bluuebunny/crossref_metadata_2025_split huggingface dataset:
Physicians’ knowledge, attitudes, and beliefs regarding practices of male and female surgical sterilization procedures in Lebanon
Authors: Najdi Jad , Armache Alexandre , Chawareb Elia Abou , Heidar Nassib Abu , Zein Marwan , Fadel Anthony , Nehme Jimmy , Bachir Bassel 
Published: August 2023

Abstract

Objective Surgical sterilization, including vasectomy in males and tubal ligation in females, is a highly effective but underutilized contraception method. Adoption rates vary globally mostly due to misconceptions by both the general public and practicing physicians. Our survey aims to explore physicians' knowledge, attitudes and beliefs about surgical sterilization techniques.Study Design A web-based survey of four parts was sent to residents and attending physicians of different specialties in Lebanon between April 2022 and April 2023. 183 physicians responded to the survey. Data was analyzed using IBM SPSS Statistics. A descriptive analysis was performed using the chi-square test for categorical variables and ANOVA for continuous variables.Results Physicians specialized in Urology, OB/GYN, Family medicine and Internal medicine, filled the survey. The majority were resident physicians (79%), male (57%), single (72%) and did not receive training in family planning (73%). Knowledge assessment showed an average score of 5/7. After setting this as a passing score, 60.7% of participants passed the assessment with higher likelihood of passing among attending physicians (84.6%), OB/GYN physicians (94.4%), married participants (80.8%), and physicians who received training in family planning (91.8%). Recommendations varied among specialties, with urologists being more likely to recommend vasectomy depending on the clinical scenario (70–97%). Most physicians perceived the general public preferring tubal ligation as a sterilization method (98%).Conclusion Surgical sterilization is underutilized mostly due to lack of knowledge and cultural beliefs among practicing physicians in our region. Ongoing education is needed in medical training to promote family planning.
Visual Interpretable and Explainable Deep Learning Models for Brain Tumor MRI and COVID-19 Chest X-ray Images
Authors: Brima Yusuf , Atemkeng Marcellin 
Published: August 2023

Abstract
Deep learning shows promise for medical image analysis but lacks interpretability, hindering adoption in healthcare. Attribution techniques that explain model reasoning may increase trust in deep learning among clinical stakeholders. This paper aimed to evaluate attribution methods for illuminating how deep neural networks analyze medical images. Using adaptive path-based gradient integration, we attributed predictions from brain tumor MRI and COVID-19 chest X-ray datasets made by recent deep convolutional neural network models. The technique highlighted possible biomarkers, exposed model biases, and offered insights into the links between input and prediction. Our analysis demonstrates the method's ability to elucidate model reasoning on these datasets. The resulting attributions show promise for improving deep learning transparency for domain experts by revealing the rationale behind predictions. This study advances model interpretability to increase trust in deep learning among healthcare stakeholders.
In-silico anti–microbial, anti-inflammatory and hypoglycemic evaluation of active phytoconstituents of the plant: Leucas aspera (Willd.)
Authors: Islam Sariful , Bhagawati Hrishikesh , Tanjim Arifa , Mehboob Masayek , Sarma Mrinal Kashyap , Borah Chinmoyee , Islam Aminul , Basumatary Pankaj 
Published: August 2023

Abstract

Leucas aspera(willd.) (Labiatae family) is a dietary herb with a wide variety of biological activities, including antimicrobial activity. In this study, we usedin-silicodocking to see how well bioactive phytoconstituents fromL. asperamight operate against an inflammatory-triggering protein. Three phytoconstituents were selected from a list of thirty based on their ability to satisfy the Lipinski rule of five. Molsoft L.L.C. databases were used to forecast the drug-likeness score and adverse effects for the chosen compounds. SwissADME was used to make predictions about the pharmacokinetics and toxicological characteristics of the selected phytoconstituents. Docking studies were carried out using the PyRx to predict the binding affinity of the selected phytoconstituents with the protein from the protein data bank. Among the drugs tested, Leucasperol A had the highest drug-likeness score (-0.11) and Amyl propionate had the lowest (-1.24). In molecular docking investigations, the ligands were found to bind to the active site of the target protein with minimal binding energy values. In terms of binding affinities with the target proteins (4DX5 and 5V5Z), Leucasperol A had the highest (-8.8 kcal/mol & -8.6 kcal/mol) and Amyl propionate had the lowest (-4.5 kcal/mol & -4.5 kcal/mol). In terms of binding affinities with the target proteins (6LW5 and 1ILR), Leucasperol A had the highest (-8.7 kcal/mol & -7.5 kcal/mol) and Amyl propionate had the lowest (-5.2 kcal/mol & -4.1 kcal/mol). In terms of binding affinities with the target proteins (3BJM and 6LN2), Leucasperol A had the highest (-8.3 kcal/mol & -8.4 kcal/mol) and Amyl propionate had the lowest (-4.3 kcal/mol & -4.5 kcal/mol). Our results suggested that the phytochemicals we examined inL. asperamight have anti-microbial, anti-inflammatory & hypoglycaemic properties, suggesting that this plant might be useful as a treatment option.
Investigating post-traumatic syringomyelia and local fluid osmoregulation via a rat model
Authors: Pukale Dipak D. , Adkins-Travis Kayla , Aryal Siddhartha R. , Shriver Leah P. , Patti Gary J. , Leipzig Nic D. 
Published: August 2023

