bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ocular surface parameters changes following endoscopic Dacryocystorhinostomy in Primary Acquired Nasolacrimal Duct Obstruction
Authors: Zhang Xiao-lin , Li Meng , Zhang Ke-ke , Gong Yan 
Published: September 2023

Abstract
Purpose: To evaluate Ocular surface parameters in patients with epiphora caused byprimary acquired nasolacrimal obstruction(PANDO), before and after endoscopic dacryocystorhinostomy (EDCR) and to compare with fellow eyes.
Materials and Methods: One hundred and twenty-nine patients who underwent successful dacryocystorhinostomy surgery were included in the study.The Sjögren’s International Collaborative Clinical Alliance (SICCA) ocular staining score (OSS), tear break-up time (TBUT), Schirmer tear test (with and without anesthesia), and Lid wiper staining score of PANDO eyes and fellow eyes were measured preoperatively and postoperatively at 1 month.
Results: Preoperatively,the SICCA OSS score of the PANDO eye was significantly lower than that of the fellow eye (P<0.001),Schirmer test I (without anesthesia) of PANDO eye was significantly higher than that of fellow eye(P=0.024).The differences observed between PANDO eyes and fellow eyes in all other parameters were not significant (p>0 .05).After surgery, the differences between the PANDO eye and fellow eye gradually disappeared(p>0 .05).
In PANDO eyes,when comparing preoperative values with postoperative values，SICCA OSS values showed a statistically significant increase（P<0.001），Schirmer tear test (without and with anesthesia)(P=0,P=0.046)、and Lid wiper staining Korb score(P=0.004)showed a statistically significant decrease.
In fellow eyes,when comparing preoperative values with postoperative 1-monthvalues, the Korb score of the fellow eye was significantly lower after surgery than before (P=0.004),all other parameters showed no significant changes.
Conclusions: Obstruction of Nasolacrimal Duct results in the decrease of tear clearance and the increase of wetness of ocular surface.After successful endoscopic dacryocystorhinostomy （EDCR）surgery,the moisturization of PANDO eyes were reduced,the differences observed between PANDO eyes and fellow eyes in all parameters were disappeared.
Botnet detection based on Markov chain and Fuzzy rough set
Authors: Ezzatneshan Aziz , Farizani Seyed Reza Kamel Tabbakh , Kheirabadi Maryam , Ghaemi Reza 
Published: September 2023

Abstract
Botnets now make up a wide range of cyber-attacks, which are a network of infected computers connected to the Internet, with remote control. So far, a lot of research has been done in this field, the proposed methods are based on the signatures of discovered botnets, anomalies, traffic behavior, and addresses. Each method has both advantages and disadvantages. This research proposes a structure for performing identification operations, which is presented in this research based on the Markov chain and is based on behavioral analysis. A disadvantage of the past methods is the inability to receive network information at a very high speed. In this research, it has tried using a solution to receive traffic at a very high speed of about 40 Gbps and analyze it. To be able to perform the analysis with a lower overhead. The proposed method can investigate the behavior of botnets by examining the area of behavior better than the previous solutions, and as a result, during the solutions used by botnets to hide their behavior, it can counter and identify suspicious flows. The accuracy of the proposed method was found to be 96.170%.
Optimal design of bamboo under transverse bending
Authors: Sato Motohiro , Chalermsin Carol Lee , Kanahama Tohya 
Published: September 2023

Abstract
Bamboo, a natural functionally graded material (FGM), exhibits self-adaptive properties that are potentially responsible for its distinct vascular bundle distribution. This study explores the optimal spatial distribution of vascular bundles to maximize flexural rigidity during transverse bending. We compared the expression for volume fraction of fibers with a verified volume fraction expression and utilized the Halpin-Tsai equation to derive expressions for transverse Young's modulus and flexural rigidity. The optimal distribution was consistent with the actual distribution in Moso bamboo, which exhibits significant ovalization of the cross section due to pure bending. Bamboo behaves as a cylindrical shell near its base where the ovalization effect is significant and as a beam at other regions. Furthermore, the spatial distribution of flexural rigidity from pure bending is optimized at all positions in wild bamboo, demonstrating its potential as a versatile FGM that can adjust its distribution under both pure and transverse bending.
Real-Time Phishing Threat Detection using Lexical URL Features and Machine Learning Techniques
Authors: K SRUTHI , S Manohar Naik S Manohar Naik 
Published: September 2023

Abstract

Phishing attacks continue to pose a significant and ever-evolving threat to individuals, organizations, and the overall cybersecurity ecosystem. Machine learning plays a vital role in phishing attack detection. Researchers have put forth numerous machine learning-based strategies. The presence of a massive number of features can significantly deteriorate the performance of machine learning algorithms. The dependence of the feature on third parties poses problems for machine learning models in the context of real-time phishing detection. In this work, we propose a strategy for detecting lightweight, third-party independent lexical features of URLs that can detect phishing attempts in real time. We used 19964 instances of legitimate and phishing URLs from the ISCXURL-2016 dataset for our test purposes. We employed a range of feature selection and the most appropriate classification methods to detect phishing URLs; out of all the strategies, the Random Forest feature extractor and the Random Forest classifier produced the best accuracy of 99.89%.
Evaluating the Efficacy of Supervised Learning vs. Large Language Models for Identifying Cognitive Distortions and Suicidal Risks in Chinese Social Media
Authors: FU Guanghui , Qi Hongzhi , Zhao Qing , Song Changwei , Zhai Wei , Luo Dan , Shuo Liu , Yu Yi Jing , Wang Fan , Zou Huijing , Yang Bing Xiang , Li Jianqiang 
Published: September 2023

