bluuebunny/crossref_metadata_2025 huggingface dataset:
Dream Painter
Authors: Canet Sola Mar , Guljajeva Varvara
Published: September 2022

This paper describes an interactive robotic art installation Dream Painter by the artistic duo Varvara & Mar that deploys artificial intelligence (AI), a KUKA industrial robot and interaction technology in order to offer the audience an artistic interpretation of their past dreams, which are then turned into a collective painting. The installation is composed of four larger parts: audience interaction design, AI-driven multicoloured drawing software, communication with an arm robot, and a kinetic part that is the automatic paper progression following each completed dream drawing. All these interconnected parts are orchestrated into an interactive and autonomous system in the form of an art installation that occupies two floors of a cultural centre. In the article, we document the technical and conceptual frameworks of the project, and the experience gained through the creation and exhibition of the interactive robotic art installation. In addition, the paper explores the creative potential of speech-to-AI-drawing transformation, which is a translation of different semiotic spaces performed by a robot as a method for audience interaction in the art exhibition context.
Dissecting My Data Body
Authors: Oliver Marilène , Smallwood Scott , Moore Stephan , Carpenter J. R. , Cohn Jonathan
Published: September 2022

We are constantly being warned that our personal data is vulnerable, that it is being used and abused by artificial intelligence, giant tech corporations and controlling governments. But do we really understand what "our data" consists of and what can be done with and to it? Is it possible to unravel the complex entanglements of data gathering and processing technologies in order to see and understand our data in a meaningful way? My Data Body is a virtual reality (VR) artwork that brings together some of our most personal and sensitive data such as medical scans, social media, biometric and social security data in an attempt to make visible and manipulable our many intersecting data corpuses so that they can be held, inspected, dissected and played with as a way to start understanding and answering these questions. My Data Body has been created as part of the interdisciplinary project Know Thyself as a Virtual Reality (KTVR), a multi-faceted project that explores the ethics and aesthetics of the contemporary "data body". KTVR brings together researchers across the arts and sciences, to innovate new creatives methodologies, educational resources and ethical guidelines for working artistically with personal data.
Field of Leaves
Authors: Silva Pedro , Lopes Daniel , Martins Pedro , Machado Penousal
Published: September 2022

Field of Leaves is an interactive installation that depicts public contracts held in Portugal and their distribution over its five regions and two autonomous regions. The installation presents information about the public contracts available at the online portal of public procurements called Portal Basegov. The installation attempts to advocate the importance and advantages of aesthetics in first-time audience engagement, and how user interaction and hedonic qualities can heighten the user's curiosity and promote more lasting explorations with a visualization.
Electromagnetic Fingerprinting of Memory Heartbeats
Authors: Shen Cheng , Huang Jun , Sun Guangyu , Chen Jingshu
Published: September 2022

This paper presents MemScope, a system that fingerprints devices via electromagnetic sensing of their memory heartbeats, i.e., the clock signal that synchronizes memory and memory controller. MemScope leverages the enhanced resolution and security of memory heartbeat fingerprint, which has enriched spectral features thanks to the spread spectrum generation of memory clock, and cannot be concealed as long as the device accesses its memory during computing. MemScope employs signal processing algorithms that allow it to hear the memory heartbeats of devices from a distance, in the presence of noise, and in crowded environments where multiple devices coexist. It then fingerprints memory heartbeats using machine learning tools. Measurements on a set of 65 devices over a month validate the robustness of fingerprint against time variation, and show a high precision and recall. We then use the neural network to build a detector to defend against possible replay attacks. Finally, we further demonstrate the effectiveness of MemScope in two application scenarios, (i) detecting wireless identity spoofing and (ii) identifying and localizing unauthorized hidden cameras.
UltraSpeech
Authors: Ding Han , Wang Yizhan , Li Hao , Zhao Cui , Wang Ge , Xi Wei , Zhao Jizhong
Published: September 2022

Speech enhancement can benefit lots of practical voice-based interaction applications, where the goal is to generate clean speech from noisy ambient conditions. This paper presents a practical design, namely UltraSpeech, to enhance speech by exploring the correlation between the ultrasound (profiled articulatory gestures) and speech. UltraSpeech uses a commodity smartphone to emit the ultrasound and collect the composed acoustic signal for analysis. We design a complex masking framework to deal with complex-valued spectrograms, incorporating the magnitude and phase rectification of speech simultaneously. We further introduce an interaction module to share information between ultrasound and speech two branches and thus enhance their discrimination capabilities. Extensive experiments demonstrate that UltraSpeech increases the Scale Invariant SDR by 12dB, improves the speech intelligibility and quality effectively, and is capable to generalize to unknown speakers.
SSpoon
Authors: Chen Yang , Fennedy Katherine , Fogel Anna , Zhao Shengdong , Zhang Chao , Liu Lijuan , Yen Chingchiuan
Published: September 2022

