bluuebunny/crossref_metadata_2025_split huggingface dataset:
Preparation and Characterization of PANI Surface Modified HMX/F2602 Microcapsule Composites
Authors: Li Yuexin , Xu Wenzheng , Guo Fengwei , Yan Tianlun , Zheng Xin , Wei Yamei , Tan Xianpeng , Wang Jingyu 
Published: July 2022

AbstractUsing HMX with a particle size of 1–10 μm as the main explosive, a dense polyaniline (PANI) shell was prepared on the surface of HMX/F2602 by in situ polymerization, and an energetic composite material HMX−F@PANI (HMX−F2602@PANI) with a microcapsule structure was constructed. The structure and properties of the samples were characterized by SEM, XRD, XPS, DSC, mechanical sensitivity and electrostatic spark sensitivity tests. The results showed that the chemical structure and polycrystalline morphology of the crystals did not change during the coating process, and remained β‐HMX. The preparation of PANI shell greatly improves the coating degree of the explosive surface and reduces the exposure of crystal. In addition, compared with HMX/F2602, HMX−F@PANI is more excellent in improving thermal stability and reducing the electrostatic spark sensitivity, which is attributed to the advantages of microcapsule structure and good electrical conductivity of PANI.
Study‐design in pandemics: From surveillance and performance‐evaluation to licensing and pharmacovigilance
Authors: Bird Sheila M. 
Published: July 2022

AbstractAndy Grieve, the first pharmaceutical statistician to be President of the Royal Statistical Society, practiced in the regulated world of drug development. With reduction in drug development costs as his motivation, Grieve advanced Bayesian methods for developing predictive methods for efficacy and toxicity ‐ to be used as early as possible in the drug development process; and his presidential address exhorted statisticians to weigh‐in wherever data are used to make decisions. Diagnostic tests for infectious diseases are less regulated than drugs and vaccines unless the blood supply is at risk. Unlike in the HIV and HCV pandemics of the late 20th century, even well‐designed surveys linked to a volunteered biological sample (to be tested for SARS‐CoV‐2 antigen or antibodies) have had modest or low consent rates. Record‐linkage, statistical design and reporting standards have seen triumph and tragedy. Among the triumphs are: Liverpool's insistence on dual testing (lateral flow device; polymerase chain reaction [PCR]) of some 6000 asymptomatic citizens who attended for SARS‐CoV‐2‐screening; two tricky randomized controlled public‐policy trials on daily contact testing for close contacts of index cases of SARS‐CoV‐2 infection versus self‐isolation (with or without initial PCR); and among already‐consented participants in surveillance, over 80% secondary consent for linkage to their health records, including the Immunization Management Service. Before the next pandemic we need to entrench better regulation of diagnostic tests, better informed consent (not via weblinks), better feedback to participants, and transparency about basic safety data.
The Acute Stroke Therapy by Inhibition of Neutrophils study – Key features and impact
Authors: Kirby Simon , Chuang‐Stein Christy 
Published: July 2022

AbstractThe Acute Stroke Therapy by Inhibition of Neutrophils (ASTIN) study, initiated in November of the year 2000, is now widely recognized as having been a landmark study in the history of clinical trials. We look at why this is the case by considering its key features and impact. These key features are: the use of Bayesian design and analysis; the use of the normal dynamic linear model; the response adaptive nature of the study; the use of real‐time dosing decisions; and the use of an integrated model to predict 90‐day response on the Scandinavian Stroke Scale. Our overall conclusion is that the ASTIN study's main impact came from showing the clinical trial community the feasibility of the novel design and analysis used when most of these key features were rarely used in industry trials, let alone used together in one trial in a disease area with a tremendous unmet medical need.
Informed decision‐making: Statistical methodology for surrogacy evaluation and its role in licensing and reimbursement assessments
Authors: Weir Christopher J. , Taylor Rod S. 
Published: July 2022

