bluuebunny/crossref_metadata_2025 huggingface dataset:
Effectiveness of a web‐based learning program for promoting local healthcare planning competencies
Authors: Yoshioka‐Maeda Kyoko , Katayama Takafumi , Fujii Hitoshi , Shiomi Misa , Hosoya Noriko , Mayama Tatsushi
Published: July 2023

Objective

To determine the effectiveness of a web‐based educational program regarding local healthcare planning by public health nurses (PHNs) in Japan.

Design

A single‐blind randomized controlled trial.

Sample

Full‐time PHNs working for local governments across Japan.

Measurements

The primary outcome was nurses’ self‐perception of competencies in public health policy. The secondary outcome was self‐perception of knowledge, skills, and perspectives regarding local healthcare planning. We analyzed intergroup differences using the intention‐to‐treat principle and the Mann–Whitney U‐test.

Intervention

The intervention group received six web‐based learning modules including substantial knowledge and skills regarding local healthcare planning based on the analysis, design, development, implementation, and evaluation model and adult learning theory.

Results

Totally, 273 PHNs registered, and 38 were excluded without completing the baseline survey; 235 were randomly allocated to either the intervention (n = 118) or control (n = 117) groups. Sixty‐four participants in the intervention group completed the program. Over 70% of the participants lacked opportunities to learn about local healthcare planning. The intervention group showed significant improvement in self‐perception of competencies, knowledge, and skills regarding local healthcare planning, except for items related to evaluation.

Conclusions

The web‐based learning program effectively improved participants’ self‐perception of competencies in local healthcare planning.
Analysis of factors related to cognitive impairment in a community‐based, complete enumeration survey in Japan: the Nakayama study
Authors: Yoshida Taku , Mori Takaaki , Shimizu Hideaki , Tachibana Ayumi , Yoshino Yuta , Ochi Shinichiro , Yamazaki Kiyohiro , Ozaki Yuki , Kawabe Kentaro , Horiuchi Fumie , Komori Kenjiro , Iga Jun‐ichi , Ueno Shu‐ichi
Published: July 2023

Background

The number of patients with cognitive disorders is rapidly increasing in the world, becoming not only a medical problem, but also a social problem. There have been many reports that various factors are associated with cognitive dysfunction, but the factors have not yet been fully identified. This was a community‐based complete enumeration study which aimed to identify risk and protective factors for dementia.

Methods

The first phase included all residents aged 65 years or older in a town in Japan. They completed many examinations, such as living conditions questionnaires, physical examination, Mini‐Mental State Examination, and brain magnetic resonance imaging. The participants with suspected cognitive impairment underwent additional examinations for detailed evaluation in the second phase. Statistical analysis was performed to identify risk and protective factors for dementia after all participants were diagnosed.

Results

There were 927 participants in the baseline evaluation; 611 (65.9%) were healthy, 165 (17.8%) had mild cognitive impairment (MCI), and 151 (16.3%) had dementia. The age‐standardised prevalence of dementia was 9.5%. Statistical analyses for amnestic MCI and Alzheimer's disease showed that risk factors for cognitive decline were diabetes mellitus, low activities of daily living, and living alone, and that protective factors were history of exercise and drinking habit.

Conclusion

The present findings suggest that several lifestyle‐related diseases and factors are associated with cognitive decline. These results support similar findings from previous studies and will be helpful for preventing dementia in the future.
More than Measurement Error: Discrepant Reporting of Contraceptive Use and the Role of Wives’ and Husbands’ Educational Attainment
Authors: Jennings Elyse A. , Pierotti Rachael S.
Published: July 2023

This paper describes discrepancies in spouses’ reports of the use of female‐controlled, nonvisible contraceptive methods using data from rural Nepal that were collected monthly from both spouses of 822 couples between 2008 and 2016. We find that spouses in about half of couples provided discrepant reports during the period of observation, and these discrepancies occurred in 14 percent of the months of observation. We then investigate these discrepant reports as possible indicators of incomplete transparency regarding reproductive choices and examine whether they are associated with wives’ education and spouses’ relative education levels. We find, first, that wife's educational attainment was negatively associated with discrepant reports of contraceptive use, independent of spouses’ relative educational attainment. At the same time, these models suggest that educational differences between husbands and wives were associated with discrepant reports. Couples in which wives had more education than their husbands faced greater odds of discrepant reports of contraceptive use, relative to couples in which spouses had similar education. Among couples in which husbands had more education than wives odds of wife‐only reporting were lower, relative to couples with similar levels of education. These findings offer important new insights into spousal dynamics that may influence transparency regarding contraceptive use.
Do birds of a feather flock together? Patterns of ethnolinguistic group representation in the Brussels Capital Region
Authors: Blanckaert Benjamin , Caluwaerts Didier
Published: July 2023

