bluuebunny/crossref_metadata_2025 huggingface dataset:
A Distinguished Subgroup of Compact Abelian Groups
Authors: Dikranjan Dikran , Lewis Wayne , Loth Peter , Mader Adolf
Published: April 2022

Here “group” means additive abelian group. A compact group G contains δ–subgroups, that is, compact totally disconnected subgroups Δ such that G/Δ is a torus. The canonical subgroup Δ(G) of G that is the sum of all δ–subgroups of G turns out to have striking properties. Lewis, Loth and Mader obtained a comprehensive description of Δ(G) when considering only finite dimensional connected groups, but even for these, new and improved results are obtained here. For a compact group G, we prove the following: Δ(G) contains tor(G), is a dense, zero-dimensional subgroup of G containing every closed totally disconnected subgroup of G, and G/Δ(G) is torsion-free and divisible; Δ(G) is a functorial subgroup of G, it determines G up to topological isomorphism, and it leads to a “canonical” resolution theorem for G. The subgroup Δ(G) appeared before in the literature as td(G) motivated by completely different considerations. We survey and extend earlier results. It is shown that td, as a functor, preserves proper exactness of short sequences of compact groups.
What Does the Brain Have to Keep Working at Its Best? Resilience Mechanisms Such as Antioxidants and Brain/Cognitive Reserve for Counteracting Alzheimer’s Disease Degeneration
Authors: Cammisuli Davide Maria , Franzoni Ferdinando , Scarfò Giorgia , Fusi Jonathan , Gesi Marco , Bonuccelli Ubaldo , Daniele Simona , Martini Claudia , Castelnuovo Gianluca
Published: April 2022

Here we performed a narrative review highlighting the effect of brain/cognitive reserve and natural/synthetic antioxidants in exerting a neuroprotective effect against cognitive deterioration during physiological and pathological aging. Particularly, we discussed pathogenesis of Alzheimer’s disease, brain and cognitive reserve as means of resilience towards deterioration, and evidence from the literature about antioxidants’ role in sustaining cognitive functioning in the preclinical phase of dementia. During aging, the effects of disease-related brain changes upon cognition are reduced in individuals with higher cognitive reserve, which might lose its potential with emerging cognitive symptoms in the transitional phase over the continuum normal aging-dementia (i.e., Mild Cognitive Impairment). Starting from this assumption, MCI should represent a potential target of intervention in which antioxidants effects may contribute—in part—to counteract a more severe brain deterioration (alongside to cognitive stimulation) causing a rightward shift in the trajectory of cognitive decline, leading patients to cross the threshold for clinical dementia later.
Biological Prognostic Value of miR-155 for Survival Outcome in Head and Neck Squamous Cell Carcinomas: Systematic Review, Meta-Analysis and Trial Sequential Analysis
Authors: Dioguardi Mario , Spirito Francesca , Sovereto Diego , La Femina Lucia , Campobasso Alessandra , Cazzolla Angela Pia , Di Cosola Michele , Zhurakivska Khrystyna , Cantore Stefania , Ballini Andrea , Lo Muzio Lorenzo , Troiano Giuseppe
Published: April 2022

Head and neck squamous cell carcinoma (HNSCC) is one of the most common cancers worldwide; in fact, it is among the top six neoplasms, with an incidence of about 370,000 new cases per year. The 5-year survival rate, despite chemotherapy, radiotherapy, and surgery for stages 3 and 4 of the disease, is low. MicroRNAs (miRNAs) are a large group of small single-stranded non-coding endogenous RNAs, approximately 18–25 nucleotides in length, that play a significant role in the post-transcriptional regulation of genes. Recent studies investigated the tissue expression of miR-155 as a prognostic biomarker of survival in HNSCC. The purpose of this systematic review is, therefore, to investigate and summarize the current findings in the literature concerning the potential prognostic expression of tissue miR-155 in patients with HNSCC. The revision was performed according to PRISMA indications: three databases (PubMed, Scopus, and the Cochrane Register) were consulted through the use of keywords relevant to the revision topic. Totally, eight studies were included and meta-analyzed. The main results report for the aggregate HR values of 1.40 for OS, 1.36 for DFS, and 1.09 for DPS. Finally, a trial sequencing analysis was also conducted to test the robustness of the proposed meta-analysis.
Gender Differences in Takotsubo Syndrome
Authors: Murakami Tsutomu , Komiyama Tomoyoshi , Kobayashi Hiroyuki , Ikari Yuji
Published: April 2022

Most patients with Takotsubo Syndrome (TTS) are postmenopausal females. TTS in males is rare and gender differences have not been sufficiently investigated. Therefore, we investigated gender differences in TTS. TTS in males and females is often triggered by physical and emotional stress, respectively. Heart failure, a severe in-hospital complication, requires greater mechanical respiratory support in males. Fatal arrhythmias such as ventricular tachycardia and ventricular fibrillation and in-hospital mortality rates are higher in males. The white blood cell (WBC) count has been shown to be higher in males than in females with cardiovascular death compared with non-cardiovascular death. Therefore, the WBC count, a simple marker, may reflect severe TTS. Decreased estrogen levels, common in postmenopausal females, are a pathogenic mechanism of TTS. Females have a more significant increase in the extracellular matrix-receptor interaction than males. Moreover, the pathological findings after hematoxylin–eosin staining were different in males and females. Males had more severe complications than females in the acute phase of TTS; thus, more careful observations and interventions are likely required. From these results, it can be considered that the mechanism of the onset of TTS may be different between males and females. Therefore, it is necessary to fully understand the gender differences in order to more effectively manage TTS.
Universal Mitochondrial Multi-Locus Sequence Analysis (mtMLSA) to Characterise Populations of Unanticipated Plant Pest Biosecurity Detections
Authors: Hiszczynska-Sawicka Ela , Li Dongmei , Armstrong Karen F.
Published: April 2022

