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Current facial image manipulation techniques have caused public concerns while achieving impressive quality. However, these techniques are mostly bound to a single frame for synthesized videos and pay little attention to the most discriminatory temporal frequency artifacts between various frames. Detecting deepfake videos using temporal modeling still poses a challenge. To address this issue, we present a novel deepfake video detection framework in this paper that consists of two levels: temporal modeling and coherence analysis. At the first level, to fully capture temporal coherence over the entire video, we devise an efficient temporal facial pattern (TFP) mechanism that explores the color variations of forgery-sensitive facial areas by providing global and local-successive temporal views. The second level presents a temporal coherence analyzing network (TCAN), which consists of novel global temporal self-attention characteristics, high-resolution fine and low-resolution coarse feature extraction, and aggregation mechanisms, with the aims of long-range relationship modeling from a local-successive temporal perspective within a TFP and capturing the vital dynamic incoherence for robust detection. Thorough experiments on large-scale datasets, including FaceForensics++, DeepFakeDetection, DeepFake Detection Challenge, CelebDF-V2, and DeeperForensics, reveal that our paradigm surpasses current approaches and stays effective when detecting unseen sorts of deepfake videos.

Adeno-associated virus (AAV) serotypes from primates are being developed and clinically used as vectors for human gene therapy. However, the evolutionary mechanism of AAV variants is far from being understood, except that genetic recombination plays an important role. Furthermore, little is known about the interaction between AAV and its natural hosts, human and nonhuman primates. In this study, natural AAV capsid genes were subjected to systemic evolutionary analysis with a focus on selection drives during the diversification of AAV lineages. A number of positively selected sites were identified from these AAV lineages with functional relevance implied by their localization on the AAV structures. The selection drives of the two AAV2 capsid sites were further investigated in a series of biological experiments. These observations did not support the evolution of the site 410 of the AAV2 capsid driven by selection pressure from the human CD4+ T-cell response. However, positive selection on site 548 of the AAV2 capsid was directly related to host humoral immunity because of the profound effects of mutations at this site on the immune evasion of AAV variants from human neutralizing antibodies at both the individual and population levels. Overall, this work provides a novel interpretation of the genetic diversity and evolution of AAV lineages in their natural hosts, which may contribute to their further engineering and application in human gene therapy.

Abstract Biometric recognition is a widely used technology for user authentication. In the application of this technology, biometric security and recognition accuracy are two important issues that should be considered. In terms of biometric security, cancellable biometrics is an effective technique for protecting biometric data. Regarding recognition accuracy, feature representation plays a significant role in the performance and reliability of cancellable biometric systems. How to design good feature representations for cancellable biometrics is a challenging topic that has attracted a great deal of attention from the computer vision community, especially from researchers of cancellable biometrics. Feature extraction and learning in cancellable biometrics is to find suitable feature representations with a view to achieving satisfactory recognition performance, while the privacy of biometric data is protected. This survey informs the progress, trend and challenges of feature extraction and learning for cancellable biometrics, thus shedding light on the latest developments and future research of this area.

Electromagnetic Side Channel Analysis (EM-SCA) is a major area of interest within the field of cybersecurity. EM-SCA makes use of the electromagnetic radiation that naturally leaks from any device that runs on electricity. Information about the observed device can be gained by gathering and analysing these electromagnetic traces. Numerous studies have demonstrated the applicability of this side channel in various environments for legal and illegal objectives. On the other hand, multi-robot systems, including swarm robotics, have received considerable attention in recent years due to their ability to conduct complex tasks using simple robots cooperating with each other. Although multi-robot and swarm robot systems are likely to be widely used in practical applications in the near future, security concerns in this context have not yet received enough attention. In particular, to the best of our knowledge, EM-SCA threats and benefits have never been thoroughly examined in this context before. In order to spotlight this matter, this work begins with a thorough introduction to EM-SCA and provides a taxonomic structure. Then, guided by this taxonomy, we present a range of EM-SCA scenarios that need to be considered in multi-robot applications.

