Miracle-dz/UniFAR-FTUs huggingface dataset:
Proinflammatory Cytokine Levels in Sepsis and in Health and TNFÎ± Association with Sepsis Mortality and patient characteristics: a Systematic Review and Meta-analysis

Background: Sepsis is a global health problem associated with significant morbidity and mortality. Detrimental sepsis effects are attributed to a "cytokine storm. " However, anti-cytokine therapies have failed to lower sepsis mortality. We aim to characterize levels of key cytokines in sepsis patients and healthy controls and relate TNF levels to patient characteristics and outcomes. Methods: We performed a systematic review and meta-analysis. Medline, Embase, Cochrane Library, and Web of Science Core Collection databases were searched from 1985 to May 2020 for studies in English. We included randomized controlled trials (RCTs), controlled trials, cohort studies, case series, and cross-sectional studies that reported mean levels of cytokines in the circulation thought to be relevant for sepsis pathogenesis. We also evaluated concentrations of these cytokines in healthy persons. Quality in Prognosis Studies tool was used to assess the methodological quality of included studies. We extracted summary data from published reports. Data analyses were performed using a random-effects model to estimate pooled odds ratios (OR) with 95% confidence intervals for cytokine levels and mortality. This systematic review is registered in PROSPERO (CRD42020179800). Findings: We identified 3654 records, and 104 studies were included with a total of 3250 participants. The pooled estimated mean TNF concentration in sepsis patients was 58.4 pg/ml (95% Confidence Interval or CI 39.8-85.8 pg/ml) and 5.5 pg/ml (95% CI 3.8-8.0 pg/ml) in healthy controls. Pooled estimate means for IL-1{beta} and IFN{gamma} in sepsis patients were 21.8 pg/ml and 63.3 pg/ml, respectively. Elevated TNF concentrations associated with increased 28-day sepsis mortality (p=0.001). In subgroup analyses, TNF levels did not relate to sepsis source, sepsis severity, or sequential organ failure assessment (SOFA) score. Interpretation: TNF concentration in sepsis is increased approximately 10-fold compared to healthy persons, and TNF associated with sepsis mortality but not with sepsis severity. The concept that elevated cytokines cause sepsis should be revisited in the context of these data.
Insecticide Resistance in Spodoptera exigua (Lepidoptera: Noctuidae) Populations in Shallot Areas of Java, Indonesia

Abstract The beet armyworm, Spodoptera exigua (Hübner), is a major insect pest in shallot in Java, Indonesia. To control this insect, farmers rely on the use of a narrow spectrum of insecticides leading to resistance development. The objective of this study was to determine the level of resistance of S. exigua populations from different areas of Java to the most widely used insecticides, chlorfenapyr, methomyl, and emamectin benzoate, as well as to cyantraniliprole which was recently introduced in Indonesia. Spodoptera exigua populations were collected from three major shallot production areas in Java, the Districts of Brebes, Nganjuk, and Bantul. Based on the LC50 values, resistance ratios of S. exigua populations compared to the most susceptible population from the sub-district Pundong in Bantul varied from very low to very high: 1.1- to 113.8-fold for chlorfenapyr, 16.4- to 223.6-fold for methomyl, and 1.5- to 51.7-fold for emamectin benzoate. Spodoptera exigua populations from the same location were more susceptible to cyantraniliprole, with a resistance ratio of 4.0- to 12.1-fold. These findings indicate that the field populations of S. exigua collected from major shallot areas in Java were resistant to chlorfenapyr and methomyl and less resistant to emamectin benzoate, whereas they were still susceptible to cyantraniliprole. The populations from Brebes and Nganjuk were more resistant to chlorfenapyr, methomyl, and emamectin benzoate than those collected from Bantul. To prevent the further increase of resistant of S. exigua populations, IPM principles need to be applied including the alternate use of insecticides with different modes of action.
Mandating better buildings: a global review of building codes and prospects for improvement in the United States