Consociational democracy is commonly discussed through the assumed premise of co‐ethnic group representation whereby representatives are institutionally incentivized to represent co‐ethnic interests. However, little attention has been paid to the process of group representation in consociations, in particular the link between descriptive and substantive ethnic group representation. This article considers representative claim‐making on behalf of ethnolinguistic groups in the Brussels Capital Region (Belgium), which is politically divided into Dutch and French speakers. Brussels constitutes an interesting case, because while we would expect co‐ethnic claim‐making to be the norm, Brussels MPs also receive electoral incentives to make representative claims on behalf of the other ethnolinguistic group (i.e., cross‐ethnic claim‐making). Based on a quantitative analysis of representative claims in parliamentary documents (2009–2019) from the Brussels Parliament, we find that cross‐ethnic claim‐making remains limited compared to co‐ethnic claim‐making, and that Dutch speakers and ethnic outbidders are more likely to speak on behalf of co‐ethnics.
How is the platform a workplace? Moving from sites to infrastructure
Authors: Richardson Lizzie
Published: July 2023

Platforms indicate a partial but critical shift from the workplace as site to the workplace as infrastructure. Instead of focusing on platforms as mediators of labour, this article asks: how is the platform a workplace? Platforms involve a distinctive geography, creating flexible space that challenges clear distinctions between the inside and outside of the workplace and its action. It is this issue of the location of the structure for action in platformised work that is the problem of infrastructure. Infrastructure, as studies in information work have demonstrated, exists through practices that resolve the tensions between broader structures for activity and specific enactments. The implications of this workplace infrastructure approach have not yet been fully addressed in contemporary social science research on platforms and work. The article begins by outlining how workplaces are not simply sites where work occurs but rather contingently structure work. It then shows how infrastructure names the socio‐technical constitution of this contingent structuring of workplaces, which allows both standardisation and flexibility in information work. These two insights are combined in the infrastructural qualities of platforms that produce workplaces as contingent structures, with the socio‐technical practices of arrangement and coordination as the spatial drivers of workplace flexibility. As infrastructure, the platform workplace is understood as compositor of work; its parameters shape the occurrence of working activity, at the same time as work taking place also alters the parameters of the workplace. This infrastructural approach illuminates other agencies shaping work beyond employer and employee, in turn implying that the study of platform workplaces as infrastructure can shed light on the functioning of contemporary economic activities beyond work.
The effect of two platelet‐rich plasma aspiration techniques on plasma cellular concentrations using a double syringe gravitational centrifugation system
Authors: Jones Rachel L. , Frederick Steven W. , Cross Alan R.
Published: July 2023

Objective

To identify which aspiration technique increased plasma platelet concentration and which technique minimized plasma leukocyte and erythrocyte concentrations using a gravitational double‐syringe platelet rich plasma (PRP) system.

Study design

Controlled laboratory study.

Animals

Thirty adult dogs.

Methods

Whole blood was collected into two autologous conditioned plasma (ACP) syringes and an ethylenediaminetetraacetic acid (EDTA tube) (control samples). The ACP syringes were centrifuged for 5 min at 1500 rpm. The proximal 2 mL of plasma from one ACP syringe was deposited in an EDTA tube (preflash samples). Plasma from the second ACP syringe was withdrawn until the buffy coat was pierced, producing a “flash” of red blood cells, agitated and deposited into an EDTA tube (flash samples). Complete blood counts were performed.

Results

Mean plasma platelet concentrations of the control, preflash, and flash samples were 2.4 × 105/dL, 3.3 × 105/dL and 4.1 × 105/dL, respectively. The mean platelet concentration of the flash samples was 7.9 × 104/dL higher than the preflash samples (p = .005). The mean platelet concentration was lower in the control samples than the preflash (p = .002) and flash (p < .0001) samples. The median plasma leukocyte concentration of the preflash samples (0/dL) was lower than in the flash samples (2.4 × 103/dL) (p = .001). The median plasma hematocrit value of the preflash samples (0%) was lower than in the flash samples (1.0%) (p = .002).

Conclusion

The flash method is not necessary to produce a PRP sample.

Clinical significance

Both methods produced PRP. However, clinicians should avoid aspirating the buffy coat when processing PRP for therapies where leukocytes and erythrocytes are contraindicated.
Future‐time reference in world Englishes
Authors: Bohmann Axel
Published: July 2023

This paper presents the first large‐scale multivariate analysis of future‐time reference (FTR) variation in world Englishes. On the basis of 7,922 hand‐coded tokens from eight corpora of the International Corpus of English suite, the study probes the effect of eight predictor variables on the choice between will and BE going to (BGT) as FTR markers. Statistically significant effects for all conditioning variables except polarity are found, as well as two significant interaction terms involving modality. By contrast, the predictor variety enters the model only as a main effect, but is not involved in any interactions. This is interpreted as a sign of strong unity in the system of FTR variation in English world‐wide. At the theoretical level, the paper argues for a stronger focus on such tendencies towards unity among global varieties of English.
Sentiment Analysis for the Natural Environment: A Systematic Review
Authors: Ibrohim Muhammad Okky , Bosco Cristina , Basile Valerio
Published: July 2023

