bluuebunny/crossref_metadata_2025_split huggingface dataset:
Application of gene expression programming for seasonal rainfall forecasting in Western Australia using potential climate indices
Authors: Islam Farhana , Imteaz Monzur Alam 
Published: April 2023

AbstractThis study presents the development of rainfall forecast models using potential climate indices for the Kimberley region of Western Australia, using 100 years of rainfall and climate indices data for four rainfall stations. Three different modeling techniques: multiple linear regression (MLR), autoregressive moving average with exogenous input (ARIMAX), and gene-expression programming (GEP) were applied to develop prediction models. Preliminary analysis suggests that Western Tropical Indian Ocean (WTIO) and Southern Oscillation Index (SOI) have significant impacts on summer rainfall generation for the region. Developed models’ performances were evaluated using Pearson correlation coefficient ($$r$$
r
), root mean square error ($$RMSE$$

RMSE

), mean absolute error $$(MAE)$$

(
M
A
E
)

, Nash–Sutcliffe efficiency $$(NSE)$$

(
N
S
E
)

, and refined Willmot index of agreement ($${d}_{r}$$

d
r

). It is found that the GEP model exclusively outperforms the other two alternatives. In the calibration period, the GEP model resulted in a Pearson correlation coefficient (r) values ranging from 0.76 to 0.85, which are significantly higher than that achieved from MLR (0.32 to 0.44) and ARIMAX (0.53 to 0.83) models, while for the validation period, the correlation values for the models ranged from 0.74 to 0.87 for GEP, 0.35 to 0.51 for MLR and 0.59 to 0.77 for ARIMAX models. Considering other statistical error statistics it can be concluded that the GEP model is the best representative seasonal rainfall forecasting model for the region.
Optical coherence tomography combined with convolutional neural networks can differentiate between intrahepatic cholangiocarcinoma and liver parenchyma ex vivo
Authors: Wolff Laura I. , Hachgenei Enno , Goßmann Paul , Druzenko Mariia , Frye Maik , König Niels , Schmitt Robert H. , Chrysos Alexandros , Jöchle Katharina , Truhn Daniel , Kather Jakob Nikolas , Lambertz Andreas , Gaisa Nadine T. , Jonigk Danny , Ulmer Tom F. , Neumann Ulf P. , Lang Sven A. , Amygdalos Iakovos 
Published: April 2023

Abstract
Purpose
Surgical resection with complete tumor excision (R0) provides the best chance of long-term survival for patients with intrahepatic cholangiocarcinoma (iCCA). A non-invasive imaging technology, which could provide quick intraoperative assessment of resection margins, as an adjunct to histological examination, is optical coherence tomography (OCT). In this study, we investigated the ability of OCT combined with convolutional neural networks (CNN), to differentiate iCCA from normal liver parenchyma ex vivo.

Methods
Consecutive adult patients undergoing elective liver resections for iCCA between June 2020 and April 2021 (n = 11) were included in this study. Areas of interest from resection specimens were scanned ex vivo, before formalin fixation, using a table-top OCT device at 1310 nm wavelength. Scanned areas were marked and histologically examined, providing a diagnosis for each scan. An Xception CNN was trained, validated, and tested in matching OCT scans to their corresponding histological diagnoses, through a 5 × 5 stratified cross-validation process.

Results
Twenty-four three-dimensional scans (corresponding to approx. 85,603 individual) from ten patients were included in the analysis. In 5 × 5 cross-validation, the model achieved a mean F1-score, sensitivity, and specificity of 0.94, 0.94, and 0.93, respectively.

Conclusion
Optical coherence tomography combined with CNN can differentiate iCCA from liver parenchyma ex vivo. Further studies are necessary to expand on these results and lead to innovative in vivo OCT applications, such as intraoperative or endoscopic scanning.
Effectiveness of physical therapy in axillary web syndrome after breast cancer: a systematic review and meta-analysis
Authors: González-Rubino Jesús Baltasar , Vinolo-Gil Maria Jesus , Martín-Valero Rocío 
Published: April 2023

Abstract
Background
The axillary web syndrome (AWS) is a surgical breast cancer sequel that limits the functionality of the patient and delays the protocol times of application of cancer treatments. This implies a long period of discomfort and limitations for the user.

