bluuebunny/crossref_metadata_2025 huggingface dataset:
Both sides of the story: comparing student-level data on reading performance from administrative registers to application generated data from a reading app
Authors: Sortkær Bent , Smith Emil , Reimer David , Oehmcke Stefan , Andersen Ida Gran
Published: August 2021

The use of various learning apps in school settings is growing and thus producing an increasing amount of usage generated data. However, this usage generated data has only to a very little extend been used for monitoring and promoting learning progress. We test if application usage generated data from a reading app holds potential for measuring reading ability, reading speed progress and for pointing out features in a school setting that promotes learning. We analyze new data from three different sources: (1) Usage generated data from a widely used reading app, (2) Data from a national reading ability test, and (3) Register data on student background and family characteristics. First, we find that reading app generated data to some degree tells the same story about reading ability as does the formal national reading ability test. Second, we find that the reading app data has the potential to monitor reading speed progress. Finally, we tested several models including machine learning models. Two of these were able to identify variables associated with reading speed progress with some degree of success and to point at certain conditions that promotes reading speed progress. We discuss the results and avenues for further research are presented.
CSRR-Loaded Crescent Slot Circular Antenna for WLAN/Wi-Fi Application
Authors: Sivashanmugavalli B. , Vijayalakshmi B. , Samiullah S.
Published: August 2021

The Complementary Split-Ring Resonator (CSRR)-loaded crescent slot circular antenna is presented in this paper. The proposed antenna is resonating at 2.4[Formula: see text]GHz with the gain of 2.38[Formula: see text]dB. The antenna is designed for a predefined shape, which has a crescent slot, star-shaped small slots and spline split on a circle. The CSRR is carved on the ground surface of the antenna to acquire 2.4[Formula: see text]GHz and eliminates the multiple resonances created by various slots. The CSRR provides the miniaturization by shifting the resonance from 2.59[Formula: see text]GHz to 2.4[Formula: see text]GHz. The measured, [Formula: see text]6[Formula: see text]dB impedance bandwidth of the antenna is 11.25%. The antenna is fabricated using an FR4 ([Formula: see text]) substrate. The proposed crescent slot circular antenna is simulated using Ansys HFSS 17.0.
Hillslope form and process: history 1960–2000+
Authors: Kirkby Mike J.
Published: August 2021

The study of hillslopes has been dominated by the expansion of studies into process rates and mechanisms. Perhaps the greatest volume of work has been on the ‘wash’ processes of soil erosion, but there has also been significant work on the diffusive mass movements of linear and non-linear ‘creep’ that shape the convexity of hilltops, on more rapid mass movements and on solution processes. There has also been fresh work on distinctive processes in coastal, arid and cold-climate environments. Accompanying and integrated with process understanding, and made possible by ubiquitous computational power, modelling has developed from soluble mathematical simplifications to complex simulations that incorporate much of our understanding of process and climate. Particular topics that have seen significant advance include a more complete understanding of drainage density and texture, and a broadening of interest to encompass the ‘critical zone’ that constructively unifies the land surface with the lower atmosphere, the biosphere and the regolith. There has also been a change of focus towards steeplands, dominated by mass movements, supply-limited removal and tectonic activity. Most recently, and now incorporated into the concept of the ‘Anthropocene’, human impact is now receiving increasing attention as we acknowledge its accelerating role in changing landscapes and their relationships.
Irrelevant Gadgets or a Source of Worry
Authors: Vargemidis Dimitri , Gerling Kathrin , Abeele Vero Vanden , Geurts Luc , Spiel Katta
Published: August 2021

Wearable activity trackers are routinely applied in physical activity (PA) interventions in late life, but there is little research that focuses on older adults' perspectives on the technology. We conducted a qualitative study with 24 older persons to explore their perspective on wearables and PA. First, we discussed their relationship with PA and wearable trackers during focus groups. Next, nine participants crafted prototypes for wearables during co-design sessions. Through Thematic Analysis, we identified two main themes: (1) PA is personal in terms of preferred activities and reasons for PA, and (2) wearables are an emotional technology, causing negative emotions when resembling medical trackers or pressurizing to perform. We followed upon these results through a survey with 41 participants, which further highlighted individual differences in the perception of wearables. We conclude with questions to guide the design of wearables and reflect on their role to support PA in late life.
Persistent software transactional memory in Haskell
Authors: Krauter Nicolas , Raaf Patrick , Braam Peter , Salkhordeh Reza , Erdweg Sebastian , Brinkmann André
Published: August 2021

