bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of the design, conduct and reporting of randomised controlled trials in the haemodialysis population: a scoping review and interview study
Authors: Kaushal Prachi , Adenwalla Sherna F , Lightfoot Courtney J , March Daniel S , Gray Laura J , Burton James O
Published: March 2022

Background

Fewer trials are conducted in nephrology than any other specialty, often failing to recruit to target, resulting in unclear evidence affecting translation to clinical practice. This mixed-methods study aims to provide guidance for designing and reporting future randomised controlled trials (RCTs) in the haemodialysis population.

Method

A scoping review was conducted. Five databases (MEDLINE, Cumulative Index to Nursing and Allied Health Literature, Embase, Cochrane Central Register of Controlled Trials and ClinicalTrials.gov) were searched for RCTs published between 2013 and 2019 involving prevalent adult haemodialysis patients. Reporting of sample size, recruitment, retention and statistical significance of primary outcome were assessed. Face-to-face semistructured interviews were conducted with individuals from a single centre during dialysis sessions. Interviews were analysed thematically.

Results

Of 786 RCTs identified, 636 (80.9%) were parallel-group, 139 (17.7%) were crossover and 11 (1.4%) were cluster (including one stepped-wedge) design. Sample size justification was reported in 73.1%, 53.8% and 45.5% of parallel-group, crossover and cluster trials, respectively.

Target recruitment was achieved by 45.5% of cluster, 53.8% of crossover and 57.7% of parallel-group trials with patient retention at 75.6%, 83.1% and 87.8%, respectively. Primary outcome reached statistical significance in 81.8% of cluster trials, 69.2% of parallel-group and 38.5% of crossover trials.

Themes identified from individual interviews: perceptions of the convenience of trial participation; group allocation; perceptions of the benefits and adverse effects of taking part in clinical trials.

Conclusion

The recruitment and reporting of RCTs involving people on haemodialysis could be improved. Involvement of all stakeholders and especially participants in the trial design process may address issues around participant burden and ultimately improve the evidence base for clinical practice.
Ultrasound imaging measures of vertebral bony landmark distances are weakly to moderately correlated with intervertebral disc height as assessed by MRI
Authors: Mitchell Ulrike H , Johnson A Wayne , Adams Lauren , Sonnefeld Tayva , Owen Patrick J
Published: March 2022

Objectives

To assess the validity and reliability of ultrasound-derived interbony landmark distances as a proxy for MRI-derived intervertebral disc (IVD) height.

Methods

This is a cross-sectional criterion validity study. Twelve college-aged participants without current low back pain completed both MRI and ultrasound imaging of the lumbar spine in a prone position. Single-segment and multisegment distances between the spinous and mammillary processes at the lumbar segments (L2/L3, L3/L4, L4/L5) were measured twice using ultrasound and analysed digitally. Sagittal slices of the lumbar spine were taken via T1-weighted MRI and IVD height, and the overall distance between IVDs L2/L3 and L4/L5 was imaged once and measured twice.

Results

There was moderate correlation between multilevel-based measurements (overall distance between L2 and L5, r=0.677, p=0.016) and the average across three levels (r=0.596, p=0.041) when using the spinous processes as bony landmarks. Single-segment measures were not significantly correlated (all: p>0.092). Accuracy and precision were better for the overall MRI-derived distance between the three IVDs from L2 and L5 MRI and the distance measured between the spinous processes L2–L5. There was excellent reliability within multiple measurements at each location, with intraclass correlation coefficient, ICC(3,1), ranging from 0.93 to 0.99 (95% CI 0.82 to 0.99) for ultrasound and from 0.98 to 0.99 (95% CI 0.92 to 0.99) for MRI.

Conclusion

Findings do not support the use of ultrasound imaging for estimating single-segment IVD height, yet it may be used to measure the change in distance over time with a certain degree of precision based on its excellent reliability.
Multicenter, single-arm, phase II trial of camrelizumab and chemotherapy as neoadjuvant treatment for locally advanced esophageal squamous cell carcinoma
Authors: Liu Jun , Yang Yang , Liu Zhichao , Fu Xiaolong , Cai Xiaoyue , Li Hongxuan , Zhu Li , Shen Yan , Zhang Hong , Sun Yifeng , Chen Hezhong , Yu Bentong , Zhang Renquan , Shao Jinchen , Zhang Ming , Li Zhigang
Published: March 2022

Background

Camrelizumab and chemotherapy demonstrated durable antitumor activity with a manageable safety profile as first-line treatment in patients with advanced esophageal squamous cell carcinoma (ESCC). This study aimed to evaluate the safety and efficacy of camrelizumab plus neoadjuvant chemotherapy, using pathologically complete response (pCR) as primary endpoint, in the treatment for locally advanced ESCC.

Methods

Patients with locally advanced but resectable thoracic ESCC, staged as T1b-4a, N2-3 (≥3 stations), and M0 or M1 lymph node metastasis (confined to the supraclavicular lymph nodes) were enrolled. Eligible patients received intravenous camrelizumab (200 mg, day 1) plus nab-paclitaxel (100 mg/m2, day 1, 8, 15) and carboplatin (area under curve of 5 mg/mL/min, day 1) of each 21-days cycle, for two cycles before surgery. The primary endpoint is pCR rate in the per-protocol population. Safety was assessed in the modified intention-to-treat population that was treated with at least one dose of camrelizumab.

