bluuebunny/crossref_metadata_2025 huggingface dataset:
Understanding HCI Practices and Challenges of Experiment Reporting with Brain Signals: Towards Reproducibility and Reuse
Authors: Putze Felix , Putze Susanne , Sagehorn Merle , Micek Christopher , Solovey Erin T.
Published: March 2022

In human-computer interaction (HCI), there has been a push towards open science, but to date, this has not happened consistently for HCI research utilizing brain signals due to unclear guidelines to support reuse and reproduction. To understand existing practices in the field, this paper examines 110 publications, exploring domains, applications, modalities, mental states and processes, and more. This analysis reveals variance in how authors report experiments, which creates challenges to understand, reproduce, and build on that research. It then describes an overarching experiment model that provides a formal structure for reporting HCI research with brain signals, including definitions, terminology, categories, and examples for each aspect. Multiple distinct reporting styles were identified through factor analysis and tied to different types of research. The paper concludes with recommendations and discusses future challenges. This creates actionable items from the abstract model and empirical observations to make HCI research with brain signals more reproducible and reusable.
Design (Not) Lost in Translation: A Case Study of an Intimate-Space Socially Assistive “Robot” for Emotion Regulation
Authors: Isbister Katherine , Cottrell Peter , Cecchet Alessia , Dagan Ella , Theofanopoulou Nikki , Bertran Ferran Altarriba , Horowitz Aaron J. , Mead Nick , Schwartz Joel B. , Slovak Petr
Published: March 2022

We present a Research-through-Design case study of the design and development of an intimate-space tangible device perhaps best understood as a socially assistive robot, aimed at scaffolding children’s efforts at emotional regulation. This case study covers the initial research device development, as well as knowledge transfer to a product development company toward translating the research into a workable commercial product that could also serve as a robust “research product” for field trials. Key contributions to the literature include: (1) sharing of lessons learned from the knowledge transfer process that can be useful to others interested in developing robust products (whether commercial or research) that preserve design values, while allowing for large scale deployment and research; (2) articulation of a design space in HCI/HRI (Human Robot Interaction) of
intimate space socially assistive robots
, with the current artifact as a central exemplar, contextualized alongside other related HRI artifacts.
It’s Complicated: The Relationship between User Trust, Model Accuracy and Explanations in AI
Authors: Papenmeier Andrea , Kern Dagmar , Englebienne Gwenn , Seifert Christin
Published: March 2022

Automated decision-making systems become increasingly powerful due to higher model complexity. While powerful in prediction accuracy, Deep Learning models are black boxes by nature, preventing users from making informed judgments about the correctness and fairness of such an automated system. Explanations have been proposed as a general remedy to the black box problem. However, it remains unclear if effects of explanations on user trust generalise over varying accuracy levels. In an online user study with 959 participants, we examined the practical consequences of adding explanations for user trust: We evaluated trust for three explanation types on three classifiers of varying accuracy. We find that the influence of our explanations on trust differs depending on the classifier’s accuracy. Thus, the interplay between trust and explanations is more complex than previously reported. Our findings also reveal discrepancies between self-reported and behavioural trust, showing that the choice of trust measure impacts the results.
Iterative Design and Prototyping of Computer Vision Mediated Remote Sighted Assistance
Authors: Xie Jingyi , Reddie Madison , Lee Sooyeon , Billah Syed Masum , Zhou Zihan , Tsai Chun-Hua , Carroll John M.
Published: March 2022

Remote sighted assistance (RSA) is an emerging navigational aid for people with visual impairments (PVI). Using scenario-based design to illustrate our ideas, we developed a prototype showcasing potential applications for computer vision to support RSA interactions. We reviewed the prototype demonstrating real-world navigation scenarios with an RSA expert, and then iteratively refined the prototype based on feedback. We reviewed the refined prototype with 12 RSA professionals to evaluate the desirability and feasibility of the prototyped computer vision concepts. The RSA expert and professionals were engaged by, and reacted insightfully and constructively to the proposed design ideas. We discuss what we learned about key resources, goals, and challenges of the RSA prosthetic practice through our iterative prototype review, as well as implications for the design of RSA systems and the integration of computer vision technologies into RSA.
Mining Order-preserving Submatrices under Data Uncertainty: A Possible-world Approach and Efficient Approximation Methods
Authors: Cheng Ji , Yan Da , Qu Wenwen , Hao Xiaotian , Long Cheng , Ng Wilfred , Wang Xiaoling
Published: March 2022

Given a data matrix\( D \), a submatrix\( S \)of\( D \)is an order-preserving submatrix (OPSM) if there is a permutation of the columns of\( S \), under which the entry values of each row in\( S \)are strictly increasing. OPSM mining is widely used in real-life applications such as identifying coexpressed genes and finding customers with similar preference. However, noise is ubiquitous in real data matrices due to variable experimental conditions and measurement errors, which makes conventional OPSM mining algorithms inapplicable. No previous work on OPSM has ever considered uncertain value intervals using the well-established possible world semantics.

We establish two different definitions of significant OPSMs based on thepossible world semantics: (1) expected support-based and (2) probabilistic frequentness-based. An optimized dynamic programming approach is proposed to compute the probability that a row supports a particular column permutation, with a closed-form formula derived to efficiently handle the special case of uniform value distribution and an accurate cubic spline approximation approach that works well with any uncertain value distributions. To efficiently check the probabilistic frequentness, several effective pruning rules are designed to efficiently prune insignificant OPSMs; two approximation techniques based on the Poisson and Gaussian distributions, respectively, are proposed for further speedup. These techniques are integrated into our two OPSM mining algorithms, based on prefix-projection and Apriori, respectively. We further parallelize our prefix-projection-based mining algorithm using PrefixFPM, a recently proposed framework for parallel frequent pattern mining, and we achieve a good speedup with the number of CPU cores. Extensive experiments on real microarray data demonstrate that the OPSMs found by our algorithms have a much higher quality than those found by existing approaches.
One Type Context Is Not Enough: Global Context-aware Neural Machine Translation
Authors: Chen Linqing , Li Junhui , Gong Zhengxian , Zhang Min , Zhou Guodong
Published: March 2022

How to effectively model global context has been a critical challenge for document-level neural machine translation (NMT). Both preceding and global context have been carefully explored in the sequence-to-sequence (seq2seq) framework. However, previous studies generally map global context into one vector, which is not enough to well represent the entire document since this largely ignores the hierarchy between sentences and words within. In this article, we propose to model global context for source language from both sentence level and word level. Specifically at sentence level, we extract useful global context for the current sentence, while at word level, we compute global context against words within the current sentence. On this basis, both kinds of global context can be appropriately fused before being incorporated into the state-of-the-art seq2seq model, i.e.,
Transformer
. Detailed experimentation on various document-level translation tasks shows that global context at both sentence level and word level significantly improve translation performance. More encouraging, both kinds of global context are complementary. This leads to more improvement when both kinds of global context are used.
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
Authors: Matsubara Yoshitomo , Levorato Marco , Restuccia Francesco
Published: March 2022

Mobile devices such as smartphones and autonomous vehicles increasingly rely on deep neural networks (DNNs) to execute complex inference tasks such as image classification and speech recognition, among others. However, continuously executing the entire DNN on mobile devices can quickly deplete their battery. Although task offloading to cloud/edge servers may decrease the mobile device’s computational burden, erratic patterns in channel quality, network, and edge server load can lead to a significant delay in task execution. Recently, approaches based on split computing (SC) have been proposed, where the DNN is split into a head and a tail model, executed respectively on the mobile device and on the edge server. Ultimately, this may reduce bandwidth usage as well as energy consumption. Another approach, called early exiting (EE), trains models to embed multiple “exits” earlier in the architecture, each providing increasingly higher target accuracy. Therefore, the tradeoff between accuracy and delay can be tuned according to the current conditions or application demands. In this article, we provide a comprehensive survey of the state of the art in SC and EE strategies by presenting a comparison of the most relevant approaches. We conclude the article by providing a set of compelling research challenges.
Survey on the Objectives of Recommender Systems: Measures, Solutions, Evaluation Methodology, and New Perspectives
Authors: Alhijawi Bushra , Awajan Arafat , Fraihat Salam
Published: March 2022

Recently, recommender systems have played an increasingly important role in a wide variety of commercial applications to help users find favourite products. Research in the recommender system field has traditionally focused on the accuracy of predictions and the relevance of recommendations. However, other recommendation quality measures may have a significant impact on the overall performance of a recommender system and the satisfaction of users. Hence, researchers’ attention in this field has recently shifted to include other recommender system objectives. This article aims to provide a comprehensive review of recent research efforts on recommender systems based on the objectives achieved: relevance, diversity, novelty, coverage, and serendipity. In addition, the definitions and measures associated with these objectives are reviewed. Furthermore, the article surveys the evaluation methodology used to measure the impact of the main challenges on performance and the new applications of the recommender system. Finally, new perspectives, open issues, and future directions are provided to develop the field.
Effect of Solvent Motion on Ion Transport in Electrolytes
Authors: Mistry Aashutosh , Grundy Lorena S. , Halat David M. , Newman John , Balsara Nitash P. , Srinivasan Venkat
Published: March 2022

We use concentrated solution theory to derive an equation governing solvent velocity in a binary electrolyte when a current passes through it. This equation, in combination with the material balance equation, enables the prediction of electrolyte concentration profiles and species velocities as a function of space and time. This framework is used to predict ion velocities in Li-Li symmetric cells containing a mixture of lithium bis(trifluoromethanesulfonyl)imide and poly(ethylene oxide) (LiTFSI/PEO), for which the cation transference number relative to the solvent velocity,

t

+

0

,

can be either positive or negative, depending on salt concentration. Accounting for the solvent motion is increasingly important at higher concentrations. Especially for negative

t

+

0

,

if solvent velocity is set to zero, the cation velocity, based on the electrode-electrolyte interface reference frame, is pointed opposite to the current flow. However, when solvent motion is taken into account, the cation velocity, based on the same reference frame, is in the same direction as the current. This analysis demonstrates the importance of accounting for solvent velocity rigorously in seemingly simple systems such as symmetric cells.
Post-metallization annealing and photolithography effects in p-type Ge/Al2O3/Al MOS structures
Authors: Ioannou-Sougleridis V. , Alafakis S. , Pécz B. , Velessiotis D. , Vouroutzis N. Z. , Ladas S. , Barozzi M. , Pepponi G. , Skarlatos D.
Published: March 2022

In this work, the combined effect of negative tone photolithography and post-metallization annealing (PMA) on the electrical behavior of Al/Al2O3/p-Ge MOS structures are investigated. During photoresist development, the exposed upper part of the Al2O3 film weakens due to the reaction with the developer. Subsequent processes of Al deposition and PMA at 350 °C result in alumina thickness reduction. The gate electrode formation seems to involve at least three processes: (a) germanium substrate out-diffusion and accumulation at the top of the alumina layer that takes place during the alumina deposition, (b) alumina destabilization, and (c) germanium diffusion into the deposited Al metal and Al diffusion into the alumina. The overall effect is the reduction of the alumina thickness due to its partial consumption. It is shown that the germanium diffusion depends on the annealing duration, and not on the annealing ambient (inert or forming gas). Although PMA passivates interface traps near the valence band edge, the insulating properties of the stacks are degraded. This degradation appears as a low-level ac loss, attributed to a hopping current that flows through the Al2O3 layer. The results are discussed and compared to recently reported on Pt/Al2O3/p-Ge structures formed and treated under the same conditions.
Assessment of Clinical Diagnostic Efficacy of Pulmonary Function Test Based on DBN-SVM of Pediatric Asthma and Cough Variant Asthma
Authors: Li Hanyi , Zhang Xinmei , Zhao Qijun , Bai Xiang , Wang Shuying
Published: March 2022

The diagnosis of asthma depends on the unprejudiced proof of the varying airflow obstruction. The pulmonary function tests are carried out to evaluate the clinical value of different types of respiratory diseases in children or infants. This study is focused on the clinical evaluation of the pulmonary function tests in the diagnosis of pediatric asthma and cough variant asthma. A differential diagnosis method for chronic obstructive pulmonary disease (COPD) and asthma-COPD overlap with complementary diagnostic value is proposed. For the pulmonary function tests, the COPD gene dataset was selected and feature selection was performed using the DBN-SVM scoring method. For analysis and comparison, the differential diagnosis models were built using ROC curves for the accuracy of the deep belief network model and the support vector machine model. The sensitive features associated with COPD and ACO classification using the deep belief network model were found to be in good agreement with known clinical diagnostic strategies. The clinical diagnosis tests for pulmonary pediatric asthma and cough variant asthma were conducted on two groups of children, with both groups containing a basis of comparison. 80 cases of pediatric asthma and cough variant asthma were admitted from 2013 to 2014 and 80 cases of children with a healthy physical examination. The results of the two groups were compared. The results showed that the levels of FEV1, PEF, and FVC were significantly lower (

P
<
0.05

), in healthy children, and FEV1/FVC%, RV, and RV/TCL% were significantly higher (

P
<
0.05

) in children with asthma and cough variant asthma during acute exacerbation and chronic persistence. There were no statistically significant differences in the duration of clinical remission (

P
>
0.05

). Thus, the study suggests that confirmed cases of the diagnosis of pediatric asthma and cough variant asthma by pulmonary function tests were significantly higher than those of conventional tests (

P
<
0.05

). From this study, we can conclude that pulmonary function tests can accurately diagnose pediatric asthma and cough variant asthma, and also accurately reflect the development of the child’s disease, which is of high clinical value.
Crushed Capsicum chacoense Hunz Fruits: A Food Native Resource of Paraguay with Antioxidant and Anthelmintic Activity
Authors: Coronel Eva , Mereles Laura , Caballero Silvia , Alvarenga Nelson
Published: March 2022

The nutritional composition and toxicity of native plants with food potential like Capsicum chacoense are important for the safe use of populations and could be used as a source for searching for new drug candidates. Infections produced by parasites such as helminths are a public health concern for many countries. The drugs used for treating these diseases are few, and the emergence of resistance is a risk. In this work, the nutritional composition, acute toxicity, antioxidant activity, and anthelmintic activity of crushed C. chacoense fruits were evaluated. The composition was analyzed by standard procedures. Antioxidant activity was evaluated using the ABTS radical and the total phenolic compound (TPC) tests. The toxicity was evaluated on Swiss albino mice by the single-DL50-dose procedure. The anthelmintic activity was tested against Eisenia foetida. The samples presented high levels of dietary fiber (47.05-49.19 g/100 g), proteins (14.43-15.60 g/100 g), and potassium (1708-1733 mg/100 g). In the samples, the absence of acute lethal effects in doses lower than 2000 mg/kg was observed. A rich composition of TPC (517.26-543.32 mg GAE/100 g sample), total carotenoids (125.72-239.57 mg/kg), β-carotene (3.29-5.60 mg/kg), and good TAC was observed (154-158 mM TEAC/g SMTC). The methanolic extracts at the doses tested (2.5 to 40 mg/mL) showed good anthelmintic activity. The presence of alkaloids was demonstrated in the methanolic extract, consistent with the levels of capsaicin (131.85 and 98.80 mg/100 g) and dihydrocapsaicin (80.75 and 63.68 mg/100 g), with significant statistical differences between samples (p<0.05). These results show that through the chemical composition of this underutilized native resource and good fruit processing procedures, the C. chacoense fruits have a great nutraceutical potential of interest for the food and pharmaceutical industries.
Experimental Study on Enhancing Heavy Oil Recovery by Multimedia-Assisted Steam Flooding Process
Authors: Cheng Keyang , Huang Zhaoting , Li Jun , Luo Taotao , Li Hongbo
Published: March 2022

Aiming to reduce the negative effect of steam channeling in the late stage of steam flooding applied in heavy oil reservoirs, experimental studies were carried out on selective plugging of solid particles, added to single-medium- and multimedia-assisted steam flooding. This work also explored the mechanisms of enhancing heavy oil recovery by applying plugging agent- and multimedia-assisted steam flooding and its optimized injection parameters. Through solid particle plugging experiments, this study clarifies the high-efficiency solid particle plugging mechanism, with an optimized slug size of 0.40 PV and plugging ratio of over 98%. Through single-medium- and multimedia-assisted steam flooding experiments, this study confirms that gas-assisted steam flooding has an effect of synergistic oil displacement and that a CO2 with urea solution-assisted steam flooding method achieves the best production performance. Its oil recovery factor is 10.7% higher than that of the steam flooding process. Parallel sand pack models with a permeability difference of more than 10 times were used to mimic a heavy oil reservoir with high-permeability channels, and the selective plugging of solid particles was carried out. The plugging ratio of high-permeability formation reached 91.20%, playing an effective plugging role. Solid particle plugging is less effective to the low-permeability formation, with the plugging ratio at only 32.39%. Based on the selective plugging of solid particles, a plugging agent- and multimedia-assisted steam flooding experiment was conducted, the high-permeability formation was effectively plugged, and the swept volume of the low-permeability formation increased significantly. The final recovery factor of the high-permeability formation was enhanced by 11%, and the recovery factor of the low-permeability formation increased by 3 times, reaching 36.38%. Therefore, solid particle plugging effectively alleviates the impact of high-permeability formation caused by permeability difference during steam flooding.
Development Status and Hotspot Visualized Analysis of Autonomous Vehicles Based on CiteSpace
Authors: Yan Lixin , Zeng Tao , Deng Guangyang , Xu Qiujie
Published: March 2022

The safety of autonomous driving has been a constant concern, with autonomous driving becoming a research focus in the world today. This study collects the literature in the field of autonomous driving in the Web of Science core data collection from 2005 to 2020. The research status, hot research topics, and the development trend of autonomous driving are reviewed based on CiteSpace from the aspects of number of articles, national collaboration, institutions, scholars, research areas, literature co-citation, keywords, and trends. The results show that the quantitative development of autonomous driving literature has experienced three stages. The research of the United States, China, and Germany is leading in this field. The development of autonomous driving will be in the stage of man-machine driving for a long time. In this study, the progress and trend of visualization in the field of autonomous driving are given, and the future research direction is forecasted, which provides a theoretical basis for the development of autonomous driving. Furthermore, it has reference significance for relevant researchers to understand the research hotspots and future trends in this field.
A Diagnostic Model of Volleyball Techniques and Tactics Based on Wireless Communication Network
Authors: Yuan Zhigang , Zhang Yongkui , Li Bo , Jin Xinlong
Published: March 2022

Volleyball is a sport of teamwork competition and tactical coordination. It tests the ability of team members to cooperate with each other and personal resilience. With the development of wireless communication networks, how to use wireless communication networks in the analysis of volleyball tactics is the subject of this article. To explore how to design the volleyball tactics analysis model of the wireless communication network, this paper proposes the method of LTE system and MIMO technology and designs the tactics analysis model based on the characteristics of the wireless network, then combined volleyball tactics and game theory to design a wireless communication network volleyball tactics analysis model. Then, this paper designs the simulation experiment of scene allocation problem, the algorithm simulation experiment, and the comparative investigation and analysis of volleyball game spiking technical ability. The results of the experiment are optimized for the tactical analysis model, and finally, the improvement of the training of the volleyball tactical analysis model based on the wireless communication network is verified through comparative experiments. The experimental results show that the team fit of the volleyball tactical analysis model training based on the wireless communication network has increased by 22.12% compared with the traditional volleyball tactical training. Compared with the traditional volleyball tactical training, the personal on-the-spot adaptability of the volleyball tactical analysis model training based on the wireless communication network has increased by 9.05%.
Real-Time Tracking of Object Melting Based on Enhanced DeepLabv3+Network
Authors: Jiang Tian-yu , Ju Feng-lan , Dai Ya-xun , Li Jie , Li Yi-fan , Bai Yun-jie , Cui Ze-qian , Xu Zheng-han , Zhang Zun-Qian
Published: March 2022

In order to reveal the dissolution behavior of iron tailings in blast furnace slag, the main component of iron tailings, SiO2, was used for research. Aiming at the problem of information loss and inaccurate extraction of tracking molten SiO2 particles in high temperature, a method based on the improved DeepLabv3+ network was proposed to track, segment, and extract small object particles in real time. First, by improving the decoding layer of the DeepLabv3+ network, construct dense ASPP (atrous spatial pyramid pooling) modules with different dilation rates to optimize feature extraction, increase the shallow convolution of the backbone network, and merge it into the upper convolution decoding part to increase detailed capture. Secondly, integrate the lightweight network MobileNet v3 to reduce network parameters, further speed up image detection, and reduce the memory usage to achieve real-time image segmentation and adapt to low-level configuration hardware. Finally, improve the expression of the loss function for the binary classification model of small object in this paper, combining the advantages of the Dice Loss binary classification segmentation and the Focal Loss balance of positive and negative samples, solving the problem of unbalanced dataset caused by the small proportion of positive samples. Experimental results show that MIoU (mean intersection over union) of the proposed model for small object segmentation is 6% higher than that of the original model, the overall MIoU is increased by 3%, and the execution time and memory consumption are only half of the original model, which can be well applied to real-time tracking and segmentation of small particles.
Data Processing Method of Distributed Parallel Database System Based on Wireless Network
Authors: Guo Zhiqi , Jiang Guangkun , Zhao Jiacong
Published: March 2022

With the development of society and the arrival of the information age, data processing has become more and more complex, so people need to manage data systems through wireless communication, and distributed systems can effectively improve data analysis, so this paper is based on wireless communication. Distributed database systems are studied. With the rapid development of database systems, how to effectively obtain useful information about massive data has gradually become an important research problem of/with the field of data management. The purpose of this paper is to study how to research the data processing of distributed parallel database system based on wireless network. This paper puts forward the basic concepts of wireless network and distributed parallel database system and proposes a clustering analysis algorithm. The preimproved clustering analysis and the improved distributed parallel clustering analysis are described and compared in detail. From the data in the figure in the experimental part of the text, it can be seen that the efficiency of the database system before the improvement is the lowest at 41% and the highest at 58%. The efficiency of the improved distributed database system is at least 65%, and the highest are 95%. It can be seen that the efficiency of the improved distributed database system is much higher than that of the preimproved database system. So it is very feasible to use the distributed database system to process data.
Research on Financial Accounting of GDP Index Based on Numerical Simulation
Authors: Li Bo
Published: March 2022

Because the traditional GDP accounting method was difficult to meet the actual needs of governments for economic and social development, aiming at the problems of unclear data sources related to GDP and inconsistent GDP accounting results in the national economic accounting system, a GDP index financial accounting method based on numerical simulation was proposed. Firstly, it summarized the traditional GDP thought and its related accounting methods and analyzed the common problems existing in the traditional GDP accounting. Secondly, it expounded the concept of green GDP and its accounting scope and theoretical model and pointed out the key problems to be solved in green GDP accounting, such as ecological resource consumption and environmental pollution cost calculation. Finally, by analyzing the relationship between green GDP and main supporting indicators, the accounting method of GDP indicators based on numerical simulation was proposed, and the accounting result detection model based on the econometric model was given. Through empirical analysis, it showed that the GDP accounting method proposed in this paper has good feasibility and effectiveness and can effectively reflect the development of the economic operation. The accounting method proposed in this paper can also provide a reference basis for the construction of a green GDP system.
Research and Model Prediction on the Performance of Recycled Brick Powder Foam Concrete
Authors: Xie Hongyang , Dong Jianjun , Deng Yong , Dai Yiwen
Published: March 2022

In order to achieve resource conservation, protect the environment and realize the sustainable development of the construction industry, the low energy resource utilization of construction waste was explored. In this paper, the effect of air bubble swarm admixture, recycled brick powder admixture, water to material ratio, and HPMC content on the physical and mechanical properties of recycled brick powder foam concrete was investigated by conducting a 4‐factor, 5‐level orthogonal test with recycled brick powder as fine aggregate, and the effect of each factor on the physical and mechanical properties of recycled brick powder foam concrete was derived, and the optimum ratio of recycled brick powder foam concrete was determined by analysing the specific strength. Five machine learning models, namely, back propagation neural network improved by particle swarm optimization (PSO‐BP), support vector machine (SVM), multiple linear regression (MLR), random forest (RF), and back propagation neural network (BP), were used to predict the compressive strength of recycled brick powder foam concrete, and the PSO‐BP model was found to have obvious advantages in terms of prediction accuracy and model stability. The experimental results and prediction models can provide experimental and theoretical references for the research and application of recycled brick powder foam concrete.
Treatment Effect of Mometasone Furoate Cream on Lichen Sclerosus et Atrophicus of External Genitalia in Boys and Its Correlation with Toll-Like Receptor 4 and Myeloid Differentiation Factor 88
Authors: Sun Hong , Zhang Wei , Wang Guibo , Sun Chuanwei , Liang Yan , Li Aijun , Li Qingfeng , Lei Jie
Published: March 2022

Objective. The objective is to explore the treatment effect of mometasone furoate cream on lichen sclerosus et atrophicus (LSeA) of external genitalia in boys and its correlation with Toll-like receptor 4 (TLR4) and myeloid differentiation factor 88 (MyD88). Methods. A total of 100 boys treated in our hospital from January 2021 to January 2023 due to clinical manifestations in the external genitalia were selected as the study objects. All boys received redundant circumcision treatment, their protein expression of TLR4 and MyD88 in foreskin tissues was measured by BCA protein assay and western blot, and mometasone furoate cream was applied to those who were pathologically diagnosed with LSeA, so as to compare the levels of serum inflammatory factors and urodynamic indicators in the child patients before and after treatment. Results. The total clinical efficacy rate of LSeA child patients was up to 90.79%; after treatment, the maximum and mean urinary flow rates of child patients were significantly higher than before treatment

P
<
0.001

; compared with the non-LSeA group, the protein expression of TLR4, MyD88, and NF-κB was increased in the LSeA group

P
<
0.001

, and the mRNA expression of TLR4, MyD88, and NF-κB was significantly increased in the LSeA group

P
<
0.001

; the results of ROC curves showed that TLR4 had the highest AUC value, and during treatment, the incidence rate of adverse reactions in child patients was 6.58%. Conclusion. LSeA will increase the inflammatory reactions in child patients, and its pathogenesis may be related to the upregulation of TLR4, MyD88, NF-κB expression and thus activation of TLR4/MyD88/NF-κB signaling pathways. Applying mometasone furoate cream to LSeA patients after redundant circumcision can effectively reduce the inflammatory reactions in the body and improve their urodynamic indicators, with exact efficacy. Further research will be conducive to establishing a better treatment scheme for such child patients.
Screening and Validation of a Carvacrol-Targeting Viability-Regulating Protein, SLC6A3, in Liver Hepatocellular Carcinoma
Authors: Yin Xieling , Chen Hongjian , Chen Shi , Zhang Suqing
Published: March 2022

Background. Liver hepatocellular carcinoma (LIHC) is the second leading cause of tumor-related death in the world. Carvacrol was also found to inhibit multiple cancer types. Here, we proposed that Carvacrol inhibited LIHC. Methods. We used MTT assay to determine the inhibition of Carvacrol on LIHC cells. BATMAN-TCM was used to predict targets of Carvacrol. These targets were further screened by their survival association and expression in cancer using TCGA data. The bioinformatic screened candidates were further validated in in vitro experiments and clinical samples. Finally, docking models of the interaction of Carvacrol and target protein were conducted. Results. Carvacrol inhibited the viability of LIHC cell lines. 40 target genes of Carvacrol were predicted, 8 of them associated with survival. 4 genes were found differentially expressed in LIHC vs. normal liver. Among these genes, the expression of SLC6A3 and SCN4A was found affected by Carvacrol in LIHC cells, but only SLC6A3 correlated with the viability inhibition of Carvacrol on LIHC cell lines. A docking model of the interaction of Carvacrol and SLC6A3 was established with a good binding affinity. SLC6A3 knockdown and expression revealed that SLC6A3 promoted the viability of LIHC cells. Conclusion. Carvacrol inhibited the viability of LIHC cells by downregulating SLC6A3.
Challenging Case of Ectopic ACTH Secretion from Prostate Adenocarcinoma
Authors: Zeng Wanling , Khoo Joan
Published: March 2022

Cushing’s syndrome (CS) secondary to ectopic adrenocorticotrophic hormone (ACTH)-producing prostate cancer is rare with less than 50 cases reported. The diagnosis can be challenging due to atypical and variable clinical presentations of this uncommon source of ectopic ACTH secretion. We report a case of Cushing’s syndrome secondary to prostate adenocarcinoma who presented with symptoms of severe hypercortisolism with recurrent hypokalaemia, limb oedema, limb weakness, and sepsis. He presented with severe hypokalaemia and metabolic alkalosis (potassium 2.5 mmol/L and bicarbonate 36 mmol/L), with elevated 8 am cortisol 1229 nmol/L. ACTH-dependent Cushing’s syndrome was diagnosed with inappropriately normal ACTH 57.4 ng/L, significantly elevated 24-hour urine free cortisol and unsuppressed cortisol after 1 mg low-dose, 2-day low-dose, and 8 mg high-dose dexamethasone suppression tests. 68Ga-DOTANOC PET/CT showed an increase in DOTANOC avidity in the prostate gland, and his prostate biopsy specimen was stained positive for ACTH and markers for neuroendocrine differentiation. He was started on ketoconazole, which was switched to IV octreotide in view of liver dysfunction from hepatic metastases. He eventually succumbed to the disease after 3 months of his diagnosis. It is imperative to recognize prostate carcinoma as a source of ectopic ACTH secretion as it is associated with poor clinical outcomes, and the diagnosis can be missed due to atypical clinical presentations.
Personalized Recommendation of Rural Tourism Based on Traffic Classification and User Data Analysis
Authors: Bi Wei , Wang Guangming , Zhang Meijuan
Published: March 2022

Recently, With the development of global agricultural industrialization, modern agriculture has the functions of improving the quality of the ecological environment and providing people with the functions of sightseeing, leisure, and vacation. How to take the user as the center and combine the user’s personalized characteristics to offer the rural tourism products they need has become a research problem with real-world application value and challenge. In this context, this study develops an intelligent recommendation model by extensively analyzing the contents of rural tourism information platforms and product recommendation factors, as well as a rough set algorithm and traffic classification. To minimize the attributes of rural tourism product information and extract the core attribute, an attribute reduction approach based on a different matrix is implemented. Moreover, user interest similarity is computed and ranked to recommend rural tourist products. In addition, a personalized tourism attraction recommendation model is presented based on geographic area and period. The model achieved the highest average accuracy of 0.87%. The relevant experimental test results reveal that the system can provide accurate recommendations and services for rural tourism products.
Study on Progressive Failure of Hard Rock Tunnel After Excavation Under High Stress
Authors: Zhong Daning , Chen Jianlin , Zhou Hui , Chen Xiangrong , Jiang Yali
Published: March 2022

The analysis and simulation of brittle failure of hard rock tunnels under high stress is essential for understanding and mastering the brittle failure characteristics of rock masses and for analyzing and regulating the stability of surrounding rocks in underground projects. The rupture and deformation of hard brittle basalt in the dam site area is one of the key problems faced by Baihetan Hydropower Station. In the paper, the spalling characteristics of the surrounding rock on the right bank of the exploratory tunnel are summarized. CDEM (continuum‐based discrete element method) is used to carry out the numerical simulation and the fracture energy model considering cohesive force weakening and friction angle strengthening is adopted. The indoor test simulations are conducted first to verify the effectiveness of the model. Then the simulation of the right bank exploratory tunnel is conducted to study the brittle failure of the surrounding rock. The results are compared with the field exposure, and the stress and displacement of characteristic points of the surrounding rock are analyzed. The numerical results are in good agreement with the damage situation in the field and reflect the brittle failure characteristics of basalt under high‐stress conditions, which helps to reasonably grasp the damage situation of the surrounding rock and take corresponding support measures, and also proves the superiority of CDEM method in solving hard rock fracture, providing a technical reference for similar hard rock brittle failure problems in engineering.
A Dish Scheduling Model for Traditional Chinese Restaurants Based on Hybrid Multiple Criteria Decision-Making Algorithms and a Double-Layer Queuing Structure
Authors: Huang Feng , Chuang Yen-Ching , Zhang Yong-Hua , Lu Bin , Xu Hong-Lan
Published: March 2022

This paper proposes a dish scheduling model for traditional Chinese restaurants based on hybrid multiple criteria decision-making (MCDM) algorithms and a double-layer queuing structure (DLQS). It simulates real-world dish scheduling and may be universally applied. First, several factors that affect the dish ranking were identified, and their weights were determined. Subsequently, a modified Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was used to rank the dishes and enhance the ranking stability after classifying the dishes based on system preset categories. A DLQS was designed to transfer the dishes from the top layer of the queue to the bottom of the queue according to certain rules, so that they could be processed and served in the correct order. The case study indicates that this model may help in the automation of dish scheduling and improve overall customer satisfaction.
Role of Qualitative and Quantitative Indocyanine Green Angiography to Assess Mastectomy Skin Flaps Perfusion in Nipple/Skin-Sparing and Skin-Reducing Mastectomies with Implant-Based Breast Reconstruction
Authors: Mastronardi Manuela , Fracon Stefano , Scomersi Serena , Fezzi Margherita , Bortul Marina
Published: March 2022

Background and Objectives. Mastectomy skin flaps (MSF) necrosis is one of the most relevant complications following mastectomies with immediate breast reconstruction. The study evaluates a possible correlation between MSF qualitative and quantitative perfusion grade, using ICG angiography, and skin necrosis. Methods. Consecutive women scheduled for nipple/skin-sparing/skin-reducing mastectomy between May 2020 and April 2021 were prospectively enrolled. Patients were divided into Group 1 in the absence of superficial and full-thickness necrosis (SN; FTN) and Group 2 in the presence of both. T1 (time between ICG injection and the initial perfusion of the least perfused MSF area), ICG-Q1, and ICG-Q% (absolute and relative perfusion values of the least vascularized area) were collected. Results. 38 breasts were considered. FTN was reported in 4 breasts (10.5%) and SN in 3 (7.9%). The two groups statistically differ in T1 (Group2 > Group1) and ICG-Q% (Group1 > Group2) (

p

 < 0.05). T1 could statistically predict ICG-Q1 and ICG-Q%. Both quantitative values have a sensitivity of 57% and a NPV of 89%; ICG-Q% shows higher specificity (81% vs 77%) and PPV (40% vs 36%). Conclusions. Quantitative ICG angiography may additionally reduce MSF necrosis. Moreover, longer T1 may indicate possible postoperative necrosis. Considering these factors, intraoperative changes of reconstructive strategy could be adopted to reduce reconstructive failure.
Spatial Variability of Soil Organic Carbon Stock in Gurje Subwatershed, Hadiya Zone, Southern Ethiopia
Authors: Addise Temesgen , Bedadi Bobe , Regassa Alemayehu , Wogi Lemma , Feyissa Samuel
Published: March 2022

Soil organic carbon contents are expected to vary from place to place because of variation in soil properties. However, the extent of variability has not been explored in the study area. This study has, therefore, been initiated to assess the spatial variability of soil organic carbon stock in Gurje subwatershed Hadiya Zone, Southern Ethiopia. A total of 40 randomly predefined sampling points were identified for soil sampling using GIS and a total of 80 composite soil samples and 80 core samples were collected from those points at two sampling soil depths (0–20 cm and 20–40 cm). The ordinary kriging (OK) method was used as a geostatistical tool and applied to model the spatial variability of soil organic carbon in this study. With respect to soil depth, the coefficient of variation (CV%) for SOC and SOCS varied from 40.87 to 51.36%, which indicated moderate variability in the study area. For the land use types, the CV% varied from 7.94 to 42.06%, indicating low to moderate variability for the variables in the study area. The exponential semivariogram model described the spatial structure of SOC at 0–20 cm depth while the spherical one was used for SOCS. Moreover, the exponential model was best suited for SOCS at a soil depth of 20–40 cm, while the circular model was appropriate for SOC at this depth. The nugget/sill ratio (C0/C0 + C) of SOC and SOCS varied from nil to 15.58, reflecting a strong spatial dependence, which could be mainly due to the influence of intrinsic factors (e.g., natural variations in soils) in the study area. Overall, the spatial distributions of SOC and SOCS were higher in the northwestern and eastern parts of the subwatershed.
[Retracted] Elevated Substance P Is a Risk Factor for Postoperative Delirium in Patients with Hip Fracture
Authors: Zhang Wenzheng , Hu Naixia , Zhang Ya , Wang Anying
Published: March 2022

Objective. Hip fractures are quite common worldwide, especially among the elderly, and are associated with a high incidence of postoperative delirium, which worsens functional results and increases death. The causes of postoperative delirium in patients with hip fractures are unknown, and a separate pathobiology has been hypothesized. Substance P is a neuropeptide that has been linked to a number of immune‐inflammatory and neurological conditions. The purpose of this study was to see if serum substance P levels could predict postoperative delirium in a group of hip fracture patients. Methods. A total of 148 hip fracture patients were enrolled in the study, all of whom had no substantial pre‐existing medical or cognitive issues. Demographic and regular laboratory data were gathered as a starting point. ELISA was used to examine substance P levels before and after surgery (after 1 day). Patients were then divided into two groups: “postoperative delirium” and “no postoperative delirium.” Intergroup comparisons, study of delirium prevalence rates in postoperative serum substance P quartile categories, and binary logistic regression for postoperative delirium category as outcome were all done. Results. Except for serum low‐density lipoprotein (LDL) levels, there were no statistically significant variations in preoperative substance P levels or other baseline characteristics between the two groups. The “postoperative delirium” group had significantly higher postoperative substance P levels than the “no postoperative delirium” group (46.36.1 versus 31.94.7 pg/ml). There was a significant difference in postoperative delirium rates between the quartile categories of postoperative substance P, with the fourth quartile having the highest rate. Regression analysis revealed that postoperative substance P levels were related with a significantly increased OR (1.265, CI: 1.172‐1.283) of postoperative delirium. Conclusion. In the current sample of hip fracture patients, a higher postoperative serum substance P level was linked to a higher risk of postoperative delirium. Further research into the utility of early postoperative serum substance P as a delirium indicator in hip fracture patients is needed.
Joint Optimization in Intelligent Reflecting Surface-Aided UAV Communication for Multiaccess Edge Computing
Authors: He Chao , Xiao Jia
Published: March 2022

Intelligent reflecting surface (IRS) is a key enabling technology for b5G and 6G networks, which can provide a reconfigurable electromagnetic environment while reducing energy consumption. In this article, the communication link between user equipment (UE) and the base station (BS) is severely blocked, so we deployed IRS on the Unmanned Aerial Vehicle (UAV) to assist UE for offloading the computing task to the multiaccess edge computing (MEC) server on the base station, which provides mobile users with low-latency edge computing services. By jointly optimizing active beamforming of UE transmitter, passive beamforming of the IRS, UAV hovering position, and computing task scheduling, the response time of user tasks is minimized. In order to solve this complex nonconvex problem, we propose an alternating optimization (AO) algorithm combined with the genetic algorithm to decouple the problem, alternate optimization, until the convergence condition is met, to find the approximate optimal solution of the problem. Numerical results show that with the assistance of IRS, MIMO channels can significantly improve the performance of edge computing and meet the needs of users for high speed and low latency.