Abstract

Background The molecular etiology of syringomyelia (SM) post-spinal cord injury (SCI) is not well understood and only invasive surgical based treatments are available to treat SM. This study builds upon our previous omics studies andin vitrocellular investigations to further understand local fluid osmoregulation in post-traumatic SM (PTSM) to highlight important pathways for future molecular interventions. Methods A rat PTSM model consisting of a parenchymal injection of 2 µL quisqualic acid (QA) and an injection of 5 µL kaolin in the subarachnoid space, followed by laminectomy at the C7 to T1 level was utilized. 6 weeks prior to the initial surgery, parenchymal fluid and cerebrospinal fluid (CSF) were collected, and the osmolality analyzed. Immunohistochemistry (IHC), metabolomics analysis using LC/MS, and mass spectrometry-based imaging (MSI) were performed on injured and laminectomy only spinal cords. Results We demonstrated that the osmolality of the local parenchymal fluid encompassing syrinxes was higher compared to control spinal cords after laminectomy, indicating a local osmotic imbalance due to SM injury. Moreover, we also found that parenchymal fluid is more hypertonic than CSF, indicating establishment of a local osmotic gradient in the PTSM injured spinal cord (syrinx area) forcing fluid into the spinal cord parenchyma to form and/or expand syrinxes. IHC results demonstrated upregulation of betaine, ions, water channels/transporters, and enzymes (BGT1, KCC4, AQP1, AQP4, CHDH) at the injury site as compared to caudal and rostral sites to the injury, implying extensive local osmoregulation activities at the injury site. Further, metabolomics analysis corroborated alterations in osmolality at the injury site by upregulation of small molecule osmolytes including betaine, carnitine, glycerophosphocholine, arginine, creatine, guanidinoacetate, and spermidine. Conclusions In summary, PTSM results in local osmotic disturbance that propagates up to 6 weeks following initial injury. This coincides with and may contribute to syrinx formation/expansion.
Home-based arm cycling exercise improves trunk control in persons with incomplete spinal cord injury: an observational study
Authors: Helden Joeri F L van , Alexander Emma , Cabral Hélio V , Strutton Paul H , Martinez-Valdes Eduardo , Falla Deborah , Chowdhury Joy Roy , Chiou Shin-Yi 
Published: August 2023

Abstract
Arm cycling is used for cardiorespiratory rehabilitation but its therapeutic effects on the neural control of the trunk after spinal cord injury (SCI) remain unclear. We investigated the effects of single session of arm cycling on corticospinal excitability, and the feasibility of home-based arm cycling exercise training on volitional control of the erector spinae (ES) in individuals with incomplete SCI. Using transcranial magnetic stimulation, we assessed motor evoked potentials (MEPs) in the ES before and after 30 minutes of arm cycling in 15 individuals with SCI and 15 able-bodied controls (Experiment 1). Both groups showed increased ES MEP size after the arm cycling. The participants with SCI subsequently underwent a six-week home-based arm cycling exercise training (Experiment 2). MEP amplitudes and activity of the ES, and movements of the trunk during reaching, self-initiated rapid shoulder flexion, and predicted external perturbation tasks were measured. After the training, individuals with SCI reached further and improved trajectory of the trunk during the rapid shoulder flexion task, accompanied by increased ES activity and MEP amplitudes. Exercise adherence was excellent. We demonstrate preserved corticospinal drive after single arm cycling session and feasible home-based arm cycling exercise training for individuals with SCI for trunk rehabilitation.
Protocol for a randomized controlled trial of a psychoeducation material to reduce psychological distress levels in women with breast cancer post mastectomy in Indonesia
Authors: Mustikaningsih Dewi , Prajoko Yan Wisnu , Setyowibowo Hari , Anggorowati Anggorowati 
Published: August 2023

