Miracle-dz/UniFAR-FTUs huggingface dataset:
Edge Machine Learning in 3GPP NB-IoT: Architecture, Applications and Demonstration

The emergence of cellular Internet of Things (IoT) standards such as NB-IoT brings novel opportunities for low-cost wide-area IoT applications. Augmenting massive IoT deployments with Machine Learning (ML) algorithms deployed at the edge enables design and implementation of a novel intelligent IoT services. In this paper, we present an architectural outlook and an overview of our recent activities that target integration of ML modules into the cellular IoT architecture. The three-layer architecture considered in this paper embeds ML modules at the edge devices (ML-EDGE), within the core network (ML-FOG) and at the cloud servers (ML-CLOUD), thus balancing between the system response time and accuracy. We discuss alignment of the proposed architecture with ongoing trends in 3GPP architecture evolution. We design, integrate and demonstrate edge ML use cases relying on our real-world deployment of about 150 static and mobile nodes integrated into the NB-IoT network.
Improved plasma confinement by ion Bernstein waves (IBWs) interacting with ions in JET (Joint European Torus)

A new coupling scheme of ion Bernstein waves (IBW) to plasma ions, by mode conversion of fast waves, has been tested in D-He plasma of the JET tokamak. Injecting 4.8 MW ion cyclotron radio frequency power, 1.8 MW IBW power absorption on deuterons occurs at the fundamental cyclotron resonant layer, which is located in the high field side near the plasma edge (R = 2.1 m). Plasma sheared flows, ponderomotively induced by IBW, are observed near the edge, producing an E×B shearing rate of 5 MHz, higher than the threshold expected for turbulence suppression. Transport analysis shows a 70% reduction of the thermal diffusivity of both electrons and ions in the edge plasma region where the sheared flows are observed.
remote sensing Vegetation Productivity and Precipitation Use Efﬁciency across the Yellow River Basin: Spatial Patterns and Controls

: In arid and semi-arid climate zones, understanding the spatial patterns and biogeographical mechanisms of net primary production (NPP) and precipitation use efﬁciency (PUE) is crucial for assessing the function and stability of ecosystem services, as well as directing ecological restoration. Although the vegetation coverage has changed dramatically after the construction of several ecological restoration projects, due to limited observation data, fewer studies have provided a thorough understanding of NPP and PUE’s recent spatial patterns and the controlling factors of different vegetation types in the Yellow River Basin (YRB). To narrow this gap, we integrated remote-sensing land-cover maps with long-term MODIS NPP and meteorological datasets to comprehend NPP and PUE spatial patterns in YRB. Furthermore, we applied structural equation models (SEM) to estimate the effect intensity of NPP and PUE controlling factors. The results showed that along geographical coordinates NPP and PUE decreased from southeast to northwest and trends were roughly consistent along latitude, longitude, and elevation gradients with segmented patterns of increasing and decreasing trends. As for climate gradients, NPP showed signiﬁcant linear positive and negative trends across the mean annual precipitation (MAP) and the arid index (AI), while segmented changes for PUE. However, the mean annual average temperature (MAT) showed a positive slope for below zero temperature and no change above zero temperature for both NPP and PUE. SEM results suggested that AI determined the spatial pattern of NPP, whereas PUE was controlled by MAP and NPP. As the AI becomes higher in the further, vegetation tends to have decreased NPP with higher sensitivity to water availability. While artiﬁcial vegetation had a substantially lower NPP than original vegetation but increased water competition between the ecosystem and human society. Hence further optimization of artiﬁcial vegetation is needed to satisfy both ecological and economic needs. This study advanced our understanding of spatial patterns and biogeographic mechanisms of NPP and PUE at YRB, therefore giving theoretical guidance for ecological restoration and ecosystem function evaluation in the face of further climate change.
Emergency Remote Education and Its Impact on Higher Education: A Temporary or Permanent Shift in Instruction?