This paper provides a global overview of the design, implementation, and evolution of building energy codes. Reflecting alternative policy goals, building energy codes differ significantly across the United States, the European Union, and China. This review uncovers numerous innovative practices including greenhouse gas emissions caps per square meter of building space, energy performance certificates with retrofit recommendations, and inclusion of renewable energy to achieve ‘nearly zero‐energy buildings’. These innovations motivated an assessment of an aggressive commercial building code applied to all US states, requiring both new construction and buildings with major modifications to comply with the latest version of the ASHRAE 90.1 Standards. Using the National Energy Modeling System (NEMS), we estimate that by 2035, such building codes in the United States could reduce energy for space heating, cooling, water heating, and lighting in commercial buildings by 16%, 15%, 20%, and 5%, respectively. Impacts on different fuels and building types, energy rates and bills as well as pollution emission reductions are also examined. WIREs Energy Environ 2016, 5:188–215. doi: 10.1002/wene.168
An Adaptive Control Strategy for a Low-Ripple Boost Converter in BLDC Motor Speed Control

Abstract Conventional boost converters are widely used for connecting low-voltage power sources and inverters in motor control. However, a large filter capacitor bank is often used to reduce DC-link ripples that occur when an inverter is connected to a boost converter. Otherwise, significant voltage and current perturbations can impact on battery performance degradation and cause torque ripple, speed ripple and vibration in brushless DC (BLDC) motors. To suppress the converter’s DC-link ripple, this paper proposes a new control strategy for boost converter controller to generate low-ripple DC-link voltage or current at different motor speeds. In the proposed method, observers are designed to adaptively estimate the DC-link voltage and current harmonics. The harmonic terms are used as feedback signals to calculate the DC converter’s duty cycle. The entire control model is implemented on an embedded system, and its robustness is verified by simulation and experimental results that show the DC-link voltage and current ripples can be reduced by about 50% and 30%, respectively.
Stable aqueous dispersions of bare and double layer functionalized superparamagnetic iron oxide nanoparticles for biomedical applications

Abstract Superparamagnetic iron oxide nanoparticles (SPIONs) have attracted the particular interest of scientists from various disciplines since their obtaining to the present day. The physicochemical and pharmacokinetic properties of SPIONs-containing magnetic nanofluids, and their applicability in biomedicine, largely depend on the stability of the colloidal system, particle size, size distribution, net magnetic moment, phase composition, and type and properties of stabilizers. Also, in some cases, when using magnetic nanoparticles for biomedical purposes, it is necessary that the stabilizing ligands of nanoparticles should not significantly change the magnetic properties. From this point of view, the preparation of stable colloidal systems containing bare iron oxide nanoparticles (BIONs) in water at physiological pH attracts particular attention and becomes increasingly popular in scientific circles. This study is focused on the development of the synthesis of aqueous suspensions of SPIONs stabilized with various organic molecules (oleic acid [OA] and poly(ethylene glycol) monooleate - with molecular weights 460 and 860) using a modified controlled chemical coprecipitation reaction, as well as stable nanofluids containing BIONs in an aqueous medium at neutral pH (near-physiological). The obtained samples were characterized using X-ray diffraction (XRD), Fourier Transform Infrared Spectroscopy, small-angle x-ray scattering (SAXS), dynamic light scattering (DLS), electrophoretic light scattering (ELS), and Vibrating Sample Magnetometry.
Design of an ASIC for a commercial small-volume fiber optic gyro

In the past, a common way to develop systems was to build prototypes and test these in the laboratory until all bugs were fixed. The final system was developed and released for production in at least another one additional design iteration. This way is no longer feasible in modern systems. It is too time consuming and for some systems not possible at all. The complex behavior of these systems must be simulated and alternatives must be proved to find the best solution to comply with the need of the market. This paper is showing, how LITEF was designing as ASIC for an (mu) FORS system. Simulation proved the correctness of the specification for this ASIC and the functionality of the complete system. The simulation of different design tools had to be interfaced to achieve the result. No prototype was built to prove correctness of the system idea.
Graphene-based biosensors for disease theranostics: Development, applications, and recent advancements