In this systematic review, Kitchenham’s framework is used to explore what tasks, techniques, and benchmarks for Sentiment Analysis have been developed for addressing topics about the natural environment. We comprehensively analyze seven dimensions including contribution, topical focus, data source and query, annotation, language, detail of the task, and technology/algorithm used. By showing how this research area has grown during the last few years, our investigation provides important findings about the results achieved and the challenges that need to be still addressed for making this technology actually helpful for stakeholders such as policymakers and governments.
An Iterative Semi-supervised Approach with Pixel-wise Contrastive Loss for Road Extraction in Aerial Images
Authors: Zhang Huijie , Li Pu , Liu Xiaobai , Yang Xianfeng , An Li
Published: July 2023

Extracting roads in aerial images has numerous applications in artificial intelligence and multimedia computing, including traffic pattern analysis and parking space planning. Learning deep neural networks, though very successful, demand vast amounts of high-quality annotations, of which acquisition is time-consuming and expensive. In this work, we propose a semi-supervised approach for image-based road extraction in which only a small set of labeled images are available for training to address this challenge. We design a pixel-wise contrastive loss to self-supervise the network training to utilize the large corpus of unlabeled images. The key idea is to identify pairs of overlapping image regions (positive) or non-overlapping image regions (negative) and encourage the network to make similar outputs for positive pairs or dissimilar outputs for negative pairs. We also develop a negative sampling strategy to filter false-negative samples during the process. An iterative procedure is introduced to apply the network over raw images to generate pseudo-labels, filter and select high-quality labels with the proposed contrastive loss, and retrain the network with the enlarged training dataset. We repeat these iterative steps until convergence. We validate the effectiveness of the proposed methods by performing extensive experiments on the public SpaceNet3 and DeepGlobe Road datasets. Results show that our proposed method achieves state-of-the-art results on public image segmentation benchmarks and significantly outperforms other semi-supervised methods.
Deep Gated Multi-modal Fusion for Image Privacy Prediction
Authors: Zhao Chenye , Caragea Cornelia
Published: July 2023

With the rapid development of technologies in mobile devices, people can post their daily lives on social networking sites such as Facebook, Flickr, and Instagram. This leads to new privacy concerns due to people’s lack of understanding that private information can be leaked and used to their detriment. Image privacy prediction models are developed to predict whether images contain sensitive information (private images) or are safe to be shared online (public images). Despite significant progress on this task, there are still some crucial problems that remain to be solved. Firstly, images’ content and tags are found to be useful modalities to automatically predict images’ privacy. To date, most image privacy prediction models use single modalities (image-only or tag-only), which limits their performance. Secondly, we observe that current image privacy prediction models are surprisingly vulnerable to even small perturbations in the input data. Attackers can add small perturbations to input data and easily damage a well-trained image privacy prediction model. To address these challenges, in this article, we propose a new decision-level Gated multi-modal fusion (GMMF) approach that fuses object, scene, and image tags modalities to predict privacy for online images. In particular, the proposed approach identifies fusion weights of class probability distributions generated by single-modal classifiers according to their reliability of the privacy prediction for each target image in a sample-by-sample manner and performs a weighted decision-level fusion, so that modalities with high reliability are assigned with higher fusion weights while ones with low reliability are restrained with lower fusion weights. The results of our experiments show that the gated multi-modal fusion network effectively fuses single modalities and outperforms state-of-the-art models for image privacy prediction. Moreover, we perform adversarial training on our proposed GMMF model using multiple types of noise on input data (i.e., images and/or tags). When some modalities are failed by input data with noise attacks, our approach effectively utilizes clean modalities and minimizes negative influences brought by degraded ones using fusion weights, achieving significantly stronger robustness over traditional fusion methods for image privacy prediction. The robustness of our GMMF model against data noise can even be generalized to more severe noise levels. To the best of our knowledge, we are the first to investigate the robustness of image privacy prediction models against noise attacks. Moreover, as the performance of decision-level multi-modal fusion depends highly on the quality of single-modal networks, we investigate self-distillation on single-modal privacy classifiers and observe that transferring knowledge from a trained teacher model to a student model is beneficial in our proposed approach.
Feeling Stressed and Unproductive? A Field Evaluation of a Therapy-Inspired Digital Intervention for Knowledge Workers
Authors: Chow Kevin , Fritz Thomas , Holsti Liisa , Barbic Skye , McGrenere Joanna
Published: July 2023

Today’s knowledge workers face cognitively demanding tasks and blurred work-life boundaries amidst rising stress and burnout in the workplace. Holistic approaches to supporting workers, which consider both productivity and well-being, are increasingly important. Taking this holistic approach, we designed an intervention inspired by cognitive behavioral therapy that consists of: (1) using the term “Time Well Spent” (TWS) in place of “productivity”, (2) a mobile self-logging tool for logging activities, feelings, and thoughts at work, and (3) a visualization that guides users to reflect on their data. We ran a 4-week exploratory qualitative comparison in the field with 24 graduate students to examine ourTherapy-inspiredintervention alongside a classicBaselineintervention. Participants who used our intervention often shifted toward a holistic perspective of their primary working hours, which included an increased consideration of breaks and emotions. No such change was seen by those who used theBaselineintervention.
Deep Learning Based Occluded Person Re-Identification: A Survey
Authors: Peng Yunjie , Wu Jinlin , Xu Boqiang , Cao Chunshui , Liu Xu , Sun Zhenan , He Zhiqiang
Published: July 2023