Objective
To investigate the different physiotherapy treatments for the AWS and how effective they are.

Methods
A systematic review based on PRISMA protocol and registered in PROSPERO (CRD42021281354) was conducted. The research was performed using PubMed, Scopus, CINAHL, PEDro, and Web of Science databases during January 2022 and March 2022. All randomized controlled trials and controlled clinical trials were included in this review.

Results
A total of 188 articles were identified, with 9 studies selected for the systematic review. These studies basically propose treatments based on exercises and stretching, manual therapy, and the combination of manual therapy and exercises.

Conclusions
Exercise and stretching are the most effective therapies within the field of physiotherapy for the rehabilitation of axillary web syndrome. They restore range of motion faster, reduce pain, improve quality of life, and reduce disabilities. Manual therapy, scar massage, and myofascial release could help improve outcomes but with worse results. The meta-analysis conclusion is that pain is the only outcome with a significant reduction after the application of physiotherapy treatments − 0.82 [− 1.67; 0.03]. This conclusion is drawn from the only three studies with small sample sizes.
Investigation of the best effective fold of data augmentation for training deep learning models for recognition of contiguity between mandibular third molar and inferior alveolar canal on panoramic radiographs
Authors: Papasratorn Dhanaporn , Pornprasertsuk-Damrongsri Suchaya , Yuma Suraphong , Weerawanich Warangkana 
Published: April 2023

Abstract
Objectives
This study aimed to train deep learning models for recognition of contiguity between the mandibular third molar (M3M) and inferior alveolar canal using panoramic radiographs and to investigate the best effective fold of data augmentation.

Materials and methods
The total of 1800 M3M cropped images were classified evenly into contact and no-contact. The contact group was confirmed with CBCT images. The models were trained from three pretrained models: AlexNet, VGG-16, and GoogLeNet. Each pretrained model was trained with the original cropped panoramic radiographs. Then the training images were increased fivefold, tenfold, 15-fold, and 20-fold using data augmentation to train additional models. The area under the receiver operating characteristic curve (AUC) of the 15 models were evaluated.

Results
All models recognized contiguity with AUC from 0.951 to 0.996. Ten-fold augmentation showed the highest AUC in all pretrained models; however, no significant difference with other folds were found. VGG-16 showed the best performance among pretrained models trained at the same fold of augmentation. Data augmentation provided statistically significant improvement in performance of AlexNet and GoogLeNet models, while VGG-16 remained unchanged.

Conclusions
Based on our images, all models performed efficiently with high AUC, particularly VGG-16. Ten-fold augmentation showed the highest AUC by all pretrained models. VGG-16 showed promising potential when training with only original images.

Clinical relevance
Ten-fold augmentation may help improve deep learning models’ performances. The variety of original data and the accuracy of labels are essential to train a high-performance model.
Brain activity during cybersickness: a scoping review
Authors: Chang Eunhee , Billinghurst Mark , Yoo Byounghyun 
Published: April 2023