Emerging persistent memory in commodity hardware allows byte-granular accesses to persistent state at memory speeds. However, to prevent inconsistent state in persistent memory due to unexpected system failures, different write-semantics are required compared to volatile memory. Transaction-based library solutions for persistent memory facilitate the atomic modification of persistent data in languages where memory is explicitly managed by the programmer, such as C/C++. For languages that provide extended capabilities like automatic memory management, a more native integration into the language is needed to maintain the high level of memory abstraction. It is shown in this paper how persistent software transactional memory (PSTM) can be tightly integrated into the runtime system of Haskell to atomically manage values of persistent transactional data types. PSTM has a clear interface and semantics extending that of software transactional memory (STM). Its integration with the language’s memory management retains features like garbage collection and allocation strategies, and is fully compatible with Haskell's lazy execution model. Our PSTM implementation demonstrates competitive performance with low level libraries and trivial portability of existing STM libraries to PSTM. The implementation allows further interesting use cases, such as persistent memoization and persistent Haskell expressions.
Modular, compositional, and executable formal semantics for LLVM IR
Authors: Zakowski Yannick , Beck Calvin , Yoon Irene , Zaichuk Ilia , Zaliva Vadim , Zdancewic Steve
Published: August 2021

This paper presents a novel formal semantics, mechanized in Coq, for a large, sequential subset of the LLVM IR. In contrast to previous approaches, which use relationally-specified operational semantics, this new semantics is based on monadic interpretation of interaction trees, a structure that provides a more compositional approach to defining language semantics while retaining the ability to extract an executable interpreter. Our semantics handles many of the LLVM IR's non-trivial language features and is constructed modularly in terms of event handlers, including those that deal with nondeterminism in the specification. We show how this semantics admits compositional reasoning principles derived from the interaction trees equational theory of weak bisimulation, which we extend here to better deal with nondeterminism, and we use them to prove that the extracted reference interpreter faithfully refines the semantic model. We validate the correctness of the semantics by evaluating it on unit tests and LLVM IR programs generated by HELIX.
A theory of higher-order subtyping with type intervals
Authors: Stucki Sandro , Giarrusso Paolo G.
Published: August 2021

The calculus of Dependent Object Types (DOT) has enabled a more principled and robust implementation of Scala, but its support for type-level computation has proven insufficient. As a remedy, we propose
F
··
ω
, a rigorous theoretical foundation for Scala’s higher-kinded types.
F
··
ω
extends
F
<:
ω
with
interval kinds
, which afford a unified treatment of important type- and kind-level abstraction mechanisms found in Scala, such as bounded quantification, bounded operator abstractions, translucent type definitions and first-class subtyping constraints. The result is a flexible and general theory of higher-order subtyping. We prove type and kind safety of
F
··
ω
, as well as weak normalization of types and undecidability of subtyping. All our proofs are mechanized in Agda using a fully syntactic approach based on hereditary substitution.
Catala: a programming language for the law
Authors: Merigoux Denis , Chataing Nicolas , Protzenko Jonathan
Published: August 2021

Law at large underpins modern society, codifying and governing many aspects of citizens' daily lives. Oftentimes, law is subject to interpretation, debate and challenges throughout various courts and jurisdictions. But in some other areas, law leaves little room for interpretation, and essentially aims to rigorously describe a computation, a decision procedure or, simply said, an algorithm.

Unfortunately, prose remains a woefully inadequate tool for the job. The lack of formalism leaves room for ambiguities; the structure of legal statutes, with many paragraphs and sub-sections spread across multiple pages, makes it hard to compute the intended outcome of the algorithm underlying a given text; and, as with any other piece of poorly-specified critical software, the use of informal, natural language leaves corner cases unaddressed.

We introduce Catala, a new programming language that we specifically designed to allow a straightforward and systematic translation of statutory law into an executable implementation. Notably, Catala makes it natural and easy to express the general case / exceptions logic that permeates statutory law. Catala aims to bring together lawyers and programmers through a shared medium, which together they can understand, edit and evolve, bridging a gap that too often results in dramatically incorrect implementations of the law. We have implemented a compiler for Catala, and have proven the correctness of its core compilation steps using the F* proof assistant.

We evaluate Catala on several legal texts that are algorithms in disguise, notably section 121 of the US federal income tax and the byzantine French family benefits; in doing so, we uncover a bug in the official implementation of the French benefits. We observe as a consequence of the formalization process that using Catala enables rich interactions between lawyers and programmers, leading to a greater understanding of the original legislative intent, while producing a correct-by-construction executable specification reusable by the greater software ecosystem. Doing so, Catala increases trust in legal institutions, and mitigates the risk of societal damage due to incorrect implementations of the law.
Propositions-as-types and shared state
Authors: Rocha Pedro , Caires Luís
Published: August 2021

We develop a principled integration of shared mutable state into a proposition-as-types linear logic interpretation of a session-based concurrent programming language. While the foundation of type systems for the functional core of programming languages often builds on the proposition-as-types correspondence, automatically ensuring strong safety and liveness properties, imperative features have mostly been handled by extra-logical constructions. Our system crucially builds on the integration of nondeterminism and sharing, inspired by logical rules of differential linear logic, and ensures session fidelity, progress, confluence and normalisation, while being able to handle first-class shareable reference cells storing any persistent object. We also show how preservation and, perhaps surprisingly, progress, resiliently survive in a natural extension of our language with first-class locks. We illustrate the expressiveness of our language with examples highlighting detailed features, up to simple shareable concurrent ADTs.
Theorems for free from separation logic specifications
Authors: Birkedal Lars , Dinsdale-Young Thomas , Guéneau Armaël , Jaber Guilhem , Svendsen Kasper , Tzevelekos Nikos
Published: August 2021