Vibration frequency and vibration duration are disturbance factors for the structural properties of clay. This study investigates how the vibration frequency and vibration duration from construction disturbances affect the mechanical properties of Zhanjiang Formation structural clay. An electric, frequency-adjustable vibration table applied varying frequencies and durations of vibration to undisturbed soil, creating structural clay samples with different disturbance degrees. Unconfined compressive strength tests and one-dimensional consolidation compression tests were conducted on these samples to obtain disturbance degrees RDq and RDS, defined by strength loss values and compression deformation characteristics, respectively. Orthogonal experiments and grey correlation analysis were used to explore the effects of vibration frequency and vibration duration on the mechanical properties of Zhanjiang Formation structural clay. The results indicated that disturbance degrees RDq and RDS increased linearly with increase in vibration frequency and vibration duration. Range analysis was conducted using two-factor three-level orthogonal experiment of disturbance degrees, and a grey relational analysis model was established to determine the primary and secondary effects of vibration duration and vibration frequency on the mechanical properties of Zhanjiang Formation structural clay. The results demonstrated that the findings from orthogonal experiments and grey relational analysis were consistent, showing that vibration duration had a more significant impact than vibration frequency on the mechanical properties of structural clay. The conclusion suggests that vibration disturbance manifests as a “fatigue damage effect”. Continuous vibration disturbance progressively weakens the cementation bonds between soil particles due to “accumulated” energy, leading to gradual fracture and destruction. With constant vibration frequency, longer durations, or with constant duration, higher frequencies intensify the “fatigue damage effect” of vibration disturbance. Furthermore, during vibration disturbance, Zhanjiang Formation structural clay shows a more pronounced “fatigue damage effect” from vibration duration than from vibration frequency, with cementation bonds between soil particles weakening more effectively due to “accumulated” energy. The research findings enhance the understanding of how vibration frequency and vibration duration from disturbance sources impact the mechanical properties of Zhanjiang Formation structural clay, offer theoretical guidance for using construction vibration machinery, and provide a reference for preventing and controlling soil disturbance.

Abstract Objective The value of tumor deposits (TDs) in the prognosis and staging of gastric cancer (GC) is still under debate. This study aims to evaluate the prognostic value of TDs and the best ways to incorporate TDs in the TNM classification of GC. Methods Patients (n = 3460) undergoing curative gastrectomy for GC in the West China Hospital from 2005 to 2017 were retrospectively reviewed and divided into two groups according to the TD status (positive vs. negative). Later, clinicopathological features and overall survival (OS) between the two groups were compared. Thereafter, the associations between the presence of TD and other clinicopathological factors were evaluated through logistic regression. In addition, univariate and multivariate Cox regression were conducted for determining prognostic factors. The possibility of selection bias was reduced through conducting the 1:1 propensity score matching (PSM) analysis. The modified classification systems proposed previously that incorporated TDs into the TNM staging system were assessed. Results There were 10.5% of patients (362/3460) diagnosed with TDs. TDs were significantly related to unfavorable factors such as advanced T stage and N stage and independently associated with poor prognosis. The 5-year OS of patients with TDs was significantly lower than that of patients without TDs (31.0% vs. 60.9%, P

Abstract Combined chemotherapy and targeted therapy holds immense potential in the management of advanced gastric cancer (GC). GC tissues exhibit an elevated expression level of protein kinase B (AKT), which contributes to disease progression and poor chemotherapeutic responsiveness. Inhibition of AKT expression through an AKT inhibitor, capivasertib (CAP), to enhance cytotoxicity of paclitaxel (PTX) toward GC cells is demonstrated in this study. A cathepsin B‐responsive polymeric nanoparticle prodrug system is employed for co‐delivery of PTX and CAP, resulting in a polymeric nano‐drug BPGP@CAP. The release of PTX and CAP is triggered in an environment with overexpressed cathepsin B upon lysosomal uptake of BPGP@CAP. A synergistic therapeutic effect of PTX and CAP on killing GC cells is confirmed by in vitro and in vivo experiments. Mechanistic investigations suggested that CAP may inhibit AKT expression, leading to suppression of the phosphoinositide 3‐kinase (PI3K)/AKT signaling pathway. Encouragingly, CAP can synergize with PTX to exert potent antitumor effects against GC after they are co‐delivered via a polymeric drug delivery system, and this delivery system helped reduce their toxic side effects, which provides an effective therapeutic strategy for treating GC.