AbstractThe desire, by patients and society, for faster access to therapies has driven a long tradition of the use of surrogate endpoints in the evaluation of pharmaceuticals and, more recently, biologics and other innovative medical technologies. The consequent need for statistical validation of potential surrogate outcome measures is a prime example on the theme of statistical support for decision‐making in health technology assessment (HTA). Following the pioneering methodology based on hypothesis testing that Prentice presented in 1989, a host of further methods, both frequentist and Bayesian, have been developed to enable the value of a putative surrogate outcome to be determined. This rich methodological seam has generated practical methods for surrogate evaluation, the most recent of which are based on the principles of information theory and bring together ideas from the causal effects and causal association paradigms. Following our synopsis of statistical methods, we then consider how regulatory authorities (on licensing) and payer and HTA agencies (on reimbursement) use clinical trial evidence based on surrogate outcomes. We review existing HTA surrogate outcome evaluative frameworks. We conclude with recommendations for further steps: (1) prioritisation by regulators and payers of the application of formal surrogate outcome evaluative frameworks, (2) application of formal Bayesian decision‐analytic methods to support reimbursement decisions, and (3) greater utilization of conditional surrogate‐based licensing and reimbursement approvals, with subsequent reassessment of treatments in confirmatory trials based on final patient‐relevant outcomes.
Simulation of analysis error contributions arising from uncertainties in the parametrisation of deep convection
Authors: Hubans Antoine , Berre Loïk , Bouteloup Yves , Loo Cécile 
Published: July 2022

AbstractIn order to simulate analysis error contributions resulting from observation errors and model errors, a possible methodology is to run a data assimilation ensemble that includes observation perturbations and a multiphysics approach. This method is examined here formally and experimentally, in order to investigate the effects of deep convection parametrisation uncertainties on analysis errors. A linearised expansion of initial condition errors is first employed to decompose them into contributions arising from observation errors and model errors. This is compared formally with a similar expansion for analysis differences resulting from the use of two different model versions, as in a multiphysics approach. Experimental results with the Action de Recherche Petite Echelle Grande Echelle global model are then examined. Observation error contributions are simulated by analysis differences between an unperturbed experiment and another experiment that includes observation perturbations that are drawn from specified observation error covariances. This is compared with analysis differences resulting from the use of two different parametrisation schemes of deep convection. The results indicate that the contribution of convection uncertainties is relatively large in the Tropics, with similar amplitudes to the contribution of observation errors. Simulated model errors are also found to be pronounced in the boundary layer and in convective areas where the analysis constraint by observations is relatively small, due to lower observation density.
Understanding spatiotemporal variability of drought in recent decades and its drivers over identified homogeneous regions of India
Authors: Saharwardi Md Saquib , Kumar Pankaj , Dubey Aditya Kumar , Kumari Amita 
Published: July 2022

AbstractDrought is a recurring hydroclimatic extreme whose frequency and intensity have increased over India in recent decades, with a detrimental effect on regional water resources. This study addresses the spatiotemporal variability of drought and its plausible mechanism over India from 1951 to 2018. Firstly, six drought‐homogeneous regions are adequately ascertained by applying rotated empirical orthogonal function analysis using the Standardized Precipitation Evapotranspiration Index that captures most of the reported drought and regional hydroclimatic patterns. For the study period, a drying trend is witnessed across the regions though not significant, whereas in recent decades, northeast India (NEI) and some parts of the Indo‐Gangetic plain (IGP) exhibit higher drought frequency. This anomalous drying is attributed to a weakening of the monsoon circulation and accelerated warming caused by changes in the land‐use pattern. The interplay between El Niño Southern Oscillation and the Indian Ocean Dipole largely affects drought's interannual variability, which shows a modified response in recent decades. However, the long‐term decadal drought pattern is found to be strongly teleconnected with a newly discovered Southern Atlantic Oscillation (SAO) index, which reveals a statistically quite significant relationship (>50%) with drought variability. The positive phase of the SAO index is generally associated with drought across the regions except for IGP and NEI. Despite the recent overall wetting trend, drought frequency has enhanced over most of the regions, modulating the regional hydrological cycle.
Performance of an environmentally‐aligned, low maintenance flood alleviation scheme on the Burn of Mosset, Forres, Scotland
Authors: Johnson Matthew F. , Edwards Patrick , Todd Rachael , Castro Janine M. , Thorne Colin R. 
Published: July 2022