Separation logic specifications with abstract predicates intuitively enforce a discipline that constrains when and how calls may be made between a client and a library. Thus a separation logic specification of a library intuitively enforces a protocol on the trace of interactions between a client and the library. We show how to formalize this intuition and demonstrate how to derive "free theorems" about such interaction traces from abstract separation logic specifications. We present several examples of free theorems. In particular, we prove that a so-called logically atomic concurrent separation logic specification of a concurrent module operation implies that the operation is linearizable. All the results presented in this paper have been mechanized and formally proved in the Coq proof assistant using the Iris higher-order concurrent separation logic framework.
Steel: proof-oriented programming in a dependently typed concurrent separation logic
Authors: Fromherz Aymeric , Rastogi Aseem , Swamy Nikhil , Gibson Sydney , Martínez Guido , Merigoux Denis , Ramananandro Tahina
Published: August 2021

Steel is a language for developing and proving concurrent programs embedded in F
⋆
, a dependently typed programming language and proof assistant. Based on SteelCore, a concurrent separation logic (CSL) formalized in F
⋆
, our work focuses on exposing the proof rules of the logic in a form that enables programs and proofs to be effectively co-developed.

Our main contributions include a new formulation of a Hoare logic of
quintuples
involving both separation logic and first-order logic, enabling efficient verification condition (VC) generation and proof discharge using a combination of tactics and SMT solving. We relate the VCs produced by our quintuple system to solving a system of associativity-commutativity (AC) unification constraints and develop tactics to (partially) solve these constraints using AC-matching modulo SMT-dischargeable equations.

Our system is fully mechanized and implemented in F
⋆
. We evaluate it by developing several verified programs and libraries, including various sequential and concurrent linked data structures, proof libraries, and a library for 2-party session types. Our experience leads us to conclude that our system enables a mixture of automated and interactive proof, making it productive to build programs foundationally verified against a highly expressive, state-of-the-art CSL.
ProbNV: probabilistic verification of network control planes
Authors: Giannarakis Nick , Silva Alexandra , Walker David
Published: August 2021

ProbNV is a new framework for probabilistic network control plane verification that strikes a balance between generality and scalability. ProbNV is general enough to encode a wide range of features from the most common protocols (eBGP and OSPF) and yet scalable enough to handle challenging properties, such as probabilistic all-failures analysis of medium-sized networks with 100-200 devices. When there are a small, bounded number of failures, networks with up to 500 devices may be verified in seconds. ProbNV operates by translating raw CISCO configurations into a probabilistic and functional programming language designed for network verification. This language comes equipped with a novel type system that characterizes the sort of representation to be used for each data structure:
concrete
for the usual representation of values;
symbolic
for a BDD-based representation of sets of values; and
multi-value
for an MTBDD-based representation of values that depend upon symbolics. Careful use of these varying representations speeds execution of symbolic simulation of network models. The MTBDD-based representations are also used to calculate probabilistic properties of network models once symbolic simulation is complete. We implement the language and evaluate its performance on benchmarks constructed from real network topologies and synthesized routing policies.
CPS transformation with affine types for call-by-value implicit polymorphism
Authors: Sekiyama Taro , Tsukada Takeshi
Published: August 2021

Transformation of programs into continuation-passing style (CPS) reveals the notion of continuations, enabling many applications such as control operators and intermediate representations in compilers. Although type preservation makes CPS transformation more beneficial, achieving type-preserving CPS transformation for implicit polymorphism with call-by-value (CBV) semantics is known to be challenging. We identify the difficulty in the problem that we call scope intrusion. To address this problem, we propose a new CPS target language Λ

open

that supports two additional constructs for polymorphism: one only binds and the other only generalizes type variables. Unfortunately, their unrestricted use makes Λ

open

unsafe due to undesired generalization of type variables. We thus equip Λ

open

with affine types to allow only the type-safe generalization. We then define a CPS transformation from Curry-style CBV System F to type-safe Λ

open

and prove that the transformation is meaning and type preserving. We also study parametricity of Λ

open

as it is a fundamental property of polymorphic languages and plays a key role in applications of CPS transformation. To establish parametricity, we construct a parametric, step-indexed Kripke logical relation for Λ

open

and prove that it satisfies the Fundamental Property as well as soundness with respect to contextual equivalence.
Kinetic Effects of Temperature on Fe–N–C Catalysts for 2e- and 4e-Oxygen Reduction Reactions
Authors: Yang Jie , Li Junfen , Ding Ruimin , Liu Chang , Yin Xi
Published: August 2021