Results

From November 20, 2019 to December 22, 2020, 60 patients were enrolled. 55 (91.7%) patients completed the full two-cycle treatment successfully. 51 patients underwent surgery and R0 resection was achieved in 50 (98.0%) patients. pCR (ypT0N0) was identified in 20 (39.2%) patients and 5 (9.8%) patients had complete response of the primary tumor but residual disease in lymph nodes alone (ypT0N+). 58 patients (96.7%) had any-grade treatment-related adverse events (TRAEs), with the most common being leukocytopenia (86.7%). 34 patients (56.7%) had adverse events of grade 3 or worse, and one patient (1.7%) occurred a grade 5 adverse event. There was no in-hospital and postoperative 30-day as well as 90-day mortality.

Conclusions

The robust antitumor activity of camrelizumab and chemotherapy was confirmed and demonstrated without unexpected safety signals. Our findings established camrelizumab and chemotherapy as a promising neoadjuvant treatment for locally advanced ESCC.

Trial registration number

ChiCTR1900026240.
Parenchymal lung abnormalities following hospitalisation for COVID-19 and viral pneumonitis: a systematic review and meta-analysis
Authors: Fabbri Laura , Moss Samuel , Khan Fasihul A , Chi Wenjie , Xia Jun , Robinson Karen , Smyth Alan Robert , Jenkins Gisli , Stewart Iain
Published: March 2022

Introduction

Persisting respiratory symptoms in COVID-19 survivors may be related to development of pulmonary fibrosis. We assessed the proportion of chest CT scans and pulmonary function tests consistent with parenchymal lung disease in the follow-up of people hospitalised with COVID-19 and viral pneumonitis.

Methods

Systematic review and random effects meta-analysis of proportions using studies of adults hospitalised with SARS-CoV-2, SARS-CoV, MERS-CoV or influenza pneumonia and followed up within 12 months. Searches performed in MEDLINE and Embase. Primary outcomes were proportion of radiological sequelae on CT scans; restrictive impairment; impaired gas transfer. Heterogeneity was explored in meta-regression.

Results

Ninety-five studies (98.9% observational) were included in qualitative synthesis, 70 were suitable for meta-analysis including 60 SARS-CoV-2 studies with a median follow-up of 3 months. In SARS-CoV-2, the overall estimated proportion of inflammatory sequelae was 50% during follow-up (0.50; 95% CI 0.41 to 0.58; I2=95%), fibrotic sequelae were estimated in 29% (0.29; 95% CI 0.22 to 0.37; I2=94.1%). Follow-up time was significantly associated with estimates of inflammatory sequelae (−0.036; 95% CI −0.068 to –0.004; p=0.029), associations with fibrotic sequelae did not reach significance (−0.021; 95% CI −0.051 to 0.009; p=0.176). Impaired gas transfer was estimated at 38% of lung function tests (0.38 95% CI 0.32 to 0.44; I2=92.1%), which was greater than restrictive impairment (0.17; 95% CI 0.13 to 0.23; I2=92.5%), neither were associated with follow-up time (p=0.207; p=0.864).

Discussion

Sequelae consistent with parenchymal lung disease were observed following COVID-19 and other viral pneumonitis. Estimates should be interpreted with caution due to high heterogeneity, differences in study casemix and initial severity.

PROSPERO registration number

CRD42020183139.
The rheological behavior and high-low temperature performance of warm SBS/rubber asphalt
Authors: Shi Zhenwu , Wang Binhua , Yu Lize , Li Bo , Zhang Haitao
Published: March 2022

Rubber asphalt pavement has the performance advantages of noise reduction, anti-rutting, and anti-cracking. In this paper, the deformation recovery ability, stress sensitivity, dynamic mechanical performance, and chemical components of a warm mix styrene-butadiene-styrene (SBS)/rubber asphalt before and after aging were analyzed. The results showed that the high-low temperature performance of the asphalt was improved, and the ratio of the viscoelastic component was changed by adding the LKW-II warm mix additive to the asphalt. The high-temperature performance of the warm mix SBS/rubber asphalt was improved after short-term aging. At a proportion of the warm mix additive of 0.1%, the high-temperature rheological performance was substantially improved, the anti-rutting ability was enhanced, and the stress sensitivity was the lowest. At high temperatures, the improvements were relatively small at warm mix additive proportions of 0.3% and 0.5%. The low-temperature performance was significantly improved at a proportion of 0.3%. The Burgers model was used to evaluate the low-temperature mechanical behavior and confirmed the low-temperature performance results. The Fourier transform infrared spectroscopy results showed that no new substances were produced after adding the LKW-II warm mix additive to the SBS/rubber asphalt, and the viscosity was reduced by changing the intermolecular force.
Effects of unregulated international fishing on recovery potential of the sandbar shark within the southeastern United States
Authors: Peterson Cassidy D. , Wilberg Michael J. , Cortés Enric , Courtney Dean L. , Latour Robert J.
Published: March 2022