AbstractFlooding of the town of Forres, Scotland prompted the implementation of a flood alleviation scheme (FAS) featuring a low earth‐fill dam constructed upstream of the town to create a flood retention area, limiting peak discharges entering the urban area. Flow through the dam is controlled by a weir, and it was recognised that if coarse sediment, large wood, and/or debris collected at the weir this could adversely affect its performance. To ensure reliable operation of the weir, the “Burn Management Works” (BMW) were designed to reconnect the embanked, elevated river channel to its floodplain, naturally retaining coarse sediment, large wood, and debris upstream of the dam. The FAS became operational on August 28, 2009 and impounded floodwater for the first time just 1 week later. Here, we report the results of monitoring of the BMW performed between 2011 and 2018. We find that the BMW have fulfilled their primary, flood alleviation functions by retaining large amounts of sediment and large wood without the need for maintenance. In so doing, the BMW has evolved from the single‐thread, trapezoidal, pilot channel constructed through the initially dry pasture, to the shifting mosaic of anastomosed channels, ponds, wetlands, and scrublands that now comprise the fully‐connected floodplain. In parallel, local interest groups have transformed this flood defence asset into a bird and wildlife area that is enjoyed by the community. Lessons learned from the BMW can inform other projects seeking to use nature‐based solutions to deliver sustainable flood risk management while simultaneously achieving goals for river restoration and community engagement.
Post‐quantum cryptography techniques for secure communication in resource‐constrained Internet of Things devices: A comprehensive survey
Authors: Kumari Swati , Singh Maninder , Singh Raman , Tewari Hitesh 
Published: July 2022

AbstractAs the number and characteristics of smart devices change, the concept of the Internet of Things (IoT) emerges. The IoT provides the connected devices with a variety of resources that enable effective communication. At this point, several security issues arise to get the sensitive information behind every communication in the IoT. To provide users with security and privacy, cryptographic schemes are adopted, the most popular being public key cryptographic systems (PKC). However, with the advent of quantum computing, the level of security that can be provided by the PKC schemes is a big question. Another important issue is that the IoT environment is resource‐constrained, which necessitates the implementation of lightweight cryptographic algorithms for better security. In response to these issues, the post‐quantum cryptographic (PQC) schemes are one of the significant developments contributing to IoT security in the post‐quantum world. This article examines the key security issues in the IoT environment and examines the effective solutions found in the literature. The problems in IoT in the quantum era are discussed and appropriate solutions by PKC schemes under limited resources in IoT are focused. As the lattice‐based cryptosystems are more effective, the importance of these schemes in the resource‐constrained IoT is highlighted. This survey also leads to feasible future directions that can support developers and researchers in this field.
Automated Windows behavioral tracing for malware analysis
Authors: Rana Shubham , Kumar Nitesh , Handa Anand , Shukla Sandeep K. 
Published: July 2022