One key strategy of combating obesity is to slow down eating; however, this is difficult to achieve due to people's habitual nature. In this paper, we explored the feasibility of incorporating shape-changing interface into an eating spoon to directly intervene in undesirable eating behaviour. First, we investigated the optimal dimension (i.e., Z-depth) and ideal range of spoon transformation for different food forms that could affect bite size while maintaining usability. Those findings allowed the development of SSpoon prototype through a series of design explorations that are optimised for user's adoption. Then, we applied two shape-changing strategies: instant transformations based on food forms and subtle transformations based on food intake) and examined in two comparative studies involving a full course meal using Wizard-of-Oz approach. The results indicated that SSpoon could achieve comparable effects to a small spoon (5ml) in reducing eating rate by 13.7-16.1% and food consumption by 4.4-4.6%, while retaining similar user satisfaction as a normal eating spoon (10ml). These results demonstrate the feasibility of a shape-changing eating utensil as a promising alternative to combat the growing concern of obesity.
Wi-Learner
Authors: Feng Chao , Wang Nan , Jiang Yicheng , Zheng Xia , Li Kang , Wang Zheng , Chen Xiaojiang
Published: September 2022

Contactless RF-based sensing techniques are emerging as a viable means for building gesture recognition systems. While promising, existing RF-based gesture solutions have poor generalization ability when targeting new users, environments or device deployment. They also often require multiple pairs of transceivers and a large number of training samples for each target domain. These limitations either lead to poor cross-domain performance or incur a huge labor cost, hindering their practical adoption. This paper introduces Wi-Learner, a novel RF-based sensing solution that relies on just one pair of transceivers but can deliver accurate cross-domain gesture recognition using just one data sample per gesture for a target user, environment or device setup. Wi-Learner achieves this by first capturing the gesture-induced Doppler frequency shift (DFS) from noisy measurements using carefully designed signal processing schemes. It then employs a convolution neural network-based autoencoder to extract the low-dimensional features to be fed into a downstream model for gesture recognition. Wi-Learner introduces a novel meta-learner to "teach" the neural network to learn effectively from a small set of data points, allowing the base model to quickly adapt to a new domain using just one training sample. By so doing, we reduce the overhead of training data collection and allow a sensing system to adapt to the change of the deployed environment. We evaluate Wi-Learner by applying it to gesture recognition using the Widar 3.0 dataset. Extensive experiments demonstrate Wi-Learner is highly efficient and has a good generalization ability, by delivering an accuracy of 93.2% and 74.2% - 94.9% for in-domain and cross-domain using just one sample per gesture, respectively.
Towards a Dynamic Inter-Sensor Correlations Learning Framework for Multi-Sensor-Based Wearable Human Activity Recognition
Authors: Miao Shenghuan , Chen Ling , Hu Rong , Luo Yingsong
Published: September 2022

Multi-sensor-based wearable human activity recognition (WHAR) is a research hotspot in the field of ubiquitous computing. Extracting effective features from multi-sensor data is essential to improve the performance of activity recognition. Despite the excellent achievements of previous works, the challenge remains for modelling the dynamic correlations between sensors. In this paper, we propose a lightweight yet efficient GCN-based dynamic inter-sensor correlations learning framework called DynamicWHAR for automatically learning the dynamic correlations between sensors. DynamicWHAR is mainly composed of two modules: Initial Feature Extraction and Dynamic Information Interaction. Firstly, Initial Feature Extraction module performs data-to-feature transformation to extract the initial features of each sensor. Subsequently, Dynamic Information Interaction module explicitly models the specific interaction intensity between any two sensors, and performs dynamic information aggregation between sensors by the learned interaction intensity. Extensive experiments on four diverse WHAR datasets and two different resource-constrained devices validate that DynamicWHAR outperforms the SOTA models in both recognition performance and computational complexity.
VECTOR
Authors: Wan Haoran , Wang Lei , Zhao Ting , Sun Ke , Shi Shuyu , Dai Haipeng , Chen Guihai , Liu Haodong , Wang Wei
Published: September 2022

Ambient temperature distribution monitoring is desired in a variety of real-life applications including indoors temperature control and building energy management. Traditional temperature sensors have their limitations in the aspects of single point/item based measurements, slow response time and huge cost for distribution estimation. In this paper, we introduce VECTOR, a temperature-field monitoring system that achieves high temperature sensing accuracy and fast response time using commercial sound playing/recording devices. First, our system uses a distributed ranging algorithm to measure the time-of-flight of multiple sound paths with microsecond resolution. We then propose a dRadon transform algorithm that reconstructs the temperature distribution from the measured speed of sound along different paths. Our experimental results show that we can measure the temperature with an error of 0.25°C from single sound path and reconstruct the temperature distribution at a decimeter-level spatial resolution.
Analysis of Evolutionary Diversity Optimization for Permutation Problems
Authors: Do Anh , Guo Mingyu , Neumann Aneta , Neumann Frank
Published: September 2022

Generating diverse populations of high-quality solutions has gained interest as a promising extension to the traditional optimization tasks. This work contributes to this line of research with an investigation on evolutionary diversity optimization for three of the most well-studied permutation problems: the Traveling Salesperson Problem (TSP), both symmetric and asymmetric variants, and the Quadratic Assignment Problem (QAP). It includes an analysis of the worst-case performance of a simple mutation-only evolutionary algorithm with different mutation operators, using an established diversity measure. Theoretical results show that many mutation operators for these problems guarantee convergence to maximally diverse populations of sufficiently small size within cubic to quartic expected runtime. On the other hand, the results regarding QAP suggest that strong mutations give poor worst-case performance, as mutation strength contributes exponentially to the expected runtime. Additionally, experiments are carried out on QAPLIB and synthetic instances in unconstrained and constrained settings, and reveal much more optimistic practical performances while corroborating the theoretical findings regarding mutation strength. These results should serve as a baseline for future studies.
Digitally Protecting and Disseminating the Intangible Cultural Heritage in Information Technology Era
Authors: Liu Jing
Published: September 2022