Hydrogen peroxide (H2O2) formed via the two-electron oxygen reduction reaction (2e-ORR) on carbon-based platinum group metal-free (PGM-free) catalysts at elevated temperature can cause catalyst degradation in fuel cells. In this work, we studied the effects of temperature on the selectivity of iron- and nitrogen-doped carbon (Fe–N–C) catalyst for 2e- and 4e-ORR in acidic electrolyte using a high-temperature rotating-ring disk electrode. The results of individual-heating experiments showed that H2O2 yield increased slightly with the increase of temperature. In the meantime, the results of sequential heating/cooling experiments suggested that prolonged high-temperature exposure at ORR polarization conditions can lead to catalyst degradation and higher selectivity for 2e-ORR. The temperature effects on the selectivity of Fe–N–C catalyst for 2e- and 4e-ORR was further explained with a kinetic model describing the competitive reactions and the thermodynamics of the system, which suggested that the increase of H2O2 yield with temperature in the individual-heating experiment was due to the promoted 2e-ORR pathway instead of catalyst degradation.
Liu Wei Di Huang Wan and the Delay of Insulin Use in Patients with Type 2 Diabetes in Taiwan: A Nationwide Study
Authors: Chen Hsin-Hung , Wu Chien-Tung , Tsai Yueh-Ting , Ho Chun-Wei , Hsieh Ming-Chia , Lai Jung-Nien
Published: August 2021

Introduction. Patients with type 2 diabetes are widely prescribed metformin for controlling blood glucose levels to avoid related comorbidities. In Taiwan, traditional Chinese medicine (TCM) is also commonly used, especially Liu Wei Di Huang Wan (LWDHW), which has been reported to delay the occurrence of kidney failure. However, the effect of combinational therapy of TCM and oral antidiabetic drugs is still unclear. This study aims to estimate their efficacy in delaying insulin use. Materials and Methods. This case-control study was conducted using one million randomized samples from the National Health Insurance Research Database in Taiwan. The effects of TCM and LWDHW were estimated using the Cox proportional hazards model. Results. In this study, 70,036 diabetic patients were enrolled; of them, 17,451 (24.9%) used insulin, while the rest (52,585, 75.1%) did not. TCM users had a lower risk for insulin use (HR: 0.58, 95% CI: 0.56–0.60). LWDHW users had a lower risk compared with patients who used other TCM (HR: 0.86, 95% CI: 0.82–0.90) and presented a dose-dependent effect. Conclusion. The use of LWDHW and oral antidiabetic drugs is highly associated with the delay in the use of insulin. Clinical practitioners may take them into consideration when treating patients with type 2 diabetes.
[Retracted] Irisin Attenuates Oxidative Stress, Mitochondrial Dysfunction, and Apoptosis in the H9C2 Cellular Model of Septic Cardiomyopathy through Augmenting Fundc1‐Dependent Mitophagy
Authors: Jiang Xiaoqing , Cai Shumin , Jin Yinghui , Wu Feng , He Jing , Wu Xixuan , Tan Ying , Wang Yu
Published: August 2021

In the present study, we used lipopolysaccharide‐ (LPS‐) stimulated H9C2 cardiomyocytes to investigate whether irisin treatment attenuates septic cardiomyopathy via Fundc1‐related mitophagy. Fundc1 levels and mitophagy were significantly reduced in LPS‐stimulated H9C2 cardiomyocytes but were significantly increased by irisin treatment. Irisin significantly increased ATP production and the activities of mitochondrial complexes I and III in the LPS‐stimulated cardiomyocytes. Irisin also improved glucose metabolism and significantly reduced LPS‐induced levels of reactive oxygen species by increasing the activities of antioxidant enzymes, glutathione peroxidase (GPX), and superoxide dismutase (SOD), as well as levels of reduced glutathione (GSH). TUNEL assays showed that irisin significantly reduced LPS‐stimulated cardiomyocyte apoptosis by suppressing the activation of caspase‐3 and caspase‐9. However, the beneficial effects of irisin on oxidative stress, mitochondrial metabolism, and viability of LPS‐stimulated H9C2 cardiomyocytes were abolished by silencing Fundc1. These results demonstrate that irisin abrogates mitochondrial dysfunction, oxidative stress, and apoptosis through Fundc1‐related mitophagy in LPS‐stimulated H9C2 cardiomyocytes. This suggests irisin is a potentially useful treatment for septic cardiomyopathy, though further investigations are necessary to confirm our findings.
Multilevel Strip Pooling-Based Convolutional Neural Network for the Classification of Carotid Plaque Echogenicity
Authors: Ma Wei , Cheng Xinyao , Xu Xiangyang , Wang Furong , Zhou Ran , Fenster Aaron , Ding Mingyue
Published: August 2021