Abstract. Background:. The results of studies comparing Billroth-I (B-I) with Roux-en-Y (R-Y) reconstruction on the quality of life (QoL) are still inconsistent. The aim of this trial was to compare the long-term QoL of B-I with R-Y anastomosis after curative distal gastrectomy for gastric cancer. Methods:. A total of 140 patients undergoing curative distal gastrectomy with D2 lymphadenectomy in West China Hospital, Sichuan University from May 2011 to May 2014 were randomly assigned to the B-I group (N = 70) and R-Y group (N = 70). The follow-up time points were 1, 3, 6, 9, 12, 24, 36, 48, and 60 months after the operation. The final follow-up time was May 2019. The clinicopathological features, operative safety, postoperative recovery, long-term survival as well as QoL were compared, among which QoL score was the primary outcome. An intention-to-treat analysis was applied. Results:. The baseline characteristics were comparable between the two groups. There were no statistically significant differences in terms of postoperative morbidity and mortality rates, and postoperative recovery between the two groups. Less estimated blood loss and shorter surgical duration were found in the B-I group. There were no statistically significant differences in 5-year overall survival (79% [55/70] of the B-I group vs. 80% [56/70] of the R-Y group, P = 0.966) and recurrence-free survival rates (79% [55/70] of the B-I group vs. 78% [55/70] of the R-Y group, P = 0.979) between the two groups. The scores of the global health status of the R-Y group were higher than those of the B-I group with statistically significant differences (postoperative 1 year: 85.4 ± 13.1 vs. 88.8 ± 16.1, P = 0.033; postoperative 3 year: 87.3 ± 15.2 vs. 92.8 ± 11.3, P = 0.028; postoperative 5 year: 90.9 ± 13.7 vs. 96.4 ± 5.6, P = 0.010), and the reflux (postoperative 3 year: 8.8 ± 12.9 vs. 2.8 ± 5.3, P = 0.001; postoperative 5 year: 5.1 ± 9.8 vs. 1.8 ± 4.7, P = 0.033) and epigastric pain (postoperative 1 year: 11.8 ± 12.7 vs. 6.1 ± 8.8, P = 0.008; postoperative 3 year: 9.4 ± 10.6 vs. 4.6 ± 7.9, P = 0.006; postoperative 5 year: 6.0 ± 8.9 vs. 2.7 ± 4.6, P = 0.022) were milder in the R-Y group than those of the B-I group at the postoperative 1, 3, and 5-year time points. Conclusions:. Compared with B-I group, R-Y reconstruction was associated with better long-term QoL by reducing reflux and epigastric pain, without changing survival outcomes. Trial Registration:. ChiCTR.org.cn, ChiCTR-TRC-10001434.

Background: Gastric cancer (GC) is one of the common and fatal cancers. Even though the Tumor, Node, Metastasis (TNM) staging system is the most classical staging system recognized worldwide, it has been controversial because there are various factors affecting the prognosis of GC patients. Objectives: The study aims to evaluate the relationship between interleukin-6 (IL-6) and several clinical indicators and construct a prognostic model to better predict the prognosis of GC. Design: A retrospective study. Methods: Data of 249 patients with GC diagnosed in GC center of West China Hospital were collected. Clinicopathological characteristics were analyzed to determine whether there were differences between IL-6 HIGH group and IL-6 LOW group. Besides, the association between the two groups and tumor marker levels was clarified. The K-M curves of 3- and 5-year were plotted with log-rank test. Afterward, we conducted univariate and multivariate analysis and a predicting nomogram. Significantly, C-index, and calibration were used to evaluate the value of nomogram in predicting prognosis. Results: The overall survival of GC in the IL-6 HIGH and IL-6 LOW groups were 47.8 months (95% CI: 42.1–53.4) and 57.9 months (95% CI: 54.1–61.7), respectively, with significant differences ( p = 0.0046). Average tumor size of GC ( p = 0.000) and nerve invasion ( p = 0.018) were statistically significant between two groups. Multivariate analysis revealed that the factors affecting prognosis were IL-6 (

IntroductionThe primary objective of this study was to investigate the potential correlation between gut microbes and postoperative pulmonary infection in gastric cancer patients. Additionally, we aimed to deduce the mechanism of differential functional genes in disease progression to gain a better understanding of the underlying pathophysiology.MethodsA nested case–control study design was utilized to enroll patients with gastric cancer scheduled for surgery at West China Hospital of Sichuan University. Patients were categorized into two groups, namely, the pulmonary infection group and the control group, based on the development of postoperative pulmonary infection. Both groups were subjected to identical perioperative management protocols. Fecal samples were collected 24 h postoperatively and upon pulmonary infection diagnosis, along with matched controls. The collected samples were subjected to 16S rDNA and metagenomic analyses, and clinical data and blood samples were obtained for further analysis.ResultsA total of 180 fecal specimens were collected from 30 patients in both the pulmonary infection and control groups for 16S rDNA analysis, and 3 fecal samples from each group were selected for metagenomic analysis. The study revealed significant alterations in the functional genes of the intestinal microbiome in patients with postoperative pulmonary infection in gastric cancer, primarily involving Klebsiella, Enterobacter, Ruminococcus, and Collinsella. During postoperative pulmonary infection, gut flora and inflammatory factors were found to be associated with the lipopolysaccharide synthesis pathway and short-chain fatty acid (SCFA) synthesis pathway.DiscussionThe study identified enriched populations of Klebsiella, Escherella, and intestinal bacteria during pulmonary infection following gastric cancer surgery. These bacteria were found to regulate the lipopolysaccharide synthesis pathway, contributing to the initiation and progression of pulmonary infections. Inflammation modulation in patients with postoperative pulmonary infection may be mediated by short-chain fatty acids. The study also revealed that SCFA synthesis pathways were disrupted, affecting inflammation-related immunosuppression pathways. By controlling and maintaining intestinal barrier function, SCFAs may potentially reduce the occurrence of pulmonary infections after gastric cancer surgery. These findings suggest that targeting the gut microbiome and SCFA synthesis pathways may be a promising approach for preventing postoperative pulmonary infections in gastric cancer patients.

