bluuebunny/crossref_metadata_2025_split huggingface dataset:
Time distribution of culturable bacteria and pathogens in the Western South China Sea by using five kinds of culture methods
Authors: Xing Mei , Fu Qiuye , Lin Sisi , Fu Xiong , Wang Xiaoxia , Wang LiCheng , Zhu Xiong , Ouyang Tinglan 
Published: January 2023

Abstract

Marine bacteria occupy an important position in the biogeochemical cycle. The study of marine bacteria can provide basis for understanding the ecosystem, microbial evolution and medical health utilization. However, most marine bacteria can not be cultured till now, and the research on culturable bacteria is significant.Wenchang sea area is located in the west of the South China Sea, representing a tropical marine biodiversity of the world. Typhoons often occur in this area after autumn. We assume that the difficulty of culturing bacteria from the ocean is different at different times.
Results
This study collected seawater at six different sites in four different quarters and cultured marine bacteria using five kinds of culture media. Then, bacteria were identified by 16S rDNA full-length sequence analysis. For the full year, 1151 strains of identifiable bacteria were isolated, which could be identified as 4 phyla, 9 classes, 20 orders, 38 families, 66 genera, and 213 species. Klebsiella, Exiguobacterium, Proteus, andEscherichia are the dominant genera, accounting for 9.64%, 8.77%, 6.52%, and 6.52% respectively. Klebsiella pneumoniae, Proteus mirabilis, Exiguobacterium indicum, and Escherichia coli are the dominant species, accounting for 7.99%, 6.34%, 4.69%, and 4.52% respectively. This study also found season-specific bacteria and pathogens appearing throughout the year.
Conclusions
It first revealed the time distribution of culturable bacteria and pathogens from the Wenchang coastline of Hainan Island, The South China Sea, and broadened understanding of the western Pacific Ocean microbes.
Fermi-Dirac entropy as a measure of electron interactions
Authors: Flores-Gallegos Nelson 
Published: January 2023

Abstract
In this work, we analyzed a simple chemical reaction using Fermi-Dirac's entropy defined in terms of the L\"owdin's occupation numbers,
this definition of entropy has not been applied or explored enough in the field of quantum chemistry, in this regard, Fermi-Dirac's entropy
maybe a good option to perform works in which the main purpose will be the study or analysis of the effect of the electron interactions.
The results presented in this work were complemented by the analysis of the kinetic energy, potential energy, and the variation of the electric potential,
along the reaction path, our results suggest that this entropy can be a useful tool to perform studies on electron interactions.
New lump solutions to the nonlinear Schrödinger equation under the few-cycle pulse propagation property
Authors: Zahran Emad H.M. , Bekir Ahmet 
Published: January 2023

Abstract
Throughout this work, we will derive new various types of lump solutions to the nonlinear Schrödinger equation that describing few-cycle pulse propagation in metamaterials. The propagation of waves through optical fibre is one of recent phenomenon that plays fundamental rule in all telecommunication processes as well as medicine devices industries, ocean engineering devices technologies. The lump solutions of this model will be firstly constructed in this article via three various techniques which are the (G’/G)-expansion method, the extended simple equation method (ESEM) and the Paul-Painleve approach method (PPAM). These three techniques have been regularly implemented in parallel paths to show the agreements between the output results. When the comparison between our achieved results with each other’s as well as by that achieved previously has been implemented, it shows the novelty of these results.
A possible feedback between dynamics and thermodynamics through the background moisture in dictating the ISO rainfall over the Bay of Bengal
Authors: Kottapalli Aditya , Vinayachandran P N 
Published: January 2023