Carotid plaque echogenicity in ultrasound images has been found to be closely correlated with the risk of stroke in atherosclerotic patients. The automatic and accurate classification of carotid plaque echogenicity is of great significance for clinically estimating the stability of carotid plaques and predicting cardiovascular events. Existing convolutional neural networks (CNNs) can provide an automatic carotid plaque echogenicity classification; however, they require a fixed-size input image, while the carotid plaques are of varying sizes. Although cropping and scaling the input carotid plaque images is promising, it will cause content loss or distortion and hence reduce the classification accuracy. In this study, we redesign the spatial pyramid pooling (SPP) and propose multilevel strip pooling (MSP) for the automatic and accurate classification of carotid plaque echogenicity in the longitudinal section. The proposed MSP module can accept arbitrarily sized carotid plaques as input and capture a long-range informative context to improve the accuracy of classification. In our experiments, we implement an MSP-based CNN by using the visual geometry group (VGG) network as the backbone. A total of 1463 carotid plaques (335 echo-rich plaques, 405 intermediate plaques, and 723 echolucent plaques) were collected from Zhongnan Hospital of Wuhan University. The 5-fold cross-validation results show that the proposed MSP-based VGGNet achieves a sensitivity of 92.1%, specificity of 95.6%, accuracy of 92.1%, and F1-score of 92.1%. These results demonstrate that our approach provides a way to enhance the applicability of CNN by enabling the acceptance of arbitrary input sizes and improving the classification accuracy of carotid plaque echogenicity, which has a great potential for an efficient and objective risk assessment of carotid plaques in the clinic.
Intrusion Detection for Industrial Control Systems Based on Open Set Artificial Neural Network
Authors: Wang Chao , Wang Bailing , Sun Yunxiao , Wei Yuliang , Wang Kai , Zhang Hui , Liu Hongri
Published: August 2021

The security of industrial control systems (ICSs) has received a lot of attention in recent years. ICSs were once closed networks. But with the development of IT technologies, ICSs have become connected to the Internet, increasing the potential of cyberattacks. Because ICSs are so tightly linked to human lives, any harm to them could have disastrous implications. As a technique of providing protection, many intrusion detection system (IDS) studies have been conducted. However, because of the complicated network environment and rising means of attack, it is difficult to cover all attack classes, most of the existing classification techniques are hard to deploy in a real environment since they cannot deal with the open set problem. We propose a novel artificial neural network based-methodology to solve this problem. Our suggested method can classify known classes while also detecting unknown classes. We conduct research from two points of view. On the one hand, we use the openmax layer instead of the traditional softmax layer. Openmax overcomes the limitations of softmax, allowing neural networks to detect unknown attack classes. During training, on the other hand, a new loss function termed center loss is implemented to improve detection ability. The neural network model learns better feature representations with the combined supervision of center loss and softmax loss. We evaluate the neural network on NF-BoT-IoT-v2 and Gas Pipeline datasets. The experiments show our proposed method is comparable with the state-of-the-art algorithm in terms of detecting unknown classes. But our method has a better overall classification performance.
Development of AI-Based Vehicle Detection and Tracking System for C-ITS Application
Authors: Tak Sehyun , Lee Jong-Deok , Song Jeongheon , Kim Sunghoon
Published: August 2021

There are various means of monitoring traffic situations on roads. Due to the rise of artificial intelligence (AI) based image processing technology, there is a growing interest in developing traffic monitoring systems using camera vision data. This study provides a method for deriving traffic information using a camera installed at an intersection to improve the monitoring system for roads. The method uses a deep-learning-based approach (YOLOv4) for image processing for vehicle detection and vehicle type classification. Lane-by-lane vehicle trajectories are estimated by matching the detected vehicle locations with the high-definition map (HD map). Based on the estimated vehicle trajectories, the traffic volumes of each lane-by-lane traveling direction and queue lengths of each lane are estimated. The performance of the proposed method was tested with thousands of samples according to five different evaluation criteria: vehicle detection rate, vehicle type classification, trajectory prediction, traffic volume estimation, and queue length estimation. The results show a 99% vehicle detection performance with less than 20% errors in classifying vehicle types and estimating the lane-by-lane travel volume, which is reasonable. Hence, the method proposed in this study shows the feasibility of collecting detailed traffic information using a camera installed at an intersection. The approach of combining AI and HD map techniques is the main contribution of this study, which shows a high chance of improving current traffic monitoring systems.
Validation of the Reference Genes for the Gene Expression Studies in Different Cell Lines of Pig
Authors: Xie Chun-Di , Wang Bingyuan , Shen Zhao-Ji , Yao Wen-Ye , Ao Hong , Li Bugao , Pei Yangli , Zhou Rong
Published: August 2021

Reverse transcription quantitative real‐time polymerase chain reaction is one of the important methods to investigate gene expression in cells and tissues. However, if the data cannot be normalized with appropriate reference genes, the results may be unreliable. In this study, we detected the expression of 15 reference genes in three pig cell lines. The results showed that SDHA and ALDOA were the most stable reference genes in 3D4/21 cells. TOP2B, TBP, and PPIA were the most stable reference genes in PK‐15 cells. SDHA and ALDOA were the most stable reference genes in IPEC‐J2 cells. In addition, each cell line only needs to use two reference genes to standardize the expression of target genes. Taken together, this study provides a reference for different pig cell lines to select reference genes and also provides a theoretical basis for the use of these cell lines in related functional researches.
miR-651-3p Enhances the Sensitivity of Hepatocellular Carcinoma to Cisplatin via Targeting ATG3-Mediated Cell Autophagy
Authors: Zou Lei , Sun Peng , Zhang Lei
Published: August 2021