Coastal sharks are challenging to manage in the United States due to their slow life history, limited data availability, history of overexploitation, and competing stakeholder interests. Furthermore, species like the sandbar shark ( Carcharhinus plumbeus) are subjected to international exploitation unmanaged by the US. We conducted a management strategy evaluation using Stock Synthesis on the sandbar shark to test the performance of various configurations of a threshold harvest control rule. In addition to uncertainties addressed in the operating model (OM), we built multiple implementation models to address uncertainties related to future levels of a partially unmanaged source of removals, the combined Mexican and US recreational (MexRec) fleet. We found that the presence of unregulated removals had the potential to significantly influence the success of the various management procedures (MPs) tested. Notably, if MexRec catches continue to increase with total stock abundance following historical trends, the rate of MexRec removals will be too large to allow the sandbar shark to recover across OMs. We present trade-offs between performance metrics across a range of 24 MPs and three implementation models.
ChiliPDF: Chebyshev interpolation for parton distributions
Authors: Diehl Markus , Nagar Riccardo , Tackmann Frank J.
Published: March 2022

Parton distribution functions (PDFs) are an essential ingredient for theoretical predictions at colliders. Since their exact form is unknown, their handling and delivery for practical applications relies on approximate numerical methods. We discuss the implementation of PDFs based on a global interpolation in terms of Chebyshev polynomials. We demonstrate that this allows for significantly higher numerical accuracy at lower computational cost compared with local interpolation methods such as splines. Whilst the numerical inaccuracy of currently used local methods can become a nontrivial limitation in high-precision applications, in our approach it is negligible for practical purposes. This holds in particular for differentiation and for Mellin convolution with kernels that have end point singularities. We illustrate our approach for these and other important numerical operations, including DGLAP evolution, and find that they are performed accurately and fast. Our results are implemented in the C++ library ChiliPDF.
Herbal Medicines for Constipation and Phytochemical Comparison of Active Components
Authors: Ma Qinge , Wang Chong-Zhi , Sawadogo Wamtinga R. , Bian Zhao-Xiang , Yuan Chun-Su
Published: March 2022

Constipation is a very common medical condition worldwide, negatively affecting patients’ quality of life and healthcare system. Rhubarb, senna leaf, and aloe are three frequently used herbal medications for achieving regular bowel movement. Rhubarb is also a key ingredient in MaZiRenWan, a Chinese medicine formula used every so often for constipation in oriental countries. We reviewed and summarized the major chemical components from these three botanicals, including dianthrones, anthraquinone glycosides, free anthraquinones, and other polyphenols. The purgative actions of these constituents have been compared. Anthraquinone, especially its dianthrone compounds such as sennoside A and sennoside B, as natural stimulant laxatives, possesses significant effects to promote gastrointestinal motility and relieve functional constipation. Furthermore, the safety, reported side effects, and other benefits of anthraquinone compounds are presented. To date, many anti-constipation natural products are being used but their research is relatively limited, and thus, more investigations in this field are indeed needed.
Single Active Element Based Electronically Controllable Capacitance Multiplier
Authors: Jaikla Winai , Huaihongthong Pintira , Siripongdee Surapong , Chaichana Amornchai , Suwanjan Peerawut
Published: March 2022

The realization of capacitance multiplier using the versatile active building block, namely voltage differencing differential difference amplifier (VDDDA) is presented in this paper. The realized capacitance multiplier is very simple consisting of one VDDDA, one MOS resistor ([Formula: see text] and one grounded capacitor which is attractive for integration. The multiplication factor (KC) of the realized circuit can be electronically controlled via the bias current ([Formula: see text] and control voltage ([Formula: see text] without the need of any matching condition of active and passive element. Moreover, the multiplication factor can be adjusted to be more or less than one. The performances of the presented capacitance multiplier are verified through Pspice simulation using CMOS VDDDA in 0.18[Formula: see text][Formula: see text]m TSMC technology with [Formula: see text][Formula: see text]V power supplies. The multiplication factor is designed to be [Formula: see text] by choosing [Formula: see text][Formula: see text]V, [Formula: see text][Formula: see text][Formula: see text]A and [Formula: see text] pF. The simulated multiplication factor is around 1.98. The simulated operational frequency range is around three decades (6.16 kHz–8.91[Formula: see text]MHz). The performances of the proposed circuit are also verified by the experiment using VDDDA implemented from the commercial ICs, AD830 and LM13700 with [Formula: see text][Formula: see text]V power supplies. The experiment is conducted under the same multiplication factor ([Formula: see text]) as the simulation by choosing [Formula: see text] k[Formula: see text] (1% passive resistor), [Formula: see text][Formula: see text][Formula: see text]A and [Formula: see text][Formula: see text]nF. The experimental multiplication factor is around 2.06. The experimental operational frequency range is around three decades (1[Formula: see text]kHz–1.25[Formula: see text]MHz). By adjusting the bias current from 17.67[Formula: see text][Formula: see text]A to 400 [Formula: see text]A, the experimental multiplication factor is controllable from 11.47 to 0.48. The percent deviation of the theoretical and experimental multiplication factor is lower than 5% when the value of bias current is greater than 39[Formula: see text][Formula: see text]A. These deviations stem from the effect of the parasitic capacitance and resistance in VDDDA. Moreover, the application example of the presented capacitance multiplier as the sinusoidal oscillator is presented. The performances of the presented oscillator verified via the experiment are well consistent with theoretical anticipation.
Improving the Performance of a Doping-Less Carbon Nanotube FET with Dual Junction Source and Drain Regions: Numerical Studies
Authors: Ghodrati Maryam , Mir Ali
Published: March 2022