Occluded person re-identification (Re-ID) focuses on addressing the occlusion problem when retrieving the person of interest across non-overlapping cameras. With the increasing demand for intelligent video surveillance and the application of person Re-ID technology, the real-world occlusion problem draws considerable interest from researchers. Although a large number of occluded person Re-ID methods have been proposed, there are few surveys that focus on occlusion. To fill this gap and help boost future research, this article provides a systematic survey of occluded person Re-ID. In this work, we review recent deep learning based occluded person Re-ID research. First, we summarize the main issues caused by occlusion as four groups: position misalignment, scale misalignment, noisy information, and missing information. Second, we categorize existing methods into six solution groups: matching, image transformation, multi-scale features, attention mechanism, auxiliary information, and contextual recovery. We also discuss the characteristics of each approach, as well as the issues they address. Furthermore, we present the performance comparison of recent occluded person Re-ID methods on four public datasets: Partial-ReID, Partial-iLIDS, Occluded-ReID, and Occluded-DukeMTMC. We conclude the study with thoughts on promising future research directions.
Improving Node Classification Accuracy of GNN through Input and Output Intervention
Authors: Chowdhury Anjan , Srinivasan Sriram , Mukherjee Animesh , Bhowmick Sanjukta , Ghosh Kuntal
Published: July 2023

Graph Neural Networks (GNNs) are a popular machine learning framework for solving various graph processing applications. This framework exploits both the graph topology and the feature vectors of the nodes. One of the important applications of GNN is in the semi-supervised node classification task. The accuracy of the node classification using GNN depends on (i) the number and (ii) the choice of the training nodes. In this article, we demonstrate that increasing the training nodes by selecting nodes from the same class that are spread out across non-contiguous subgraphs, can significantly improve the accuracy. We accomplish this by presenting a novel input intervention technique that can be used in conjunction with different GNN classification methods to increase the non-contiguous training nodes and, thereby, improve the accuracy. We also present an output intervention technique to identify misclassified nodes and relabel them with their potentially correct labels. We demonstrate on real-world networks that our proposed methods, both individually and collectively, significantly improve the accuracy in comparison to the baseline GNN algorithms. Both our methods are agnostic. Apart from the initial set of training nodes generated by the baseline GNN methods, our techniques do not need any other extra knowledge about the classes of the nodes. Thus, our methods are modular and can be used as pre-and post-processing steps with many of the currently available GNN methods to improve their accuracy.
A New Method for Fault Detection and Location in a Low-Resistance Grounded Power Distribution Network Using Voltage Phasor of D-PMUs Data
Authors: Najafzadeh Masoud , Pouladi Jaber , Daghigh Ali , Beiza Jamal , Shahmohamadi Sima
Published: July 2023

Electric power grids are always affected by numerous unexpected faults. Occurrence of these faults will have a negative impact on network availability and reliability indices of the network. But the indicators of reliability and quality of electrical energy in the network can be augmented by locating the fault in the shortest time. Special features of distribution networks such as load and network imbalance, existence of different types of load with different connections, existence of multiphase branches, effects related to different conductors, capacitive effects of distribution lines, and limited numbers of measuring devices complicate the process of fault localization in distribution networks. On the other hand, increasing penetration of distributed generation units has caused conventional methods of fault localization. Therefore, it is mandatory to introduce new methods of fault locating by considering the mentioned features. Hence, in the current study, nonlinear methods are presented for identifying the location of ground faults in the power distribution network with the help of voltage phasor measurement at different network buses by the D-PMU phased distribution unit. In the first technique, the genetic optimization algorithms and particle swarm optimization for nonlinear modeling of fault position along the distribution line have been utilized for different single-phase, two-phase, and three-phase faults, and in the second technique, neural fuzzy network training has been proposed by different phasor measurement devices. In this case, it is enough to access the phase information of the network bus voltage. In order to show the effectiveness of the proposed algorithms, a 9-bus system is defined by MATLAB software and also defining different line lengths and line characteristics in different buses. Moreover, after applying single-phase, two-phase, and three-phase faults, as well as presenting the results, fault localization is detected in the shortest time.
Shared Lifestyle-Related Risk Factors of Cardiovascular Disease and Cancer: Evidence for Joint Prevention
Authors: Masoudkabir Farzad , Mohammadifard Noushin , Mani Arya , Ignaszewski Andrew , Davis Margot K. , Vaseghi Golnaz , Mansourian Marjan , Franco Christopher , Gotay Carolyn , Sarrafzadegan Nizal
Published: July 2023

Cardiovascular disease (CVD) and cancer are leading causes of mortality and morbidity worldwide and are the major focus of the World Health Organization’s joint prevention programs. While, diverse diseases, CVD and cancer, have many similarities. These include common lifestyle-related risk factors and shared environmental, metabolic, cellular, inflammatory, and genetic pathways. In this review, we will discuss the shared lifestyle-related and environmental risk factors central to both diseases and how the strategies commonly used to prevent atherosclerotic vascular disease can be applied to cancer prevention.
Self-Disclosure on Professional Social Networking Sites: A Privacy Calculus Perspective
Authors: Eitiveni Imairi , Hidayanto Achmad Nizar , Dwityafani Yuvitri Annisa , Kumaralalita Larastri
Published: July 2023