The COVID-19 pandemic has led to problems and upheaval throughout the higher-education sector, with university campuses ceasing face-to-face instruction and with assessments shifting to an online model for a few years. As a result, the pandemic prompted educators to teach online, utilizing online lectures, narrated power points, audio snippets, podcasts, instant messaging, and interactive videos, whereas traditional universities had primarily relied on in-person courses. Evaluations, which included assignments and multiple-choice questions, were conducted online, forcing lecturers to reconsider how deliverables were set up to prevent students from having easy access to the answers in a textbook or online. Learning from college students’ experiences throughout this time period will assist higher-education stakeholders (administration, faculty, and students) in adapting future online course delivery selections for higher education. In this study, we investigated the experiences of students learning from a distance, as well as aspects of their learning. We provide recommendations for higher education. The COVID-19 pandemic has clearly resulted in the largest distance-learning experiment in history.
WaterBear: Practical Asynchronous BFT Matching Security Guarantees of Partially Synchronous BFT

Asynchronous Byzantine fault-tolerant (BFT) protocols assuming no timing assumptions are inherently more robust than their partially synchronous counterparts, but typically have much weaker security guarantees. We design and implement WaterBear, a family of new and efficient asynchronous BFT protocols matching all security guarantees of partially synchronous protocols. To achieve the goal, we have developed the local coin (flipping a coin locally and independently at each replica) based BFT approach—one long deemed as being inefficient—and designed more efficient asynchronous binary agreement (ABA) protocols and their reproposable ABA (RABA) versions from local coins. Our techniques on ABA and RABA are of independent interests and also allow us to build more efficient ABA protocols from common coins (distributively generating the same random coins for all replicas), helping improve various other protocols such as distributed key generation and BFT assuming trusted setup. We implemented in total five BFT protocols in a new golang library, including four WaterBear protocols and BEAT. Via extensive evaluation, we show that our protocols are efficient under both failure-free and failure scenarios, achieving at least comparable or superior performance to BEAT with much weaker security guarantees. Specifically, the most efficient WaterBear protocol consistently outperforms BEAT in terms of all metrics. For instance, when the number of replicas is 16, the latency of our protocol is about 1/8 of that of BEAT and the throughput of our protocol is 1.23x that of BEAT. Our work
A Stacking Ensemble Learning Method to Classify the Patterns of Complex Road Junctions

Recognizing the patterns of road junctions in a road network plays a crucial role in various applications. Owing to the diversity and complexity of morphologies of road junctions, traditional methods that rely heavily on manual settings of features and rules are often problematic. In recent years, several studies have employed convolutional neural networks (CNNs) to classify complex junctions. These methods usually convert vector-based junctions into raster representations with a predefined sampling area coverage. However, a fixed sampling area coverage cannot ensure the integrity and clarity of each junction, which inevitably leads to misclassification. To overcome this drawback, this study proposes a stacking ensemble learning method for classifying the patterns of complex road junctions. In this method, each junction is first converted into raster images with multiple area coverages. Subsequently, several CNN-based base-classifiers are trained using raster images, and they output the probabilities of the junction belonging to different patterns. Finally, a meta-classifier based on random forest is used to combine the outputs of the base-classifiers and learn to arrive at the final classification. Experimental results show that the proposed method can improve the classification accuracy for complex road junctions compared to existing CNN-based classifiers that are trained using raster representations of junctions with a fixed sampling area coverage.
The Combination of Individual Herb of Mi-Jian-Chang-Pu Formula Exerts a Synergistic Effect in the Treatment of Ischemic Stroke in Rats