Abstract
The northward propagating intraseasonal Oscillation (ISO) is one of the dominant modes of tropical variability during Boreal summers. Several mechanisms have been proposed to explain northward propagation. Yet the factors that decide the ISO rainfall over a particular region remain elusive. This study shows that the ISO rainfall anomalies weaken across the south Bay of Bengal (SBoB) before they re-strengthen over the north Bay of Bengal (NBoB). We use the moisture budget to understand the reason for these weakening-strengthening cycles. We find that the horizontal moisture flux convergence predominantly controls the ISO rainfall anomalies over the two regions. The convergence of background moisture by the ISO wind perturbations decides the ISO rainfall structure. Since past literature suggests that the Planetary Boundary Layer (PBL) convergence is caused by barotropic vorticity, we further conduct the vorticity budget to understand the rainfall structure. We find that though vorticity tilting helps in the generation of the positive tendency of ISO vortex, it is the vorticity stretching that enhances the PBL convergence and hence the ISO rainfall over the NBoB. Further splitting of the stretching term helps us conclude that the convergence due to wind perturbations is the predominant term, representing feedback between dynamics and thermodynamics. We hypothesize that the weaker rainfall anomalies in the SBoB result from the weaker background column relative humidity and moisture, which do not allow the initial dynamic perturbations to grow as fast as they do in an environment with stronger background relative humidity and moisture (NBoB). Our study helps us understand the factors that control the ISO rainfall over a particular region and could help improve the model simulations.
Cuckoo Search Optimization based Energy Efficient Job Scheduling Approach for IoT-Edge Environment
Authors: Bakshi Mohana , Chowdhury Chandreyee , Maulik Ujjwal 
Published: January 2023

Abstract
Recently developed IoT devices are capable of gathering, storing, and processing more data than ever before. This calls for the need for scalability. Through the use of edge computing, more processing functions can be relocated closer to where the data is gathered through the IoT devices. Here, processing tasks may be placed in the Edge Computing Units (ECUs). Each ECU may host a cloudlet consisting of a number of Virtual Machines (VMs), where tasks, that is, jobs could be executed. Due to the need to ensure near real-time response of the jobs, efficient job scheduling is required. Few recent works addressed this issue of job scheduling at the edges. However, many important constraints such as job dependency, job conflict along with heterogeneous edge infrastructure are not found to be considered. Accordingly, in this paper, we proposed an optimal job scheduling approach based on the cuckoo search algorithm to handle these challenges subject to energy efficiency and resource utilization.
The proposed algorithm is simulated and found to work notably well as compared to state-of-the-art edge computing-based job scheduling techniques, especially when the jobs have conflict or dependency among them. The work is reported to achieve above 85% resource utilization even in presence of job conflicts and de-pendencies.
Management and Outcome of Intracardiac Heartworms in Dogs
Authors: Vila Jorge , Alost Elisabeth 
Published: January 2023

Abstract
Background

Intracardiac heartworm (IH) disease is a serious condition that can become life threatening if the patient develops caval syndrome. We aim to describe the management and outcome of IH in dogs evaluated by Medvet’s New Orleans cardiology service from November 2015 to December 2021.

Methods

Records of 27 dogs with IH were examined retrospectively. Follow-up information was obtained from phone conversations with referring veterinarians and owners.

Results

9/27 dogs had a previous diagnosis of heartworm disease and were undergoing “slow kill” treatment, 12/27 dogs heartworm disease was a new diagnosis, and 6/27 had either scheduled or started adulticide therapy. Nine dogs had heartworm extraction. No dogs died during the heartworm extraction procedure. 4/9 dogs have died (survival time 1; 676; 1,815 and 2,184 days). One dog died the day after the procedure secondary to continued respiratory distress, the other 3 died of non-cardiac causes. 5/9 are alive (median follow up 1062 and range of 648 to 1831 days). Eleven dogs had IH resolution. In 7/11 this occurred while undergoing stabilization for heartworm extraction. In 4/11 heartworm extraction was not recommended due to low IH burden. All dogs with IH resolution were discharged from the hospital. 4/11 have died (survival time 6; 22, 58 and 835 days) and 6/11 are alive (median follow up 523 and range of 268 to 2,081 days). One was lost to follow up after 18 days. Five dogs were medically managed. In 1/5 dogs, extraction was not recommended due low IH burden. In 4/5 extraction was recommended but declined. 1/5 has died (survival of 26 days) and 4/5 are alive (follow up of 155; 371; 935 and 947 days). Two dogs were euthanized at the time of diagnosis. 15/27 dogs were considered to have caval syndrome.