Abstract Graphene, owing to its unique chemical structure and extraordinary chemical, electrical, thermal, optical, and mechanical properties, has opened up a new vista of applications, specifically as novel sensing platforms. The last decade has seen an extensive exploration of graphene and graphene-based materials either alone or modified with nanoparticles and polymers for the fabrication of nanoscale biosensors. These biosensors displayed excellent conductivity, high sensitivity, and selectivity, good accuracy, and precision, rapid detection with low detection limits as well as long-term stability. The unmatched properties of graphene and graphene-based materials have been applied for the detection of a number of chemical and biological molecules successfully for the diagnosis of a variety of diseases, pathogens, and biomarkers of the diseases. This review is aimed to cover the fabrication methods, functionalization techniques, and biomedical applications along with the recent advancements in the field of development of graphene-based biosensors. Recent clinical trials and patents as well as market trends and opportunities associated with graphene-based biosensors are also summarized. The application of graphene-based biosensors in the detection of SARS-CoV-2 causing COVID-19 is also reviewed.
Twitch’s CDN as an Open Population Ecosystem

The quality and continuity of the video services such as Twitch depend on the scale and well-being of their content distribution networks (CDNs). Each CDN may consist of 1000s of servers, physically feeding the videos to the clients. Opting for a better understanding, researchers have attempted to measure and analyze the CDNs of popular video services [10, 11, 12, 19]. These works are, however, one-time effort. Given the widespread use of Twitch, we find continuous survey of its CDN an important subject of study. The challenge lies in the cost of performing the Internet-scale scans – the probing traffic. The larger the CDNs and the more frequent the scans are, the higher the overhead. Instead of performing full scans repeatedly, we envision a cost-effective alternative that samples and estimates the CDN size (i.e., the number of servers). Only when the size change is significant, does the system trigger a full scan. To this end and inspired by Capture-Mark-Recapture (CMR), a methodology widely used in Ecology to estimate animal population with little human effort, we propose two mechanisms to estimate the CDN size with lightweight traffic. Using a data set collected in Nov 2019, we find a 7.25% average estimation error. Provided an estimation error bound, we can identify as well the best parameter combination to minimize the probing traffic.
capsAEUL: Slow HTTP DoS Attack Detection using Autoencoders through Unsupervised Learning

Slow HTTP Denial of Service (DoS) attacks are defined as application layer vulnerabilities that make HTTP services degrade their performance or reach a denial state. The Slow HTTP DoS attacks can evade the generic DoS attack detection techniques because of their low volume but long lasting attack traffic. Existing solutions on Slow HTTP DoS attack detection mainly rely on static threshold based detection techniques or supervised machine learning techniques. However, the use of unsupervised learning techniques has not been widely studied. This paper proposes capsAEUL, which uses multiple Autoencoders as an unsupervised learning technique for detecting all of Slowloris, Slowread, and Slow POST of Slow HTTP DoS attack as an integrated system. The PoC implementation of capsAEUL exhibits the comparable prediction performance in terms of the high accuracy and the decent AUC scores.
Situational factor affecting energy-saving behavior in direct approaches in Hanoi City. The role of socio-demographics