AbstractVirtual reality (VR) experiences can cause a range of negative symptoms such as nausea, disorientation, and oculomotor discomfort, which is collectively called cybersickness. Previous studies have attempted to develop a reliable measure for detecting cybersickness instead of using questionnaires, and electroencephalogram (EEG) has been regarded as one of the possible alternatives. However, despite the increasing interest, little is known about which brain activities are consistently associated with cybersickness and what types of methods should be adopted for measuring discomfort through brain activity. We conducted a scoping review of 33 experimental studies in cybersickness and EEG found through database searches and screening. To understand these studies, we organized the pipeline of EEG analysis into four steps (preprocessing, feature extraction, feature selection, classification) and surveyed the characteristics of each step. The results showed that most studies performed frequency or time-frequency analysis for EEG feature extraction. A part of the studies applied a classification model to predict cybersickness indicating an accuracy between 79 and 100%. These studies tended to use HMD-based VR with a portable EEG headset for measuring brain activity. Most VR content shown was scenic views such as driving or navigating a road, and the age of participants was limited to people in their 20 s. This scoping review contributes to presenting an overview of cybersickness-related EEG research and establishing directions for future work.
Incidentally exploring natural course of a rare entity: representative case for rosette-forming glioneuronal tumors
Authors: Altunbüker Hira , Hinz Felix , Sahm Felix , Brehmer Stefanie , Wenz Holger 
Published: April 2023

AbstractRosette-forming glioneuronal tumors (RGNT) are extremely rare mostly benign tumors of the central nervous system, which are often studied for its histological aspects despite relatively small numbers of clinical especially radiological knowledge.Despite the increasing number of publications on different localizations and treatment protocols, the morphologic and temporal development process of this rare tumor entity is not clear. We were able to coincidentally observe the entire course of the tumor growth of a RGNT on subsequent MRI examinations in a typical case with mild clinical symptoms and no other neurological illnesses, thus possible clinical complications were prevented.
A systematic review of social network sentiment analysis with comparative study of ensemble-based techniques
Authors: Tiwari Dimple , Nagpal Bharti , Bhati Bhoopesh Singh , Mishra Ashutosh , Kumar Manoj 
Published: April 2023

AbstractSentiment Analysis (SA) of text reviews is an emerging concern in Natural Language Processing (NLP). It is a broadly active method for analyzing and extracting opinions from text using individual or ensemble learning techniques. This field has unquestionable potential in the digital world and social media platforms. Therefore, we present a systematic survey that organizes and describes the current scenario of the SA and provides a structured overview of proposed approaches from traditional to advance. This work also discusses the SA-related challenges, feature engineering techniques, benchmark datasets, popular publication platforms, and best algorithms to advance the automatic SA. Furthermore, a comparative study has been conducted to assess the performance of bagging and boosting-based ensemble techniques for social network SA. Bagging and Boosting are two major approaches of ensemble learning that contain various ensemble algorithms to classify sentiment polarity. Recent studies recommend that ensemble learning techniques have the potential of applicability for sentiment classification. This analytical study examines the bagging and boosting-based ensemble techniques on four benchmark datasets to provide extensive knowledge regarding ensemble techniques for SA. The efficiency and accuracy of these techniques have been measured in terms of TPR, FPR, Weighted F-Score, Weighted Precision, Weighted Recall, Accuracy, ROC-AUC curve, and Run-Time. Moreover, comparative results reveal that bagging-based ensemble techniques outperformed boosting-based techniques for text classification. This extensive review aims to present benchmark information regarding social network SA that will be helpful for future research in this field.
Evaluation of long-chain omega-3 canola oil on Atlantic salmon growth, performance, and essential fatty acid tissue accretion across the life cycle: a review
Authors: Davis Barbara A , Devine Malcolm D 
Published: April 2023

AbstractAquaculture plays an important role in providing protein and fatty acids to meet human nutritional requirements. The finite supply of marine ingredients has led to increased use of vegetable oils in aquafeed. While these oils can meet energy and growth needs, they lack nutritionally required ω3 long-chain polyunsaturated fatty acids (ω3 LCPUFAs). Development of land-based alternative oil sources to safeguard global aquaculture production, while meeting the nutritional needs of both fish and human consumers, is critical. This review summarizes studies using a new land-based ω3 LCPUFA canola oil in fish feed to support both energy and ω3 LCPUFA requirements of Atlantic salmon during each phase of the life cycle, while producing fish with adequate ω3 LCPUFA content to meet human nutritional needs. In all cases, growth and performance were comparable to fish fed conventional fish-oil-based diets; no adverse effects were attributed to the use of ω3 canola oil. Fatty acid deposition in muscle (fillet) reflected feed composition, resulting in accumulation of alpha-linolenic acid (ALA), eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA) and a low ω6:ω3 ratio, making salmon fed ω3 LCPUFA canola oil a sustainable source of these nutrients for human consumption. Additionally, reduced melanin deposits were observed in fish fed ω3 LCPUFA canola oil.
Modular Homes as a New Form of Accommodation to Tackle Homelessness: A Case Study From Cambridge, England
Authors: Ehwi Richmond Juvenile , Oti-Sarpong Kwadwo , Burgess Gemma , Lenhard Johannes , Meng Eana 
Published: April 2023