Biosecurity responses to post-border exotic pest detections are more effective with knowledge of where the species may have originated from or if recurrent detections are connected. Population genetic markers for this are typically species-specific and not available in advance for any but the highest risk species, leaving other less anticipated species difficult to assess at the time. Here, new degenerate PCR primer sets are designed for within the Lepidoptera and Diptera for the 3′ COI, ND3, ND6, and 3′ plus 5′ 16S gene regions. These are shown to be universal at the ordinal level amongst species of 14 and 15 families across 10 and 11 dipteran and lepidopteran superfamilies, respectively. Sequencing the ND3 amplicons as an example of all the loci confirmed detection of population-level variation. This supported finding multiple population haplotypes from the publicly available sequences. Concatenation of the sequences also confirmed that higher population resolution is achieved than for the individual genes. Although as-yet untested in a biosecurity situation, this method is a relatively simple, off-the-shelf means to characterise populations. This makes a proactive contribution to the toolbox of quarantine agencies at the time of detection without the need for unprepared species-specific research and development.
Probing Structural Perturbation of Biomolecules by Extracting Cryo-EM Data Heterogeneity
Authors: DeVore Kira , Chiu Po-Lin
Published: April 2022

Single-particle cryogenic electron microscopy (cryo-EM) has become an indispensable tool to probe high-resolution structural detail of biomolecules. It enables direct visualization of the biomolecules and opens a possibility for averaging molecular images to reconstruct a three-dimensional Coulomb potential density map. Newly developed algorithms for data analysis allow for the extraction of structural heterogeneity from a massive and low signal-to-noise-ratio (SNR) cryo-EM dataset, expanding our understanding of multiple conformational states, or further implications in dynamics, of the target biomolecule. This review provides an overview that briefly describes the workflow of single-particle cryo-EM, including imaging and data processing, and new methods developed for analyzing the data heterogeneity to understand the structural variability of biomolecules.
Continuance Intention to Use and Perceived Net Benefits as Perceived by Streaming Platform Users: An Application of the Updated IS Success Model
Authors: Kuo Chan-Sheng , Hsu Chia-Chien
Published: April 2022

Video game streaming has become a popular leisure activity which, depending on content, level of skill, and user interest, reaches a wide array of audiences. Using streaming platforms, client users are able to broadcast their video gameplays and use chat rooms to interact with other viewers and game players in real time. This paper aimed to develop and examine a theoretical explanation concerning the formation of client users’ satisfaction with, perceived net benefits of, and continuance intention to use a particular streaming platform. The study employed the updated IS Success Model. An online questionnaire was designed for individuals who were game streaming users of a streaming platform. A total of 632 usable responses were used in the data analysis. The results pointed out that system quality, information quality, service quality, and user motivation all positively and significantly affected user satisfaction. User satisfaction had a positive and significant effect on perceived net benefits and continuance intention to use a streaming platform. Perceived net benefits positively and significantly related to continuance intention to use a particular streaming platform. The moderating effect of emotional involvement between user satisfaction and perceived net benefits was confirmed.
Institutional Factors Impacting on International Construction Market Selection: Evidence from Chinese Contractors
Authors: Li Liping , Martek Igor , Chen Chuan
Published: April 2022

Institutions can be understood as the mechanism by which the rules societies operate under are formulated. As such, the international construction market is heavily affected by institutional factors. International market selection (IMS) is a fundamental decision that project contractors must make when entering the overseas arena. A variety of clues show that institutional factors have a complex impact on contractors’ IMS, but papers in this field tend to cover just one or two institutional factors or even ignore their role. Institutional factors exist in a multi-level social system, and the role of broader institutional factors in contractors’ IMS needs to be systematically explored. This study extensively collects institutional factors predicted to impact contractors’ IMS by literature review, selects 10 specific institutional factors from different perspectives, theoretically deduces their effects on contractor’s IMS, and takes international Chinese contractors’ IMS practice as the empirical research material and collects data for logistic regression analysis to test the assumptions. The results show that the IMS of contractors is affected by institutional factors from different levels and the effect of some factors on IMS must be weighted in a specific context. Specifically, IMSs of Chinese contractors are negatively affected by institutional distance but are not sensitive to the institutional environment. The results also confirm that if the host country and China have signed a trade agreement, belong to the same regional organization, or if China has provided foreign aid to a host country, Chinese contractors are more willing to choose the host market and central enterprises become more active in IMS than other firms. These findings can be expected to supplement IMS decision-making, with the empirical data presented affording an extension to the body of knowledge on contractors’ IMS process.
Limits in Laparoscopic Partial Splenectomy in Children
Authors: Tomuschat Christian , Aftzoglou Michail , Hagens Johanna , Boettcher Michael , Reinshagen Konrad
Published: April 2022