Mi-Jian-Chang-Pu formula (MJCPF), composed of Crocus sativus L. and Acorus tatarinowii Schott, is a well-known TCM for treatment of hemiplegia, facial paralysis as well as language dysfunction caused by stroke both in ancient and modern times. By using pharmacodynamics, pharmacokinetics, and metabolomics, our present study discusses whether the combination of individual herbs or major active components of MJCPF possess synergistic neuroprotective effects against ischemic stroke (IS). 108 adult male Sprague-Dawley rats were randomly and equally divided into 9 groups, including sham group (N, vehicle), middle cerebral artery occlusion (MCAO) model group (M, vehicle), positive group (P, 36 mg/kg/day nimodipine), crocin I (A1, 40 mg/kg/day), β-asarone (B1, 15 mg/kg/day), crocin I + β-asarone (A1B1, 55 mg/kg/day), C. sativus (A, 580 mg/kg/day), A. tatarinowii (B, 480 mg/kg/day), and C. sativus + A. tatarinowii, also named MJCPF (AB, 1060 mg/kg/day) groups. All drugs were orally administered to rats once a day for 14 consecutive days. Neurological deficit score, cerebral infarct volume, body weight change, TTC, HE and IHC staining, behavioral evaluation, metabolic profiles, and pharmacokinetic parameters were determined. MCAO led to severe brain damage including large infarct volume, more severe brain tissue injury, and worse neurological function as compared to the sham rats. All treatment groups showed a significant neuroprotective effect on MCAO rats. Furthermore, the pharmacodynamics' results demonstrated that MJCPF had a synergistic effect evidenced by small infarct volume, more regular arrangement of neuronal cells, and more improved neural function, and the levels of inflammatory factors were closer to normality. A total of 53 differential metabolites between MCAO and sham groups were screened by integration of serum and brain metabolisms, all of which were restored at varying degrees in treatment. PCA and PLS-DA analysis showed that the levels of differential metabolites treated with MJCPF were closer to the sham group than the individual herb and single compound alone or A1B1 combination. The pharmacokinetic parameters further verified the above results that MJCPF could synergistically promote drug absorption greater than others. Our integrated pharmacodynamics, metabolomics, and pharmacokinetic approach reveals the synergistic effect of MJCPF on treatment of IS, which powerfully contribute to the understanding of scientific connotation of TMC formula.
Quantifying how 1 post-transcriptional noise and gene 2 copy number variation bias 3 transcriptional parameter inference 4 from mRNA distributions

Transcriptional rates are often estimated by ﬁtting the distribution of mature mRNA 15 numbers measured using smFISH (single molecule ﬂuorescence in situ hybridization) with the 16 distribution predicted by the telegraph model of gene expression, which deﬁnes two promoter 17 states of activity and inactivity. However, ﬂuctuations in mature mRNA numbers are strongly 18 affected by processes downstream of transcription. In addition, the telegraph model assumes 19 one gene copy, but in experiments cells may have two gene copies as cells replicate their genome 20 during the cell cycle. Whilst it is often presumed that post-transcriptional noise and gene copy 21 number variation affect transcriptional parameter estimation, the size of the error introduced 22 remains unclear. To address this issue, here we measure both mature and nascent mRNA 23 distributions of GAL10 in yeast cells using smFISH and classify each cell according to its cell cycle 24 phase. We infer transcriptional parameters from mature and nascent mRNA distributions, with 25 and without accounting for cell cycle phase and compare the results to live-cell transcription 26 measurements of the same gene. We ﬁnd that: (i) correcting for cell cycle dynamics decreases the 27 promoter switching rates and
Open Research Online A typology of dis/value in public service delivery

Public service providers are now expected to play a central role in public value creation by designing and delivering services that are both relevant and bene ﬁ cial to the wider society in which they operate. In this article the authors explore the view that service providers can destroy, as well as create value. Drawing on a scoping review of the public value and co-creation literatures, the article proposes a typology of the types of dis/value emerging from the review. The ﬁ ndings will be useful for professionals engaged in designing and delivering public services, including managers, planners and commissioners. ABSTRACT Dis/value or public value destruction is now increasingly recognized as part of the public value process. Despite this, confusion about the concept remains. This article assesses the use of public value destruction, and synonymous terms, in the public value and co-creation literatures, and considers their meaning in public service delivery. The article provides ﬁ ndings from a scoping review to explore the conceptualization of value destruction to date. We progress the conceptualization of the term dis/value by identifying a typology and suggesting future avenues for its exploration.
ratings and firm value”