AbstractIn England, provision of temporary accommodation for people experiencing homelessness has often entailed using traditional construction approaches to deliver housing. However, recent experiments are using modular homes to provide temporary accommodation, accompanied by support services for people experiencing homelessness. Given the early nature of these trials, it is unclear what impacts these modular homes have on their occupants and how these projects in turn impact surrounding residents and businesses. We present a case study of the first modular homes for people experiencing homelessness in Cambridge, England, drawing on longitudinal interviews with the six residents occupying these homes. We found that the physical features of the homes, coupled with wrap-around support services, yielded positive short- and mid-term outcomes for occupants, including improved management of their substance use and money, skills development and readiness for employment, social relations, and a burgeoning sense of community, safety, and security. These positive outcomes have spurred wider interest, including the incorporation of modular homes as alternative temporary accommodation in the Homelessness and Rough Sleeping Strategy of Cambridge City Council, alongside a growing research interest in modular homes and other new schemes by the national government. We argue for further empirical studies of the impacts of different modular home projects, including those that admit more diverse resident cohorts and offer different accommodation types to establish a clear methodology for future modular homes projects in England and beyond.
Rigid toric matrix Schubert varieties
Authors: Portakal Irem 
Published: April 2023

AbstractFulton proves that the matrix Schubert variety $$\overline{X_{\pi }} \cong Y_{\pi } \times \mathbb {C}^q$$

X
π

¯

≅

Y
π

×

C

q

can be defined via certain rank conditions encoded in the Rothe diagram of $$\pi \in S_N$$

π
∈

S
N

. In the case where $$Y_{\pi }:={{\,\textrm{TV}\,}}(\sigma _{\pi })$$

Y
π

:
=

TV

(

σ
π

)

is toric (with respect to a $$(\mathbb {C}^*)^{2N-1}$$

(

C

∗

)

2
N
-
1

action), we show that it can be described as a toric (edge) ideal of a bipartite graph $$G^{\pi }$$

G
π

. We characterize the lower dimensional faces of the associated so-called edge cone $$\sigma _{\pi }$$

σ
π

explicitly in terms of subgraphs of $$G^{\pi }$$

G
π

and present a combinatorial study for the first-order deformations of $$Y_{\pi }$$

Y
π

. We prove that $$Y_{\pi }$$

Y
π

is rigid if and only if the three-dimensional faces of $$\sigma _{\pi }$$

σ
π

are all simplicial. Moreover, we reformulate this result in terms of the Rothe diagram of $$\pi $$
π
.
Subtropical Mode Water in a recent persisting Kuroshio large-meander period: part II—formation and temporal evolution in the Kuroshio recirculation gyre off Shikoku
Authors: Nishikawa Hatsumi , Oka Eitarou , Sugimoto Shusaku 
Published: April 2023