Abstract Recently, scholars worldwide have been focusing on conducting studies on energy-saving behavior to promote efficient usage and conservation of energy. However, in Vietnam and some other developing countries, the research on energy-saving behavior is still limited, especially for the residence and household sectors. This research involved the conducting of a survey on the factors that affect the energy-saving behavior of households in Hanoi city. With 698 randomly collected samples, this research aimed to investigate the mechanism of the direct impact of external factors, such as policy, energy cost, energy-saving products’ quality, and social norms. The analysis was carried out using structural equation modeling (SEM); the present results showed that the quality of energy-efficient appliances and social norms affect energy-saving behavior positively. Meanwhile, energy cost and energy-related policies do not affect the energy-saving behavior directly but rather indirectly. Demographic factors, such as gender, income, educational level act as stimulant factors, promote the formation of energy-saving behavior. This research would help policy-makers in the field of energy get a more specific view on the effects of external factors on energy-saving behavior and thence establish more sustainable and environment-friendly energy-saving policies.
An Algorithmic Readout Approach for Thermal Conductivity Based CO2 Sensors

This thesis presents a new approach to reading out thermal-conductivity-based gas sensors. This method is intended for the readout of a CMOS compatible resistive thermal-conductivity transducer for indoor CO2 sensing applications, without requiring precision off-chip components. Instead of accurately regulating the power dissipated in the transducer and measuring its temperature, the temperature and power dissipation are both measured using an algorithmic approach. A high-resolution ADC digitizes the voltage drop across the transducer and the current through it, measured using an on-chip reference resistor. Moreover, by digitizing several base-emitter voltages of an on-chip bipolar transistor, a precision bandgap voltage reference is constructed in the digital domain, and accurate information about the ambient temperature is obtained, which is used to temperature compensate the voltage reference and the reference resistor. Thus, all necessary ingredients are obtained to calculate the power dissipation and temperature of the transducer, from which the thermal conductivity of the surrounding air, and hence CO2 concentration, can be obtained. A prototype integrated circuit implementing this readout approach has been realized in 0.16um CMOS. It has been tested in a climate chamber in combination with a platinum resistor mimicking the transducer. The digitally-constructed voltage reference has a temperature coefficient of 9ppm/°C, while ambient temperature is sensed with accuracy of ±0.2°C, with a resolution of 0.15°C. The resistance readings have an inaccuracy ranging between -1mOhms to 4mOhms on a nominal resistance of about 100Ohms (-10ppm - 40ppm) with a resolution of around 2mOhms in the temperature range from 10°C to 40°C; for the power measurements, the circuit achieved an accuracy between -0.03% and 0.06%, with an 800nW of resolution (in the same temperature range) which is one order of magnitude better than results presented in previous work. Although no CO2 measurements have been performed, an estimated thermal resistance accuracy of around 2862ppm with a resolution of 155.64[K/W] should be possible, which would enable detection of the CO2 levels in the air with an accuracy of around 0.72% and a resolution of 7705ppm.
Active-notch antennas stabilized with a slotline-ring resonator for wireless applications

This article discusses the design and measurements of two compact and low-cost Gunn-diode active-notch antennas and compares the results with a similarly configured passive antenna. The antennas feature a slotline-ring resonator for frequency stabilization and external Q improvement. The active antennas' radiation patterns compare well with those measured for a similar passive antenna. The active antenna can incorporate a varactor for frequency tuning with a frequency range of about 400 MHz or 4.2%. This circuit should have many applications in wireless communication, sensor, RF identification, or Doppler radar applications.
Are Crescia and Piadina the Same? Towards Identifying Synonymy or Non-Synonymy between Italian Words to Enable Crowdsourcing from Language Learners

We introduce a method to generate candidate pairs of related Italian words sharing (or not) synonymous relations from the ConceptNet knowledgebase. The pairs are intended to generate questions for a vocabulary trainer which combines exercises to enhance vocabulary skills with the implicit crowdsourcing of linguistic knowledge about the semantic relations between words. Our method relies on the idea that pairs of synonyms in a language tend to translate to pairs of synonyms in other languages. We generated 85 k candidate pairs of Italian synonyms that can be used to produce questions for both teaching ( 3 . 8 k pairs) and crowdsourcing purposes ( 80 k pairs). Follow-up efforts are however needed in order to generate a complementary set of questions.
Moving from Human Ratings to Word Vectors to Classify People With Focal Dementias: Are We There Yet?