The aim of this paper is to assess the effectiveness and perioperative complications of splenic surgeries in children. In 41 splenectomies, an anterior abdominal laparoscopic approach was used, with 35 including a partial laparoscopic splenectomy. Of these, three needed a conversion to open. Six patients had a total splenectomy, three of which were open. Patients ranged in age from 5 to 18 years. Splenectomy was performed for a variety of causes, including hereditary spherocytosis (n = 20), splenic cysts (n = 13), sickle cell disease (n = 3), primary malignancy (n = 1), sepsis (n = 1), embolism (n = 1), anemia (n = 1), and hypersplenism (n = 1). The average length of stay was 7.6 days, and the average operation time was 169.3 min. Pleural effusion in the left hemithorax was found in 31.6% of the patients, with 5.3% requiring a thorax drain. The majority of patients had the highest platelet count two weeks after surgery. There was no evidence of wound infection, pancreatic leak, colon perforation, or postoperative sepsis. The most encountered perioperative complication was bleeding with the need of transfusion (n = 6), and one patient needed a diaphragm repair. A partial splenectomy (PS) can be a difficult procedure with a steep learning curve. For most children who require a splenic operation, this should be the primary procedure of choice.
Liver Frailty Index for Prediction of Short-Term Rehospitalization in Patients with Liver Cirrhosis
Authors: Kaps Leonard , Lukac Lejla , Michel Maurice , Kremer Wolfgang Maximilian , Hilscher Max , Gairing Simon Johannes , Galle Peter R. , Schattenberg Jörn M. , Wörns Marcus-Alexander , Nagel Michael , Labenz Christian
Published: April 2022

Background: Stratifying patients with liver cirrhosis for risk of rehospitalization is challenging with established scoring systems for chronic liver disease. Frailty captures the physical characteristics of patients with cirrhosis. Its value for predicting short-term rehospitalizations in hospitalized patients remains to be defined. Methods: Eighty-three non-electively hospitalized patients with liver cirrhosis were analyzed in this study. Frailty was assessed during the last 48 h of hospital stay with the liver frailty index (LFI). Patients were followed for 30-day rehospitalization. Results: In total, 26 (31%) patients were rehospitalized within 30 days. The median LFI was 4.5, and 43 (52%) patients were identified as frail. Rehospitalized patients had a significant higher LFI compared to patients without a rehospitalization within 30 days. In multivariable analysis, LFI as a metric variable (OR 2.36, p = 0.02) and lower platelet count (OR 0.98, p < 0.01) were independently associated with rehospitalization. LFI and its subtest chair stands had the best discriminative ability to predict rehospitalization, with AUROCs of 0.66 and 0.67, respectively. An LFI cut-off of >4.62 discriminated best between patients with and without elevated risk for rehospitalization within 30 days. Conclusions: Measures of frailty could be useful to identify patients at higher risk for short-term rehospitalization.
A Framework for Lane-Change Maneuvers of Connected Autonomous Vehicles in a Mixed-Traffic Environment
Authors: Du Runjia , Chen Sikai , Li Yujie , Alinizzi Majed , Labi Samuel
Published: April 2022

In the transition era towards connected autonomous vehicles (CAVs), the sharing of the roadway by CAVs and human-driven vehicles (HDVs) in a mixed-traffic stream is expected to pose safety and flow efficiency concerns even though CAVs may tend to adopt rather conservative maneuvering policies. Unfortunately, this will likely cause HDV drivers to unduly exploit such conservativeness by driving in ways that imperil safety. A context of this situation is lane-changing by the CAV, a potential major source of traffic disturbance at multi-lane highways that could impair their traffic flow efficiency. In dense, high-speed traffic conditions, it will be extremely unsafe for the CAV to change lanes without cooperation from neighboring vehicles in the traffic stream. To help address this issue, this paper developed a framework through which connected HDVs (CHDVs) could cooperate to facilitate safe and efficient lane-changing by the CAV. A numerical experiment was carried out to demonstrate the efficacy of the framework. The results indicated the CAVs’ lane-changing feasibility and the overall duration of the lane-changing if the CAV carries out that maneuver. It was observed that throughout the lane-changing process, the safety of not only the CAV but also of all neighboring vehicles, was promoted through the framework’s collision avoidance mechanism. The overall traffic flow efficiency was analyzed in terms of the ambient level of CHDV–CAV cooperation. Overall, the results of the study present evidence of how CHDV–CAV cooperation can help enhance the overall system efficiency.
Probabilistic Plan Recognition for Multi-Agent Systems under Temporal Logic Tasks
Authors: Yu Wentao , Li Shanghao , Tian Daiying , Cui Jinqiang
Published: April 2022

This paper studies the plan recognition problem of multi-agent systems with temporal logic tasks. The high-level temporal tasks are represented as linear temporal logic (LTL). We present a probabilistic plan recognition algorithm to predict the future goals and identify the temporal logic tasks of the agent based on the observations of their states and actions. We subsequently build a plan library composed of Nondeterministic Bu¨chi Automation to model the temporal logic tasks. We also propose a Boolean matrix generation algorithm to map the plan library to multi-agent trajectories and a task recognition algorithm to parse the Boolean matrix. Then, the probability calculation formula is proposed to calculate the posterior goal probability distribution, and the cold start situation of the plan recognition is solved using the Bayes formula. Finally, we validate the proposed algorithm via extensive comparative simulations.
Vehicle Re-Identification with Spatio-Temporal Model Leveraging by Pose View Embedding
Authors: Huang Wenxin , Zhong Xian , Jia Xuemei , Liu Wenxuan , Feng Meng , Wang Zheng , Satoh Shin’ichi
Published: April 2022