Conclusion

The results suggest that patients with IH resolution have a good long-term prognosis. Most often IH resolution occurred while undergoing stabilization for heartworm extraction. When IH are present, heartworm extraction should still be considered the treatment of choice and recommended as 1st line therapy whenever possible.
Global dynamics of a Lotka-Volterra competition-diffusion-advection model with stage structure
Authors: Yan Shuling , Du Zengji 
Published: January 2023

Abstract
In this paper, we study a classical two species stage structure Lotka-Volterra diffusion-advection system. By employing the Krein-Rutman theorem, analyzing the principal eigenvalue, and combining with the theory of monotone dynamical systems, a complete classification of the global dynamics is given for the weak competition case.
2020 MSC: 35B35, 35K57, 35Q92, 92D25
A Supervised Method to Enhance Distance-based Neural Networks' Clustering Performance by Discovering Perfect Representative Neurons
Authors: Fu Qiang , Li Yuefeng , Albathan Mubarak 
Published: January 2023

Abstract
Distance-based neural network clustering has an intrinsic assumption that a particular neuron in the network represents a cluster centroid. However, not all these neurons can perfectly represent the training data; these neurons can only represent part of the training samples. This paper proposes an effective training data splitting method (TDSM) to find perfect representative neurons and improve the clustering results in a distance-based neutral network without changing the original network's internal algorithm or the training data quality. The method allows a network with N neurons to be enlarged to a new m×N neurons network. These neurons represent m sub-networks, and each sub-network perfectly represents a part of the training set with the clustering qualification indicators (purity, Normalized Mutual Information, and Adjusted Rand Index), all equalling 1. Results are statistically validated with a t-test, and we demonstrate that TDSM performs better than the original clustering paradigm on some real data sets.
Study of the effect nitro and hydroxyl substituents of two Imidazopyridines derivatives on inhibitory efficacy: Combining Theoretical and Experimental Study
Authors: Elyoussfi Abdellah , Daoudi Walid , Salhi Amin , Azghay Issam , Ahari Mhamed , Amhamdi Hassan , Benchat Noureddine , Dafali Ali , Aatiaoui Abdelmalik El 
Published: January 2023

Abstract
The present study was devoted to the investigation of the effect of NO2 and OH group in the structure of two imidazopyridine, namely: (E)-N-(7-methyl-2-phenylimidazo[1,2-a]pyridin-3-yl)-1-(4-nitrophenyl)methanimine (Imid1) and (E)-4-(((7-methyl-2-phenylimidazo[1,2-a]pyridin-3-yl)imino)methyl)phenol (Imid2) on the corrosion inhibition efficiency of steel in acidic medium, to remediate mild steel in hydrochloric acid. The evaluation of this activity was revealed using several experimental techniques such as: weight loss measurements (WL), potentiodynamic polarization (PDP) and electrochemical impedance spectroscopy (EIS), UV-visible spectroscopy study, surface characterization by SEM, as well as the impact of temperature and concentration on the inhibition efficiency for two inhibitors. The results obtained from these techniques show that the inhibition efficiency is reduced by the effect of the nitro electron withdrawing group (NO2) in the Imid1 molecule, but increased by the presence of the donor group (-OH) in the Imid2 molecule. The comparison of the results obtained by UV-visible spectroscopy of the inhibited and uninhibited electrolyte provided evidence for the formation of an Fe-ligand complex of two imidazopyridine molecules in 1M HCl. Surface morphological studies by SEM revealed a uniform surface in the presence of the imid1 − 2 in contrast a destroyed surface in the absence of the inhibitors. Finally the results of DFT and MDS simulations are in good order with the experimental data and showed that the obtained order of the inhibition efficiency is always respected (Imid2 > Imid1).
Machine learning-based QoT estimation over optical wireless communication
Authors: Sahu Shakrajit , Clement J. Christopher 
Published: January 2023