The prevalence of social networking sites (SNS) raises questions about what information is private and what is not. Some users willingly share information on the site expecting some benefits, but others may be reluctant to do so due to fear of losing control of the shared information. To better understand the delicate relationship between privacy, perceived benefits, and self-disclosure, this study examines the antecedents of self-disclosure behavior on professional SNS (i.e., LinkedIn). A model contextualizing privacy calculus theory combined with the trust factor was developed and evaluated using 661 quantitative data collected through a questionnaire. Then, the data was analyzed using covariance-based structural equation modeling method. The results show that perceived benefit (e.g., self-presentation, career advancement, professional network development, learning, and information exchange), privacy concerns, and perceived control are the factors that directly influence LinkedIn users to disclose personal information. These factors become significant predictors of self-disclosure behavior. Meanwhile, trust in LinkedIn members, perceived severity, and perceived likelihood indirectly influence self-disclosure through privacy concerns. Finally, perceived control directly influences trust in LinkedIn members and trust in the LinkedIn provider. The findings of this study help to understand SNS users' behavior, particularly self-disclosure behavior. SNS users can become more aware of the benefits and risks of their disclosure behavior, allowing them to make more informed decisions. These findings can also be helpful for SNS providers to improve product experience and strategy by effectively encouraging and facilitating self-disclosure practices.
Seasonal and Spatial Variability of Chlorophyll-a in Response to ENSO and Ocean Current in the Maritime Boundary of Bangladesh
Authors: Fardoshi Tamanna , Golder Md Rony , Rouf Muhammad Abdur , Masud-Ul-Alam Md
Published: July 2023

Chlorophyll-a (Chl-a) and its correlation with different parameters are one of the major indicators to understand marine ecosystems. This study was conducted to explore the seasonal and spatial variability of Chl-a at three different stations (onshore, midshore, and offshore) across the maritime boundary of Bangladesh in the northern BoB with its response to the surface current speed and recent ENSO (El Niño/La Niña-Southern Oscillation) events by using satellite data. Moderate resolution imaging spectroradiometer (MODIS) aqua satellite level-3 data of Chl-a was used in this study. Ocean currents datasets were obtained from the Asia-Pacific Data Research Center (APDRC) live access service, LAS8.6.13 of NOAA (National Oceanic and Atmospheric Administration), whereas the SST anomalies dataset was collected from NOAA Climate Prediction Center. This study revealed that the onshore region showed the highest (1.121 mgm-3) abundance of Chl-a, whereas the offshore region showed the lowest (0.136 mgm-3). The offshore and midshore regions showed a homogenous distribution of Chl-a, whereas the observed trend of seasonal fluctuation was southwest monsoon > postmonsoon > northeast monsoon > premonsoon. There is a seasonal variation in the relationship between Chl-a and surface current speed, with moderate correlations during northeast (Dec-Feb) and premonsoon (Mar-May). The effect of ENSO on Chl-a was observed as insignificant (

P
>
0.05

) in the northern BoB. However, Chl-a variability in response to ENSO events across the northern region of BoB requires more investigation.
Latent Toxoplasmosis among Breast Cancer Patients in Jahrom, South of Iran
Authors: Haghbin Marzeieh , Maani Salar , Bagherzadeh Mohammad Aref , Bazmjoo Ahmadreza , Shakeri Heshmatollah , Taghipour Ali , Falahi Shahab , Kenarkoohi Azra , Badri Milad , Abdoli Amir
Published: July 2023

Objective. Reactivation of latent toxoplasmosis is the main cause of severe infection among immunocompromised patients, including patients with cancer. Hence, this study is aimed at screening the status of Toxoplasma gondii infection among breast cancer patients by serological and molecular methods and determining their associated risk factors in Jahrom County, Fars Province, south of Iran. Methods. One hundred and seven women with breast cancer (aged 34 to 80 years) were screened for anti-T. gondii antibodies (IgG and IgM) during 2019–2020. A questionnaire regarding demographic factors was filled out by participants. Molecular detection was performed by polymerase chain reaction (PCR) using the primer pair targeting the repetitive element (RE) gene of T. gondii. The risk factors and demographic data were analyzed by SPSS software (ver. 20, Chicago, IL, USA) using the Chi-squared test. Results. Anti-T. gondii IgG was detected in 45.8% (49/107) of the patients, which indicates latent infection, but anti-T. gondii IgM and PCR were negative in all samples. Demographic factors revealed a statistically significant increased T. gondii seropositivity among nonmenopause cancer patients (