AbstractBackgroundThe prevalence of post-mastectomy patients who experience symptoms of anxiety and depression are 40%, the impact of psychological stress due to mastectomy includes loss of self-confidence, specifically, body image, by 63%, lifestyle changes by 51.8%, feeling useless as much as 58% and feel less cared for by 75%. Patients who experience post-mastectomy stress may experience psychological side effects, including a 24% increased chance of cancer recurrence, a 30% increased risk of all-cause mortality, and a 29% greater risk of developing a particular type of breast cancer. The current study will examine whether our recently created nurse-guided psychoeducational material for Indonesian breast cancer survivors who have had mastectomy reduces psychological discomfort.MethodsIn Indonesia's Kariadi Hospital Semarang Centre Java, 84 patients will participate in a randomised controlled experiment. Data will be gathered at the start of the intervention (pre-assessment), 3 to 5 days later (post-assessment − 1), and 1 to 2 weeks later (follow-up assessment). The primary outcome is level of psychological distress and serotonin levels. Secondary outcomes are breast cancer post mastectomy score pain level, body image and fatigue.DiscussionIf effective, psychoeducation nurse-guided materials will be tested and put into practise in a variety of contexts for city local cares that offer health education/psychoeducation to women with breast cancer following mastectomy (such as in the hospital in West Java, Indonesia).Trial registrationTCTR20230501003. Date: Mei 1th,2023.
Brillouin light storage for 100 pulse widths
Authors: Stiller Birgit , Jaksch Kevin , Piotrowski Johannes , Merklein Moritz , Schmidt Mikolaj K. , Vu Khu , Ma Pan , Madden Stephen , Steel Michael , Poulton Christopher G. , Eggleton Benjamin 
Published: August 2023

Abstract

Signal processing based on stimulated Brillouin scattering (SBS) is limited by the narrow linewidth
of the optoacoustic response, which confines many Brillouin applications to continuous wave signals
or optical pulses longer than several nanoseconds. In this work, we experimentally demonstrate
Brillouin interactions at the 150 ps time scale and a delay for a record 15 ns which corresponds to a
delay of 100 pulse widths. This breakthrough experimental result was enabled by the high local gain
of the chalcogenide waveguides as the optoacoustic interaction length reduces with pulse width. We
successfully transfer 150ps-long pulses to traveling acoustic waves within a Brillouin-based memory
setup. The information encoded in the optical pulses is stored for 15 ns in the acoustic field. We
show the retrieval of eight amplitude levels, multiple consecutive pulses and low distortion in pulse
shape. The extension of Brillouin-based storage to the ultra-short pulse regime is an important step
for the realisation of practical Brillouin-based delay lines and other optical processing applications.
RT-Ranked: towards network resiliency by anticipating demand in TSCH/RPL communication environments.
Authors: Junior Ivanilson França Vieira , Granjal Jorge , Curado Marilia 
Published: August 2023

Abstract
Time-slotted Channel Hopping (TSCH) Media Access Control (MAC) was specified to target the Industrial Internet of Things needs. This MAC balances energy, bandwidth, and latency for deterministic communications in unreliable wireless environments. Building a TSCH schedule is arduous because the node negotiates cells with its neighbours based on queue occupancy, latency, and consumption metrics. The Minimal TSCH Configuration defined by RFC 8180 was specified for bootstrapping a 6TiSCH network and detailed configurations necessary to be supported. In particular, it adopts Routing Protocol for Low Power and Lossy networks (RPL) Non-Storing mode, which reduces the node's network awareness. Dealing with unpredicted traffic far from the forwarding node is difficult due to limited network information. Anticipating this unexpected flow from multiple network regions is essential because it can turn the forwarding node into a network bottleneck leading to high latency, packet discard or disconnection rates, forcing RPL to change the topology. To cope with that, this work proposes a new mechanism that implements an RPL control message option for passing forward the node's cell demand, allowing the node to anticipate the proper cell allocation for supporting the traffic originating by nodes far from the forwarding point embedded in Destination-Oriented Directed Acyclic Graph (DODAG) Information Object (DIO) and Destination Advertisement Object (DAO) RPL control messages. Implementing this mechanism in a distributed TSCH Scheduling developed in Contiki-NG yielded promising results in supporting unforeseen traffic bursts and has the potential to significantly improve the performance and reliability of TSCH schedules in challenging network environments.
Maxillary Sinus Ameloblastoma in Turner syndrome: A Comprehensive Case Report and Long-Term Follow-Up.
Authors: Maghous Abdelhak , Lalya Issam , marnouche El-Amin , Hommadi Mouhcine , Benlemlih Maroua , Zaghba Noha , Ennadif Ayoub , Bazine Amine , Andaloussi Khalid , Elmarjany Mohamed , Hadadi Khalid , sifat hassan 
Published: August 2023