This paper studies the digital protection and dissemination of digital information based on intangible cultural heritage. The “digital era” is an important milestone, and there is no problem with the development of martial arts heritage. First, this paper explains that the development of the current martial arts heritage is not without drawbacks, for example, ecological marginalization and imperfect development. Then, the author of this study determined the benefits of combining incomplete martial arts technology with digital technology, and took the digital protection of Xingyi boxing as an example to explore its practice and specialize in digital protection. Then, the problems and procedures in digital protection are studied. Finally, with Xingyi boxing as an experiment, this form leads to separation. The experimental results show that, according to the full understanding of the status quo of nondigital defense culture, the protection measures to improve digital protection are put forward: determining the digital distribution process and writing models, completing knowledge presentation view, document creation, multimedia interactive platform development, and digital technology protection of public heritage, without any problems.
Grid Supervision Path of Platform Food Safety Collaborative Governance Based on Big Data
Authors: Zhang Xin , Tian Weiguo
Published: September 2022

Food safety is the focus of people’s attention. This year, there have a lot of bad pieces of news related to food safety, which has increased people’s attention to it. According to the requirements for traceability of platform food information attributes, the article aims to study the implementation of grids for food safety governance in the context of big data. This paper designs a nonconformity rate evaluation index to screen qualified food through the grid management of the area using big data processing under the DHRA copy management strategy to achieve the function of assisting food safety supervision. The experimental results of this paper show that grid management can effectively and quickly manage food safety problems. The method of this paper can also optimize the error of food detection, and the error can reach less than 2% in the end. It makes people’s lives safer and ensures that the food they consume is safer.
Investigation on the Construction of Urban Intelligent Emergency Management System Based on Data Mining Technology
Authors: Jiang Faming
Published: September 2022

Urban management is one of the most prominent problems in modern and contemporary social governance. With the scale of the city, the flow of industries and population becomes larger and larger, and the city takes on more and more risks and emergencies. In order to minimize losses and prevent in advance, early drills are often the best way to prevent emergencies. This paper aims to use data mining technology to design a smart city emergency management system to achieve the role of early warning of emergency time and emergency response to emergency events. In response to this, this paper proposes the EIM-DS algorithm, an optimized data mining algorithm based on high-utility itemset mining. This paper improves the threshold setting and data classification method, which greatly develops the execution efficiency of the algorithm. In addition, an intelligent emergency management system is also designed based on the optimization algorithm. The system focuses on analyzing enterprise data, emergency data and emergency command data. The combination of the three data can evaluate the order of the entire city. The experimental results of this paper prove that the calculation time of the algorithm in this paper is about 50% lower than that of other algorithms under different datasets, which has better efficiency, and the designed system can run more smoothly through black-box and white-box experiments.
Construction of Online Teaching Mode of College Sports Based on Neural Network Technology
Authors: Chen Qiao , Xia Jun , He Ping
Published: September 2022

Online teaching is becoming familiar in recent days for bridging the educational gap and long-distance coverage. The online teaching modes provide better visualization and representation of different subjects, including sports and physical education classes. Data-to-representation provides a better teaching model without interrupting the knowledge-based performance. For leveraging this feature, this article introduces a Conveyance-dependent Teaching Mode (CTM) paradigm. The proposed paradigm employs neural learning to improve the performance of different online sports sessions. This learning assigns rewards for different training modes based on student assessment and data representation. The flawless data and student performance assessments are balanced for improving teaching modes. The reward is updated using the sports curriculum, teacher recommendations, and student interests. Based on the assessment, the rewards are identified using the activation function. This generates the rewards for different sports training sessions improving the data accuracy. Further metrics such as representation, analysis rate, and time factors are verified to improve the performance of the proposed paradigm.
Main Controlling Factors and Formation Mode of Geothermal Anomaly in Eastern Chenghe Mining Area of Weibei Coalfield
Authors: Peng Tao , Liu Kaixiang , Chen Yue , Wang Feng , Li Bing
Published: September 2022