In this paper, a new structure using dual junctions in the source and drain regions is proposed to improve the electronic characteristics of the doping less carbon nanotube FET. To simulate the proposed structure, the Poisson–Schrodinger equation self-consistent solution method through the nonequilibrium Green’s function (NEGF) procedure has been used. The proposed structure is formed by dividing the source and drain regions into two parts with the same lengths and different work function for each part. The simulation results show that the short channel effects (SCEs) including the sub-threshold swing (SS) and the drain-induced barrier lowering (DIBL) are improved. Also, OFF current has a significant reduction, which in turn increases the current ratio. Besides, improving the ambipolar behavior and leakage current are other benefits of the proposed structure.
Wind Tunnel Study on Aerodynamic Characteristics of the Train on Viaducts with a New Type of Wind–Noise Barrier Under Cross Wind
Authors: Jiang Shuo , Zou Yunfeng , He Xuhui , Cai Chenzhi , Zhai Lihua , Nong Xingzhong
Published: March 2022

Traditional noise barriers are often designed only by considering its noise reduction effects, but designer ignores that it may transfer too much aerodynamic force to the bridge. In order to meet the wind resistance and noise reduction requirements of the elevated lines crossing an urban area at the same time, a new type of wind–noise barrier (NT-WNB) is proposed. The noise reduction effect is evaluated by a numerical method, and the influence of the wind–noise barriers’ rotation angle on the aerodynamic characteristics of a train–bridge system was studied by sectional model wind tunnel tests. The results show that the NT-WNB has effective noise reduction in the frequency range of 500–1600 Hz, and the noise reduction can be increased when install barriers with upward incline blade. Although an angle combination type of wind–noise barrier can optimize the lateral force and the lift of the train at the same time, which may cause high turbulence in the corresponding area. The NT-WNB can reduce the wind load of the bridge–barrier system by 22%, which is more conducive to the safety of the bridge and the barrier.
A Renewed Study on Charitable Giving Among Canadians
Authors: Chan Alan TY , MacDonald Robert A.
Published: March 2022

Using the Survey of Household Spending from 2012 to 2015, this paper studies (1) the distributions and characteristics of giver types and (2) the determinants of religious and non-religious giving in Canada. The paper employs both multinominal logistic regression and pooled Tobit regression to re-examine the results of two earlier studies [Chan and Lee (2016). Interdisciplinary Journal of Research on Religion, 12, 1–15; Chan and Lee (2018). Atlantic Canada Economics Review, 1] and adds additional variables (e.g. geography and behavioural addiction). It summarises the most likely characteristics of each of the giver types and re-examines the determinants of religious and non-religious giving among Canadians, ultimately determining that Eastern Canadians are more likely to participate in giving, although Western Canadians give more in terms of monetary amount.
Emerging Cybersecurity Capability Gaps in the Industrial Internet of Things: Overview and Research Agenda
Authors: Axon Louise , Fletcher Katherine , Scott Arianna Schuler , Stolz Marcel , Hannigan Robert , Kaafarani Ali El , Goldsmith Michael , Creese Sadie
Published: March 2022

Internet of Things (IoT)-enabled devices are becoming integrated into a significant and increasing proportion of critical infrastructures, changing the cybersecurity-risk landscape. Risk is being introduced to industry sectors such as transport, energy, and manufacturing, with new attack surfaces exposed and potential for increased harm. Furthermore, risk and harm arising in the Industrial IoT (IIoT) could propagate across interconnected organisations and sectors, resulting in systemic risk. Aspects of this changing risk landscape are not addressed by current cybersecurity approaches, leaving cybersecurity-capability gaps. In this article, we show how current and emerging cybersecurity needs in the IIoT align with a key industry cybersecurity standard, the NIST Cyber Security Framework. The key capability gaps emerging in the IIoT are identified based on our findings from a series of workshops with over 100 expert participants. We present a comprehensive research agenda to enable researchers to prioritise research focus to address these gaps; this research agenda covers the full lifecycle of IIoT development (design, implementation, use and decommission). Furthermore, we conclude that there is a significant gap in understanding of the nature of systemic risk, which should be a key priority if we are to develop effective solutions for cybersecurity and safety in IIoT environments.
Using Reinforcement Learning to Perform Qubit Routing in Quantum Compilers
Authors: Pozzi Matteo G. , Herbert Steven J. , Sengupta Akash , Mullins Robert D.
Published: March 2022

‘‘Qubit routing” refers to the task of modifying quantum circuits so that they satisfy the connectivity constraints of a target quantum computer. This involves inserting SWAP gates into the circuit so that the logical gates only ever occur between adjacent physical qubits. The goal is to minimise the circuit depth added by the SWAP gates.

