of increasing its network size, as it is confronted with more and more data, especially if the data is of noisy and/or overlapping nature. To remedy this problem a number of researchers have designed modifications to the training phase of fuzzy ARTMAP that had the beneficial effect of reducing this phenomenon. In this paper we propose a new approach to handle the category proliferation problem in fuzzy ARTMAP by evolving trained FAM architectures. We refer to the resulting FAM architectures as GFAM We demonstrate through extensive experimentation that an evolved FAM (GFAM) exhibits good generalization, small size, and produces an optimal or a good sub-optimal network with a reasonable computational effort. Furthermore, comparisons of the GFAM with other approaches, proposed in the literature, that address the FAM category proliferation problem, illustrate that the GFAM has a number of advantages (i.e. produces smaller or equal size architectures, of better or as good generalization, with reduced computational complexity).
Architectural abstractions for hybrid programs

Modern cyber-physical systems interact closely with continuous physical processes like kinematic movement. Software component frameworks do not provide an explicit way to represent or reason about these processes. Meanwhile, hybrid program models have been successful in proving critical properties of discrete-continuous systems. These programs deal with diverse aspects of a cyber-physical system such as controller decisions, component communication protocols, and mechanical dynamics, requiring several programs to address the variation. However, currently these aspects are often intertwined in mostly monolithic hybrid programs, which are difficult to understand, change, and organize. These issues can be addressed by component-based engineering, making hybrid modeling more practical. This paper lays the foundation for using architectural models to provide component-based benefits to developing hybrid programs. We build formal architectural abstractions of hybrid programs and formulas, enabling analysis of hybrid programs at the component level, reusing parts of hybrid programs, and automatic transformation from views into hybrid programs and formulas. Our approach is evaluated in the context of a robotic collision avoidance case study.
IIb-7 edge-MOM diode: An integrated, optical, non-linear device

We report here on the fabrication of a new integrated device, with non-linear characteristics that extend from DC up to optical frequencies. Electrons that unnelofrom one metal to the other through a thin oxide (-10 A), in less than sec are responsible for a fast response, and the non-linear I-V characteristic of the device. ’m2 Due to the extremely small effective area of tunneling (-lO-’O cm2), the “Point-Contact Metal-Oxide-Metal’’ (MOM) junction performs well as a detector and mixer, up to optical frequen~ i e s , ~ but is not practical as a device due to its mechanical instability. Evaporated structures, utilized thus far have been reproducible and stable, but inferior due to the large tunneling area (-IO-’ cm2) . 4
Diagnosis and management of acute kidney injury in patients with cirrhosis: revised consensus recommendations of the International Club of Ascites

Acute renal failure (ARF) is a common complication in patients with decompensated cirrhosis. The traditional diagnostic criteria of renal failure in these patients were proposed in 19961 and have been refined in subsequent years.2 According to these criteria, ARF is defined as an increase in serum creatinine (sCr) of ≥50% from baseline to a final value >1.5 mg/dL (133 µmol/L). However, the threshold value of 1.5 mg/dL (133 µmol/L) sCr to define renal failure in patients with decompensated cirrhosis has been challenged.3 ,4 In addition, the timeframe to distinguish acute from chronic renal failure has not been clearly identified, the only exception being type 1 hepatorenal syndrome (HRS). Meanwhile, new definitions for ARF, now termed acute kidney injury (AKI), have been proposed and validated in patients without cirrhosis.5–7 Recently these new criteria were also proposed and applied in the diagnosis of AKI in patients with cirrhosis.3 ,8–15 Thus, in December 2012, the International Club of Ascites (ICA) organised a consensus development meeting in Venice, Italy, in order to reach a new definition of AKI in patients with cirrhosis. The discussion among the experts continued thereafter for 2 years, both online and through several meetings, between those experts who had different positions on crucial points on the subject. This paper reports the scientific evidence supporting the final proposal of a new approach to the diagnosis and treatment of this condition, on which the experts agreed. AKI is defined as an acute significant reduction in the glomerular filtration rate (GFR). sCr remains the most practical biomarker of renal function in patients with ARF (with or without cirrhosis). However, sCr as a biomarker of renal function has many limitations in clinical practice since it is influenced by bodyweight, race, age, and gender. The use of sCr in patients with cirrhosis is …
Shared Genetic Susceptibility to Ischemic Stroke and Coronary Artery Disease: A Genome-Wide Analysis of Common Variants

Background and Purpose— Ischemic stroke (IS) and coronary artery disease (CAD) share several risk factors and each has a substantial heritability. We conducted a genome-wide analysis to evaluate the extent of shared genetic determination of the two diseases. Methods— Genome-wide association data were obtained from the METASTROKE, Coronary Artery Disease Genome-wide Replication and Meta-analysis (CARDIoGRAM), and Coronary Artery Disease (C4D) Genetics consortia. We first analyzed common variants reaching a nominal threshold of significance (P<0.01) for CAD for their association with IS and vice versa. We then examined specific overlap across phenotypes for variants that reached a high threshold of significance. Finally, we conducted a joint meta-analysis on the combined phenotype of IS or CAD. Corresponding analyses were performed restricted to the 2167 individuals with the ischemic large artery stroke (LAS) subtype. Results— Common variants associated with CAD at P<0.01 were associated with a significant excess risk for IS and for LAS and vice versa. Among the 42 known genome-wide significant loci for CAD, 3 and 5 loci were significantly associated with IS and LAS, respectively. In the joint meta-analyses, 15 loci passed genome-wide significance (P<5×10−8) for the combined phenotype of IS or CAD and 17 loci passed genome-wide significance for LAS or CAD. Because these loci had prior evidence for genome-wide significance for CAD, we specifically analyzed the respective signals for IS and LAS and found evidence for association at chr12q24/SH2B3 (PIS=1.62×10−7) and ABO (PIS=2.6×10−4), as well as at HDAC9 (PLAS=2.32×10−12), 9p21 (PLAS=3.70×10−6), RAI1-PEMT-RASD1 (PLAS=2.69×10−5), EDNRA (PLAS=7.29×10−4), and CYP17A1-CNNM2-NT5C2 (PLAS=4.9×10−4). Conclusions— Our results demonstrate substantial overlap in the genetic risk of IS and particularly the LAS subtype with CAD.
Exploring the Impact of Web-Based vs. In-Person Exercise Training on Benefits and Adherence in Substance Use Disorder Interventions: A Pilot Study

Background: Substance use disorders pose unique challenges, affecting individuals physiologically and socially. This study addresses the fundamental question of how adherence to exercise programs impacts those with substance use disorders, examining both in-person and online interventions. Methods: A 12-week analysis involving 26 participants assessed physical fitness, strength, and quality of life. Participants were categorized into in-person and online exercise groups, with their adherence tracked through attendance and a dedicated app. Results: The in-person group exhibited higher adherence rates and significant improvements, in contrast to the challenges encountered by the online groups, particularly in substance use disorder cohorts. Statistical analyses highlighted these differences, emphasizing the pivotal role of the exercise program delivery format. Conclusions: This research advocates for hybrid models, blending professional supervision with online flexibility, recognizing the distinct challenges of substance use disorders. Insights from this study will contribute to shaping more effective, personalized interventions in the complex landscape of substance use disorders, offering guidance for advancing treatment strategies.