Abstract
Maxillary sinus ameloblastoma is an uncommon locally aggressive odontogenic tumor. In this case report, we present a comprehensive long-term follow-up of maxillary sinus ameloblastoma occurring in a patient with Turner syndrome, managed through partial right maxillectomy followed by adjuvant operative bed radiotherapy.
Quantum spin liquid signatures in monolayer 1T-NbSe2
Authors: Wang Ye-Liang , Zhang Quanzhen , He Wen-Yu , Zhang Yu , Chen Yaoyao , Jia Liangguang , Hou Yanhui , Ji Hongyan , Yang Huixia , Zhang Teng , Liu Liwei , Gao Hong-Jun , Jung Thomas 
Published: August 2023

Abstract
Quantum spin liquids (QSLs) are in a quantum disordered state that is highly entangled and has fractional excitations. As a highly sought-after state of matter, QSLs were predicted to host spinon excitations and to arise in frustrated spin systems with large quantum fluctuations. Here we report on the experimental observation and theoretical modelling of QSL signatures in monolayer 1T-NbSe2, which is a newly emerging two-dimensional material that exhibits both charge-density-wave (CDW) and correlated insulating behaviors. By using scanning tunneling microscopy and spectroscopy (STM/STS), we confirm the presence of spin fluctuations in monolayer 1T-NbSe2 by observing the Kondo resonance as they interact with metallic 1H-NbSe2 monolayers. Subsequent STM/STS imaging of monolayer 1T-NbSe2 at the Hubbard band energy further reveals a long-wavelength charge modulation, in agreement with the spinon modulation expected for QSLs. By depositing manganese-phthalocyanine (MnPc) molecules with spin S = 3/2 onto monolayer 1T-NbSe2, new STS resonance peaks emerge at the Hubbard band edges of monolayer 1T-NbSe2. This observation is consistent with the spinon Kondo effect induced by a S = 3/2 magnetic impurity embedded in a QSL. Taken together, these experimental observations indicate that monolayer 1T-NbSe2 is a new promising QSL material.
A Reinforcement Method for Passenger Flow Control Based on Asynchronous Advantage Actor-Critic Neural Network
Authors: Wang Bao , Jin Peter J. , Luo Xia , Su Qiming 
Published: August 2023

Abstract
Effective passenger flow management is critical for improving service quality and alleviating congestion in metro networks. However, the dynamic nature of travel demand and the complex structure of metro networks present significant challenges in building and solving control models. Additionally, the high computational costs of existing methods limit their practical applications. To address these challenges, this study proposes a new reinforcement learning (RL) based method for passenger flow control. The method has three components: the network state characterization, the control model, and the reinforcement learning model. Then, the study outlines the “action”, “state”, and “reward” concepts in RL based on the definition of decision variables, constraints, and objective functions in the constructed passenger flow control programming model. An iterative interaction mechanism is introduced to synchronize the control schemes generated by the reinforcement learning unit and the network states. Furthermore, effectively utilizing computational resources, the Asynchronous Advantage Actor-Critic Neural Network (A3C-NN) is trained to optimize the complex programming model. Finally, the proposed approach is validated through a case study using data from Chengdu Urban Rail Transit (URT), demonstrating its effectiveness in achieving various objectives, such as minimizing passenger waiting time, maximizing passenger turnover, and maximizing passenger numbers.
Using logistic regression and random forest techniques to predict the degradation of forest roads
Authors: shabani saeid , ahmadi akram , mostafa mohsen , faramarzi hassan 
Published: August 2023