In this article, we propose a qubit routing procedure that uses a modified version of the deep Q-learning paradigm. The system is able to outperform the qubit routing procedures from two of the most advanced quantum compilers currently available (Qiskit and t

\( | \)

ket

\( \rangle \)

), on both random and realistic circuits, across a range of near-term architecture sizes (with up to 50 qubits).
Blockchain-enabled Federated Learning: A Survey
Authors: Qu Youyang , Uddin Md Palash , Gan Chenquan , Xiang Yong , Gao Longxiang , Yearwood John
Published: March 2022

Federated learning (FL) has experienced a boom in recent years, which is jointly promoted by the prosperity of machine learning and Artificial Intelligence along with emerging privacy issues. In the FL paradigm, a central server and local end devices maintain the same model by exchanging model updates instead of raw data, with which the privacy of data stored on end devices is not directly revealed. In this way, the privacy violation caused by the growing collection of sensitive data can be mitigated. However, the performance of FL with a central server is reaching a bottleneck, while new threats are emerging simultaneously. There are various reasons, among which the most significant ones are centralized processing, data falsification, and lack of incentives. To accelerate the proliferation of FL, blockchain-enabled FL has attracted substantial attention from both academia and industry. A considerable number of novel solutions are devised to meet the emerging demands of diverse scenarios. Blockchain-enabled FL provides both theories and techniques to improve the performance of FL from various perspectives. In this survey, we will comprehensively summarize and evaluate existing variants of blockchain-enabled FL, identify the emerging challenges, and propose potentially promising research directions in this under-explored domain.
Towards an Advanced Deep Learning for the Internet of Behaviors: Application to Connected Vehicles
Authors: Mezair Tinhinane , Djenouri Youcef , Belhadi Asma , Srivastava Gautam , Lin Jerry Chun-Wei
Published: March 2022

In recent years, intensive research has been conducted to enable people to live more comfortably. Developments in the
Internet of Things (IoT)
, big data, and artificial intelligence have taken this type of research to a new level and led to the emergence of the
Internet of Behaviors (IoB)
, which analyzes behavioral patterns. However, current IoB technologies are not capable of handling heterogeneous data. While it is quite common to have different formats of sensor data for the same behavioral observation, the use of these different data formats can significantly help to obtain a more accurate classification of the observation. Another limitation is that existing IoB deep learning models rely on inefficient hyperparameter tuning strategies. In this paper, we present an
Advanced Deep Learning framework for IoB (ADLIoB)
applied to connected vehicles. Several deep learning architectures are employed in this framework: CNN, Graph CNN (GCNN), and LSTM are used to train sensor data of different formats. In addition, a branch-and-bound technique is used to intelligently select hyperparameters. To validate ADLIoB, experiments were conducted on four databases for connected vehicles. The results clearly show that ADLIoB is superior to the baseline solutions in terms of both accuracy and runtime.
Hardware Approximate Techniques for Deep Neural Network Accelerators: A Survey
Authors: Armeniakos Giorgos , Zervakis Georgios , Soudris Dimitrios , Henkel Jörg
Published: March 2022

Deep Neural Networks (DNNs) are very popular because of their high performance in various cognitive tasks in Machine Learning (ML). Recent advancements in DNNs have brought levels beyond human accuracy in many tasks, but at the cost of high computational complexity. To enable efficient execution of DNN inference, more and more research works, therefore, are exploiting the inherent error resilience of DNNs and employing Approximate Computing (AC) principles to address the elevated energy demands of DNN accelerators. This article provides a comprehensive survey and analysis of hardware approximation techniques for DNN accelerators. First, we analyze the state of the art, and by identifying approximation families, we cluster the respective works with respect to the approximation type. Next, we analyze the complexity of the performed evaluations (with respect to the dataset and DNN size) to assess the efficiency, potential, and limitations of approximate DNN accelerators. Moreover, a broad discussion is provided regarding error metrics that are more suitable for designing approximate units for DNN accelerators as well as accuracy recovery approaches that are tailored to DNN inference. Finally, we present how Approximate Computing for DNN accelerators can go beyond energy efficiency and address reliability and security issues as well.
Flexibility can Hurt Dynamic Matching System Performance
Authors: Cadas Arnaud , Doncel Josu , Fourneau Jean-Michel , Busic Ana
Published: March 2022

We study the performance of stochastic matching models with general compatibility graphs. Items of different classes arrive to the system according to independent Poisson processes. Upon arrival, an item is matched with a compatible item according to the First Come First Matched discipline and both items leave the system immediately. If there are no compatible items, the new arrival joins the queue of unmatched items of the same class. Compatibilities between item classes are defined by a connected graph, where nodes represent the classes of items and the edges the compatibilities between item classes. We show that such a model may exhibit a non intuitive behavior: increasing the matching flexibility by adding new edges in the matching graph may lead to a larger average population at the steady state. This performance paradox can be viewed as an analog of the Braess paradox. We show sufficient conditions for the existence or non-existence of this paradox. This performance paradox in matching models appears when specific independent sets are in saturation, i.e., the system is close to the stability condition.
Updating the Theory of Buffer Sizing
Authors: Spang Bruce , Arslan Serhat , McKeown Nick
Published: March 2022

Internet routers have packet buffers which reduce packet loss during times of congestion. Sizing the router buffer correctly is important: if a router buffer is too small, it can cause high packet loss and link under-utilization. If a buffer is too large, packets may have to wait an unnecessarily long time in the buffer during congested periods, often up to hundreds of milliseconds. While an operator can reduce the operational size of a router buffer, the maximum size of a router buffer is decided by the router manufacturer, and the operator typically configures the router to use all the available buffers. Without clear guidance about how big a buffer needs to be, manufacturers tend to oversize buffers and operators tend to configure larger buffers than necessary, leading to increased cost and delay.
On the Capacity Region of Bipartite and Tripartite Entanglement Switching
Authors: Vardoyan Gayane , Guha Saikat , Nain Philippe , Towsley Don
Published: March 2022