Vehicle re-identification (Re-ID) research has intensified as numerous advancements have been made along with the rapid development of person Re-ID. In this paper, we tackle the vehicle Re-ID problem in open scenarios. This research differs from the early-stage studies that focused on a certain view, and it faces more challenges due to view variations, illumination changes, occlusions, etc. Inspired by the research of person Re-ID, we propose leveraging pose view to enhance the discrimination performance of visual features and utilizing keypoints to improve the accuracy of pose recognition. However, the visual appearance information is still limited by the changing surroundings and extremely similar appearances of vehicles. To the best of our knowledge, few methods have been aware of the spatio-temporal information to supplement visual appearance information, but they neglect the influence of the driving direction. Considering the peculiar characteristic of vehicle movements, we observe that vehicles’ poses on camera views indicating their directions are closely related to spatio-temporal cues. Consequently, we design a two-branch framework for vehicle Re-ID, including a Keypoint-based Pose Embedding Visual (KPEV) model and a Keypoint-based Pose-Guided Spatio-Temporal (KPGST) model. These models are integrated into the framework, and the results of KPEV and KPGST are fused based on a Bayesian network. Extensive experiments performed on the VeRi-776 and VehicleID datasets related to functional urban surveillance scenarios demonstrate the competitive performance of our proposed approach.
A Graph Attention Mechanism-Based Multiagent Reinforcement-Learning Method for Task Scheduling in Edge Computing
Authors: Li Yinong , Li Jianbo , Pang Junjie
Published: April 2022

Multi-access edge computing (MEC) enables end devices with limited computing power to provide effective solutions while dealing with tasks that are computationally challenging. When each end device in an MEC scenario generates multiple tasks, how to reasonably and effectively schedule these tasks is a large-scale discrete action space problem. In addition, how to exploit the objectively existing spatial structure relationships in the given scenario is also an important factor to be considered in task-scheduling algorithms. In this work, we consider indivisible, time-sensitive tasks under this scenario and formalize the task-scheduling problem to minimize the long-term losses. We propose a multiagent collaborative deep reinforcement learning (DRL)-based distributed scheduling algorithm based on graph attention neural networks (GATs) to solve task-scheduling problems in the MEC scenario. Each end device creates a graph representation agent to extract potential spatial features in the scenario and a scheduling agent to extract the timing-related features of the tasks and make scheduling decisions using a gated recurrent unit (GRU). The simulation results show that, compared with several baseline algorithms, our proposed algorithm can take advantage of the spatial positional relationship of devices in the environment, significantly reduce the average delay and drop rate, and improve link utilization.
Enhancing Communication Reliability from the Semantic Level under Low SNR
Authors: Liu Yueling , Zhang Yichi , Luo Peng , Jiang Shengteng , Cao Kuo , Zhao Haitao , Wei Jibo
Published: April 2022

In the low signal-to-noise ratio region, a large number of bit errors occur, and it may exceed the channel error correction capability of the receiver. Traditional communication system may use the technology of automatic repeat-request to deal with this problem, which is time consuming and a waste of resources. To enhance the reliability of the communication system, we investigate reasoning and decoding at the semantic level instead of the grammar level. In particular, we propose a semantic communication model for text transmission, assisting the communication system to be more robust in terrible channel environments. Based on the traditional communication system, the language model BERT, part of speech tagging, and prior information concerning bit-flipping are introduced to enhance the semantic reasoning ability of the transceiver. Furthermore, this paper analyzes the effects of the sub-strategies on the performances of the improved communication model, such as the existence of a candidate set and language model. The numerical results show the effectiveness of our model in terms of improving the semantic accuracy measured by BLUE, the METEOR score, and the similarity score based on BERT between transmitted messages and recovered messages.
An Adaptive Finite Element/Finite Difference Domain Decomposition Method for Applications in Microwave Imaging
Authors: Beilina Larisa , Lindström Eric
Published: April 2022

A new domain decomposition method for Maxwell’s equations in conductive media is presented. Using this method, reconstruction algorithms are developed for the determination of the dielectric permittivity function using time-dependent scattered data of an electric field. All reconstruction algorithms are based on an optimization approach to find the stationary point of the Lagrangian. Adaptive reconstruction algorithms and space-mesh refinement indicators are also presented. Our computational tests show the qualitative reconstruction of the dielectric permittivity function using an anatomically realistic breast phantom.
Monascus purpureus Fermented Product Ameliorates Learning and Memory Impairment in the Amyloid Precursor Protein Transgenic J20 Mouse Model of Alzheimer’s Disease
Authors: Fang Ming-Chih , Cheng Irene Han-Juo , Chen Chien-Li
Published: April 2022