Drug resistance is a major challenge for hepatocellular carcinoma (HCC) treatment in a clinic, which limits the therapeutic effect of the chemotherapeutic drugs, including cisplatin (CDDP), in this disease. Mounting evidence has identified that miRNAs dysfunction is related to the resistance of tumor cells to CDDP, and miR-651-3p has been identified as a tumor inhibitor to suppress the progression of multiple tumors. However, the role of miR-651-3p in HCC remains unclear. In this study, the relative expression of miR-651-3p in HCC tissues and cell lines were measured, and the functions of miR-651-3p were also observed by CCK-8 assay, flow cytometry assay, and Western blot. Moreover, the downstream target of miR-651-3p was predicted and verified via TargetScan and dual-luciferase reporter assay, and its functions were also investigated. The results showed that miR-651-3p was significantly downregulated in HCC tissues and cell lines, and the decreased miR-651-3p was also observed in CDDP-induced cells. miR-651-3p upregulation could effectively inhibit the proliferation and induce the apoptosis of R-HepG2. It was also found that ATG3 was a downstream target of miR-651-3p, and ATG3 was highly upregulated in HCC tissues. Moreover, the upregulated ATG3 could partly reverse the effects of miR-651-3p on R-HepG2. Besides, miR-651-3p involved the autophagy pathway of the HCC cells via targeting ATG3. In conclusion, miR-651-3p could regulate the autophagy to enhance the sensitivity of HepG2 cells to CDDP via targeting ATG3.
A Randomized Clinical Hypnosis Pilot Study: Improvements in Self-Reported Pain Impact in Adults with Sickle Cell Disease
Authors: Wallen Gwenyth R. , Middleton Kimberly R. , Kazmi Narjis B. , Yang Li , Brooks Alyssa T.
Published: August 2021

Sickle cell disease (SCD) is characterized by recurrent painful vasoocclusive crises. Current evidence focuses on the frequency of acute pain crises resulting in emergency department use and nonplanned inpatient hospital admissions; yet few studies focus on pain sequelae outside the healthcare system or how individuals self-manage their chronic SCD-related pain. This study investigated the feasibility of a biobehavioral intervention as an adjunct nonpharmacological therapy to assist in the self-management of chronic pain. A randomized, controlled clinical trial of hypnosis was conducted in outpatients with SCD (n = 31). Patient-reported outcomes (PROs) administered at baseline, five, and twelve weeks from both groups included pain frequency, intensity, and quality (Pain Impact Scale (PIQ) and Numerical Rating Scales); anxiety (State-Trait Anxiety Inventory), coping strategies (Coping Strategies Scale), sleep (Pittsburgh Sleep Quality Index (PSQI)), and depression (Beck Depression Inventory (BDI)). The same PROs were collected at weeks seventeen and twenty-four from the control group after the crossover. No significant group by time interaction effects were found in any of the PROs based on the repeated-measures mixed models. The PIQ and PSQI scores decreased over time in both groups. Post hoc pairwise comparisons with the Bonferroni adjustment indicated that the mean PIQ score at baseline decreased significantly by week 12 (

p

 = 0.01) in the hypnosis group. There were no significant changes across time before and after the crossover in any of the PROs in the control group. As suggested by these findings, pain impact and sleep in individuals with SCD may be improved through guided mind-body and self-care approaches such as hypnosis.
Developing a Predictive Tool for Hospital Discharge Disposition of Patients Poststroke with 30-Day Readmission Validation
Authors: Cho Jin , Place Krystal , Salstrand Rebecca , Rahmat Monireh , Mansouri Misagh , Fell Nancy , Sartipi Mina
Published: August 2021

After short-term, acute-care hospitalization for stroke, patients may be discharged home or other facilities for continued medical or rehabilitative management. The site of postacute care affects overall mortality and functional outcomes. Determining discharge disposition is a complex decision by the healthcare team. Early prediction of discharge destination can optimize poststroke care and improve outcomes. Previous attempts to predict discharge disposition outcome after stroke have limited clinical validations. In this study, readmission status was used as a measure of the clinical significance and effectiveness of a discharge disposition prediction. Low readmission rates indicate proper and thorough care with appropriate discharge disposition. We used Medicare beneficiary data taken from a subset of base claims in the years of 2014 and 2015 in our analyses. A predictive tool was created to determine discharge disposition based on risk scores derived from the coefficients of multivariable logistic regression related to an adjusted odds ratio. The top five risk scores were admission from a skilled nursing facility, acute heart attack, intracerebral hemorrhage, admission from “other” source, and an age of 75 or older. Validation of the predictive tool was accomplished using the readmission rates. A 75% probability for facility discharge corresponded with a risk score of greater than 9. The prediction was then compared to actual discharge disposition. Each cohort was further analyzed to determine how many readmissions occurred in each group. Of the actual home discharges, 95.7% were predicted to be there. However, only 47.8% of predictions for home discharge were actually discharged home. Predicted discharge to facility had 15.9% match to the actual facility discharge. The scenario of actual discharge home and predicted discharge to facility showed that 186 patients were readmitted. Following the algorithm in this scenario would have recommended continued medical management of these patients, potentially preventing these readmissions.
DC‐LTM: A Data Collection Strategy Based on Layered Trust Mechanism for IoT
Authors: He An , Wu Guangwei , Zhang Jinhuan
Published: August 2021