AbstractIn malware analysis, there are two problem scenarios—detection and prevention. In prevention, analysts try to quarantine the file before it gets executed in a real system. The file is further analyzed in a sandbox to observe the behavior. Hence, our work shows that our agent captures events for malware analysis. After integration with the sandbox, it produces robust and efficient models. ETW is a Windows in‐build tool with kernel‐level access. We develop an agent using ETW in C++ with proper usage details. We collect data using cuckoo sandbox and ETW agent for 11 546 samples and perform comparative frequency analysis. The performance of various machine learning classifiers is examined on the behavioral data. Random Forest classifier performs the best on the combined (cuckoo+ETW) data with an accuracy of 99.68% and FPR of 0.45%. The improved performance of combined data over cuckoo data on packed and un‐seen malware is also significantly good. In the detection, if malware somehow escapes from the deployed prevention mechanism and gets executed, the analyst tries to detect malicious actions and respond before it is too late. Our agent can tackle such issues and can function as a standalone host‐based monitoring agent to extract kernel‐level information.
Successful management of gastroesophageal intussusception in an adult puma (Puma concolor)
Authors: Koeppel Katja , O'Dell Jacques , Stevenage Craig , van der Zee Hans , Schoeman Johan P. 
Published: July 2022

AbstractA 7‐year‐old entire female puma (Puma concolor)presented with a history of regurgitation and diarrhoea of 2 weeks duration. Conventional medical management was initially attempted to alleviate the clinical signs, which were unsuccessful. Oesophagoscopy allowed for the diagnosis of gastroesophageal intussusception (GEI) through the observation of gastric mucosa in the lumen of the distal portion of the oesophagus. The intussusception was reduced using the endoscope, which did not result in resolution because the intussusception recurred within 24 hours. A ventral midline celiotomy was performed, followed by a left‐sided belt loop gastropexy. No postoperative complications related to the surgery were observed. The puma resumed eating within 48 hours following the surgery, and no regurgitation recurred for a period of 24 months following surgery. To the knowledge of the authors, this is the first report of GEI in a puma that was successfully resolved.
Electrospun fibers as carriers for topical drug delivery and release in skin bandages and patches for atopic dermatitis treatment
Authors: Krysiak Zuzanna J. , Stachewicz Urszula 
Published: July 2022

AbstractThe skin is a complex layer system and the most important barrier between the environment and the organism. In this review, we describe some widespread skin problems, with a focus on eczema, which are affecting more and more people all over the world. Most of treatment methods for atopic dermatitis (AD) are focused on increasing skin moisture and protecting from bacterial infection and external irritation. Topical and transdermal treatments have specific requirements for drug delivery. Breathability, flexibility, good mechanical properties, biocompatibility, and efficacy are important for the patches used for skin. Up to today, electrospun fibers are mostly used for wound dressing. Their properties, however, meet the requirements for skin patches for the treatment of AD. Active agents can be incorporated into fibers by blending, coaxial or side‐by‐side electrospinning, and also by physical absorption post‐processing. Drug release from the electrospun membranes is affected by drug and polymer properties and the technique used to combine them into the patch. We describe in detail the in vitro release mechanisms, parameters affecting the drug transport, and their kinetics, including theoretical approaches. In addition, we present the current research on skin patch design. This review summarizes the current extensive know‐how on electrospun fibers as skin drug delivery systems, while underlining the advantages in their prospective use as patches for atopic dermatitis.This article is categorized under:
Implantable Materials and Surgical Technologies > Nanomaterials and Implants
Implantable Materials and Surgical Technologies > Nanotechnology in Tissue Repair and Replacement
Therapeutic Approaches and Drug Discovery > Emerging Technologies
J‐aggregation strategy of organic dyes for near‐infrared bioimaging and fluorescent image‐guided phototherapy
Authors: Tian Youliang , Yin Dalong , Yan Lifeng 
Published: July 2022