Geothermal anomaly has gradually become a prominent issue affecting the efficient mining of coal with the depth of coal mining increasing in eastern Chenghe mining area of Weibei coalfield. Here, we comprehensively investigated the distribution characteristics of the present geothermal field, analyzed the main controlling factors, and constructed forming mode of geothermal anomaly by the temperature measurement in surface borehole and underground water, combined with coal-rock thermal conductivity test. The results show that the geothermal gradient ranged from 25.7°C/km to 54.3°C/km. The areas with geothermal gradient greater than 30°C/km accounted for 88.31%, and there was the highest gradient value in the southeast F1 fault zone. The heat flow was between 66.81 mW/m2 to 128.49 mW/m2, which belonged to the obvious high heat flow area. Under the action of the main controlling factors such as fault, fold, coal-rock thermal conductivity, and groundwater activity in the region, the geothermal gradient and geothermal heat flow values showed an increasing trend from northwest to southeast. According to the distribution characteristics of heat flow and the action mechanism of main controlling factors, the geothermal anomaly in the study area was finally divided into two forming modes, i.e., fault-deep circulating hot water uplifting type and coal seam heat resistance-fold type. The research provides guidance for the geothermal hazard prevention of coal mine and the rational development and utilization of geothermal resources.
Polypyrrole/Magnetic/Tea Waste Composites for PO43− Ions Removal: Adsorption-Desorption, Kinetics, and Thermodynamics Studies
Authors: Saleem Fatima , Bhatti Haq Nawaz , Khan Amina , Farhat Lamia Ben , Elqahtani Zainab Mufarreh , Alwadai Norah , Iqbal Munawar
Published: September 2022

The polypyrrole (PPY/TW) and magnetic (MG/TW) composite with tea waste (TW) was prepared and used as an adsorbent for PO4 3− ions removal from aqueous media. The composite were characterized with SEM and FTIR techniques. Batch study was conducted to investigate the effect of different reaction parameters on the adsorption of PO4 3− ions. The native TW, PPY/TW, and MG/TW showed the PO4 3− ions removal of 7.2, 7.3, and 7.9 (mg/g), respectively, using 0.05 g adsorbent dose and 10 mg/L initial concentration of PO4 3− ions at pH of 6, 10, and 3, respectively, and equilibrium was reached in 90 min. Kinetics and isotherm models were employed on the PO4 3− ions adsorption data and PO4 3− ions adsorption followed the pseudo-second order kinetics, intraparticle diffusion, and Langmuir isotherm models. Thermodynamics analysis reveals an exothermic process and spontaneous adsorption of PO4 3− ions on the composites. Results revealed that the magnetic and polypyrrole composites with tea waste have auspicious potential as an adsorbent and this class of the composites can be utilized for the removal of PO4 3− ions from the effluents.
Analysis of the Role of 5G Communication Technology Based on Intelligent Sensor Network in the Construction and Design of the Internet of Things in Free Trade Zones
Authors: Meng LingYan
Published: September 2022

With the rapid development of the Internet today, the number of various mobile communication terminals has increased rapidly, and 5G has also appeared. With the popularization of 5G technology, new development trends have emerged in more business scenarios. For example, in the 5G era, there will be a lot of room for development in traditional business applications. On this basis, we design a resource allocation that utilizes DQN technology to achieve 5G high-band services. First of all, considering the characteristics of boundary operation, the connection between the base station and the user, the transmission capacity of the base station to the user, and other factors are used as judgment factors, the total energy efficiency is maximized as the goal, and the requirements of mobile users are the constraints. Based on customer service quality assurance, QoS assurance is the object. On the basis of DQN, the convex optimization method is used to solve the given maximum transmission energy between nodes, and DQN is used for iterative iteration to obtain the optimal node and optimal power distribution. Through simulation experiments, the results show that the algorithm has high learning efficiency and convergence and can effectively optimize the allocation of network resources under the premise of ensuring the mobile terminal’s requirements for service quality.
Combining Phenotypes of Nucleotide Excision Repair Pathway to Predict the Risk of Head and Neck Squamous Cell Carcinomas in a Chinese Population
Authors: Liang Yiqian , Zhang Ling , Chen Zichen , Chen Jiayu , Fang Sui , Zhang Ting , Zhu Yun , Liu Jie , Li Baiya , Shao Yuan , Xing Juanli , Zhang Shaoqiang , Han Peng
Published: September 2022

Background. Nucleotide excision repair (NER) is pivotal in the development of smoking-related malignancies. Nine core genes (XPA, XPB, XPC, XPD, XPF, XPG, ERCC1, DDB1, and DDB2) are highly involved in the NER process. We combined two phenotypes of NER pathway (NER protein and NER gene mRNA expression) and evaluated their associations with the risks of the head and neck squamous cell carcinomas (HNSCCs) in a Chinese population. Methods. We conducted a case-control study of 337 HNSCC patients and 285 cancer-free controls by measuring the expression levels of nine core NER proteins and NER gene mRNA in cultured peripheral lymphocytes. Results. Compared with the controls, cases had statistically significantly lower protein expression levels of XPA (

P
<
0.001

) and lower mRNA expression levels of XPA and XPB (

P
=
0.005

and 0.001, respectively). After dividing the subjects by controls’ medians of expression levels, we found an association between increased risks of HNSCCs and low XPA protein level (

P

trend

=
0.031

), as well as low mRNA levels of XPA and XPB (

P

trend

=
0.024

and 0.001, respectively). Subsequently, we correlated the two phenotypes and found associations between the NER mRNA and protein levels. Finally, the sensitivity of the expanded model with protein and mRNA expression levels, in addition to demographic variables, on HNSCCs risk was significantly improved. Conclusions. Combining two phenotypes of NER pathway may be more effective than the model only including one single phenotype for the assessment of risks of HNSCCs.
Dynamic Fire Monitoring Analysis and Risk Assessment Based on Multisource Satellite Remote Sensing
Authors: Chen Jie , Zheng Wei , Shan Tianchan
Published: September 2022