In Section 4.3 (Analysis), the last few lines of the proof of Claim 3 should be replaced with the following
Current Perspectives on Food Oral Processing
Authors: He Yue , Wang Xinmiao , Chen Jianshe
Published: March 2022

Food oral processing (FOP) is a fast-emerging research area in the food science discipline. Since its first introduction about a decade ago, a large amount of literature has been published in this area, forming new frontiers and leading to new research opportunities. This review aims to summarize FOP research progress from current perspectives. Food texture, food flavor (aroma and taste), bolus swallowing, and eating behavior are covered in this review. The discussion of each topic is organized into three parts: a short background introduction, reflections on current research findings and achievements, and future directions and implications on food design. Physical, physiological, and psychological principles are the main concerns of discussion for each topic. The last part of the review shares views on the research challenges and outlooks of future FOP research. It is hoped that the review not only helps readers comprehend what has been achieved in the past decade but also, more importantly, identify where the knowledge gaps are and in which direction the FOP research will go.
Electrochemistry of Protein Electron Transfer
Authors: Matyushov Dmitry V.
Published: March 2022

Protein fold and slow relaxation times impose constraints on configurations sampled by the protein. Incomplete sampling leads to the violation of fluctuation-dissipation relations underlying the traditional theories of electron transfer. The effective reorganization energy of electron transfer is strongly reduced thus leading to lower barriers and faster rates (catalytic effect). Electrochemical kinetic measurements support low activation barriers for protein electron transfer. The distance dependence of the rate constant displays a crossover from a plateau at short distances to a long-distance exponential decay. The transition between these two regimes is controlled by the protein dynamics.
From the Editor: Starting the Next 30 Years
Authors: Kelly Rob
Published: March 2022

As noted in the Winter 2021 issue of Interface, we have now wrapped up three decades of bringing ECS members and the outside world insights into the wonderful world of electrochemical and solid state science. To calibrate, thirty years ago I had a full head of hair. More or less (less). In going through the archives—a reminder that ALL issues are available in pdf in our archive—one can follow the major changes that have taken place in scientific publishing.
Podcasts of Note - Spring 2022
Authors: Suroviec Alice H.
Published: March 2022

Podcasts of Note suggested for you by Alice Suroviec
Low-Resistance Ti/Au Ohmic Contact on (001) Plane Ga2O3 Crystal
Authors: Kim Yukyung , Kim Man-Kyung , Baik Kwang Hyeon , Jang Soohwan
Published: March 2022

We studied the Ti/Au Ohmic contact on (001) plane Ga2O3 single crystal wafer as a function of annealing temperature and crystallographic orientation. The lowest specific contact resistances of ∼5 × 10−4 Ω·cm2 were obtained at 400 °C annealing temperature under nitrogen ambience. The Ohmic metal contact became degraded when annealed above 500 °C. The formation of Ti oxide might be responsible for the degradation of Ohmic contact at high annealing temperatures. In comparison with (010) Ga2O3 substrate, the Ohmic contact could be more easily formed on

(

2
¯

01
)

and (001) Ga2O3 planes, possibly due to high surface energy and dangling bond density. It is notable that the contact properties did not show any particular dependence on in-plane azimuth angles on (001) Ga2O3 substrate.
Network Host Cardinality Estimation Based on Artificial Neural Network
Authors: Jie Xu , Haoliang Lan , Wei Ding , Ao Ju
Published: March 2022

Cardinality estimation plays an important role in network security. It is widely used in host cardinality calculation of high-speed network. However, the cardinality estimation algorithm itself is easy to be disturbed by random factors and produces estimation errors. How to eliminate the influence of these random factors is the key to further improving the accuracy of estimation. To solve the above problems, this paper proposes an algorithm that uses artificial neural network to predict the estimation bias and adjust the cardinality estimation value according to the prediction results. Based on the existing algorithms, the novel algorithm reduces the interference of random factors on the estimation results and improves the accuracy by adding the steps of cardinality estimation sampling, artificial neural network training, and error prediction. The experimental results show that, using the cardinality estimation algorithm proposed in this paper, the average absolute deviation of cardinality estimation can be reduced by more than 20%.
Preparation and Safety Evaluation of Centella asiatica Total Glycosides Nitric Oxide Gel and Its Therapeutic Effect on Diabetic Cutaneous Ulcers
Authors: Liu Yi-qiu , Zhang Dandan , Deng Junyu , Liu Ye , Li Wei , Nie Xuqiang
Published: March 2022