Evidence suggests that various hallmarks such as amyloid overproduction, tau dysfunction, insulin resistance/diabetic mechanisms, and neuroinflammation are associated with Alzheimer’s disease (AD). This study investigated the bioactive functions of ankaflavin (AK) and monascin (MS) in the fermented product of Monascus purpureus and found their abilities to ameliorate AD by modifying several important pathogenic factors including improved cognitive function, reversed behavioral deficits, reduced hippocampal β-amyloid peptide (Aβ) burden, decreased tau hyper-phosphorylation, and reduced neuroinflammation in the J20 mouse model of AD compared to wild type. Monascus purpureus fermented product (MPFP) was suggested to act as a peroxisome proliferator-activated receptor (PPAR)-γ agonist and it was compared against the action of a well-known anti-diabetic PPAR-γ agonist rosiglitazone. MPFP could be a promising therapeutic strategy for disease modification in AD.
In Utero Heat Stress Has Minimal Impacts on Processed Pork Products: A Comparative Study
Authors: Xue Siwen , Park Jun-young , Tuell Jacob R. , Maskal Jacob M. , Johnson Jay S. , Dinh Thu , Kim Yuan H. Brad
Published: April 2022

This study aimed to determine what effects in utero heat stress (IUHS) in pigs may have on quality of processed pork products. In two experiments, patties and emulsion sausages were prepared from lean and fat from pigs subjected to IUHS or in utero thermoneutral (IUTN) conditions. Patties formulated to contain 25% added fat had altered textural properties compared to those without additional fat, as shown by lower hardness, cohesiveness, springiness, and chewiness values (p < 0.05), which was not affected by IUHS treatment. Neither fat content nor IUHS treatment affected fluid losses of patties (p > 0.05). In general, 25% added fat patties had greater L*, a*, b*, hue angle, and chroma values than lean patties (p < 0.05). However, 25% added fat patties from the IUHS treatment maintained superior color stability during aerobic display, despite lean patties from this treatment exhibiting increased lipid oxidation (p < 0.05). For emulsion sausages, minimal differences in quality attributes and oxidative stability were found between treatment groups. Subcutaneous fat from IUHS pigs had greater C20:1 and C20:2 than IUTN (p < 0.05), although the magnitude of these differences was slight. Overall, the findings of this study suggest IUHS would have minimal impacts on the functional properties of raw pork, resulting in similar final quality of processed products to IUTN.
How Does Starch Structure Impact Amylolysis? Review of Current Strategies for Starch Digestibility Study
Authors: Wang Yuzi , Ral Jean-Philippe , Saulnier Luc , Kansou Kamal
Published: April 2022

In vitro digestibility of starch is a common analysis in human nutrition research, and generally consists of performing the hydrolysis of starch by α-amylase in specific conditions. Similar in vitro assays are also used in other research fields, where different methods can be used. Overall, the in vitro hydrolysis of native starch is a bridge between all of these methods. In this literature review, we examine the use of amylolysis assays in recent publications investigating the complex starch structure-amylolysis relation. This review is divided in two parts: (1) a brief review of the factors influencing the hydrolysis of starch and (2) a systematic review of the experimental designs and methods used in publications for the period 2016–2020. The latter reports on starch materials, factors investigated, characterization of the starch hydrolysis kinetics and data analysis techniques. This review shows that the dominant research strategy favors the comparison between a few starch samples most frequently described through crystallinity, granule type, amylose and chain length distribution with marked characteristics. This strategy aims at circumventing the multifactorial aspect of the starch digestion mechanism by focusing on specific features. An alternative strategy relies on computational approaches such as multivariate statistical analysis and machine learning techniques to decipher the role of each factor on amylolysis. While promising to address complexity, the limited use of a computational approach can be explained by the small size of the experimental datasets in most publications. This review shows that key steps towards the production of larger datasets are already available, in particular the generalization of rapid hydrolysis assays and the development of quantification approaches for most analytical results.
Preparation and Characterization of Solid Acid Catalysts for the Conversion of Sesamin into Asarinin in Sesame Oil
Authors: Yu Qiong , Wang Xue-De , Liu Hua-Min , Ma Yu-Xiang
Published: April 2022

Asarinin, an isomer of sesamin, has attracted attention because it has stronger biological properties than sesamin. The research on the conversion of sesamin into asarinin is limited. In this study, solid acid catalysts were screened and applied to promote the conversion of sesamin into asarinin in sesame oil. The results showed that citric acid loaded on zeolite beta (CTAH) was the optimal catalyst for asarinin production among the prepared catalysts. Characterization showed that CTAH had the greatest pore volume, largest surface area and strongest acid content. Response surface methodology (RSM) was applied to optimize the reaction conditions for asarinin yield using CTAH. The optimal reaction conditions were as follows: temperature, 85 °C; time, 2.7 h; catalyst amount, 1.6%. The predicted and experimental values of asarinin yield were 50.79 and 51.80 mg/100 g, respectively. The peroxide value and color in sesame oil samples treated with CTAH were clearly improved. In short, CTAH is a solid acid catalyst with potential application in the industrial conversion of sesamin into asarinin and in the improvement of sesame oil.
A Comparison Study on the Therapeutic Effect of High Protein Diets Based on Pork Protein versus Soybean Protein on Obese Mice
Authors: Jiang Songsong , Ji Shanshan , Tang Xinlei , Wang Tao , Wang Hengpeng , Meng Xiangren
Published: April 2022