Abstract Drug resistance remains the greatest challenge in improving outcomes for cancer patients who receive chemotherapy and targeted therapy. Surmounting evidence suggests that a subpopulation of cancer cells could escape intense selective drug treatment by entering a drug‐tolerant state without genetic variations. These drug‐tolerant cells (DTCs) are characterized with a slow proliferation rate and a reversible phenotype. They reside in the tumor region and may serve as a reservoir for resistant phenotypes. The survival of DTCs is regulated by epigenetic modifications, transcriptional regulation, mRNA translation remodeling, metabolic changes, antiapoptosis, interactions with the tumor microenvironment, and activation of signaling pathways. Thus, targeting the regulators of DTCs opens a new avenue for the treatment of therapy‐resistant tumors. In this review, we first provide an overview of common characteristics of DTCs and the regulating networks in DTCs development. We also discuss the potential therapeutic opportunities to target DTCs. Last, we discuss the current challenges and prospects of the DTC‐targeting approach to overcome acquired drug resistance. Reviewing the latest developments in DTC research could be essential in discovering of methods to eliminate DTCs, which may represent a novel therapeutic strategy for preventing drug resistance in the future.

Abstract Background Intraperitoneal paclitaxel is proved to be efficient for peritoneal metastasis of gastric cancer. It remains uncertain the efficacy and safety of the triplets regimen which combined intraperitoneal high‐dose paclitaxel with systemic SOX in gastric cancer patients with peritoneal metastasis. This study aimed to evaluate the efficacy and safety of intraperitoneal administration of high‐dose paclitaxel, intravenous oxaliplatin and S‐1 in patients with peritoneal metastatic gastric cancer. Methods This single‐center, prospective, single‐arm phase II study was conducted between January 2017 and May 2019 in West China Hospital, Sichuan University. Patients diagnosed with primary gastric cancer by histopathology and confirmed synchronous peritoneal metastasis were enrolled. This study aimed to evaluate efficacy and safety of intraperitoneal administration of high‐dose paclitaxel (80 mg/m2, d1), intravenous oxaliplatin (100 mg/m2, d1), and S‐1 (80 mg/m2, d1‐14) of patients. The primary endpoint was 1‐year overall survival rate, and the second endpoints were progression‐free survival (PFS), overall survival (OS), overall response rate (ORR), disease control rate (DCR) and adverse events. Results In this single‐arm phase II clinical trial, 49 patients received SOX combined intraperitoneal high‐dose paclitaxel treatment. One‐year survival rate was 81.6% (95% CI, 68.6–90.0%). Median PFS and OS were 6.50 months (95% CI, 2.89–10.11) and 16.9 months (95% CI, 13.58 to 20.22), respectively; ORR was 55.3% (95% CI, 41.3–68.6) and DCR was 76.6% (95% CI, 62.8–86.4). Thirteen patients underwent second laparoscopic detection, but only nine ultimately underwent radical gastrectomy. Subgroup analysis showed that sPCI ≤12 was a good index for a favorable prognosis. The most frequent grade 3/4 toxicities were neutropenia (40.8%), anemia (22.4%), leukopenia (18.4%), nausea (14.3%), and vomiting (12.2%). None of the patients had any intraperitoneal catheter‐related complications. Conclusions Intraperitoneal high‐dose paclitaxel with systemic SOX is an effective and tolerable first‐line treatment for patients with peritoneal metastatic gastric cancer and patients with sPCI≤12 scores might be recommended crowd for this regimen as conversion therapy.