A topic of relevance to financial managers is the relation between a credit rating and firm value ( V L ). The general aim of this paper is to elucidate this relation with a spe- cific objective of helping C corp managers choose an optimal target rating ( OTR ). To achieve these goals, we use the Capital Structure Model (CSM) to compute a series of firm value ( V L ) outcomes matched to credit ratings. The maximum V L ( max V L ), among all V L outcomes, identifies OTR . This identification begins with the matching of credit spreads and ratings by Damodaran (2019) for three firm categories: small , large , and financial service ( FS) . Given these spreads, we can compute costs of borrow- ing with these costs needed to compute V L and other numerical outcomes. Besides costs of borrowing, our numerical outcomes are based on other key inputs including US $1,000,000 in before-tax cash flows, C corp tax rates, and a sustainable growth rate. Major findings that guide managers include the following. First , Moody’s A3 is the most common OTR . Second , growth firms generally require higher ranked OTRs . Third , compared to small and large firms, FS firms attain greater max V L values, higher optimal debt-to-firm value ratios ( ODVs ), and generally lower ranked OTRs . Fourth , relative to small firms, large firms gain less from growth even though they attain greater max V L outcomes. Fifth , only for FS firms can we find outcomes where operational cash flows are better spent on interest payments than retained internally for growth. a
Positioning systems in Minkowski spacetime: from emission to inertial coordinates

The coordinate transformation between emission coordinates and inertial coordinates in Minkowski spacetime is obtained for arbitrary configurations of the emitters. It appears that a positioning system always generates two different coordinate domains, namely, the front and the back emission coordinate domains. For both domains, the corresponding covariant expression of the transformation is explicitly given in terms of the emitter world lines. This task requires the notion of the orientation of an emitter configuration. The orientation is shown to be computable from the emission coordinates for the users of a ‘central’ region of the front emission coordinate domain. Other spacetime regions associated with the emission coordinates are also outlined.
Invisible Gender Inequality in Rural China: Exploring the Reasons for Son Preference and Daughter Preference

Son preference seems to be disappearing in the current discursive environment, replaced by the emergence of daughter preference in social communication. Different from previous studies of son preference in China that focus on quantitative data, this paper is based on an ethnographic study of a remote rural village in Shanxi Province, China. Through the use of participant observation, focus groups, and semi-structured interviews, the paper concludes that patriarchal systems persist, and son preference develops dynamically. Among others, the author concludes that (1) the patriarchal system is a necessary foundation for the existence of son preference, (2) the concealment of son preference is an aftereffect of gender reproductive selection and a major variable of marital stress, and (3) daughter preference is a variant of traditional gender expectations.
METHODS TO DETERMINE NITROGEN IN SUNFLOWER GRAINS

In sunflower, grain oil concentration (O G ) determines oil industrial yield and protein concentration (P G ) determines the protein of by-products as pellets and meals (P M ). There are various methodologies to quantify total grain nitrogen concentration (N G ): a) Kjeldahl (wet digestion); b) Dumas (dry combustion); c) NIRS (analysis of the spectrum of the light reflected by the sample). NIRS represents a promissory method for determining N G because it is simple and fast. The objective of this work was to compare the methods of NIRS, Kjeldahl and Dumas to quantify N G in sunflower grains and to adjust models to estimate P M from N G . Eighty-four samples of sunflower grains were selected covering a wide range of N G (from 1.75 to 3.80% taking Dumas reference) from a net of experiments conducted in southeastern Buenos Aires Province (2013-2014 and 2014-2015). NIRS was the most precise method for the determination of N G and presented a greater degree of agreement with Dumas than Kjeldahl. P M was satisfactorily estimated with N G . Therefore, NIRS could be a better alternative to the other techniques because its low cost, quickness, low toxicity, and adaptability to determine other components of the grain simultaneously with the determination of N G .
OF DECISION-MAKING STYLES OF SCHOOL ADMINISTRATORS ACCORDING TO VARIOUS VARIABLES ANALYSIS