In recent years, forest fires have not only destroyed a large amount of vegetation but also the number and burning area of forest fires in the world have increased significantly. In order to reflect the dynamic monitoring analysis and risk assessment of fires in my country in the past 14 years, this paper selects the national terrestrial forest as an area and uses satellite sensing products with a long time series to analyze the time and space of forest burning biomass and forest fires from a qualitative and quantitative perspective. Feature. A power-law distribution-based estimation model for forest burning biomass was established, and the accuracy of the estimation results and the interannual variation pattern reached more than 98%, forming the regional sensitivity of the remote sensing evaluation method. With the emergence of new sensors such as NPP-VIIRS and HIGH, the emergence of high-resolution data has enhanced the ability of forest fire area extraction and fire point information identification, which provides more data sources for forest burning NG biomass estimation and forest fire spatial and temporal pattern analysis using these thermal infrared sensors.
Construction and Application of Physical Education Teacher Literacy Evaluation System Based on Grey Relational Model
Authors: Kong Xianglong , Guo Daowei
Published: September 2022

With the development of our country, more and more attention is paid to the education of students. However, the issue of teachers’ literacy is the one that has the greatest impact on students in the campus. This paper makes an in-depth study and discussion on the quality evaluation system and application of physical education teachers. Better improving the literacy of physical education teachers also has a more positive impact on student education. The following conclusions are drawn from the research and analysis: (1) The basis of selecting evaluation indicators and the corresponding evaluation process for the evaluation of physical education teachers'literacy are multifaceted, and the evaluation criteria should be fully considered. (2) For the construction of the grey relational model, its algorithms are divided into many categories, and different algorithms are applied to different situations of relational degree. The grey relational model based on point relationship and generalized range is introduced, which provides convenience for subsequent computational research. (3) The difference between the basic information distribution and teaching quality of physical education teachers was obtained through the investigation experiment. The form of questionnaire survey determined the specific indicators of evaluation and the distinction between the first and second grades. Through multiple sets of experiments, the main evaluation indicators and the grey correlation degree of teacher literacy are tested, and the correlation degree between evaluation indicators and teacher literacy is obtained.
[Retracted] Deep Fusion of Intelligent Meridian Sensing Technology and Huoluo Xiaoling Pills in the Treatment of Knee Osteoarthritis
Authors: Sun Jiye , Wang Ziou , Wu Piao , Wei Jinxuan , Sun Xiaowei , Li Hongtao
Published: September 2022

Based on the deep fusion of intelligent meridian sensing technology and Huoluo Xiaoling Pill (HXP) in the treatment of knee osteoarthritis (KOA), firstly, the effective components and targets of Salvia miltiorrhiza, Angelica sinensis, frankincense, and myrrh were obtained by using TCMSP, SwissADME, and Swisstarget databases. Similarly, relevant targets of KOA were collected through GeneCards, OMIM, TTD, PharmGKB, and DrugBank databases. Next, the potential targets of ZXP in the treatment of KOA were obtained by intersection of drug and disease targets. Finally, Cytoscape 3.7.1 software was used to construct a “disease‐drug‐component‐target” network, and Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Gnomes (KEGG) signaling pathway enrichment analysis were performed on the core targets through Metascape website. A total of 99 active components and 203 corresponding potential therapeutic targets were obtained from the components of HXP. And KOA has 2543 potential therapeutic targets, of which 120 cross targets correspond to 120 active compounds in HXP. Then, topology analysis displayed that the six targets form the core PPI network. In addition, GO and KEGG enrichment analyses showed that these core targets were mainly enriched in inflammatory response, apoptosis, oxidation reaction, and other related pathways.
Ovarian Hyperstimulation Syndrome after GnRH Agonist Triggering and Freeze-All Protocol? Never Not, Hardly Ever: A Systematic Review of Case Reports
Authors: Iorio Giuseppe Gabriele , Carbone Luigi , Conforti Alessandro , Rovetto Marika Ylenia , Picarelli Silvia , Cariati Federica , Strina Ida , Papanikolaou Evangelos , Alviggi Carlo
Published: September 2022

Introduction: Ovarian hyperstimulation syndrome (OHSS) is a severe complication associated with controlled ovarian stimulation (COS). GnRH agonist (GnRH-a) triggering is considered an efficient strategy to prevent OHSS in the high-risk patient. Methods: We performed a review of 11 cases of early and severe OHSS following GnRH-a triggering and freeze-all protocol. Electronic databases were searched from inception of each database until October 2021, to identify case reports and case series that reported OHSS after GnRH-a triggering and freeze-all approach describing patient demographics, COS protocol, and patient outcomes. Results: From the literature review, it is possible to suggest that (1) following GnRH-a triggering, the risk of early and severe OHSS is not totally cancelled; (2) despite it is not possible to predict the event, polycystic ovary syndrome is the most common risk factor; (3) the use of GnRH antagonist starting from the day of PU may represent a valid strategy for preventing OHSS in women with high-risk profile; (4) following the unexpected onset of OHSS, measuring serum levels of human chorionic gonadotropin (hCG) is helpful to exclude an inadvertent exogenous administration or a pregnancy. Conclusion: The statement that OHSS risk is eliminated when GnRH-a triggering, a freeze-all strategy, and no hCG in the luteal phase may generate the idea that this event cannot occur. Although rare, these cases have been observed in a relatively short period of time.
Weight Loss, Type 2 Diabetes, and Nutrition in 355 Patients with Obesity Undergoing Sleeve Gastrectomy with Transit Bipartition: Two-Year Outcomes
Authors: Al Muzaffer , Taskin Halit Eren
Published: September 2022