A large number of Internet of Things (IoT) devices such as sensor nodes are deployed in various urban infrastructures to monitor surrounding information. However, it is still a challenging issue to collect data in a low‐cost, high‐quality, and reliable manner through IoT technique. Although the recruitment of mobile vehicles (MVs) to collect urban data has proved to be an effective method, most existing data collection systems lack a trust detection mechanism for malicious terminal nodes and malicious vehicles, which should lead to security vulnerabilities in practice. This paper proposes a novel data collection strategy based on a layered trust mechanism (DC‐LTM). The strategy recruits MVs as data collectors of the sensor nodes based on the data value in the city, evaluates the trustworthiness of the data reported by the nodes, and records the results to the cloud data center. Furthermore, in order to make the data collection system more efficient and trust mechanism more reliable, we introduce unmanned aerial vehicles (UAVs) dispatched by data centers to actively verify the core sensor node data and use the core sensor data as baseline data to evaluate the credibility of the vehicles and the trust value of the whole network sensor nodes. Different from the previous strategies, UAVs adopts the DC‐LTM method to obtain the node data while actively obtaining the trust value of MVs and nodes, which effectively improves the quality of data acquisition. Simulation results show that the mechanism effectively distinguishes malicious vehicles that provide false data in exchange for payment and reduces the total cost of system recruitment payments. At the same time, the proposed incentive mechanism encourages vehicle to complete the evaluation task and improves the accuracy of node trust evaluation. The recognition rates of false data attacks and flooding attacks as well as the recognition error rate of normal nodes are 100%, 98.9%, and 3.9%, respectively, which improves the quality of system data collection as a whole.
Quality Evaluation Method of a Mathematics Teaching Model Reform Based on an Improved Genetic Algorithm
Authors: Yang Yun
Published: August 2021

The poor comprehensiveness of the evaluation indexes of quality evaluation methods for the traditional college mathematics teaching model reform results in low accuracy of the evaluation outcomes. In this paper, aiming at this problem, a quality evaluation method for the college mathematics teaching model reform, based on the genetic algorithm, is proposed. The simulated annealing algorithm uses the weighted comprehensive objective evaluation multiobjective optimization effect that can effectively improve the accuracy of the evaluation results. In the training process, the gradient descent back-propagation training method is used to obtain new weights for the quality evaluation of college mathematics teaching mode reforms and to score various indicators and evaluate the indicators. The mean value of the outcomes is the result of mathematics teaching quality evaluation. The experimental results show that the training error of the convolutional network of the proposed method is significantly small. Based on the genetic algorithm that improves the convolutional network training process, the obtained quality evaluation outcomes are higher in accuracy, better in goodness of fitness function, and considerably lower than other state-of-the-art methods. We observed that the improved genetic algorithm has a more than 90% goodness of fit and the error is significantly lower, that is, 0.01 to 0.04, than the classical genetic algorithm.
Integration and Optimization of Ancient Literature Information Resources Based on Big Data Technology
Authors: Gu Lingling
Published: August 2021

Big data refers to a collection of data that cannot be captured, managed, and processed with conventional software tools within a certain time frame. It is a massive, high-volume, high-volume data that requires new processing models to have stronger decision-making power, insight and discovery, process optimization capabilities, growth rate, and diversified information assets. This article aims to study the integration and optimization of ancient literature information resources of big data technology, that is, to integrate and optimize ancient literature information resources through big data technology and make the literature more systematic and complete, allowing readers to find and browse literature more conveniently. This paper focuses on the literary works and the related collation, annotation, and textual research results and divides the scope of each subtopic according to the genre. The biggest difference between the information platform built in this paper and the existing ancient books database is that it has the functions of semantic analysis, subject retrieval, data generation, and so on. After text learning, the computer can automatically classify related vocabulary. Based on the effective integration of big data and cultural resources, the experimental results of this article show that, so far, through technical optimization and resource integration, the number of ancient literature reincorporated has exceeded 12,000 copies, and more than 10,000 publications have been restored. Therefore, big data technology is essential for the integration and optimization of cultural resources.
Finite Cartan Graphs Attached to Nichols Algebras of Diagonal Type
Authors: Qian Chen , Wang Jing
Published: August 2021

Nichols algebras are fundamental objects in the construction of quantized enveloping algebras and in the classification of pointed Hopf algebras by the lifting method of Andruskiewitsch and Schneider. The structure of Cartan graphs can be attached to any Nichols algebras of diagonal type and plays an important role in the classification of Nichols algebras of diagonal type with a finite root system. In this paper, the main properties of all simply connected Cartan graphs attached to rank 6 Nichols algebras of diagonal type are determined. As an application, we obtain a subclass of rank 6 finite dimensional Nichols algebras of diagonal type.
LLSFIoT: Lightweight Logical Security Framework for Internet of Things
Authors: Batra Isha , Hamatta Hatem S. A. , Malik Arun , Baz Mohammed , Albogamy Fahad R. , Goyal Vishal , Alshamrani Sultan S.
Published: August 2021