AbstractWith the continuous development of organic materials for optoelectronic devices and biological applications, J‐aggregation has attracted a great deal of interest in both dye chemistry and supramolecular chemistry. Except for the characteristic red‐shifted absorption and emission, such ordered head‐to‐tail stacked structures may be accompanied by special properties such as enhanced absorption, narrowed spectral bandwidth, improved photothermal and photodynamic properties, aggregation‐induced emission enhancement (AIEE) phenomenon, and so forth. These excellent properties add great potential to J‐aggregates for optical imaging and phototherapy in the near‐infrared (NIR) region. Despite decades of development, the challenge of rationally designing the molecular structure to adjust intermolecular forces to induce J‐aggregation of organic dyes remains significant. In this review, we discuss the formation of J‐aggregates in terms of intermolecular interactions and summarize some recent studies on J‐aggregation dyes for NIR imaging and phototherapy, to provide a clear direction and reference for designing J‐aggregates of near‐infrared organic dyes to better enable biological applications.This article is categorized under:
Therapeutic Approaches and Drug Discovery > Emerging Technologies
Diagnostic Tools > In Vivo Nanodiagnostics and Imaging
Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease
Resource Scheduling Strategy for Spark in Co-allocated Data Centers
Authors: Liang Yi , Zhang Chaohui 
Published: July 2022

AbstractThe co-allocated data centers are to deploy online services and offline workloads in the same cluster to improve the utilization of resources. Spark application is a typical offline batch workload. At present, the resource scheduling strategy for co-allocated data centers mainly focuses on online services. Spark applications still use the original resource scheduling, which can’t solve the data dependency and deadline problems between spark applications and online services. This paper proposes a data-aware resource-scheduling model to meet the deadline requirement of Spark application and optimize the throughput of data processing on the premise of ensuring the quality of service of online services.
Multi-level Network Software Defined Gateway Forwarding System Based on Multus
Authors: Wang Zhengqi , Ji Yuan , Zheng Weibo , Li Mingyan 
Published: July 2022

AbstractIn order to solve the problem that the data forwarding performance requirements of the security gateway are becoming higher and higher, the difficulty of operation and maintenance is increasing day by day, and the physical resource configuration strategy is constantly changing, a multi-level network software defined gateway forwarding system based on Multus is proposed and implemented. On the basis of kubernetes' centralized management and control of the service cluster, different types of CNI plugins are dynamically called for interface configuration, At the same time, it supports the multi-level network of kernel mode and user mode, separates the control plane and data plane of the forwarding system, and enhances the controllability of the system service. At the same time, the load balancing module based on user mode protocol stack is introduced to realize the functions of dynamic scaling, smooth upgrade, cluster monitoring, fault migration and so on without affecting the forwarding performance of the system.
An Improved Chicken Swarm Optimization Algorithm for Feature Selection
Authors: Wang Haoran , Chen Zhiyu , Liu Gang 
Published: July 2022

AbstractIn recent years, feature selection is becoming more and more important in data mining. Its target is that reduce the dimensionality of the datasets while at least maintaining the classification accuracy. There are some researches about chicken swarm optimization algorithm (CSO) applied to feature selection, the effect is extraordinary compared with traditional swarm intelligence algorithms. However, there is a complex search space in the challenging task feature selection, the CSO algorithm still has a default that quickly gets stuck in the local minimum problem. An improved chicken swarm optimization algorithm (ICSO) is proposed in this paper, which introduces the Levy flight strategy in the hen location update strategy and the nonlinear strategy of decreasing inertial weight in the chick location update strategy to increase the global search ability and avoid getting stuck in the local minimum problem. Compared with the other three algorithms on eighteen UCI datasets shows that the ICSO algorithm can greatly reduce the redundant features while ensuring classification accuracy.
Recognition Model Based on BP Neural Network and Its Application
Authors: Nong Yingxiong , Chen Zhibin , Huang Cong , Pan Jian , Liang Dong , Lu Ying 
Published: July 2022

AbstractThe BP neural network model used in data classification can change the traditional manual classification, which has the disadvantages of low efficiency and subjective interference. According to the principle of BP, this paper determines the relevant parameters of network structure, and establishes an optimized BP. The BP model is used to analyze the chemical composition data of tobacco leaves to determine the grade of tobacco leaves. Experiments show that this model has better recognition accuracy than KNN and random forest model. It effectively improves the efficiency of classification and reduces the interference of subjective factors in classification.