Introduction: This study examined whether 355 obese patients with type 2 diabetes (T2D) of varying duration and severity experienced equivalent weight loss and T2D remission following a newer sleeve gastrectomy (SG) procedure – SG with transit bipartition (SG-TB). Methods: Primary outcomes were changes in body mass index (BMI), total weight loss (TWL), excess BMI loss (EBMIL), A1C, and diabetes medication use through 24 months. Results: Between December 2015 and December 2019, 399 patients who underwent SG-TB reached the 2-year time point. Follow-up was possible in 355 patients (89.0%): 206 females (58.0%), mean age 51.5 years (24.0–73.0), BMI 34.0 kg/m2 (28.0–50.5), and T2D duration 12.0 years (4.0–37.0). At 2 years, total sample respective mean TWL and EBMIL were 20.2 ± 6.1% (95% CI: 19.5, 20.8) and 87.7 ± 35.2% (84.1, 91.4) corresponding to mean BMI change of 7.0 ± 2.7 kg/m2 (6.7, 7.3) (p < 0.001). T2D duration and severity subgroups experienced comparable BMI and A1C change from baseline (p < 0.001); 281 (79.2%) maintained complete remission. ANOVA showed significant mean increases in vitamin D, calcium, and albumin: overall complication rate, 10.2%; no mortality. Discussion/Conclusion: In 355 patients with obesity who underwent SG-TB, excellent weight loss, T2D, and nutritional outcomes were seen at 2-year follow-up regardless of preoperative T2D duration and severity.
Abstract P304: Persistent Use Of Aktiia 24/7 Blood Pressure Monitor Is Associated With Lower Blood Pressure In Hypertensive Patients
Authors: Sola Josep , Perruchod David , Cortes Meritxell , Pandit Jay , Lobo Melvin D , Wuerzner Gregoire , Fisher Naomi D , Shah Jay
Published: September 2022

Introduction:
Cuffless blood pressure (BP) monitors are a promising innovation for hypertension diagnosis and monitoring. Aktiia (Aktiia 24/7 BP Monitor, Aktiia, Switzerland) is a CE-marked, commercially available BP monitor that passively and continually collects BP waveforms via optical sensors at the wrist. After initial cuff calibration, the app displays BP estimates on a smartphone. No data exist on the impact of persistently showing a person their BP data with this device.

Hypothesis:
If people with hypertension persistently use Aktiia and are exposed to their BP data, there will be a change in behavior resulting in measurably lower BP.

Methods:
We analyzed the systolic BP (SBP) trends of 838 real-world users (Age 57±11, 14% female) who consistently used Aktiia for 6 months (375±287 app interactions, 3646±1417 cuffless readings per user, and 9±7 cuff readings per user). Users were grouped by the mean monthly SBP (<140 and >140 mmHg) calculated over 6 months. Then, cuff SBP averages were calculated monthly and compared to the SBP average of the first month. A t-test analysis was used to detect the difference in SBP between the first and successive months.

Results:
Hypertensive users (SBP > 140 mmHg) saw a reduction in SBP of -3.2 mmHg (CI: -0.70, -5.59, p<0.02), beginning at 3 months and sustained through 6 months. For users with SBP < 140 mm Hg, the mean SBP remained unchanged (Fig. 1).

Conclusion:
Persistent BP monitoring with Aktiia’s cuffless device is associated with a significant, sustained reduction in SBP for hypertensive patients at 6 months. Possible explanations include the Hawthorne effect or intensified treatment.
Nocturnal Hypertension in Children With Chronic Kidney Disease Is Common and Associated With Progression to Kidney Replacement Therapy
Authors: Guzman-Limon Monica L. , Jiang Shuai , Ng Derek , Flynn Joseph T. , Warady Bradley , Furth Susan L. , Samuels Joshua A. ,   for the Chronic Kidney Disease in Children study*
Published: September 2022

BACKGROUND:

Nocturnal hypertension is a risk factor for chronic kidney disease (CKD) progression among adults. In children, effects of nocturnal hypertension on CKD progression is less studied.

METHODS:

We investigated the relationships between nocturnal, daytime, or sustained hypertension and progression to kidney replacement therapy in children using Cox proportional hazards models. Nocturnal and diurnal hypertension respectively defined as: mean blood pressure >95th percentile and/or load >25% for either systolic or diastolic blood pressure within sleep or wake periods.

RESULTS:

One thousand five hundred seventy-seven ambulatory blood pressure monitoring studies from 701 CKiD participants were reviewed. Nighttime, daytime, and both types of hypertension were 19%, 7%, and 33%, respectively. Participants with both daytime and nocturnal hypertension had the highest risk of kidney replacement therapy. Among children with CKD, compared with those who were normotensive, those with isolated nocturnal hypertension had a hazard ratio of 1.49 ([CI, 0.97–2.28];
P
=0.068) while those with both daytime and nocturnal hypertension had a HR of 2.23 ([CI, 1.60–3.11];
P
<0.001) when adjusted for age, race, sex, and baseline proteinuria and glomerular filtration. Estimates for risk were similar among glomerular and nonglomerular participants but not significant in glomerular due to smaller sample size.