As the potential of directed acyclic graph (DAG)-based distributed ledgers in IoT systems unfolds, a need arises to understand their intricate dynamics in real-world scenarios. It is well known that discrete event simulations can provide high-fidelity evaluations of protocols. However, there is a lack of public discrete event simulators capable of assessing DAG-based distributed ledgers. In this paper, a discrete-event-based distributed ledger simulator is introduced, with which we investigate a custom Python-based implementation of IOTA’s Tangle DAG protocol. The study reveals the dynamics of Tangle (particularly Poisson processes in transaction dynamics), the efficiency and intricacies of the random walk in Tangle, and the quantitative assessment of node convergence. Furthermore, the research underscores the significance of weight updates without depth limitations and provides insights into the role, challenges, and implications of the coordinator/validator in DAG architectures. The results are striking, and although the findings are reported only for Tangle, they demonstrate the need for adaptable and versatile discrete event simulators for DAG architectures and tip selection methodologies in general.

Abstract Background Acoustic conditions in the operating room have different impacts on surgeon’s performance. Their effects on the performance of surgical teams are not well documented. We investigated if laparoscopic teams operating under pleasant acoustic conditions would perform better than under noisy conditions. Methods We recruited 114 surgical residents and built 57 two-person teams. Each team was required to perform two laparoscopic tasks (object transportation and collaborative suturing) on a simulation training box under music, neutral, and noisy acoustic conditions. Data were extracted from video recordings of each performance for analysis. Task performance was measured by the duration of time to complete a task and the total number of errors, and objective performance scores. The measures were compared over the three acoustic conditions. Results A music environment elicited higher performance scores than a noisy environment for both the object transportation (performance score: 66.3 ± 8.6 vs. 57.6 ± 11.2; p

Background The 13C-urea breath test (13C-UBT) is a kind of safe, noninvasive, and reliable measure for Helicobacter pylori (H. pylori) infection diagnosis in patients with complete stomach. Nevertheless, the test has not indicated fully precision in people who have had a gastrectomy. So, we made the use of a systematic review of plentiful published resources and research and meta-analysis. There are prominent research achievements regarding of utilizing 13C-UBT for H. pylori infection patients diagnosis with the residual stomach. Method We searched publications available on Cochrane, PubMed, and Embase databases, and on the web of science. The last search was performed in May 2021. The basis model for this meta-analysis was fixed-effect through Metadisc Beta 1.4 software (Universidad Complutense, Madrid, Spain). When measuring the precision of 13C-urea breath test, we utilized the ratio analysis such as Ratio of diagnostic odds ratio (DOR) and its corresponding 95% confidence interval (95%CI). Results Ten associated researches were analyzed with a total of 1065 patients. In general, the sensitivity of included studies ranged from 0.40 to 1.00 (I2 = 81.2%), whereas the specificity ranged from 0.59 to 1.00 (I2 = 90.1%). The pooled sensitivity, specificity were 0.83 (95% CI, 0.79–0.86; P

The Internet of Drones (IoD) has attracted increasing attention in recent years because of its portability and automation, and is being deployed in a wide range of fields (e.g., military, rescue and entertainment). Nevertheless, as a result of the inherently open nature of radio transmission paths in the IoD, data collected, generated or handled by drones is plagued by many security concerns. Since security and privacy are among the foremost challenges for the IoD, in this paper we conduct a comprehensive review on security issues and solutions for IoD security, discussing IoD-related security requirements and identifying the latest advancement in IoD security research. This review analyzes a host of important security technologies with emphases on authentication techniques and blockchain-powered schemes. Based on a detailed analysis, we present the challenges faced by current methodologies and recommend future IoD security research directions. This review shows that appropriate security measures are needed to address IoD security issues, and that newly designed security solutions should particularly consider the balance between the level of security and cost efficiency.

Biometric authentication is, over time, becoming an indispensable complementary component to traditional authentication methods that use passwords and tokens. As a result, the research interest in the protection techniques for the biometric template has also grown considerably. In this paper, we present a light-weight AI-based biometric authentication that operates based on the binary representation of a biometric instance. In details, a binary classifier will be trained using the binary strings that represent the intraclass and interclass biometric subjects. The Support Vector Machine and Multi-layer Perceptron Neural Network are chosen as the classifier to evaluate the fingerprint-based and iris-based authentication capability. Afterward, the authenticated biometric string is fed to a hash function to produce a hash value, which is to be used in a Zero-Knowledge-Proof Protocol for the purpose of privacy preservation. In order to improve the recognition of the classifier, we devise a simple yet efficient strategy to enhance the discriminativeness of the binary strings and name it the Composite Features Retrieval. We evaluated the proposed method with the four publicly available fingerprint datasets FVC2002-DB1, FVC2002-DB2, FVC2002-DB3, and FVC2004-DB2 and the iris dataset UBIRISv1. The promising performance shows this method's capability.