Abstract
Fiber-optic communication and networks play an important role in communications technology. According to the last few decades, in this area, research development is growing rapidly. Machine learning (ML) algorithms for optical communications (OC) are certainly a hot topic in the current generation. To overcome the current limitation and different issues of fiber-optic communication and network, a machine learning (ML) algorithm is essential for us. Machine learning techniques are proof that it has superiority in solving complex problems. Machine learning algorithms generally focused on the education field, business organization, and health sectors. Currently, many researchers work in the optical communications area by using machine learning algorithms techniques. A machine learning algorithm is an emerging technology because it helps in the optical communication field for a better quality of service (QoS). According, to the first time, this work reviews machine learning for optical communication literature from a machine learning viewpoint. Only fiber-optic communication and network experts work on machine learning for optical networks, and they are not ML algorithms experts. This paper uses machine learning algorithms for calculating the quality of transmission (QoT) of light paths in optical 1 Springer Nature 2021 L A T E X template Article Title networks link. For better quality of transmission (QoT) estimation tools, it can show the performance analysis of machine learning-based algorithms by using such as bit error rate (BER), optical signal to noise ratio (OSNR), quality factor (Q-factor), blocking probability, and signal to noise ratio (SNR) data. This paper presents a novel concept of quality of transmission (QoT) based on the machine learning algorithm.
Identification of the Breach of Short-Term Rental Regulations in Irish Rent Pressure Zones
Authors: Liu Guowen , Arnedillo-Sanchez Inmaculada , Chen Zhenshuo 
Published: January 2023

Abstract
The housing crisis in Ireland has rapidly grown in recent years. To make a more significant profit, many landlords are no longer renting out their houses under long-term tenancies but under short-term tenancies. The shift from long-term to short-term rentals has harmed the supply of private housing rentals. Regulating rentals in Rent Pressure Zones with the highest and rising rents is becoming a tricky issue.
In this paper, we develop a breach identifier to check short-term rentals located in Rent Pressure Zones with potential breaches only using publicly available data from Airbnb (an online marketplace focused on short-term home-stays) and Irish government websites. First, we use a Residual Neural Network to filter out outdoor landscape photos that negatively impact identifying whether an owner has multiple rentals in a Rent Pressure Zone. Second, a Siamese Neural Network is used to compare the similarity of indoor photos to determine if multiple rental posts correspond to the same residence. Next, we use the Haversine algorithm to locate short-term rentals within a circle centered on the coordinate of a permit. Short-term rentals with a permit will not be restricted. Finally, we improve the occupancy estimation model combined with sentiment analysis, which may provide higher accuracy.
Because Airbnb does not disclose accurate house coordinates and occupancy data, it is impossible to verify the accuracy of our breach identifier. The accuracy of the occupancy estimator cannot be verified either. It only provides an estimate within a reasonable range. Users should be skeptical of short-term rentals that are flagged as possible breaches.
Copper-Bisbenzimidazole Complexes as Biomimetic Catalysts in Organic
Transformations
Authors: Jain Manisha , Yadav Shilpa , Mansi  , Misra Neeti , Khanna Pankaj , Khanna Leena 
Published: January 2023

Abstract:
Copper complexes have biological applications as well as catalytic importance in
performing organic reactions. Besides finding utilities in metalloproteins, enzymes, or molecular
magnetism, they have been used extensively as catalysts in the formation ofcarbon–carbon or carbon–
heteroatom bonds, in addition to many other reactions. This has been achieved by the rational
selection of the organic ligands with suitable coordination sites for attaining ideal geometries. The
binding of one such organic ligand, bisbenzimidazole, with copper results in complexes with varied
shapes, structures, and activities, enhancing their catalytic potential and hence useful applications.
This review describes the catalytic applications of bisbenzimidazole based copper (II)/(I) complexes
in various useful organic transformations. It also provides an in-depth knowledge of the mechanistic
pathways of the transformations involving copper complexes and their multipurpose utility as
catalysts, which may help in the development of improved copper catalysts for the future.
Breast Cancer Knowledge, Attitude and Practice among Metro Women of
India Using Social Media: A Cross-Sectional Study
Authors: Nibha Sinha , Alka Sharma 
Published: January 2023

Background:
In recent times, social media has been playing an important role in spreading
health awareness including breast cancer awareness. Breast cancer is the most common form of cancer
among Indian women. Awareness of its risk factors, symptoms, screening methods and positive attitude
towards screening and practices are essential to reduce the advanced stage diagnosis and further
its mortality rate. This study aims to evaluate knowledge, attitude, and practice about breast cancer in
social media users’ in women in the metro city Delhi of India.