In this study, an obese C57BL/6J mice model was induced to compare the effect of different high protein diets (soybean protein and pork protein) on obesity. The obese mice were randomly divided into four groups: natural recovery (NR), high-fat diet (HF), high soybean protein diet (HSP), and high pork protein diet (HPP) groups. After 12 weeks of dietary intervention, the obesity-related indexes of mice were measured, such as body weight, fat coefficients, blood lipid indexes and so on. Results showed that HSP and HPP decreased the weight and fat coefficients of mice, the levels of serum total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C) and leptin (p < 0.05). Soybean protein was shown to be more effective in reducing the weight and fat mass of obese mice, although pork protein seemed to have a better effect on regulating serum triglyceride (TG). In addition, the two high protein diets both alleviated hepatic fat deposition effectively. Furthermore, HPP and HSP decreased the expression of hepatic peroxisome proliferator-activated receptor-γ (PPAR-γ) and increased the protein expression of phosphorylated AMP-activated protein kinase (pAMPK), phosphorylated acetyl CoA carboxylase (pACC), and uncoupling protein 2 (UCP2) (p < 0.05). In conclusion, the study shows that high protein diets based on both pork protein and soybean protein alleviated abdominal obesity in mice effectively by regulating lipid metabolism, probably via the UCP2-AMPK-ACC signaling pathway.
Weakened PAM/PEI Polymer Gel for Oilfield Water Control: Remedy with Silica Nanoparticles
Authors: Amir Zulhelmi , Saaid Ismail Mohd , Mohd Junaidi Mohd Usman , Wan Bakar Wan Zairani
Published: April 2022

Polymer gel treatment is one of the most popular conformance control methods used in the petroleum industry. The advantage of the polymer gel system used in harsh reservoir conditions is an integrated process that must take into account all elements of gelation kinetics. In high-temperature applications, NH4Cl has been selected as a retarder to extend the gelation time of a PAM/PEI gel system. However, the gel network loses gel strength when salt and retarder increase, resulting in a weak gel structure, and becomes susceptible. The combination of these two variables leads to the development of a weak gel network, making it fragile and susceptible. To strengthen the weakened PAM/PEI polymer gel, the addition of silica nanoparticles (silica NP) is considered an effective remedy. This article presents the performance of PAM/PEI polymer gel strengthened with silica NP, especially the performance in terms of viscosity, gelation time, and gel strength, as well as performance in porous media. For example, the results exhibited a high storage modulus, G′, which is almost 800 Pa, compared to the loss modulus, G″, throughout the frequency and strain range, indicating solid-like behavior, at significantly high amounts of silica NP. This finding provides a better understanding and knowledge on the influence of solid particles in enhancing the performance of PAM/PEI polymer gel that has been weakened by salinity and retarder.
Analysis of the Knowledge, Attitudes, and Practice Model of Healthcare Professionals on Hearing Loss at Elderly Dementia Residences in Korea
Authors: Kwak Chanbeom , Seo Young Joon , Park Kyoung Ho , Han Woojae
Published: April 2022

Due to a lack of knowledge about age-related hearing loss, its early identification and appropriate intervention are not being carried out in the field of dementia care. Since the untreated hearing loss of the elderly leads to a more rapid cognitive decline, the present study aimed to understand the hearing-related knowledge, attitudes, and practices of healthcare professionals in long-term care (LTC) facilities in Korea. A total of 557 workers (104 facility managers and 453 healthcare professionals) in residential LTC participated in this cross-sectional multicenter survey study. The Korean version of the knowledge, attitudes, and practice (KAP) on-line survey with a five-point scale or yes/no response was applied as the experimental tool. The results of structural equation modeling showed that knowledge significantly affected the attitudes and health-seeking practices of the facility manager, and allied healthcare professionals demonstrated similar results, which showed the significant effects of that knowledge on attitudes and health-seeking practices. This clearly indicated that sufficient knowledge is the driving force for the health-seeking practices and positive attitudes of both the facility manager and the healthcare professionals. Thus, we suggest that a further step, such as the development of comprehensive and professional guidelines regarding hearing care information for these professionals in residential LTC facilities, should be followed, and believe that this effort could lead to improving hearing-related knowledge, attitudes, and practices in order to clinically and politically care for the elderly population.
Developing Relative Spatial Poverty Index Using Integrated Remote Sensing and Geospatial Big Data Approach: A Case Study of East Java, Indonesia
Authors: Putri Salwa Rizqina , Wijayanto Arie Wahyu , Sakti Anjar Dimara
Published: April 2022