Abstract
The destruction of forest roads has significant adverse impacts on forest accessibility, resulting in heightened maintenance and environmental expenses, as well as posing potential threats to human life. Enhancing the sustainable and secure accessibility to forest regions necessitates a more comprehensive knowledge of the deterioration of forest pathways across temporal and spatial dimensions. The objective of this study is to utilize destruction prediction models to identify pertinent variables related to the state of the Hyrcanian forest road network in Golestan province. Additionally, the study aims to determine the most significant explanatory variables. To achieve the research objective, a set of 50 points were established along forest roads at a uniform distance of 200 meters from each other. The response variable, which pertains to the visibility of the forest road infrastructure, was recorded in binary format. Additionally, the explanatory variables were documented along a strip transect with a width of 10 meters perpendicular to the road. The study employed two conventional logistic regression models and a novel random forest model to forecast the deterioration of forest roads. Three indicators consist of TWI, natural ground gradient and cover density of cut slope, can predict road damage, as demonstrated by the implementation of two models. The random forest model exhibited superior accuracy to logistic regression, as evidenced by its success rate of 0.73 and prediction rate of 0.68. The results indicate that machine learning models offer significant insights into predicting road conditions and ensuring access to the Hyrcanian forests.
A generalization of a theorem of von Neumann
Authors: Eshkaftaki Ali Bayati 
Published: August 2023

Abstract

In 1953 von Neumann has proved that every n × n doubly substochastic matrix A can be increased to a doubly stochastic matrix, i.e., there is an n × n doubly stochastic matrix D for which A ≤ D. In this paper, we will discuss this result for a class of I × I doubly substochastic matrices. In fact, by a constructive method, we find an equivalent condition for existence of a doubly stochastic matrix D which satisfies A ≤ D, for all A ∈ A, where A is assumed a class of (finite or infinite) doubly substochastic matrices. Such a matrix D is called a cover of A. The uniqueness of the cover will also be discussed. Then we obtain an application of this concept to a system of (infinite) linear equations and inequalities.
MSC(2020): 15B48, 15B51, 15A45, 47A50
Filling the Gap Between ESG Discloser and Financial Performance in Indian Companies: The Moderating Role of the Sustainability Committee
Authors: Pasupuleti Akhil , Ayyagari Lakshmana Rao , Devarapalli Suman , Akuthota Sankar Rao 
Published: August 2023

Abstract
The present study aims to examine the impact of sustainable disclosure on firm performance, while considering the moderating role of the Sustainability Committee (SC) within the Indian business landscape. The sample size consists of 84 ESG-disclosing companies with 336 firm-year observations and employed correlation analysis, balanced panel data analysis with fixed effect method, and random effect method. The study has identified ESG disclosure and sustainability committees are significant and positively associated with profitability and firm performance, while moderate variables have received mixed results. To the best of the authors’ knowledge, this is the first research work to address a sustainability committee as a moderator in panel data analysis along with the support of two theories i.e., signaling and stakeholders theory. In the future, researchers may examine the possibility of advancements in this field, while acknowledging the significant role of green initiatives.
Investigating Hostile Post Detection in Gujarati: A Machine Learning Approach
Authors: Boda Jagruti 
Published: August 2023

Abstract
Hostile post on social media is a crucial issue for individual, government, and organizations. There is a critical need for an automated system that can investigate and identify hostile posts from large-scale data. In India, Gujarati is the sixth most spoken language. In this work, we have constructed a major hostile post dataset in the Gujarati language. The data are collected from Twitter, Instagram, and Facebook. Our dataset consists of 1,51,000 distinct tweets having 10,000 manually annotated posts. These posts are labeled into the Hostile and Non-Hostile categories. We have used the dataset in two ways: (i) Original Gujarati Text Data and (ii) Gujarati Data translated into English. We have also checked the performance of pre-processing and without pre-processing data by removing extra symbols and substituting emoji descriptions in the text. Finally, we have conducted experiments using machine learning models based on supervised learning such as Support Vector Machine, Decision Tree, Random Forest, Gaussian Naive-Bayes, Logistic Regression, K-Nearest Neighbor and unsupervised learning based model such as k-means clustering. We have evaluated performance of these models for Bag-of-Words and TF-IDF feature extraction methods. It is observed that classification using TF-IDF features is efficient. Among these methods Logistic regression outperforms with an Accuracy of 0.68 and F1-score of 0.67.