Smart grids are vulnerable to stealthy false data injection attacks (SFDIAs), as SFDIAs can bypass residual-based bad data detection mechanisms. Methods based on deep learning technology have shown promising accuracy in the detection of SFDIAs. However, most existing methods rely on the temporal structure of a sequence of measurements but do not take account of the spatial structure between buses and transmission lines. To address this issue, we propose a spatiotemporal deep network, PowerFDNet, for the SFDIA detection in AC-model power grids. The PowerFDNet consists of two sub-architectures: spatial architecture (SA) and temporal architecture (TA). The SA is aimed at extracting representations of bus/line measurements and modeling the spatial structure based on their representations. The TA is aimed at modeling the temporal structure of a sequence of measurements. Therefore, the proposed PowerFDNet can effectively model the spatiotemporal structure of measurements. Case studies on the detection of SFDIAs on the benchmark smart grids show that the PowerFDNet achieved significant improvement compared with the state-of-the-art SFDIA detection methods. In addition, an IoT-oriented lightweight prototype of size 52 MB is implemented and tested for mobile devices, which demonstrates the potential applications on mobile devices. The trained model will be available at [Online]. Available: https://github.com/FrankYinXF/PowerFDNet.

Objective To explore patients’ experiences of enhanced recovery after surgery (ERAS) and to identify issues in the implementation of ERAS from the patient’s perspective.Design The systematic review and qualitative analysis were based on the Joanna Briggs Institute’s methodology for conducting synthesis.Data sources Relevant studies published in four databases, that is, Web of Science, PubMed, Ovid Embase and the Cochrane Library, were systematically searched, and some studies were supplemented by key authors and reference lists.Study selection Thirty-one studies were identified, involving 1069 surgical patients enrolled in the ERAS programme. The inclusion and exclusion criteria were formulated based on the Population, Interest of phenomena, Context, Study design criteria recommended by the Joanna Briggs Institute to determine the scope of article retrieval. The inclusion criteria were as follows: ERAS patients’ experiences; qualitative data; English language and published from January 1990 to August 2021.Data extraction Data were extracted from relevant studies using the standardised data extraction tool from Joanna Briggs Institute Qualitative Assessment and Review Instrument for qualitative research.Data synthesis The themes in the structure dimension are as follows: (1) patients cared about the timeliness of healthcare professionals’ help; (2) patients cared about the professionalism of family care; and (3) patients misunderstood and worried about the safety of ERAS. The themes in the process dimension are as follows: (1) patients needed adequate and accurate information from healthcare professionals; (2) patients needed to communicate adequately with healthcare professionals; (3) patients hoped to develop a personalised treatment plan and (4) patients required ongoing follow-up services. The theme in the outcome dimension is as follows: patients wanted to effectively improve severe postoperative symptoms.Conclusions Evaluating ERAS from the patient’s perspective can reveal the omissions and deficiencies of healthcare professionals in clinical care so that problems in patients’ recovery process can be solved in a timely manner, reducing potential barriers to the implementation of ERAS.PROSPERO registration number CRD42021278631.

Deep insight into the forces driving chloramine decay in different pipe materials is the key to taking sound action to cope with pipe water quality deterioration. By using the newly developed RTCDM (refined Total Chloramine Decay Model) and pipe section reactor, the role of four typical pipes in disinfection chemistry was qualitatively and quantitatively compared, and the mechanism of pipe wall mediated chloramine decay was further described. As for the four typical pipes studied, the characteristics of deteriorating water quality, especially for accelerating total chloramine decay was in the order of cast iron pipe > steel pipe > cement lined ductile iron pipe > polypropylene-random pipe. Cast iron pipes, cement-lined ductile iron pipes, and steel pipes of long service age are characterized by one or two driving forces leading to TCR decay. Aged cast iron pipes could take up chloramine by Fe(0) and microbes (especially nitrifiers) spreading over the inner wall. Aged steel pipe is characterized by aggressive electrochemical corrosion and weak nitrification. Lime and gypsum leaching is the main cause, and nitrification/denitrification may also occur in aged cement-lined ductile iron pipe. Polypropylene-random pipes have a minimum effect on disinfection chemistry. This knowledge is of value in speculating the reasons leading to TCR loss in the full scale distribution system. HIGHLIGHTS The developed RTCDM has good performance for predicting TCR decay in the bulk phase.; The ability to accelerate TCR decay was in the order cast iron (CI) > steel > cement lined DI > polypropylene random copolymer (PPR).; Chemical corrosion and nitrification are the main causes of TCR consumption for aged CI pipe.; Aggressive corrosion and weak nitrification are the main causes of TCR lost for aged steel pipe.;