Fine-grained variables based on semantic proximity of words can provide helpful diagnostic information when applied to the analysis of Verbal Fluency tasks. However, before leaving human-based ratings in favour of measures derived from distributional approaches, it is essential to assess the performance of the latter against that of the former. In this work, we analysed a Verbal Fluency task using measures of semantic proximity derived from Distributional Semantic Models of language, and we show how Machine Learning models based on them are less accurate in classifying patients with focal dementias than the same models built on human-based ratings. We discuss the possible interpretation of these results and the implications for the application of distributional semantics in clinical settings.
Introducing a Gold Standard Corpus from Young Multilinguals for the Evaluation of Automatic UD-PoS Taggers for Italian

Part-of-speech (PoS) tagging constitutes a common task in Natural Language Processing (NLP) given its widespread applicability. However, with the advance of new information technologies and language variation, the contents and methods for PoS-tagging have changed. The majority of Italian existing data for this task originate from standard texts, where language use is far from multifaceted informal real-life situations. Automatic PoS-tagging models trained with such data do not perform reliably on non-standard language, like social media content or language learners’ texts. Our aim is to provide additional training and evaluation data from language learners tagged in Universal Dependencies (UD), as well as testing current automatic PoS-tagging systems and evaluating their performance on such data. We use Italian texts from a multilingual corpus of young language learners, LEONIDE, to create a tagged gold standard for evaluating UD PoS-tagging performance on non-standard language. With the 3.7 version of Stanza, a Python NLP package, we apply available automatic PoS-taggers, namely ISDT, ParTUT, POSTWITA, TWITTIRÒ and VIT, trained with diversified data, on our dataset. Our results show that the above taggers, trained on non-standard data or multilingual treebanks, can
On the Role of Textual Connectives in Sentence Comprehension: A New Dataset for Italian

In this paper we present a new evaluation resource for Italian aimed at assessing the role of textual connectives in the comprehension of the meaning of a sentence. The resource is arranged in two sections (acceptability assessment and cloze test), each one corresponding to a distinct challenge task conceived to test how subtle modifications involving connectives in real usage sentences influence the perceived acceptability of the sentence by native speakers and Neural Language Models (NLMs). Although the main focus is the presentation of the dataset, we also provide some pre-liminary data comparing human judgments and NLMs performance in the two tasks 1 .
An Unsupervised Approach to Extract Life-Events from Personal Narratives in the Mental Health Domain

Personal Narratives are an important source of knowledge in the mental health domain. Over an extended period of time, the psychologist learns about the patient’s life-events and participants from the Personal Narratives shared during each therapy session. The acquired knowledge is then used to support the patient to reach a healthier mental state by appropriate targeted feedback during each conversation. In this work, we propose an unsupervised approach to automatically extract personal life-events and participants from the patient’s narratives and represent them as a personal graph. This personal graph is then updated at each interaction with the patient. We have evaluated our proposed approach on a dataset of longitudinal Italian Personal Narratives as well as a dataset of English commonsense stories.
Leveraging Bias in Pre-Trained Word Embeddings for Unsupervised Microaggression Detection

Microaggressions are subtle manifestations of bias (Breitfeller et al., 2019). These demonstrations of bias can often be classified as a subset of abusive language. However, not as much focus has been placed on the recognition of these instances. As a result, limited data is available on the topic, and only in English. Being able to detect microaggressions without the need for labeled data would be advantageous since it would allow content moderation also for languages lacking annotated data. In this study, we introduce an unsupervised method to detect microaggressions in natural language expressions. The algorithm relies on pre-trained wordembeddings, leveraging the bias encoded in the model in order to detect microaggressions in unseen textual instances. We test the method on a dataset of racial and gender-based microaggressions, reporting promising results. We further run the algorithm on out-of-domain unseen data with the purpose of bootstrapping corpora of microaggressions “in the wild”, and discuss the benefits and drawbacks of our proposed method.