P
<
0.0005

), patients without previous breast cancer (

P
=
0.0001

), and human epidermal growth factor receptor 2- (HER2-) negative patients (

P
=
0.00002

). As such, patients with a history of previous abortions and who were at stages II, III, and IIII of cancer had higher seropositivity rates than patients without a history of previous abortions or who were at stage I cancer, but the statistical analysis was not significant. We did not find a statistically significant association between T. gondii seropositivity and other risk factors of toxoplasmosis (e.g., education level, type of water source, washing raw fruits and vegetables, consumption of raw or undercooked meat, and contact with soil, cats, and domestic animal). Conclusion. A high seroprevalence rate of latent T. gondii infection was detected among patients with breast cancer; hence, these patients may be at high risk for reactivation of latent infection. Screening of T. gondii infection is recommended to detect active infection among patients with malignancies.
Enclosed Karst Depression Identification and Analysis for the Pumped Storage Power Station Reservoir Construction Using DEM
Authors: Bo Yu , Tao Zhang , Kexun Zheng , Shuangying Zuo , Xiao Han , Senlin Wang , Shiwan Chen
Published: July 2023

An enclosed karst depression, a typical natural negative terrain, has the advantage of less engineering excavation when constructing a reservoir. In this study, the enclosed karst depression and its range identification technique have been developed. What is more, the geometric parameters and spatial distribution of enclosed karst depressions in Anlong County, Guizhou Province of China, have also been analyzed. Results show that (1) the focus statistic method and local terrain contour tree model were developed to identify enclosed karst depression and its range using regular grid DEM data with 12.5 m spatial resolution, which has been applied to enclosed karst depression identification in Anlong County. (2) 7262 independent and nested depressions with an average density of 3.7/km2 were identified by using the proposed method. The effectiveness and reliability of the proposed model have been verified through comparative analysis and visual recognition comparison. (3) High-density depression areas (5.6 depressions/km2), medium-density depression areas (2.9 depressions/km2), and low-density depression areas (1.1 depressions/km2) were well classified through kernel density analysis. (4) The geometric parameters of enclosed karst depressions (area, perimeter, circularity, depth, elevation, slope, and volume) were all analyzed in the study area. In addition, an indicator called DCK (depression is caused by karstification) was proposed to evaluate the dissolution degree and karstification stage of the enclosed karst depression. Based on the DCK, we determined that around 2.7% of depressions were identified as middle-stage and suitable for reservoir construction with enough volume and good slope stability. The idea and method in this research could provide a technological support for the engineering utilization of enclosed karst depressions.
Extracellular‐Matrix Mechanics Regulate the Ocular Physiological and Pathological Activities
Authors: Zhang Ran , Li Bo , Li Heng
Published: July 2023

The extracellular matrix (ECM) is a noncellular structure that plays an indispensable role in a series of cell life activities. Accumulating studies have demonstrated that ECM stiffness, a type of mechanical forces, exerts a pivotal influence on regulating organogenesis, tissue homeostasis, and the occurrence and development of miscellaneous diseases. Nevertheless, the role of ECM stiffness in ophthalmology is rarely discussed. In this review, we focus on describing the important role of ECM stiffness and its composition in multiple ocular structures (including cornea, retina, optic nerve, trabecular reticulum, and vitreous) from a new perspective. The abnormal changes in ECM can trigger physiological and pathological activities of the eye, suggesting that compared with different biochemical factors, the transmission and transduction of force signals triggered by mechanical cues such as ECM stiffness are also universal in different ocular cells. We expect that targeting ECM as a therapeutic approach or designing advanced ECM‐based technologies will have a broader application prospect in ophthalmology.
Synthesis of Thinned Planar Concentric Circular Antenna Arrays Using a Modified Artificial Bee Colony Algorithm
Authors: Sun Yuqi , Sun Jianbang , Ye Lei
Published: July 2023

The artificial bee colony (ABC) algorithm is a biomimetic optimization algorithm based on the intelligent foraging behavior of a bee colony. It has obvious advantages in dealing with complex nonlinear optimization problems. However, the random neighborhood search leads to the ABC algorithm being good at exploration but neglected in exploitation. Therefore, a modified artificial bee colony algorithm (MABC) is proposed in this paper. The modified artificial bee colony algorithm is applied to the thinned optimization of large multiple concentric circular antenna arrays. The aim is to make the thinned array obtain the narrow beam pattern with the best peak sidelobe level (PSLL) in the vertical plane. The elements in the concentric circular antenna arrays are uniformly excited and isotropic. Two different cases have been considered in this study for thinning of concentric circular antenna arrays using MABC, one with fixed uniform interelement spacing and another with optimum uniform interelement spacing. In both the cases, the thinning percentage of the array is kept equal to or more than 50%. Simulation results of the proposed thinned arrays are compared with a fully populated array to illustrate the effectiveness of our proposed method.
Integrating the “Quit and Stay Quit Monday” Model into Smoking Cessation Services for Smokers with Mental Health Conditions: A Pilot Randomized Controlled Trial
Authors: Vojjala Mahathi , Wysota Christina N. , Oketunbi Ololade , King Quiann , Rogers Erin S. , Ward Kenneth
Published: July 2023