AbstractSince August 2017, the large meander (LM) path of the Kuroshio south of Japan has continued to date for more than five years, with a local Kuroshio recirculation gyre off Shikoku (RGOS). The formation and temporal evolution of Subtropical Mode Water (STMW) in the isolated RGOS were examined by using Argo profiling float and shipboard observation data. Since the late winter of 2018, which was the first winter of the current LM event, multiple STMW layers with different densities overlapping with each other have been observed for more than three years inside the RGOS. The deeper STMW layer of 18.0 °C, which was formed in the late winter of 2017 before the current LM event began, has survived at least until September 2021 while its thickness and horizontal extent has decreased gradually. We may have succeeded in discovering the longest survived STMW ever observed due to a suitable environment for STMW conservation inside the RGOS. On the other hand, the shallower STMW layer(s) of 19.0–19.8 °C, which was originally formed over the deeper STMW layer in the late winter of 2018, has been repeatedly renewed or dissipated in the following years. The oxygen utilization rate at the core of the deeper STMW at a depth of 500 dbar was estimated to be 4.6 μmol kg−1 yr−1, which is believed to be explained solely by oxygen consumption due to remineralization.
The importance of monitoring cerebral oxygenation in non brain injured patients
Authors: Robba Chiara , Battaglini Denise , Rasulo Francesco , Lobo Francisco A. , Matta Basil 
Published: April 2023

AbstractOver the past few years, the use of non-invasive neuromonitoring in non-brain injured patients has increased, as a result of the recognition that many of these patients are at risk of brain injury in a wide number of clinical scenarios and therefore may benefit from its application which allows interventions to prevent injury and improve outcome. Among these, are post cardiac arrest syndrome, sepsis, liver failure, acute respiratory failure, and the perioperative settings where in the absence of a primary brain injury, certain groups of patients have high risk of neurological complications. While there are many neuromonitoring modalities utilized in brain injured patients, the majority of those are either invasive such as intracranial pressure monitoring, require special skill such as transcranial Doppler ultrasonography, or intermittent such as pupillometry and therefore unable to provide continuous monitoring. Cerebral oximetry using Near infrared Spectroscopy, is a simple non invasive continuous measure of cerebral oxygenation that has been shown to be useful in preventing cerebral hypoxemia both within the intensive care unit and the perioperative settings. At present, current recommendations for standard monitoring during anesthesia or in the general intensive care concentrate mainly on hemodynamic and respiratory monitoring without specific indications regarding the brain, and in particular, brain oximetry. The aim of this manuscript is to provide an up-to-date overview of the pathophysiology and applications of cerebral oxygenation in non brain injured patients as part of non-invasive multimodal neuromonitoring in the early identification and treatment of neurological complications in this population.
The determinants of the mobility patterns of the elderly in Switzerland
Authors: Huggenberger Yashka , Wagner Joël , Wanzenried Gabrielle 
Published: April 2023

AbstractLike most industrialized countries, Switzerland is characterized by an aging population. However, it differentiates itself from other countries because of its low home-ownership rate. Also, the lack of specialized housing for the elderly encourages them to stay in their current accommodation instead of moving to a smaller place thereby vacating space for family housing. Given this challenge, it is important for governments as well as other housing providers to understand better the needs of the elderly with respect to their housing situation and their patterns of mobility. Accordingly, our research aims to identify the key determinants that trigger the intention, on the part of the elderly in Switzerland, to move. For this purpose, we use secondary data obtained from the Swiss Household Panel, which is a longitudinal social sciences survey which annually interviews a random sample of private households in Switzerland using computer-assisted interviewing. We focus on respondents aged 60 and older over the period from 2013 to 2018, and our sample includes 13 313 observations. We investigate the role of intentions to move as a function of socio-demographic factors, health and life events, dwelling characteristics as well as satisfaction factors. By exploring the importance of these elements on the intention to move within a logistic regression model framework, we confirm the findings of recent studies including those that affirm that the willingness to move decreases with age, and that the elderly do not adapt the size of their home for a smaller dwelling. In addition, while we find no evidence for the impact of dwelling characteristics and health or life events, we provide empirical evidence for socio-demographic and satisfaction factors that influence the intention to move. From our results, we observe that not being satisfied with the accommodation, living in the German-speaking region of Switzerland, judging the accommodation to be too large, and being a tenant, all increase the intention to move. Our findings contribute to the welfare of the elderly in Switzerland by helping governments as well as private entities to plan housing adapted to their needs better.