Diabetic cutaneous ulcers (DCU) are a chronic and refractory complication of diabetes mellitus, which can lead to amputation or even death in extreme cases. Promoting the early healing of DCU and reducing the disability rate and treatment cost are important research topics in treating with integrated traditional Chinese and Western medicine. Centella asiatica total glycosides are extracted from the traditional Chinese medicine Centella asiatica and have angiogenic, anticancer, antioxidant, and wound healing effects. Nitric oxide (NO) is a critical component of wound healing. During the development of DCU, endogenous NO secretion is insufficient. It has been reported that exogenous nitric oxide can promote wound healing, but it is difficult to adhere to the skin because of its short half-life. Therefore, in this study, we used the polymer excipient hydroxyethyl cellulose as the matrix, combined with Centella asiatica total glycosides and NO, and developed a new type of topical gel that can promote wound healing. At the same time, we made a comprehensive research and evaluation on the preparation technology, quality standard, skin toxicity, reproductive toxicity, and pharmacodynamics against diabetic skin ulcers of the gel. According to our research results, the combination of Centella asiatica total glycosides and nitric oxide can accelerate the healing speed of DCU wounds, and 8% Centella asiatica total glycosides nitric oxide gel (CATGNOG) has the best effect in ulcer wound healing. CATGNOG has the advantages of feasible preparation method, controllable quality, good stability at low temperature, and no apparent skin toxicity and reproductive toxicity. It can effectively inhibit the growth of bacteria on the wound surface, relieve the inflammatory reaction of the wound surface, and promote the healing of ulcer wound, which provides a basis for further research of the preparation in the future.
Ecosphere Management Model of Farmer Cooperatives Based on Intelligent Data Sampling Technology
Authors: Fang Ting , Liao Xiaoming , Fang Ming
Published: March 2022

In order to improve the operation effect of farmer cooperatives, this paper combines the intelligent data sampling technology to analyze the ecological circle operation mode of farmer cooperatives. Moreover, this paper strives to promote the ecosphere business model, accelerate regional development, build agricultural pastoral complex projects and in-depth study TI-ADC modeling, error estimation, mismatch compensation, and other technologies, and carry out engineering realization. In addition, this paper uses technology to analyze intelligent data and builds a system structure based on the actual needs of the farmer cooperative ecosphere management. Finally, this paper analyzes the structure and flow of the data processing layer. The test results show that the ecosystem business model of farmer cooperatives based on intelligent data sampling technology proposed in this paper has good results.
Hybrid Hypercube Optimization Search Algorithm and Multilayer Perceptron Neural Network for Medical Data Classification
Authors: Tunay Mustafa , Pashaei Elnaz , Pashaei Elham
Published: March 2022

The hypercube optimization search (HOS) approach is a new efficient and robust metaheuristic algorithm that simulates the dove’s movement in quest of new food sites in nature, utilizing hypercubes to depict the search zones. In medical informatics, the classification of medical data is one of the most challenging tasks because of the uncertainty and nature of healthcare data. This paper proposes the use of the HOS algorithm for training multilayer perceptrons (MLP), one of the most extensively used neural networks (NNs), to enhance its efficacy as a decision support tool for medical data classification. The proposed HOS-MLP model is tested on four significant medical datasets: orthopedic patients, diabetes, coronary heart disease, and breast cancer, to assess HOS’s success in training MLP. For verification, the results are compared with eleven different classifiers and eight well-regarded MLP trainer metaheuristic algorithms: particle swarm optimization (PSO), biogeography-based optimizer (BBO), the firefly algorithm (FFA), artificial bee colony (ABC), genetic algorithm (GA), bat algorithm (BAT), monarch butterfly optimizer (MBO), and the flower pollination algorithm (FPA). The experimental results demonstrate that the MLP trained by HOS outperforms the other comparative models regarding mean square error (MSE), classification accuracy, and convergence rate. The findings also reveal that the HOS help the MLP to produce more accurate results than other classification algorithms for the prediction of diseases.
Multi-Class Skin Problem Classification Using Deep Generative Adversarial Network (DGAN)
Authors: Heenaye-Mamode Khan Maleika , Gooda Sahib-Kaudeer Nuzhah , Dayalen Motean , Mahomedaly Faadil , Sinha Ganesh R. , Nagwanshi Kapil Kumar , Taylor Amelia
Published: March 2022

The lack of annotated datasets makes the automatic detection of skin problems very difficult, which is also the case for most other medical applications. The outstanding results achieved by deep learning techniques in developing such applications have improved the diagnostic accuracy. Nevertheless, the performance of these models is heavily dependent on the volume of labelled data used for training, which is unfortunately not available. To address this problem, traditional data augmentation is usually adopted. Recently, the emergence of a generative adversarial network (GAN) seems a more plausible solution, where synthetic images are generated. In this work, we have developed a deep generative adversarial network (DGAN) multi-class classifier, which can generate skin problem images by learning the true data distribution from the available images. Unlike the usual two-class classifier, we have developed a multi-class solution, and to address the class-imbalanced dataset, we have taken images from different datasets available online. One main challenge faced during our development is mainly to improve the stability of the DGAN model during the training phase. To analyse the performance of GAN, we have developed two CNN models in parallel based on the architecture of ResNet50 and VGG16 by augmenting the training datasets using the traditional rotation, flipping, and scaling methods. We have used both labelled and unlabelled data for testing to test the models. DGAN has outperformed the conventional data augmentation by achieving a performance of 91.1% for the unlabelled dataset and 92.3% for the labelled dataset. On the contrary, CNN models with data augmentation have achieved a performance of up to 70.8% for the unlabelled dataset. The outcome of our DGAN confirms the ability of the model to learn from unlabelled datasets and yet produce a good diagnosis result.
Creation and Validation of a Survival Nomogram Based on Immune-Nutritional Indexes for Colorectal Cancer Patients
Authors: Liu Yulan , Meng Yang , Zhou Chenliang , Liu Ya , Tian Shan , Li Jiao , Dong Weiguo
Published: March 2022