Introduction. People with mental health conditions (MHCs) are less likely to achieve long-term abstinence than people without MHCs. The Quit and Stay Quit Monday (QSQM) model offers a long-term approach to treating tobacco use by encouraging people to quit, requit, or recommit to quit smoking every Monday. Aim. To evaluate the efficacy, patient satisfaction, and patient engagement with an intervention that integrated the QSQM model into multicomponent smoking cessation services among people with an MHC. Methods. This was a randomized controlled pilot trial. Eligibility criteria were as follows: (1) ≥18 years old, (2) smoked a cigarette in the past 30 days, (3) diagnosis of an ICD-10 MHC, (4) interest in quitting smoking, (5) able to receive services in English, and (5) had an active email and a cell phone. The intervention group (n = 33) received QSQM-focused telephone coaching, a weekly QSQM email newsletter, a SmokefreeTXT anchored around a Monday quit date, and 4 weeks of nicotine replacement therapy (NRT). The control group (n = 36) received information about contacting their state Quitline for usual services. Primary outcomes were self-reported quit attempts, 7-day abstinence, and intervention satisfaction at 3 months. Results. Twenty-four participants (73%) in the intervention group began telephone coaching, 26 (79%) enrolled in the QSQM email newsletter, 19 (58%) enrolled in SmokefreeTXT, and 15 (46%) used NRT. Using a penalized intent-to-treat approach, quit attempts in the intervention and control groups were 63.6% and 38.9% (OR 2.75, 95% CI 1.03-7.30), respectively. Seven-day abstinence in the two groups was 12.1% and 5.6% (OR 2.35, 95% CI 0.40-13.74), respectively. Of the 15 intervention group participants who set a quit date during the intervention, 13 (86.7%) selected a Monday quit day. Qualitative interviews revealed positive participant experiences with picking a Monday quit day. On follow-up surveys, 89.5%, 69.3%, and 64.3% of intervention participants reported that the counseling, QSQM email, and text messaging, respectively, were very or somewhat helpful. Conclusions. The QSQM model was acceptable and potentially efficacious among people with MHCs, but intervention engagement and satisfaction were modest. Future research should adapt or develop new QSQM delivery approaches to improve patient engagement and potential efficacy of the model. This trial is registered with clinicaltrials.gov (NCT04512248).
A Deep Transfer Learning Network for Structural Condition Identification with Limited Real-World Training Data
Authors: Bao Nengxin , Zhang Tong , Huang Ruizhi , Biswal Suryakanta , Su Jingyong , Wang Ying
Published: July 2023

Structural condition identification based on monitoring data is important for automatic civil infrastructure asset management. Nevertheless, the monitoring data are almost always insufficient because the real-time monitoring data of a structure only reflect a limited number of structural conditions, while the number of possible structural conditions is infinite. With insufficient monitoring data, the identification performance may significantly degrade. This study aims to tackle this challenge by proposing a deep transfer learning (TL) approach for structural condition identification. It effectively integrates physics-based and data-driven methods by generating various training data based on the calibrated finite element (FE) model, pretraining a deep learning (DL) network, and transferring its embedded knowledge to the real monitoring/testing domain. Its performance is demonstrated in a challenging case, vibration-based condition identification of steel frame structures with bolted connection damage. First, disparate subsets of test data are used as training data, and the identification accuracy of the whole dataset is evaluated. The results demonstrate that the proposed approach can achieve high identification accuracy with limited types of training data, with the identification accuracy increasing up to 8.57%. Second, numerical simulation data are used as training data, and then different TL strategies and different DL architectures are compared on the performance of structural condition identification. The results show that even though the training data are from a different domain and with different types of labels, intrinsic physics can be learned through the pretraining process, and the TL results can be clearly improved, with the identification accuracy increasing from 81.8% to 89.1%. The comparative studies show that SHMnet with three convolutional layers stands out as the pretraining DL architecture, with 21.8% and 25.5% higher identification accuracy values over the other two networks, VGGNet-16 and ResNet-18. The findings of this study advance the potential application of the proposed approach towards expert-level condition identification based on limited real-world training data.
The World Turned Outside In: Settler Colonial Studies and Political Economy
Authors: Davies Jack
Published: July 2023

This article criticises the political-economic analysis of settler colonial studies, which it draws out through an immanent critique of its most famous practitioners. It then offers a critical genealogy of the wider theoretical trend that secures it: the post-Cold War vogue of asserting the ever-increasing centrality of primitive accumulation in global capitalism – what we might term a mode of predation. Finally, it teases out the tensions and confusions in the reliance of settler colonial studies upon Marx’s concept of surplus populations, as well as problems abounding in Patrick Wolfe’s ‘logic of elimination’. Overall, it argues that the frequent claim that we inhabit a global settler modernity cannot be sustained through these notions, and that this claim is profoundly moral and academic, lacking political and analytical value. The insistence on the durability of settler colonialism amounts, in this literature, to a claim on behalf of settler colonial studies itself.
“Damit siegt Hrabanus mühelos über Apelles”
Authors: Endrődi Gábor
Published: July 2023