Poverty data are usually collected through on-the-ground household-based socioeconomic surveys. Unfortunately, data collection with such conventional methods is expensive, laborious, and time-consuming. Additional information that can describe poverty with better granularity in scope and at lower cost, taking less time to update, is needed to address the limitations of the currently existing official poverty data. Numerous studies have suggested that the poverty proxy indicators are related to economic spatial concentration, infrastructure distribution, land cover, air pollution, and accessibility. However, the existing studies that integrate these potentials by utilizing multi-source remote sensing and geospatial big data are still limited, especially for identifying granular poverty in East Java, Indonesia. Through analysis, we found that the variables that represent the poverty of East Java in 2020 are night-time light intensity (NTL), built-up index (BUI), sulfur dioxide (SO2), point-of-interest (POI) density, and POI distance. In this study, we built a relative spatial poverty index (RSPI) to indicate the spatial poverty distribution at 1.5 km × 1.5 km grids by overlaying those variables, using a multi-scenario weighted sum model. It was found that the use of multi-source remote sensing and big data overlays has good potential to identify poverty using the geographic approach. The obtained RSPI is strongly correlated (Pearson correlation coefficient = 0.71 (p-value = 5.97×10−7) and Spearman rank correlation coefficient = 0.77 (p-value = 1.58×10−8) to the official poverty data, with the best root mean square error (RMSE) of 3.18%. The evaluation of RSPI shows that areas with high RSPI scores are geographically deprived and tend to be sparsely populated with more inadequate accessibility, and vice versa. The advantage of RSPI is that it is better at identifying poverty from a geographical perspective; hence, it can be used to overcome spatial poverty traps.
A Novel Method for Digital Reconstruction of the Mucogingival Borderline in Optical Scans of Dental Plaster Casts
Authors: Brandenburg Leonard Simon , Schlager Stefan , Harzig Lara Sophie , Steybe David , Rothweiler René Marcel , Burkhardt Felix , Spies Benedikt Christopher , Georgii Joachim , Metzger Marc Christian
Published: April 2022

Adequate soft-tissue dimensions have been shown to be crucial for the long-term success of dental implants. To date, there is evidence that placement of dental implants should only be conducted in an area covered with attached gingiva. Modern implant planning software does not visualize soft-tissue dimensions. This study aims to calculate the course of the mucogingival borderline (MG-BL) using statistical shape models (SSM). Visualization of the MG-BL allows the practitioner to consider the soft tissue supply during implant planning. To deploy an SSM of the MG-BL, healthy individuals were examined and the intra-oral anatomy was captured using an intra-oral scanner (IOS). The empirical anatomical data was superimposed and analyzed by principal component analysis. Using a Leave-One-Out Cross Validation (LOOCV), the prediction of the SSM was compared with the original anatomy extracted from IOS. The median error for MG-BL reconstruction was 1.06 mm (0.49–2.15 mm) and 0.81 mm (0.38–1.54 mm) for the maxilla and mandible, respectively. While this method forgoes any technical work or additional patient examination, it represents an effective and digital method for the depiction of soft-tissue dimensions. To achieve clinical applicability, a higher number of datasets has to be implemented in the SSM.
3D Printing and Virtual Surgical Planning in Oral and Maxillofacial Surgery
Authors: Zoabi Adeeb , Redenski Idan , Oren Daniel , Kasem Adi , Zigron Asaf , Daoud Shadi , Moskovich Liad , Kablan Fares , Srouji Samer
Published: April 2022

Compared to traditional manufacturing methods, additive manufacturing and 3D printing stand out in their ability to rapidly fabricate complex structures and precise geometries. The growing need for products with different designs, purposes and materials led to the development of 3D printing, serving as a driving force for the 4th industrial revolution and digitization of manufacturing. 3D printing has had a global impact on healthcare, with patient-customized implants now replacing generic implantable medical devices. This revolution has had a particularly significant impact on oral and maxillofacial surgery, where surgeons rely on precision medicine in everyday practice. Trauma, orthognathic surgery and total joint replacement therapy represent several examples of treatments improved by 3D technologies. The widespread and rapid implementation of 3D technologies in clinical settings has led to the development of point-of-care treatment facilities with in-house infrastructure, enabling surgical teams to participate in the 3D design and manufacturing of devices. 3D technologies have had a tremendous impact on clinical outcomes and on the way clinicians approach treatment planning. The current review offers our perspective on the implementation of 3D-based technologies in the field of oral and maxillofacial surgery, while indicating major clinical applications. Moreover, the current report outlines the 3D printing point-of-care concept in the field of oral and maxillofacial surgery.
Vasoactive Inotropic Score as a Prognostic Factor during (Cardio-) Respiratory ECMO
Authors: Sandrio Stany , Krebs Joerg , Leonardy Eva , Thiel Manfred , Schoettler Jochen J.
Published: April 2022

The vasoactive inotropic score (VIS) is calculated as a weighted sum of all administered vasopressor and inotropic medications and quantifies the amount of pharmacological cardiovascular support in patients with the most severe combined cardiopulmonary failure supported with extracorporeal membrane oxygenation (ECMO). This study evaluated (1) whether VIS prior to the initiation of ECMO is an independent predictor of survival in these patients and (2) whether VIS might guide the selection of the appropriate extracorporeal cannulation modality (Veno-Venous ‘V-V’ or Veno-VenoArterial ‘V-VA’). In this study, 39 V-VA and 182 V-V ECMO runs were retrospectively analyzed. VIS immediately prior to ECMO initiation (pre-ECMO) was 40 (10/113) in all patients, 30 (10/80) in patients with V-V ECMO and 207 (60/328) in patients with V-VA ECMO. Pre-ECMO VIS was an independent predictor of survival in univariate (AUC = 0.68, p = 0.001) and multi-variable analyses (p = 0.02). Pre-ECMO VIS was clearly associated with mortality (p = 0.001) in V-V ECMO group; however, V-VA ECMO disrupted this association (p = 0.18). Therefore, in conjunction with echocardiography, VIS might assist in selecting the appropriate ECMO cannulation strategy as patients with a pre-ECMO VIS ≥ 61.4 had significantly lower odds of survival compared to those with lower VIS.
LungNet22: A Fine-Tuned Model for Multiclass Classification and Prediction of Lung Disease Using X-ray Images
Authors: Shamrat F. M. Javed Mehedi , Azam Sami , Karim Asif , Islam Rakibul , Tasnim Zarrin , Ghosh Pronab , De Boer Friso
Published: April 2022