The emerging field of privacy-preserving biometrics is attracting significant attention, as a paradigm that can address several of the key concerns in cryptographic authentication processes, whilst simultaneously addressing the issues of privacy. This paper contributes to the understanding of the field from following perspectives: (1) It proposes a novel and comprehensive taxonomy of privacy-preserving biometrics for the classification of the knowledge/existing literature in the field. (2) It provides a taxonomy-guided literature survey. (3) Open research problems/future works are discussed. (4) As the techniques used in privacy-preserving biometric authentication systems rely upon, or integrate with, general biometric matching techniques, a taxonomy and summary of the state-of-art biometrics matching techniques has also been developed. Such system-level knowledge organization will help produce excellent self-contained contents of reference materials for researchers in both the biometrics community and cryptography community who would otherwise have difficulty in understanding the relevant materials from the other community.

Blockchain and smart contracts have seen significant application over the last decade, revolutionising many industries, including cryptocurrency, finance and banking, and supply chain management. In many cases, however, the transparency provided potentially comes at the cost of privacy. Blockchain does have potential uses to increase privacy-preservation. This paper outlines the current state of privacy preservation utilising Blockchain and Smart Contracts, as applied to a number of fields and problem domains. It provides a background of blockchain, outlines the challenges in blockchain as they relate to privacy, and then classifies into areas in which this paradigm can be applied to increase or protect privacy. These areas are cryptocurrency, data management and storage, e-voting, the Internet of Things, and smart agriculture. This work then proposes PPSAF, a new privacy-preserving framework designed explicitly for the issues that are present in smart agriculture. Finally, this work outlines future directions of research in areas combining future technologies, privacy-preservation and blockchain.

Contactless fingerprint biometrics has achieved rapid development in the past decades thanks to its inherent advantages, such as no physical contact between a finger and a sensor, no contamination by latent fingerprints, and more hygienic. These advantages have paved the way for new 2D or 3D contactless fingerprint-based applications and have promoted a larger number of academic publications in recent years. Therefore, it is necessary and important to conduct a comprehensive survey on contactless fingerprint biometric technology, review the latest research findings on 2D and 3D contactless fingerprint recognition systems, and point out the future development direction of contactless fingerprint biometrics. In this work, a comprehensive survey is presented to review the 2D and 3D contactless fingerprint biometrics from four essential aspects: contactless fingerprint capture, fingerprint preprocessing, feature extraction, and template comparison. To serve as a good reference, we provide a well-structured taxonomy about contactless fingerprint biometrics. We also identify related research problems and future research directions.

Objective To explore the attitudes and barriers encountered in the implementation of enhanced recovery after surgery (ERAS) in China from the perspective of multidisciplinary team members.Design Based on Donabedian’s structure–process–outcome (SPO) model, a multicentre qualitative study using semistructured interviews was conducted.Setting From September 2020 to December 2020, the participants of this study were interviewed from six tertiary hospitals in Sichuan province (n=3), Jiangsu province (n=2) and Guangxi province (n=1) in China.Participants A total of 42 members, including surgeons (n=11), anaesthesiologists (n=10), surgical nurses (n=14) and dietitians(n=7) were interviewed.Results Multidisciplinary team (MDT) members still face many barriers during the process of implementing ERAS. Eight main themes are described around the barriers in the implementation of ERAS. Themes in the structure dimension are: (1) shortage of medical resources, (2) lack of policy support and (3) outdated concepts. Themes in the process dimension are: (1) poor doctor–patient collaboration, (2) poor communication and collaboration among MDT members and (3) lack of individualised management. Themes in the outcome dimension are: (1) low compliance and (2) high medical costs. The current implementation of ERAS is still based on ideas more than reality.Conclusions In general, barriers to ERAS implementation are broad. Identifying key elements of problems in the application and promotion of ERAS from the perspective of the MDT would provide a starting point for future quality improvement of ERAS, enhance the clinical effect of ERAS and increase formalised ERAS utilisation in China.