The first printed edition of the Liber sanctae crucis by Hrabanus Maurus, published in 1503 in Pforzheim, was a milestone in the reception history of the Carolingian figured poems, and a typographic and philological masterpiece as well. This article argues for considering Johannes Reuchlin as the principal initiator of the publication. This sheds new light on the excursus about the cross in Reuchlin’s De verbo mirifico and De arte cabalistica. Reuchlin’s theory of the miracle-working word implies a Cabbalistic-Pythagorean exegesis of the cross as a complement, which he left unelaborated. In view of this hiatus, Hrabanus’ figured poems on the cross might have appeared particularly promising for the humanist, both on grounds of their compositional methods and contents. Besides the demonstration of these affinities, the article provides an analysis of Reuchlin’s introductory poem to the edition, which confirms that he regarded the cycle of figured poems as a prospective source for his Christian Cabbala.
Heat Generation and Thermal Radiation Effects on Magneto Hydrodynamics Non Newtonian Casson Nanofluid with Gyro Tactic Microorganisms Over a Plate, Stagnation and Wedge Through Porous Media
Authors: Hymavathi D. , Ramachandru M. , Reddy M. Chenna Krishna , Kishan N.
Published: July 2023

The numerical interventions of two dimensional steady flow of MHD Non-Newtonian nanofluids containing the gyro-tactic microorganisms through porous media over a plate, wedge, and stagnation point are highlighted in this paper. Mainly the Peclet number, bioconvection, Brownian motion,
thermophoresis, and heat generation impacts are addressed to consolidate thermal and nanofluid concentration conservative equations with passively controlled boundary conditions for three different geometrical conditions of flow over a plate, wedge, and stagnation point. By considering the
impacts of the varying pertinent parameters, namely thermophoresis, Brownian motion, Prandtl number, heat generation, chemical reaction, bio convectional and magnetic parameters, results are analysed graphically for the momentum, temperature, nanoparticle volume fractions, and the density
of motile microorganisms profile, as well as the local Nusselt and motile microorganism numbers. Relevant similarity transformations are used to obtain the system of ordinary differential equations and the equations are solved numerically by using Bvp4c via MATLAB based on the shooting technique.
Multiple Slip Effects of Boundary Layer Maxwell-Nanofluid Flow Past a Stretching Sheet: Magnetic Field and Cross Diffusion Effects
Authors: Hassan Khaja , VijayaKumar R. , Srinivas G.
Published: July 2023

The authors are interested in understanding how a magnetic field and cross diffusion influence non-Newtonian Maxwell-Nanofluid boundary layer flow towards a nonlinearly stretched sheet when there are also Thermophoresis and Brownian motion reaction present in the system. Specifically,
the purpose of this research is to learn more about the Maxwell and nanofluid properties of a stretched sheet in a normal magnetic field, as well as the reactions of three distinct slip situations (velocity, thermal, and solutal). Partially differential equations with nonlinear coefficients
are used to obtain the governing conditions. These conditions are changed into profitable non-direct common differential conditions by utilizing the suitable change factors and change coefficients. To explore the mathematical results of the diminished arrangement of non-direct customary differential
conditions, it was created and utilized the Keller box technique, which was produced for mathematical results. The reproduction considers the nanofluid speed, temperature, focus, skin grating coefficients, heat move rate, and mass exchange rate, among different factors. The validity of this
strategy is shown through a correlation of the current outcomes with past discoveries in the writing. From this exploration work, the speed profiles are expanding with expanding upsides of Maxwell liquid boundary and diminishes with expanding upsides of Magnetic field and speed slip boundaries.
With expanding impacts of Thermophoresis and Brownian movement, the temperature profiles are increment. As the upsides of Dufour number builds, the temperature profiles are additionally increments. A development of the Thermophoresis boundary prompts expanded nano particle volume focus circulation
and the opposite impact is seen in the event of Brownian movement impact. The focus profiles are expanding with rising upsides of Soret number boundary.
Oscillatory Modes on the Onset of Electrohydrodynamic Instability in Oldroydian Nanofluid Saturated Anisotropic Porous Layer
Authors: Sharma Veena , Kavita  , Chowdhary Anuradha
Published: July 2023

This work deals with an analytical study on the initiation of oscillatory convection in a rheological nanofluid saturating anisotropic porous layer with inclusion of vertical AC electric field using modified boundary conditions with negligible flux of volume fraction of nanoparticles.
The rheological properties of the nanofluid are described using Oldroyd model. The Darcy model extended by Brinkman model is deployed to characterize the solid matrix behavior. The used model for nanofluid with inclusion of electric field integrates the additional effect of electrophoresis
with that of thermophoresis and Brownian motion in the conservation equations of motion. The partial differential equations are simplified to non-dimensional linear equations using infinitesimal perturbations, Boussinesq approximation, normal mode technique and linearized stability theory.
The characteristic equation is solved analytically for stress-free boundary conditions and the expressions for Rayleigh number of non-oscillatory and oscillatory modes initiation are determined. The oscillatory modes are found to occur for both the cases of top-/bottom-heavy nanoparticles
distributions. The electric Rayleigh number, thermal Prandtl number and stress relaxation parameter advances whereas the Brinkman-Darcy number are found to delay initiation of both stationary and oscillatory convection.