Nutritional and inflammatory status was associated with prognosis in various types of malignant cancer, including colorectal cancer (CRC). This clinical research was performed to estimate the prognostic role of immune-nutritional indexes CRC in patients and to set up a survival nomogram based on the significant immune-nutritional indexes. 1024 CRC patients underwent surgical resection from Wuhan Union Hospital were enrolled and divided into the test cohort (

n
=
717

) and validation cohort (

n
=
307

). A total of 19 immune-nutritional indexes were included into our analysis. The Cox regression analysis was utilized to identify the informative immune-nutritional indexes which were closely associated with overall survival (OS) and disease-free survival (DFS). Survival nomograms were created in the test set and further verified in the validation set. Td-ROC was curved to estimate the predictive performance of survival nomograms for CRC patients. Body mass index (BMI), chemotherapy, TNM stage, T stage, lactate dehydrogenase (LDH)/prealbumin (PA), monocytes (MON)/albumin (ALB), and prognostic nutritional index (PNI) were seven potent prognostic biomarkers of CRC patients. We created an OS-nomogram based on the seven risk indexes, and the predictive accuracy expressed with area under curve (AUC) was 0.826 for 1-year, 0.809 for 3-year, and 0.80 for 5-year OS rates in the test set and 0.795 for 1-year, 0.749 for 3-year, and 0.647 for 5-year OS rates in the validation set. TNM stage, T stage, LDH/ALB, and MON/ALB were risk factors for unfavorable DFS in CRC patients. We further built a DFS-nomogram based on the four risk factors, and the predictive performance presented with AUC was 0.806 for 1-year, 0.763 for 3-year, and 0.82 for 5-year DFS rates in the test set, and 0.704 for 1-year, 0.692 for 3-year, and 0.692 for 5-year DFS rates in the validation set. Our survival nomogram based on immune-nutritional indexes is a useful and potential prognostic tool in CRC patients.
An Optimized Artificial Intelligence System Using IoT Biosensors Networking for Healthcare Problems
Authors: Khan Shadab , Singh Yash Veer , Singh Pushpendra , Singh Ram Sewak
Published: March 2022

In today’s environment, electronics technology is growing rapidly because of the availability of the numerous and latest devices which can be deployed for monitoring and controlling the various healthcare systems. Due to the limitations of such devices, there is a dire need to optimize the utilization of the devices. In healthcare systems, Internet of things (IoT) based biosensors networking has minimal energy during transmission and collecting data. This paper proposes an optimized artificial intelligence system using IoT biosensors networking for healthcare problems for efficient data collection from the deployed sensor nodes. Here, an optimized tunicate swarm algorithm is used for optimizing the route for data collection and transmission among the patient and doctor. The fitness function of the optimized tunicate swarm algorithm used the distance, proximity, residual, and average energy of nodes parameters. The proposed method is attributed to the optimal CH chosen under TSA operation having a lower energy consumption. The performance of the proposed method is compared to the existing methods in terms of various metrics like stability period, lifetime, throughput, and clusters per round.
Postoperative Ileus with the Topical Application of Tongfu Decoction Based on Network Pharmacology and Experimental Validation
Authors: Zuo Ximeng , Shi Xiaoguang , Zhang Xuedan , Chen Zhenzhou , Yang Zhenrui , Pan Xiaojuan , Lai Rui , Zhao Ze
Published: March 2022

Objective. Postoperative gastrointestinal dysfunction is a common and important complication of surgery. This study aimed to explore the key pharmacological mechanisms of Tongfu decoction in treating postoperative ileus (POI). Methods. The active ingredients of Tongfu decoction and their targets were screened using the TCMSP database and STITCH and SwissTargetPrediction databases, respectively. The GeneCards and DisGeNET databases were used to obtain POI dysfunction-related therapeutic targets. After screening, a drug-active-ingredient-therapeutic target network was constructed and the key target functional enrichment analysis was carried out. The Sprague–Dawley rats with POI were used for in vivo experimental verification. The serum levels of IL-1β, IL-6, IL-10, IFN-γ, and MCP-1 were measured after surgery using enzyme-linked immunosorbent assay. The Western blot analysis was used to determine the expression of key proteins of the PI3K-Akt signaling pathway in colon tissues. Results. An interaction network was constructed containing 7 Chinese medicine components, 36 compounds, and 85 target proteins. The functional enrichment analysis showed that the target proteins mainly acted on the POI through the PI3K-Akt signaling pathway. In in vivo experiments, Tongfu decoction had a promoting effect on the serum level of IL-10, an inhibitory effect on the serum levels of IL-1β and CCL2, and an inhibitory effect on the local expression of PI3K, pAkt, and mTOR in colon tissue. In addition, the Tongfu decoction increased the intestinal ink advancing rate. Conclusion. Nonoral Tongfu decoction can also be used to treat POI; its mechanism is affected by IL-10 and IL-1β.The inhibition of the PI3K-Akt signaling pathway affected the treatment with Tongfu decoction by inducing an immune-inflammatory storm in POI.