CONCLUSIONS:

The presence of both daytime and nocturnal hypertension is significantly associated with risk of kidney replacement therapy. Our study confirms the utility of ambulatory blood pressure monitoring in children with CKD. Identifying and controlling both daytime and nocturnal hypertension using ambulatory blood pressure monitoring may improve outcomes and delay CKD progression in this population.
Type-2 Diabetes and the Clinical Importance of Exaggerated Exercise Blood Pressure
Authors: Schultz Martin G. , Otahal Petr , Kovacevic Ann-Marie , Roberts-Thomson Philip , Stanton Tony , Hamilton-Craig Christian , Wahi Sudhir , La Gerche Andre , Hare James L. , Selvanayagam Joseph , Maiorana Andrew , Venn Alison J. , Marwick Thomas H. , Sharman James E. ,   on behalf of the Exercise Stress Test Collaboration (EXERTION) Investigators
Published: September 2022

Background:

Exaggerated exercise blood pressure (EEBP) during clinical exercise testing is associated with poor blood pressure (BP) control and cardiovascular disease (CVD). Type-2 diabetes (T2DM) is thought to be associated with increased prevalence of EEBP, but this has never been definitively determined and was the aim of this study.

Methods:

Clinical exercise test records were analyzed from 13 268 people (aged 53±13 years, 59% male) who completed the Bruce treadmill protocol (stages 1–4, and peak) at 4 Australian public hospitals. Records (including BP) were linked to administrative health datasets (hospital and emergency admissions) to define clinical characteristics and classify T2DM (n=1199) versus no T2DM (n=12 069). EEBP was defined as systolic BP ≥90th percentile at each test stage. Exercise BP was regressed on T2DM history and adjusted for CVD and risk factors.

Results:

Prevalence of EEBP (age, sex, preexercise BP, hypertension history, CVD history and aerobic capacity adjusted) was 12% to 51% greater in T2DM versus no T2DM (prevalence ratio [95% CI], stage 1, 1.12 [1.02–1.24]; stage 2, 1.51 [1.41–1.61]; stage 3, 1.25 [1.10–1.42]; peak, 1.18 [1.09–1.29]). At stages 1 to 3, 8.6% to 15.8% (4.8%–9.7% T2DM versus 3.5% to 6.1% no-T2DM) of people with ‘normal’ preexercise BP (<140/90 mm Hg) were identified with EEBP. Exercise systolic BP relative to aerobic capacity (stages 1–4 and peak) was higher in T2DM with adjustment for all CVD risk factors.

Conclusions:

People with T2DM have higher prevalence of EEBP and exercise systolic BP independent of CVD and many of its known risk factors. Clinicians supervising exercise testing should be alerted to increased likelihood of EEBP and thus poor BP control warranting follow-up care in people with T2DM.
A converging global research system
Authors: Adams Jonathan , Szomszor Martin
Published: September 2022

We examine the hypothesis that research collaboration has enabled a global research network to evolve, with self-organizing properties transcending national research policy. We examine research output, bilateral and multilateral collaboration, subject diversity, and citation impact over 40 years, in detail for the G7 and BRICK groups of countries and in summary for 26 other nations. We find that the rise in national output was strongly associated with bilateral collaboration until the 2000s but after that by multilateral partnerships, with the shift happening at much the same time across countries. There was a general increase in research subject diversity, with evenness across subjects converging on a similar index value for many countries. Similar diversity is not the same as actual similarity but, in fact, the G7 countries became increasingly similar. National average citation impact (CNCI) rose and groups converged on similar impact values. The impact of the largest economies is above world average, which is a phenomenon we discuss separately. The similarities in patterns and timing occur across countries despite variance in their research policies, such as research assessment. We suggest that the key agent facilitating global network self-organization is a shared concept of best practice in research.
Moving Closer to Village Tradition: Analysis of the Problem of “Dividing up Brigades and Teams” in the Early 1960s
Authors: Guo (郭心钢) Xingang
Published: September 2022

From the end of the 1950s to the early 1960s, the Party Central made positive adjustments to the people’s commune system. Each tier of the system was downsized. Existing scholarship has mainly provided macro-narratives or policy analyses of this episode of history from the perspective of political history or institutional history. Using archives on “dividing up brigades and teams” of the communes in Pucheng county in Shaanxi province, this article contends that, during the collectivization period, the state neglected the pre-existing environmental, social, economic, and cultural characteristics of villages, as well as the relationships among the villages, in the process of integrating them into larger units of collective production with a higher degree of public ownership, thus engendering new conflicts and ruptures in local society. During the process of “dividing up brigades and teams,” individuals and collectives used their legitimate rights of division to maximize their own interests, during which multiple forces were at play. To some extent, this led to a return to long-standing village traditions. These findings are crucial for us to understand the history of the adjustments in the people’s commune system and to reflect on the institution of agricultural collectives in the pre-Reform era.
Systemic Immune-Inflammation Index and C-Reactive Protein/Albumin Ratio Could Predict Acute Stent Thrombosis and High SYNTAX Score in Acute Coronary Syndrome
Authors: Akboga Mehmet Kadri , Inanc Ibrahim Halil , Sabanoglu Cengiz , Akdi Ahmet , Yakut Idris , Yuksekkaya Baran , Nurkoc Serdar , Yalcin Ridvan
Published: September 2022