Methods:
We conducted a cross-sectional study from June to December 2019, obtaining data from
397 women using a structured questionnaire. It had four sections that included the socio-demographic
profile, knowledge of the risk factors of breast cancer, symptoms and screening, attitude towards
screening methods, and its practice to prevent the disease.

Results:
The mean age of the respondents was 39.34 (±10.22) years. Interestingly, while 92.4%
(367/397) knew about the dreaded disease, only 74% knew about its risk factors. The participants who
were married, educated and working had reported significantly higher knowledge scores (p ≤ 0.001)
Almost three fourth of these women did have a positive attitude towards breast self-examination and
clinical examination, and 58% of them were practicing breast self-examination, on monthly basis.

Conclusion:
Our study findings revealed that women using social media were having a good awareness
of risk factors, symptoms, and screening methods. They also have a positive attitude toward
screening methods for breast cancer. It highlights the need for awareness generation for practicing early
screening behavior.
1,3,4-Thiadiazole: A Versatile Pharmacophore of Medicinal Significance
Authors: Bala Madhu , Piplani Poonam , Ankalgi Amardeep , Jain Ankit , Chandel Lalit 
Published: January 2023

Abstract:
The 1,3,4-thiadiazole nucleus has attracted the attention of medicinal chemists during the
last decades due to its multiple pharmacological activities, such as antiviral, anticancer, antibacterial,
and anticonvulsant activity. This scaffold's structural alteration could aid in developing novel therapeutically
effective drugs. Incorporating this adaptable pharmacophore into a well-established medicinally
active molecule results in hybrid molecules with diverse pharmacological effects. These encouraging
reports suggested that this privileged skeleton should be thoroughly researched for medicinal
purposes. Hence, an attempt has been made in this compilation to review the structure-activity
relationships of numerous thiadiazole derivatives reported in the literature with varied pharmacological
properties. This review provides an up-to-date explanation of the various synthesized 1,3,4- thiadiazole
analogs and their pharmacological importance.
Nurses’ Work Concerns and Disenchantment During the COVID-19 Pandemic: Machine Learning Analysis of Web-Based Discussions
Authors: Jiang Haoqiang , Castellanos Arturo , Castillo Alfred , Gomes Paulo J , Li Juanjuan , VanderMeer Debra 
Published: January 2023

Background
Web-based forums provide a space for communities of interest to exchange ideas and experiences. Nurse professionals used these forums during the COVID-19 pandemic to share their experiences and concerns.

Objective
The objective of this study was to examine the nurse-generated content to capture the evolution of nurses’ work concerns during the COVID-19 pandemic.

Methods
We analyzed 14,060 posts related to the COVID-19 pandemic from March 2020 to April 2021. The data analysis stage included unsupervised machine learning and thematic qualitative analysis. We used an unsupervised machine learning approach, latent Dirichlet allocation, to identify salient topics in the collected posts. A human-in-the-loop analysis complemented the machine learning approach, categorizing topics into themes and subthemes. We developed insights into nurses’ evolving perspectives based on temporal changes.

Results
We identified themes for biweekly periods and grouped them into 20 major themes based on the work concern inventory framework. Dominant work concerns varied throughout the study period. A detailed analysis of the patterns in how themes evolved over time enabled us to create narratives of work concerns.

Conclusions
The analysis demonstrates that professional web-based forums capture nuanced details about nurses’ work concerns and workplace stressors during the COVID-19 pandemic. Monitoring and assessment of web-based discussions could provide useful data for health care organizations to understand how their primary caregivers are affected by external pressures and internal managerial decisions and design more effective responses and planning during crises.