58 Analysis of decision-making styles of according This study aims to reveal how the decision-making styles of administrators change according to demographic characteristics. In the study, Decision Making Styles Scale was used. The sample of the study consists of 365 administrators working in state high schools. The data were analyzed by the SPSS statistical program. The independent sample t-test was used to determine whether the decision-making styles of the administrators differed from demographic factors according to gender and education level. One-way analysis of variance (ANOVA) was used to determine whether or not they differ according to age, location, working time and total working time. Levene test was used to determine the variance of the groups before the analysis and variance homogeneity tests were applied in case of differences. As a result of the research, it has been found that decision making styles differ partially according to age, gender, working time in the position and total working time, but not according to education level and position.
Medical and Dental Applications of Titania Nanoparticles: An Overview

Currently, titanium oxide (TiO2) nanoparticles are successfully employed in human food, drugs, cosmetics, advanced medicine, and dentistry because of their non-cytotoxic, non-allergic, and bio-compatible nature when used in direct close contact with the human body. These NPs are the most versatile oxides as a result of their acceptable chemical stability, lower cost, strong oxidation properties, high refractive index, and enhanced aesthetics. These NPs are fabricated by conventional (physical and chemical) methods and the latest biological methods (biological, green, and biological derivatives), with their advantages and disadvantages in this epoch. The significance of TiO2 NPs as a medical material includes drug delivery release, cancer therapy, orthopedic implants, biosensors, instruments, and devices, whereas their significance as a dental biomaterial involves dentifrices, oral antibacterial disinfectants, whitening agents, and adhesives. In addition, TiO2 NPs play an important role in orthodontics (wires and brackets), endodontics (sealers and obturating materials), maxillofacial surgeries (implants and bone plates), prosthodontics (veneers, crowns, bridges, and acrylic resin dentures), and restorative dentistry (GIC and composites).
A Translation of OCaml GADTs into Coq

Proof assistants based on dependent types are powerful tools for building certified software. In order to verify programs written in a different language, however, a representation of those programs in the proof assistant is required. When that language is sufficiently similar to that of the proof assistant, one solution is to use a shallow embedding to directly encode source programs as programs in the proof assistant. One challenge with this approach is ensuring that any semantic gaps between the two languages are accounted for. In this paper, we present GSet , a mixed embedding that bridges the gap between OCaml GADTs and inductive datatypes in Coq. This embedding retains the rich typing information of GADTs while also allowing pattern matching with impossible branches to be translated without additional axioms. We formalize this with GADTml, a minimal calculus that captures GADTs in OCaml, and gCIC, an impredicative variant of the Calculus of Inductive Constructions. Furthermore, we present the translation algorithm between GADTml and gCIC, together with a proof of the soundness of this translation. We have integrated this technique into coq-of-ocaml
Experimental Measurement of Ice-Curling Stone Friction Coefficient Based on Computer Vision Technology: A Case Study of “Ice Cube” for 2022 Beijing Winter Olympics

In the curling sport, the coefficient of friction between the curling stone and pebbled ice is crucial to predict the motion trajectory. However, the theoretical and experimental investigations on stone–ice friction are limited, mainly due to the limitations of the field measurement techniques and the inadequacy of the experimental data from professional curling rinks. In this paper, on-site measurement of the stone–ice friction coefficient in a prefabricated ice rink for the Beijing Winter Olympics curling event was carried out based on computer vision technology. Firstly, a procedure to determine the location of the curling stone was proposed using YOLO-V3 (You Only Look Once, Version 3) deep neural networks and the CSRT Object tracking algorithm. Video data was recorded during the curling stone throwing experiments, and the friction coefficient was extracted. Furthermore, the influence of the sliding velocity on the friction coefficient was discussed. Comparison with published experimental data and models and verification of the obtained results, using a sensor-based method, were conducted. Results show that the coefficient of friction (ranging from 0.006 to 0.016) decreased with increasing sliding velocity, due to the presence of a liquid-like layer. Our obtained results were consistent with the literature data and the friction model of Lozowski. In addition, the experimental results of the computer vision technique method and the accelerometer sensor method showed remarkable agreement, supporting the accuracy and reliability of our proposed measurement procedure based on deep learning.