Acute stent thrombosis (AST) is associated with increased morbidity and mortality. The main aim of this study was to evaluate the prognostic value of the systemic immune-inflammation index (SII) and C-reactive protein (CRP) to albumin ratio (CAR) in predicting AST and high SYNTAX score in patients with acute coronary syndrome (ACS) undergoing percutaneous coronary intervention (PCI). The criteria of the Academic Research Consortium were used to determine definite stent thrombosis. A total of 2077 consecutive patients with ACS undergoing PCI were retrospectively enrolled. Platelet, white blood cell and neutrophil counts, as well as SII, CRP, CAR, and peak cardiac troponin I (cTnI) values were significantly higher, whereas the lymphocyte count and albumin values were significantly lower in the AST (+) group compared with the AST (−) group (P < .05). SYNTAX score showed significant positive correlations with SII (r = .429, P < .001) and CRP (r = .402, P < .001). Multivariate logistic regression analysis showed that SII and CAR, as well as age, diabetes mellitus, stent length, and peak cTnI are independent predictors of AST and high SYNTAX score. In conclusion, the SII and CAR are simple, relatively cheap, and reliable inflammatory biomarkers that can predict AST and high SYNTAX scores in ACS.
Leading Equitable Distance Education During Times of Crisis
Authors: Anthony Anika Ball , Miller Dustin
Published: September 2022

COVID-19 shed light on persistent disparities among K-12 school districts, particularly regarding technology access, its use, and educational outcomes. Although resources exist to inform the design and delivery of distance education at the course level, there is a lack of resources to inform program-level decisions about equitable distance education that impact educators and learners across a school system. Drawing on existing literature on educational equity, this article discusses four ways schools may frame equitable distance education. This article also discusses critical lessons schools learned from the COVID-19 pandemic about sustaining equitable technology-supported learning environments during times of crisis.
Disproportionately Punished, Yet Still Neglected: Variation in Official Police Responses to American Indian/Alaska Native Offending and Victimization
Authors: Lantz Brendan , Ward Cole
Published: September 2022

Objectives

While a great deal of research has considered racial disparities in the criminal justice system, empirical research on the American Indian/Alaska Native (AIAN) population is still in its infancy. Instead, AIAN people are most often grouped in the “other race” category. In this research, we move beyond this categorization and advance research by considering differential handling of AIAN-involved violent crime.

Methods

We use 2016 NIBRS data—including information on 5,740 AIAN victims and 6,591 AIAN suspects—to examine variation in the likelihood of clearance by arrest and variation in these patterns according to victim race, offender race, and offense type.

Results

Results indicate that incidents involving AIAN suspects and White victims are especially likely to result in arrest, but incidents involving AIAN suspects and AIAN victims are less likely to result in arrest. AIAN sexual assault victimization is particularly unlikely to result in arrest.

Conclusions

The AIAN population is both disproportionately arrested when suspected of crime, and disproportionately neglected when victimized. If we wish to better understand the role of race in the criminal justice, it is imperative that we move beyond simple Black-White dichotomies, and begin centering attention on other marginalized populations, including the AIAN population, as well.
Evaluating the effect of online psychoeducational support group on perceived stress in family caregivers of COVID-19 survivors
Authors: Mirhosseini Seyedmohammad , Heshmati Nabavi Fatemeh , Behnam Vashani Hamidreza , Vaghee Saeed
Published: September 2022

Introduction

This study aimed to determine the effect of an online psychoeducational support group on the perceived stress of caregivers of coronavirus disease 2019 (COVID-19) survivors.

Methods

This randomized clinical trial included 70 caregivers of COVID-19 survivors discharged from Imam Reza Hospital in Mashhad, Iran in 2021. The intervention group received a psychoeducational support training program for stress management through six online group sessions (each 35–45 minutes) in a week. An expert psychiatric nurse provided this program on WhatsApp immediately after the patient’s discharge from the hospital. The participants completed the Perceived Stress Scale (PSS-14) before starting the intervention and 1 week after that. Data analysis was conducted by descriptive and inferential statistics, including Chi-square, Fisher’s exact test, independent, and paired t-test.

Results

Both groups were homogeneous in terms of demographic variables of survivors and caregivers. In terms of the PSS, there was no significant difference between the two groups before the intervention ( p=0.28). The pre- and post-intervention scores of PSS in the intervention group were 39.57 ± 5.89 and 35.48 ± 5.68, respectively, indicating a significant decrease (p<0.001). Also, the pre- and post-intervention differences between the intervention and control groups were −4.08 ± 3.52 and −0.11 ± 2.66, respectively, indicating a greater decrease in the total PSS of the intervention group ( p < 0.001).

Conclusion

The family caregivers of COVID-19 survivors suffered from psychological distress during their patient care. It is recommended to perform online psychoeducational support group interventions for these individuals based on psychological and educational support program.