PurposeTo develop a bounding box (BBOX)-based radiomics model for the preoperative diagnosis of occult peritoneal metastasis (OPM) in advanced gastric cancer (AGC) patients.Materials and Methods599 AGC patients from 3 centers were retrospectively enrolled and were divided into training, validation, and testing cohorts. The minimum circumscribed rectangle of the ROIs for the largest tumor area (R_BBOX), the nonoverlapping area between the tumor and R_BBOX (peritumoral area; PERI) and the smallest rectangle that could completely contain the tumor determined by a radiologist (M_BBOX) were used as inputs to extract radiomic features. Multivariate logistic regression was used to construct a radiomics model to estimate the preoperative probability of OPM in AGC patients.ResultsThe M_BBOX model was not significantly different from R_BBOX in the validation cohort [AUC: M_BBOX model 0.871 (95% CI, 0.814–0.940) vs. R_BBOX model 0.873 (95% CI, 0.820–0.940); p = 0.937]. M_BBOX was selected as the final radiomics model because of its extremely low annotation cost and superior OPM discrimination performance (sensitivity of 85.7% and specificity of 82.8%) over the clinical model, and this radiomics model showed comparable diagnostic efficacy in the testing cohort.ConclusionsThe BBOX-based radiomics could serve as a simpler reliable and powerful tool for the preoperative diagnosis of OPM in AGC patients. And M_BBOX-based radiomics is simpler and less time consuming.

Deep learning has become a predominant method for solving data analysis problems in virtually all fields of science and engineering. The increasing complexity and the large volume of data collected by diverse sensor systems have spurred the development of deep learning methods and have fundamentally transformed the way the data are acquired, processed, analyzed, and interpreted. With the rapid development of deep learning technology and its ever-increasing range of successful applications across diverse sensor systems, there is an urgent need to provide a comprehensive investigation of deep learning in this domain from a holistic view. This survey paper aims to contribute to this by systematically investigating deep learning models/methods and their applications across diverse sensor systems. It also provides a comprehensive summary of deep learning implementation tips and links to tutorials, open-source codes, and pretrained models, which can serve as an excellent self-contained reference for deep learning practitioners and those seeking to innovate deep learning in this space. In addition, this paper provides insights into research topics in diverse sensor systems where deep learning has not yet been well-developed, and highlights challenges and future opportunities. This survey serves as a catalyst to accelerate the application and transformation of deep learning in diverse sensor systems.

The basic properties of blockchain, such as decentralization, security, and immutability, show promising potential for IoT applications. The main feature—decentralization of blockchain technology—depends on the consensus. However, consensus algorithms are mostly designed to work in extensive computational and communication environments for network security and immutability, which is not desirable for resource-restricted IoT applications. Many solutions are proposed to address this issue with modified consensus algorithms based on the legacy consensus, such as the PoW, PoS, and BFT, and new non-linear data structures, such as DAG. A systematic classification and analysis of various techniques in the field will be beneficial for both researchers and industrial practitioners. Most existing relevant surveys provide classifications intuitively based on the domain knowledge, which are infeasible to reveal the intrinsic and complicated relationships among the relevant basic concepts and techniques. In this paper, a powerful tool of systematic knowledge classification and explanation is introduced to structure the survey on blockchain consensus algorithms for resource-constrained IoT systems. More specifically, an ontology was developed for a consensus algorithm apropos of IoT adaptability. The developed ontology is subdivided into two parts—CONB and CONIoT—representing the classification of generic consensus algorithms and the ones that are particularly proposed for IoT, respectively. Guided by this ontology, an in depth discussion and analysis are provided on the major consensus algorithms and their IoT compliance based on design and implementation targets. Open research challenges and future research directions are provided.

Cognitive biometrics is an emerging branch of biometric technology. Recent research has demonstrated great potential for using cognitive biometrics in versatile applications, including biometric recognition and cognitive and emotional state recognition. There is a major need to summarize the latest developments in this field. Existing surveys have mainly focused on a small subset of cognitive biometric modalities, such as EEG and ECG. This article provides a comprehensive review of cognitive biometrics, covering all the major biosignal modalities and applications. A taxonomy is designed to structure the corresponding knowledge and guide the survey from signal acquisition and pre-processing to representation learning and pattern recognition. We provide a unified view of the methodological advances in these four aspects across various biosignals and applications, facilitating interdisciplinary research and knowledge transfer across fields. Furthermore, this article discusses open research directions in cognitive biometrics and proposes future prospects for developing reliable and secure cognitive biometric systems.