In recent years, lung disease has increased manyfold, causing millions of casualties annually. To combat the crisis, an efficient, reliable, and affordable lung disease diagnosis technique has become indispensable. In this study, a multiclass classification of lung disease from frontal chest X-ray imaging using a fine-tuned CNN model is proposed. The classification is conducted on 10 disease classes of the lungs, namely COVID-19, Effusion, Tuberculosis, Pneumonia, Lung Opacity, Mass, Nodule, Pneumothorax, and Pulmonary Fibrosis, along with the Normal class. The dataset is a collective dataset gathered from multiple sources. After pre-processing and balancing the dataset with eight augmentation techniques, a total of 80,000 X-ray images were fed to the model for classification purposes. Initially, eight pre-trained CNN models, AlexNet, GoogLeNet, InceptionV3, MobileNetV2, VGG16, ResNet 50, DenseNet121, and EfficientNetB7, were employed on the dataset. Among these, the VGG16 achieved the highest accuracy at 92.95%. To further improve the classification accuracy, LungNet22 was constructed upon the primary structure of the VGG16 model. An ablation study was used in the work to determine the different hyper-parameters. Using the Adam Optimizer, the proposed model achieved a commendable accuracy of 98.89%. To verify the performance of the model, several performance matrices, including the ROC curve and the AUC values, were computed as well.
From World Factory to Global City-Region: The Dynamics of Manufacturing in the Pearl River Delta and Its Spatial Pattern in the 21st Century
Authors: Li Xiaowen , Tan Yiming , Xue Desheng
Published: April 2022

Since the 21st century, the PRD has gradually been transforming from a world factory to a global city-region. Based on the manufacturing and urban economic data, this paper uses the upgrade (UPG) index of industrial structure, comparative advantage and economies of scale to evaluate the development level of manufacturing in the PRD from 2000 to 2019. Through geographically and temporally weighted regression (GTWR), this paper measures the impact of four types of components (R & D, service economy, production capability and foreign investment) on the development of manufacturing in different periods. The results show the disharmony between the scale, structure and quality of the manufacturing in different cities. The impact on the manufacturing from R & D has spatiotemporal differences; the impact of foreign investment on the west coast cities of PRD is stronger than that of the east coast cities with varied impact mechanisms. The impact of the service economy is strong in sub-core cities. The impact of production capability has a transmission effect from core cities to the sub-core cities, indicating the manufacturing subdivision of function within the region. By 2019, The PRD has gradually transformed into a dual-core structure and the two cores have differentiated development paths.
An Intelligent Fault Analysis and Diagnosis System for Electromagnet Manufacturing Process Based on Fuzzy Fault Tree and Evidence Theory
Authors: Pang Jihong , Dai Jinkun , Li Yong
Published: April 2022

Because an electromagnet has a complex structure and manufacturing process, it is difficult to analyze the overall failure of the electromagnet. In order to solve this problem, a fault intelligent analysis and diagnosis system based on fuzzy fault tree and evidence theory is proposed in this paper. First, the failure structure and fuzzy fault tree are generated according to the experience. Second, the probability of failure caused by basic events is obtained based on the data statistics of the insufficient holding force of the electromagnet in the past. Then, the probability of the basic events is given by using the synthesis rules of evidence theory. Next, the belief interval of the basic event is set as the fuzzy number, and the intelligent analysis is completed by using the calculated fuzzy importance. Finally, the validity and feasibility of the proposed method is proved by using the failure of insufficient retention force in the electromagnet manufacturing process as an example.
Tracking Control for Triple-Integrator and Quintuple-Integrator Systems with Single Input Using Zhang Neural Network with Time Delay Caused by Backward Finite-Divided Difference Formulas for Multiple-Order Derivatives
Authors: Guo Pengfei , Zhang Yunong
Published: April 2022

Tracking control for multiple-integrator systems is regarded as a fundamental problem associated with nonlinear dynamic systems in the physical and mathematical sciences, with many applications in engineering fields. In this paper, we adopt the Zhang neural network method to solve this nonlinear dynamic problem. In addition, in order to adapt to the requirements of real-world hardware implementations with higher-order precision for this problem, the multiple-order derivatives in the Zhang neural network method are estimated using backward finite-divided difference formulas with quadratic-order precision, thus producing time delays. As such, we name the proposed method the Zhang neural network method with time delay. Moreover, we present five theorems to describe the convergence property of the Zhang neural network method without time delay and the quadratic-order error pattern of the Zhang neural network method with time delay derived from the backward finite-divided difference formulas with quadratic-order precision, which specifically demonstrate the effect of the time delay. Finally, tracking controllers with quadratic-order precision for multiple-integrator systems are constructed using the Zhang neural network method with time delay, and two numerical experiments are presented to substantiate the theoretical results for the Zhang neural network methods with and without time delay.