Abstract
Large language models, particularly those akin to the rapidly progressing GPT series, are gaining traction for their expansive influence. While there is keen interest in their applicability within medical domains such as psychology, tangible explorations on real-world data remain scant. Concurrently, users on social media platforms are increasingly vocalizing personal sentiments; under specific thematic umbrellas, these sentiments often manifest as negative emotions, sometimes escalating to suicidal inclinations. Timely discernment of such cognitive distortions and suicidal risks is crucial to effectively intervene and potentially avert dire circumstances. Our study ventured into this realm by experimenting on two pivotal tasks: suicidal risk and cognitive distortion identification on Chinese social media platforms. Using supervised learning as a baseline, we examined and contrasted the efficacy of large language models via three distinct strategies: zero-shot, few-shot, and fine-tuning. Our findings revealed a discernible performance gap between the large language models and traditional supervised learning approaches, primarily attributed to the models' inability to fully grasp subtle categories. Notably, while GPT-4 outperforms its counterparts in multiple scenarios, GPT-3.5 shows significant enhancement in suicide risk classification after fine-tuning. To our knowledge, this investigation stands as the maiden attempt at gauging large language models on Chinese social media tasks. This study underscores the forward-looking and transformative implications of using large language models in the field of psychology. It lays the groundwork for future applications in psychological research and practice.
Understanding Left-behind Places in a Contrastive Approach
Authors: Sandu Dumitru 
Published: September 2023

Abstract
Purpose
Left-behindness is usually studied on the regional level or, in a qualitative way, on the local level. Here we expand the approach to all the rural and urban localities of a country. How are left-behind places in a country compared to other types of places in an Eastern European country? A typology of local human development is designed and tested for the case of the Romanian society.
Methods
One starts from the United Nations model of human development, structured by the three dimensions of education, economic development and health. This is adapted to the local level and two contrasting measures are designed – an index and a typology of local human development. The typology resulted from a cluster analysis. It is validated by bi-variate and multivariate analysis (multinomial regression).
Results
High emigration rates, irrespective of the destination, do not bring lower development, and, implicitly, left behindness, to the local origin in Romania. A key finding of the analysis is that the destination of migration counts. There is a higher probability, in 2018, of bringing left-behindness in localities with a higher number of emigrants to Italy, in the years preceding 2011. In a contrasting vay, high emigration to Italy favours lower comprehensive or economic development later on, at the local level.
Conclusions
Human development is highly differentiated by destination countries of emigration and by historical subregions. It is especially for such contexts that the quantitative approach could be misleading. Public policy targets could be better identified if qualitative and quantitative approaches are simultaneously used. The proposed approach could be adopted by keeping the same dimensions and measuring them by available and appropriate indicators.
Feature selection for constructing datasets toward automated lifecycle assessment for additive manufacturing
Authors: Naser Ahmed Z. , Defersha Fantahum , Yang Sheng 
Published: September 2023

Abstract
Additive Manufacturing (AM) is considered an innovative technology to fabricate goods with green characteristics. In comparison to conventional manufacturing (CM) approaches, AM technologies have shown impressive results in enhancing sustainability in production systems. Various research has been conducted to assess the environmental impacts of AM based on the well-known life cycle assessment (LCA) framework. However, this approach requires intensive domain knowledge to build the environmental impact model and interpret the impacts of input variances. This knowledge barrier may cause delays and challenges in the selection of the optimal design and process parameters for additively manufactured parts in the product design and planning stages due to the iterative design-evaluation process. As such, the research community demands an automated LCA tool for supporting AM toward elevated sustainability. To achieve this ambitious goal, this paper particularly investigates the fundamental question – “What are the key influential parameters that pose an impact on the environmental sustainability of AM?”. A methodological framework for identifying the key influential parameters for AM is proposed. The framework was demonstrated by taking the fused filament fabrication (FFF) process as an example. Based on instantiation, LCA of over 200 AM instances, and correlation analysis, the key influential parameters are identified. Finally, a dataset with the identified features could be constructed. This dataset is expected to establish a common base for scale-up with joint efforts from the AM community.
Modeling and indexing the drought severity by augmenting SPI metric system to incorporate multi-modal ground temperature data
Authors: Karunarathne Sachini , De Silva Kushani , Perera Sanjeewa 
Published: September 2023

Abstract
Drought is a global threat caused by the persistent challenges of climate change. It is important to identify drought conditions based on the weather variables and their patterns. In this study, we enhanced the Standardized Precipitation Index (SPI) by integrating ground temperature data to develop a more comprehensive metric for evaluating drought severity. