bluuebunny/crossref_metadata_2025 huggingface dataset:
Can Computers Outperform Humans in Detecting User Zone-Outs? Implications for Intelligent Interfaces
Authors: Bosch Nigel , D'Mello Sidney K.
Published: January 2022

The ability to identify whether a user is “zoning out” (mind wandering) from video has many HCI (e.g., distance learning, high-stakes vigilance tasks). However, it remains unknown how well humans can perform this task, how they compare to automatic computerized approaches, and how a fusion of the two might improve accuracy. We analyzed videos of users’ faces and upper bodies recorded 10s prior to self-reported mind wandering (i.e., ground truth) while they engaged in a computerized reading task. We found that a state-of-the-art machine learning model had comparable accuracy to aggregated judgments of nine untrained human observers (area under receiver operating characteristic curve [AUC] = .598 versus .589). A fusion of the two (AUC = .644) outperformed each, presumably because each focused on complementary cues. Furthermore, adding more humans beyond 3–4 observers yielded diminishing returns. We discuss implications of human–computer fusion as a means to improve accuracy in complex tasks.
Exploring the Design Space for Human-Food-Technology Interaction: An Approach from the Lens of Eating Experiences
Authors: Gayler Tom , Sas Corina , Kalnikaitė Vaiva
Published: January 2022

Embedded in everyday practices, food can be a rich resource for interaction design. This article focuses on eating experiences to uncover how bodily, sensory, and socio-cultural aspects of eating can be better leveraged for the design of user experience. We report a systematic literature review of 109 papers, and interviews with 18 professional chefs, providing new understandings of prior HFI research, as well as how professional chefs creatively design eating experiences. The findings inform a conceptual framework of designing for user experience leveraging eating experiences. These findings also inform implications for HFI design suggesting the value of multisensory flavor experiences, external and internal sensory stimulation and deprivation, aspects of eating for communicating meaning, and designing with contrasting pleasurable and uncomfortable experiences. The article concludes with six charts as novel generative design tools for HFI experiences focused on sensory, emotional, communicative, performative, and temporal experiences.
Social Contexts, Agency, and Conflicts: Exploring Critical Aspects of Design for Future Smart Home Technologies
Authors: Garg Radhika , Cui Hua
Published: January 2022

Smart devices are increasingly being designed for, and adopted in, the home environment. Prior scholarship has investigated the challenges that users face as they take up these devices in their homes. However, little is known about when and how users or potential users would prefer future domestic Internet of Things (IoT) to support their activities in home settings. To fill this gap, we conducted two co-design workshops, an in-home activity between the two sessions, and pre- and post-study interviews with 18 adult participants, who had diverse levels of prior experience of IoT use. Our findings contribute new insights into how smart home devices could adapt their behavior based on social contexts; how to re-imagine agency and support useful intelligibility; and how to resolve user-driven conflict by providing appropriate information about those with whom devices are shared. Finally, based on these findings, we discuss the implications of our work and provide a set of design considerations from which designers of future smart home technologies can benefit.
Manipulating Interface Design Features Affects Children's Stop-And-Think Behaviours in a Counterintuitive-Problem Game
Authors: Gauthier Andrea , Porayska-Pomsta Kaska , Dumontheil Iroise , Mayer Sveta , Mareschal Denis
Published: January 2022

The human–computer interaction (HCI) design of educational technologies influences cognitive behaviour, so it is imperative to assess how different HCI strategies support intended behaviour. We developed a neuroscience-inspired game that trains children's use of “stopping-and-thinking” (S&T)—an inhibitory control-related behaviour—in the context of counterintuitive science problems. We tested the efficacy of four HCI features in supporting S&T: (1) a readiness mechanic, (2) motion cues, (3) colour cues, and (4) rewards/penalties. In a randomised eye-tracking trial with 45 7-to-8-year-olds, we found that the readiness mechanic increased S&T duration, that motion and colour cues proved equally effective at promoting S&T, that combining symbolic colour with the readiness mechanic may have a cumulative effect, and that rewards/penalties may have distracted children from S&T. Additionally, S&T duration was related to in-game performance. Our results underscore the importance of interdisciplinary approaches to educational technology research that actively investigates how HCI impacts intended learning behaviours.
Can Unhappy Pictures Enhance the Effect of Personas? A User Experiment
Authors: Salminen Joni , Şengün Sercan , Santos João M. , Jung Soon-Gyo , Jansen Bernard
Published: January 2022

There has been little research into whether a persona's picture should portray a happy or unhappy individual. We report a user experiment with 235 participants, testing the effects of happy and unhappy image styles on user perceptions, engagement, and personality traits attributed to personas using a mixed-methods analysis. Results indicate that the participant's perceptions of the persona's realism and pain point severity increase with the use of unhappy pictures. In contrast, personas with happy pictures are perceived as more extroverted, agreeable, open, conscientious, and emotionally stable. The participants’ proposed design ideas for the personas scored more lexical empathy scores for happy personas. There were also significant perception changes along with the gender and ethnic lines regarding both empathy and perceptions of pain points. Implications are the facial expression in the persona profile can affect the perceptions of those employing the personas. Therefore, persona designers should align facial expressions with the task for which the personas will be employed. Generally, unhappy images emphasize realism and pain point severity, and happy images invoke positive perceptions.
Chatbots Language Design: The Influence of Language Variation on User Experience with Tourist Assistant Chatbots
Authors: Chaves Ana Paula , Egbert Jesse , Hocking Toby , Doerry Eck , Gerosa Marco Aurelio
Published: January 2022

Chatbots are often designed to mimic social roles attributed to humans. However, little is known about the impact of using language that fails to conform to the associated social role. Our research draws on sociolinguistic to investigate how a chatbot’s language choices can adhere to the expected social role the agent performs within a context. We seek to understand whether chatbots design should account for linguistic register. This research analyzes how register differences play a role in shaping the user’s perception of the human-chatbot interaction. We produced parallel corpora of conversations in the tourism domain with similar content and varying register characteristics and evaluated users’ preferences of chatbot’s linguistic choices in terms of appropriateness, credibility, and user experience. Our results show that register characteristics are strong predictors of user’s preferences, which points to the needs of designing chatbots with register-appropriate language to improve acceptance and users’ perceptions of chatbot interactions.
Documentation Matters: Human-Centered AI System to Assist Data Science Code Documentation in Computational Notebooks
Authors: Wang April Yi , Wang Dakuo , Drozdal Jaimie , Muller Michael , Park Soya , Weisz Justin D. , Liu Xuye , Wu Lingfei , Dugan Casey
Published: January 2022

Computational notebooks allow data scientists to express their ideas through a combination of code and documentation. However, data scientists often pay attention only to the code, and neglect creating or updating their documentation during quick iterations. Inspired by human documentation practices learned from 80 highly-voted Kaggle notebooks, we design and implement Themisto, an automated documentation generation system to explore how human-centered AI systems can support human data scientists in the machine learning code documentation scenario. Themisto facilitates the creation of documentation via three approaches: a deep-learning-based approach to generate documentation for source code, a query-based approach to retrieve online API documentation for source code, and a user prompt approach to nudge users to write documentation. We evaluated Themisto in a within-subjects experiment with 24 data science practitioners, and found that automated documentation generation techniques reduced the time for writing documentation, reminded participants to document code they would have ignored, and improved participants’ satisfaction with their computational notebook.
Creating and Augmenting Keyboards for Extended Reality with the

K

eyboard

A

ugmentation

T

oolkit
Authors: McGill Mark , Brewster Stephen , De Sa Medeiros Daniel Pires , Bovet Sidney , Gutierrez Mario , Kehoe Aidan
Published: January 2022

This article discusses the
Keyboard Augmentation Toolkit
(KAT), which supports the creation of virtual keyboards that can be used both for standalone input (e.g., for mid-air text entry) and to augment physically tracked keyboards/surfaces in mixed reality. In a user study, we firstly examine the impact and pitfalls of visualising shortcuts on a tracked physical keyboard, exploring the utility of virtual per-keycap displays. Supported by this and other recent developments in XR keyboard research, we then describe the design, development, and evaluation-by-demonstration of KAT. KAT simplifies the creation of virtual keyboards (optionally bound to a tracked physical keyboard) that support
enhanced display
—2D/3D per-key content that conforms to the virtual key bounds;
enhanced interactivity
—supporting extensible per-key states such as tap, dwell, touch, swipe;
flexible keyboard mappings
that can encapsulate groups of interaction and display elements, e.g., enabling application-dependent interactions; and
flexible layouts
—allowing the virtual keyboard to merge with and augment a physical keyboard, or switch to an alternate layout (e.g., mid-air) based on need. Through these features, KAT will assist researchers in the prototyping, creation and replication of XR keyboard experiences, fundamentally altering the keyboard’s form and function.
Pennington Corner: Expanding Horizons
Authors: Calvo Roque J.
Published: January 2022

I recently had an opportunity to read the member comments attached to our 1998 membership satisfaction survey, and the responses reflect strong opinions about the Society’s activities.
The Luminescence and Display Division (LDM) of The Electrochemical Society
Authors: Soules T. F.
Published: January 2022

Luminescence, the emission of light by a material after it has been exposed to ultraviolet radiation (UV) radiation, electron bombardment, X-rays or some other method of excitation, has fascinated people since ancient times. Even as late as the early 1900s, Thomas Edison and E. L. Nichols concluded “unless someone discovers a means of making luminescent bodies that are vastly brighter than the best known now, luminescence may be excluded altogether as a factor in artificial lighting.”
Continuous Electrodeionization: Production of High-Purity Water without Regeneration Chemicals
Authors: DiMascio Felice , Wood Jonathan , Fenton James M.
Published: January 2022

Electrochemical deionization (EDI), also called electrodeionization, is a process that removes ionizable species from liquids using ionically active media and an electrical potential to influence ionic transport. Electrodeionization processes can be batch or continuous. Continuous Electrodeionization (CEDI) is an electrodeionization process where the ion transport properties of the active media are the primary scale-up parameters. There are also batch electrodeionization processes, such as capacitive deionization, where the ion capacity properties of the active media are the primary sizing parameters.
Analysis of the Structure and Magnetic Properties of New Multicomponent Transition Metal Nitride Compounds
Authors: Scott Weil K.
Published: January 2022

A Summary Report to The Electrochemical Society for the 1997 F. M. Becket Memorial Award.
(Invited) Transition from Coupon Observation to Part Processing and the Challenges in Between
Authors: Hall Timothy D , Garich Holly , Vijapur Santosh H. , Radhakrishnan Rajeswaran , Xu Jing , Skinn Brian , Snyder Stephen , Taylor E. J. , Inman Maria
Published: January 2022

Faraday Technology Inc. is a research, development and engineering firm developing electrochemical innovations based has pulse/pulse reverse electrolytic principles [1]. With that founding basis Faraday has been able to bridge the gap between academic understanding and small scale demonstration to commercialization of practical components over 27 years of operation.

This presentation will discuss some of the observations and challenges that have come up as a result of moving from exemplar “coupon scale” materials toward practical part shapes and geometries. Furthermore, we will discuss a range of applications and materials including but not limited to Cu/Cr single metal plating, CoMn/NiCr alloy plating, included deposit formations with CrAlY or WC particles, electropolishing of additively manufactured components, and Nb cavity polishing. Within each of these very distinct processes and applications we will try to identify similarities and differences in the observed challenges and ways that many could be overcome either by electrochemical engineering, process developments, or program planning.

References:

J. Taylor “Adventures in Pulse/Pulse Reverse Electrolytic Processes: Explorations and Applications in Surface Finishing” J. Applied Surface Finsihing,
3(4), 178-189 (2008).
Nine‐Axis Sensor for Athlete Physical Training Load Characteristics
Authors: Liang Meifu , Zhao Ningning , Li Yamei
Published: January 2022

In order to understand the characteristic data of athletes’ training load, a method based on nine‐axis sensor was proposed. Twenty‐seven male college athletes were tested twice with a time interval of more than 48 hours. In part 1, participants take the 1 Repetition Maximum (1RM) test. The results show that maximum strength is one of the basic factors to develop the output power of athletes. In the process of skeletal muscle contraction, the curve of speed, force, and power is closely related. When the external load is 10%∼70%, the average power increases with the increase in the average force, it increases with the decrease in the average speed, and at 70%1RM, the average power reaches the peak and then decreases at an inflection point. It is proved that the accurate weight ratio of strength training is the basis of winning athletes, the focus of high level physical coach, and the premise of scientific sports training.
A Large-Scale Dataset and Deep Learning Model for Detecting and Counting Olive Trees in Satellite Imagery
Authors: Abozeid Amr , Alanazi Rayan , Elhadad Ahmed , Taloba Ahmed I. , Abd El-Aziz Rasha M.
Published: January 2022

Since the Pre-Roman era, olive trees have a significant economic and cultural value. In 2019, the Al-Jouf region, in the north of the Kingdom of Saudi Arabia, gained a global presence by entering the Guinness World Records, with the largest number of olive trees in the world. Olive tree detecting and counting from a given satellite image are a significant and difficult computer vision problem. Because olive farms are spread out over a large area, manually counting the trees is impossible. Moreover, accurate automatic detection and counting of olive trees in satellite images have many challenges such as scale variations, weather changes, perspective distortions, and orientation changes. Another problem is the lack of a standard database of olive trees available for deep learning applications. To address these problems, we first build a large-scale olive dataset dedicated to deep learning research and applications. The dataset consists of 230 RGB images collected over the territory of Al-Jouf, KSA. We then propose an efficient deep learning model (SwinTUnet) for detecting and counting olive trees from satellite imagery. The proposed SwinTUnet is a Unet-like network which consists of an encoder, a decoder, and skip connections. Swin Transformer block is the fundamental unit of SwinTUnet to learn local and global semantic information. The results of an experimental study on the proposed dataset show that the SwinTUnet model outperforms the related studies in terms of overall detection with a 0.94% estimation error.
Identification of Novel Diagnostic Biomarkers in Prostate Adenocarcinoma Based on the Stromal-Immune Score and Analysis of the WGCNA and ceRNA Network
Authors: Zhang Tengfei , Wang Yaxuan , Dong Yiming , Liu Lei , Han Yikai , Wang Huanrong , Wei Qian , Xia Peige , Ma Wang , Li Lifeng
Published: January 2022

Prostate cancer is still a significant global health burden in the coming decade. Novel biomarkers for detection and prognosis are needed to improve the survival of distant and advanced stage prostate cancer patients. The tumor microenvironment is an important driving factor for tumor biological functions. To investigate RNA prognostic biomarkers for prostate cancer in the tumor microenvironment, we obtained relevant data from The Cancer Genome Atlas (TCGA) database. We used the bioinformatics tools Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm and weighted coexpression network analysis (WGCNA) to construct tumor microenvironment stromal-immune score-based competitive endogenous RNA (ceRNA) networks. Then, the Cox regression model was performed to screen RNAs associated with prostate cancer survival. The differentially expressed gene profile in tumor stroma was significantly enriched in microenvironment functions, like immune response, cancer-related pathways, and cell adhesion-related pathways. Based on these differentially expressed genes, we constructed three ceRNA networks with 152 RNAs associated with the prostate cancer tumor microenvironment. Cox regression analysis screened 31 RNAs as the potential prognostic biomarkers for prostate cancer. The most interesting 8 prognostic biomarkers for prostate cancer included lncRNA LINC01082, miRNA hsa-miR-133a-3p, and genes TTLL12, PTGDS, GAS6, CYP27A1, PKP3, and ZG16B. In this systematic study for ceRNA networks in the tumor environment, we screened out potential biomarkers to predict prognosis for prostate cancer. Our findings might apply a valuable tool to improve prostate cancer clinical management and the new target for mechanism study and therapy.
Existence of Fixed Points in Fuzzy Strong b-Metric Spaces
Authors: Kanwal Shazia , Kattan Doha , Perveen Saba , Islam Sahidul , Shagari Mohammed Shehu
Published: January 2022

In the present research, modern fuzzy technique is used to generalize some conventional and latest results. The objective of this paper is to construct and prove some fixed-point results in complete fuzzy strong b-metric space. Fuzzy strong b-metric (sb-metric) spaces have very useful properties such as openness of open balls whereas it is not held in general for b-metric and fuzzy b-metric spaces. Due to its properties, we have worked in these spaces. In this way, we have generalized some well-known fixed-point theorems in fuzzy version. In addition, some interesting examples are constructed to illustrate our results.
Eco-Environmental Civilization Construction System in Remote Areas Based on Multiple Data Collection and the Internet of Things
Authors: Du Xinyi , Ma Haijun
Published: January 2022

For a long time, the development strategy of remote areas is basically resource-oriented. Large-scale exploitation of resources not only damages the corresponding balance of resource reserves but also causes serious damage to the ecological environment. To this end, this paper has carried out research on the construction of ecological environment civilization in remote areas based on multidata collection and edge computing. Based on the understanding of the connotation, composition, and characteristics of ecological civilization, this paper selects representative indicators to reflect the specific requirements of ecological civilization, constructs an evaluation index system for the construction of ecological civilization in remote areas, and uses the evaluation indicators analysis and sorting. Second, edge computing and sensor technologies are applied to the process of data collection and information transmission and providing solutions for data collection and transmission in remote areas. This paper also presents the security method to protect the information transmission. Through testing, the program has shown good adaptability and can provide ideas for the construction of ecological environment in remote areas.
Coupling Relationship between Lithofacies and Brittleness of the Shale Oil Reservoir: A Case Study of the Shahejie Formation in the Raoyang Sag
Authors: Song Zhaojing , Li Junqian , Li Xiaoyan , Chen Ketong , Wang Chengyun , Li Peng , Wei Yongbo , Zhao Rixin , Wang Xiaocheng , Zhang Shasha , Yan Mengying , Geng Haiyan
Published: January 2022

Analyzing the characteristics of rock brittleness in low-permeability mudstone and shale (MS) formations is imperative for efficient hydraulic fracturing stimulation. Rock brittleness depends on the mineral composition, organic matter abundance, and bedding structure. Based on the MS from Shahejie Formation mineral composition (clay mineral, felsic mineral, and calcareous mineral contents), total organic content, and bedding structure (laminated, laminar, and massive), six types of lithofacies were identified: clay-rich MS, felsic-rich MS, calcareous-rich MS, clay MS, felsic MS, and calcareous MS. The quartz, feldspar, calcite, and dolomite of the Shahejie Formation are brittle minerals. Consequently, lithofacies with high felsic and calcareous mineral contents are more brittle. In addition, laminated and laminar MS are also conducive to hydraulic fracturing. Therefore, laminated, organic-rich, and calcareous-rich MS are the dominant lithofacies for hydraulic fracturing in the Shahejie Formation. The lithofacies and brittleness index were predicted by the response characteristics between mineral compositions and logging curves. The 3521–3552 m section of well B11x is dominated by calcareous-rich MS with developed laminae, representing a favorable section for hydraulic fracturing. Fragile minerals and oil are widely developed in the lower part of the lower 1st member of the Shahejie Formation (Es1L) in the southwestern part of Zhaohuangzhuang-Suning, where hydraulic fracturing can be used to increase shale oil production.
Perfusion‐Diffusion Ratio: A New IVIM Approach in Differentiating Solid Benign and Malignant Primary Lesions of the Liver
Authors: Podgórska Joanna , Pasicz Katarzyna , Skrzyński Witold , Gołębiewski Bogumił , Kuś Piotr , Jasieniak Jakub , Kiliszczyk Anna , Rogowska Agnieszka , Benkert Thomas , Pałucki Jakub , Grabska Iwona , Fabiszewska Ewa , Jagielska Beata , Kukołowicz Paweł , Cieszanowski Andrzej
Published: January 2022

Introduction. In order to improve the efficacy of intravoxel incoherent motion (IVIM) parameters in characterising specific tissues, a new concept is introduced: the perfusion–diffusion ratio (PDR), which expresses the relationship between the signal S(b) decline rate as a result of IVIM and the rate of signal S(b) decline due to diffusion. The aim of this study was to investigate this novel approach in the differentiation of solid primary liver lesions. Material and Methods. Eighty‐three patients referred for liver MRI between August 2017 and January 2020 with a suspected liver tumour were prospectively examined with the standard liver MRI protocol extended by DWI‐IVIM sequence. Patients with no liver lesions, haemangiomas, or metastases were excluded. The final study population consisted of 34 patients with primary solid liver masses, 9 with FNH, 4 with regenerative nodules, 10 with HCC, and 11 with CCC. The PDR coefficient was introduced, defined as the ratio of the rate of signal S(b) decrease due to the IVIM effect to the rate of signal S(b) decrease due to the diffusion process, for b = 0. Results. No significant differences were found between benign and malignant lesions in the case of IVIM parameters (f, D, or D∗) and ADC. Significant differences were observed only for PDR, with lower values for malignant lesions (p = 0.03). The ROC analysis yielded an AUC value for PDR equal to 0.74, with a cut‐off value of 5.06, sensitivity of 81%, specificity of 77%, and accuracy of 79%. Conclusion. PDR proved to be more effective than IVIM parameters and ADC in the differentiation of solid benign and malignant primary liver lesions.
Anatomical Evaluation of Root and Root Canal Configuration of Permanent Maxillary Dentition in the Population of the Kingdom of Saudi Arabia
Authors: Mashyakhy Mohammed , Awawdeh Mohammed , Abu-Melha Abdulaziz , Alotaibi Bushra , AlTuwaijri Nada , Alazzam Nouf , Almutairi Rahaf , Alessa Reuof
Published: January 2022

Aim. This study is aimed at combining the sample sizes of all studies on permanent maxillary teeth conducted in different regions of the Kingdom of Saudi Arabia (KSA) to obtain a large sample size that represents the population of the KSA. The outcome of these combined studies is compared with international studies in terms of the number of roots, number of canals, and canal configurations on the basis of Vertucci’s classification. Methodology. The studies were systematically reviewed using the Preferred Reporting Items for Systematic Review and Meta‐analysis chart. Studies were included in the analysis if they were conducted in the KSA, involved permanent human maxillary teeth, and had a sample of more than 10 teeth (power). By contrast, studies were excluded if they involved deciduous teeth in the sample size, investigated nonhuman teeth, were not conducted in the KSA, and were case reports, case series, review studies, and anomalies. Relevant literature was searched from PubMed, Scopus, Web of Science, Embase, Cochrane, and Direct Science by two calibrated teams, starting in August 2020, without time limits or language restrictions. Results. The database searches and cross‐referencing identified a total of 19 relevant studies. All maxillary canines (N = 1,018) had one root, whereas 98.4% had one canal and 98.3% had Vertucci type I. Moreover, 63.2% of the maxillary first premolars had two roots, and 91.4% had two canals. The most common Vertucci root canal configuration was type IV (64.6%). The maxillary second premolars mostly had one root (84.4%) and one canal (50.4%). The most common canal configuration was Vertucci type I (47.1%). The majority of maxillary first molars had three roots (98.9%), 48.7% of which had three canals, and 46.4% had four canals. The most prevalent feature of the canal morphology of mesiobuccal roots was Vertucci type II (35.3%). The investigated maxillary second molars had three roots, 88.0% of which had three canals. Conclusion. This systematic review represents the Saudi population since samples were combined from different studies from different regions of the country. Variations in findings were observed in the same group of teeth from different regions and the same region, while the overall combined samples results fell within the range of other international studies.
A Novel Energy-Efficient, Static Scenario-Oriented Routing Method of Wireless Sensor Network Based on Edge Computing
Authors: Liu Gang , Liu Zhaobin , Sheng Victor S. , Zhang Liang , Yang Yuanfeng
Published: January 2022

In wireless sensor network (WSN), the energy of sensor nodes is limited. Designing efficient routing method for reducing energy consumption and extending the WSN’s lifetime is important. This paper proposes a novel energy-efficient, static scenario-oriented routing method of WSN based on edge computing named the NEER, in which WSN is divided into several areas according to the coverage of gateway (or base station), and each of the areas is regarded as an edge area network (EAN). Each edge area network is abstracted into a weighted undirected graph model combined with the residual energy of the sensor nodes. The base station (or a gateway) calculates the optimal energy consumption path for all sensor nodes within its coverage, and the nodes then perform data transmission through their suggested optimal paths. The proposed method is verified by the simulations, and the results show that the proposed method may consume about 37% less energy compared with the conventional WSN routing protocol and can also effectively extend the lifetime of WSN.
A Novel Clustering-Based Algorithm for Continuous and Noninvasive Cuff-Less Blood Pressure Estimation
Authors: Farki Ali , Baradaran Kazemzadeh Reza , Akhondzadeh Noughabi Elham
Published: January 2022

Extensive research has been performed on continuous and noninvasive cuff-less blood pressure (BP) measurement using artificial intelligence algorithms. This approach involves extracting certain features from physiological signals, such as ECG, PPG, ICG, and BCG, as independent variables and extracting features from arterial blood pressure (ABP) signals as dependent variables and then using machine-learning algorithms to develop a blood pressure estimation model based on these data. The greatest challenge of this field is the insufficient accuracy of estimation models. This paper proposes a novel blood pressure estimation method with a clustering step for accuracy improvement. The proposed method involves extracting pulse transit time (PTT), PPG intensity ratio (PIR), and heart rate (HR) features from electrocardiogram (ECG) and photoplethysmogram (PPG) signals as the inputs of clustering and regression, extracting systolic blood pressure (SBP) and diastolic blood pressure (DBP) features from ABP signals as dependent variables, and finally developing regression models by applying gradient boosting regression (GBR), random forest regression (RFR), and multilayer perceptron regression (MLP) on each cluster. The method was implemented using the MIMIC-II data set with the silhouette criterion used to determine the optimal number of clusters. The results showed that because of the inconsistency, high dispersion, and multitrend behavior of the extracted features vectors, the accuracy can be significantly improved by running a clustering algorithm and then developing a regression model on each cluster and finally weighted averaging of the results based on the error of each cluster. When implemented with 5 clusters and GBR, this approach yielded an MAE of 2.56 for SBP estimates and 2.23 for DBP estimates, which were significantly better than the best results without clustering (DBP: 6.27, SBP: 6.36).
Diagnostic Value of Serum Golgi Protein 73 for Liver Inflammation in Patients with Autoimmune Hepatitis and Primary Biliary Cholangitis
Authors: Yao Mingjie , Wang Leijie , Wang Jianwen , Liu Yanna , Liu Shuhong , Zhao Jingmin , Lu Fengmin
Published: January 2022

Background. There is lack of reliable serum biomarkers to reflect the severity of liver necroinflammation for those who suffer autoimmune liver diseases (AILDs). In this study, a previously established patient cohort was used to explore the potential of serum Golgi protein 73 (GP73) as a noninvasive marker of AILD-related liver necroinflammation. Methods. Serum GP73 concentration was measured in a retrospective cohort of 168 AILD patients, which included 74 patients with autoimmune hepatitis (AIH) and 94 with primary biliary cholangitis (PBC) who had undergone liver biopsy. Spearman’s correlation and multivariate analysis were used to evaluate the relationship between serum GP73 and liver necroinflammation. A receiver operating characteristic curve was constructed to evaluate the value of GP73 for the prediction of moderate or severe liver necroinflammation. The diagnostic value of serum GP73 was also compared with that of alkaline phosphatase (ALP) in patients with PBC. Histologically, immunohistochemical analysis was performed to assess hepatic GP73 expression. Results. Both the serum level and hepatic tissue expression of GP73 protein were aberrantly elevated and correlated well with the severity of necroinflammation in both AIH (

rho
=
0.655

,

P
<
0.001

) and PBC (

rho
=
0.547

,

P
<
0.001

) patients. The results here suggested that serum GP73 could be an independent biomarker to reflect the severity of liver necroinflammation. The AUROCs for GP73 to predict moderate necroinflammation (≥G2) and severe necroinflammation (≥G3) in patients with AIH were 0.828 and 0.832, respectively. Moreover, the AUROCs of serum GP73 for the identification of moderate necroinflammation (≥G2) (

AUROC
=
0.820

,

P
<
0.001

) and severe necroinflammation (≥G3) (

AUROC
=
0.803

,

P
<
0.001

) were superior to those of ALP (≥G2:

AUROC
=
0.607

,

P
=
0.028

and ≥G3:

AUROC
=
0.559

,

P
=
0.357

) in patients with PBC. Mechanically, interlukin-6 (IL-6), the proinflammatory and prohepatic regenerating cytokine, could transcriptionally upregulate GP73 gene expression. Conclusion. Serum GP73 is a potential noninvasive biomarker to evaluate the severity of liver necroinflammation in patients with AILDs.
Experimental Investigation on the Hydraulic Characteristics of the Two-Phase Flow in the Intersecting Rock Fractures
Authors: Wang Chen , Zhou Lujie , Jiang Yujing , Zhang Xuepeng , Liu Jiankang
Published: January 2022

An appropriate understanding of the hydraulic characteristics of the two-phase flow in the rock fracture network is important in many engineering applications. To investigate the two-phase flow in the fracture network, a study on the two-phase flow characteristics in the intersecting fractures is necessary. In order to describe the two-phase flow in the intersecting fractures quantitatively, in this study, a gas-water two-phase flow experiment was conducted in a smooth 3D model with intersecting fractures. The results in this specific 3D model show that the flow structures in the intersecting fractures were similar to those of the stratified wavy flow in pipes. The nonlinearity induced by inertial force and turbulence in the intersecting fractures cannot be neglected in the two-phase flow, and the Martinelli-Lockhart model is effective for the two-phase flow in intersecting fractures. Delhaye’s model can be adapted for the cases in this experiment. The turbulence of the flow can be indicated by the values of

C

in Delhaye’s model, but resetting the appropriate range of the values of

C

is necessary.
MuLSi-Co: Multilayer Sinks and Cooperation-Based Data Routing Techniques for Underwater Acoustic Wireless Sensor Networks (UA-WSNs)
Authors: Ali Munsif , Shah Sahar , Khan Mahnoor , Ali Ihsan , Alroobaea Roobaea , Baqasah Abdullah M. , Ahmad Muneer
Published: January 2022

Designing an efficient, reliable, and stable algorithm for underwater acoustic wireless sensor networks (UA-WSNs) needs immense attention. It is due to their notable and distinctive challenges. To address the difficulties and challenges, the article introduces two algorithms: the multilayer sink (MuLSi) algorithm and its reliable version MuLSi-Co using the cooperation technique. The first algorithm proposes a multilayered network structure instead of a solid single structure and sinks placement at the optimal position, which reduces multiple hops communication. Moreover, the best forwarder selection amongst the nodes based on nodes’ closeness to the sink is a good choice. As a result, it makes the network perform better. Unlike the traditional algorithms, the proposed scheme does not need location information about nodes. However, the MuLSi algorithm does not fulfill the requirement of reliable operation due to a single link. Therefore, the MuLSi-Co algorithm utilizes nodes’collaborative behavior for reliable information. In cooperation, the receiver has multiple copies of the same data. Then, it combines these packets for the purpose of correct data reception. The data forwarding by the relay without any latency eliminates the synchronization problem. Moreover, the overhearing of the data gets rid of duplicate transmissions. The proposed schemes are superior in energy cost and reliable exchanging of data and have more alive and less dead nodes.
Score Prediction of Sports Events Based on Parallel Self-Organizing Nonlinear Neural Network
Authors: Ling Junyao
Published: January 2022

This paper introduces the basic concepts and main characteristics of parallel self-organizing networks and analyzes and predicts parallel self-organizing networks through neural networks and their hybrid models. First, we train and describe the law and development trend of the parallel self-organizing network through historical data of the parallel self-organizing network and then use the discovered law to predict the performance of the new data and compare it with its true value. Second, this paper takes the prediction and application of chaotic parallel self-organizing networks as the main research line and neural networks as the main research method. Based on the summary and analysis of traditional neural networks, it jumps out of inertial thinking and first proposes phase space. Reconstruction parameters and neural network structure parameters are unified and optimized, and then, the idea of dividing the phase space into multiple subspaces is proposed. The multi-neural network method is adopted to track and predict the local trajectory of the chaotic attractor in the subspace with high precision to improve overall forecasting performance. During the experiment, short-term and longer-term prediction experiments were performed on the chaotic parallel self-organizing network. The results show that not only the accuracy of the simulation results is greatly improved but also the prediction performance of the real data observed in reality is also greatly improved. When predicting the parallel self-organizing network, the minimum error of the self-organizing difference model is 0.3691, and the minimum error of the self-organizing autoregressive neural network is 0.008, and neural network minimum error is 0.0081. In the parallel self-organizing network prediction of sports event scores, the errors of the above models are 0.0174, 0.0081, 0.0135, and 0.0381, respectively.
Strategic Alliance Pattern Evaluation Model for Taiwan’s Machine Tool Industry: A Hierarchical DEMATEL Method
Authors: Sun Chia-Chi
Published: January 2022

The worldwide machine tool market is anticipated to reach a value of USD 68.9 billion by 2021, from USD 65.6 billion in 2020. This projection is based on the progressive production drop within the car industry, which is the largest customer of machine devices, and supply chain disruption. The machine tool industry in Taiwan faces a severe challenge and has been unobtrusively experiencing an inner reshuffling and innovative transformation. The developing strategic alliances reflect a basic endeavor by numerous firms to improve their specialized capabilities. This study applied the DEMATEL, a suitable method for gathering group knowledge to form a structural model and visualize the casual relationship between subsystems through a casual diagram, revealing that the causal relationships between measurement criteria and the proposed model can provide a viable assessment of the alliance with satisfactory criteria that fit the decision-makers requirements, especially when the assessment criteria are various and interrelated. Financial resources were the strongest factor within the strategic behavior dimension (D1), whereas the minimize manufacturing cost was the foremost basic determinant in the cost perspective (D2). The specialists also demonstrated that obtaining dominant technology was a determinative component within organizational learning (D3). This paper offers proposals for government authorities to plan a machine tools industry strategy for Taiwan and for companies to formulate business directions for long-run advancement.
Visual Communication Design Based on Collaborative Wireless Communication Video Transmission
Authors: Ma Cong , Chung Wonjun
Published: January 2022

With the development of wireless communication technology, video and multimedia have become an integral part of visual communication design. Designers want higher interactivity, diversity, humanization, and plurality of attributes in the process of visual communication. This makes the process of visual communication have high requirements for the quality and real-time data transmission. To address the problem of transmitting HD video in a heterogeneous wireless network with multiple concurrent streams to improve the transmission rate and thus enhance the user experience, with the optimization goal of minimizing the system transmission delay and the delay difference between paths, the video sender and receiver are jointly considered, and the video transmission rate and the cache size at the receiver are adaptively adjusted to improve the user experience, and a cooperative wireless communication video transmission based on the control model for video transmission based on cooperative wireless communication is established, and video streams with self-similarity and long correlation are studied based on Pareto distribution and

P
/
P
/
l

queuing theory, based on which an adaptive streaming decision method for video streams in heterogeneous wireless networks is proposed. Simulation results show that the proposed multistream concurrent adaptive transmission control method for heterogeneous networks is superior in terms of delay and packet loss rate compared with the general load balancing streaming decision method, in terms of transmission efficiency and accuracy.
Genetic Polymorphism of Matrix Metalloproteinase-9 and Susceptibility to Myocardial Infarction: A Meta-Analysis
Authors: Feng Beili , Li Hengdong
Published: January 2022

Objective. Current findings on the association between MMP-9 rs3918242 and susceptibility to myocardial infarction (MI) are inconsistent, and their definite relationship is discussed in this meta-analysis. Methods. Eligible literatures reporting MMP-9 rs3918242 and susceptibility to MI were searched in PubMed, Cochrane Library, CNRI, and VIP using keywords such as “MMP-9”, “matrix metallopeptidase-9” and “myocardial infarction”, “acute myocardial infarction”, “AMI”, and “polymorphism”. Data from eligible literatures were extracted for calculating OR and corresponding 95% CI using RevMan 5.3 and STATA12.0. Results. Ten independent literatures reporting MMP-9 rs3918242 and susceptibility to MI were enrolled. Compared with subjects carrying CT&TT genotype of MMP-9 rs3918242, susceptibility to MI was lower in those carrying CC genotype (

OR
=
1.49

,

95
%
CI
=
1.19
–
1.86

,

P
=
0.0004

). Such a significance was observed in the overdominant (

OR
=
1.27

,

95
%
CI
=
1.14
–
1.41

,

P
<
0.0001

) and allele genetic models (

OR
=
1.43

,

95
%
CI
=
1.17
–
1.74

,

P
=
0.0005

) as well. This finding was also valid in the Asian population. Conclusions. Mutation on MMP-9 rs3918242 has a potential relevance with susceptibility to MI.
Optimization of Ideological and Political Education under the Epidemic via Mobile Learning Auxiliary Platform in the Era of Digitization
Authors: Zhang Lizhe , He Juan
Published: January 2022

In the digitized era, life has become simpler with the increased information technology. The Education Department in the whole world is facing a tremendous revolution with the development. The traditional classroom study is converted to a modernized and digitized classroom with visualization. This modernization has increased the learning capability of the students with an increase in student and teacher interaction. From this teaching and learning process, most colleges and universities have improved performance in preparing course materials, effective teaching, and independent learning among the students in the theoretical courses. Ideological and political education (IPE) is a theoretical subject that is taught and understood at higher education institutions, such as colleges and universities. A hybrid hierarchicalK-means clustering for optimizing clustering with unsupervised machine learning is proposed to analyze the student performance and concluded that the proposed algorithm shows improved performance than theK-means algorithm.
MicroRNA-517c Functions as a Tumor Suppressor in Hepatocellular Carcinoma via Downregulation of KPNA2 and Inhibition of PI3K/AKT Pathway
Authors: Ma Limin , Tao Changming , Zhang Yingying
Published: January 2022

Objective. Hepatocellular carcinoma (HCC) is a kind of solid and highly aggressive malignant tumor with poor prognosis. MicroRNA (miRNA/miR) has been confirmed to be involved in HCC development. The current study focused on the functions and mechanisms of miR-517c in HCC. Methods. Expressions of miR-517c and Karyopherin α2 (KPNA2) mRNA in HCC cell lines and tissue samples were examined using quantitative real-time polymerase chain reaction (qRT-PCR). Western blot was conducted for detections of epithelial-to-mesenchymal transition (EMT) and PI3K/AKT markers. 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) and Transwell assays were utilized to investigate the influence of miR-517c on HCC cell proliferation, invasion, and migration. TargetScan and luciferase reporter assay were performed to search for the potential target gene of miR-517c. Results. We demonstrated that miR-517c expressions were decreased in HCC tissues and cells. Moreover, the clinical analysis showed that decreased miR-517c expressions in HCC tissues correlated with shorter overall survival and malignant clinicopathologic features of HCC patients. MTT assay showed that miR-517c upregulation prominently repressed HCC cell proliferation. In addition, miR-517c restoration could significantly suppress HCC cell invasion and migration as demonstrated by Transwell assays. We also found that miR-517c directly targeted KPNA2 and regulated the PI3K/AKT pathway and EMT, exerting prohibitory functions in HCC. Conclusion. Taken together, this study stated that miR-517c inhibited HCC progression via regulating the PI3K/AKT pathway and EMT and targeting KPNA2 in HCC, providing a novel insight into HCC treatment.