Current research in Internet of Things (IoT) is focused on the security enhancements to every communicated message in the network. Keeping this thought in mind, researcher in this work emphasizes on a security oriented cryptographic solution. Commonly used security cryptographic solutions are heavy in nature considering their key size, operations, and mechanism they follow to secure a message. This work first determines the benefit of applying lightweight security cryptographic solutions in IoT. The existing lightweight counterparts are still vulnerable to attacks and also consume calculative more power. Therefore, this research work proposes a new hybrid lightweight logical security framework for offering security in IoT (LLSFIoT). The operations, key size, and mechanism used in the proposed framework make its lightweight. The proposed framework is divided into three phases: registration, authentication, and light data security (LDS). LDS offers security by using unique keys at each round bearing small size. Key generation mechanism used is comparatively fast making the compromise of keys as a difficult task. These steps followed in the proposed algorithm design make it lightweight and a better solution for IoT‐based networks as compared to the existing solutions that are relatively heavy weight in nature.
The Role and Function of Regulatory T Cells in Toxoplasma gondii-Induced Adverse Pregnancy Outcomes
Authors: Gao Xuyang , Zhong Yue , Liu Yifan , Ding Runmin , Chen Jinling
Published: August 2021

Infection with Toxoplasma gondii (T. gondii) during the pregnant period and its potentially miserable outcomes for the fetus, newborn, and even adult offspring continuously occur worldwide. People acquire infection through the consumption of infected and undercooked meat or contaminated food or water. T. gondii infection in pregnant women primarily during the gestation causes microcephaly, mental and psychomotor retardation, or death. Abnormal pregnancy outcomes are mainly associated with regulatory T cell (Treg) dysfunction. Tregs, a special subpopulation of T cells, function as a vital regulator in maintaining immune homeostasis. Tregs exert a critical effect on forming and maintaining maternal-fetal tolerance and promoting fetal development during the pregnancy period. Forkhead box P3 (Foxp3), a significant functional factor of Tregs, determines the status of Tregs. In this review, we summarize the effects of T. gondii infection on host Tregs and its critical transcriptional factor, Foxp3.
Prediction Algorithm of Young Students’ Physical Health Risk Factors Based on Deep Learning
Authors: Yin Xianping
Published: August 2021

Young people’s physical and mental health is the foundation of society’s overall development and the key to improving people’s health quality. Middle school students’ physical examinations and monitoring work are a surefire way to ensure their healthy development. Poor vision, dental caries, overweight and obesity, and high blood pressure are the most common adverse health outcomes of students caused by adolescent health risk behavior factors. Researchers have been concerned about the retinal fundus vascular system, which is the only internal vascular system that can be observed in a noninvasive state of the human body. Fundus images contain a wealth of disease-related information. Fundus images have been widely used in the field of medical auxiliary diagnosis because many important systemic diseases of the human body cause specific reactions in the fundus. Aiming to solve the problem of inseparable tiny blood vessels, this paper proposes a model of retinal vessel segmentation based on attention mechanisms. In light of the retinal arteriovenous division of discontinuous challenges, the topological structure of the constraint system along with overcoming the network and topology restrictions is monitored. Finally, simulation experiments were conducted on two publicly available datasets. The findings show that the proposed method is reliable, effective, and accurate in predicting physical health risk factors in adolescent students.
Systemic Proteomic Analysis Reveals Distinct Exosomal Protein Profiles in Rheumatoid Arthritis
Authors: Qin Qiu , Song Ronghua , Du Peng , Gao Chaoqun , Yao Qiuming , Zhang Jin-an
Published: August 2021

Objective. Rheumatoid arthritis (RA) is a complex disease with unknown pathogenesis. In recent years, fewer have paid attention to the broad spectrum of systemic markers of RA. The aim of this study was to identify exosomal candidate proteins in the pathogenesis of RA. Methods. Totally, 12 specimens of plasma from 6 RA patients and 6 age- and gender-matched controls from the Chinese population were obtained for nanoscale liquid chromatography coupled to tandem mass spectrometry (nano-LC-MS/MS) analysis to identify exosomal profiles. Results. A total of 278 exosomal proteins were detected. Among them, 32 proteins were significantly upregulated (

FC
≥
2.0

and

P
<
0.05

) and 5 proteins were downregulated (

FC
≤
0.5

and

P
<
0.05

). Bioinformatics analysis revealed that transthyretin (TTR), angiotensinogen (AGT), lipopolysaccharide-binding protein (LBP), monocyte differentiation antigen CD14 (CD14), cartilage oligomeric matrix protein (COMP), serum amyloid P (SAP/APCS), and tenascin (TNC) can interact with each other. Subsequently, these cross-linked proteins may be mainly involved in the inflammatory-related pathways to mediate the onset of RA. Noteworthy, the LBP/CD14 complex can promote the expression of IL-8 and TNF-α, eventually leading to the development of RA. Conclusions. Our findings suggest distinct plasmatic exosomal protein profiles in RA patients. These proteins not only take important parts in the vicious circle in the pathogenic process of RA but also serve as novel biomarkers in RA diagnosis and prognosis.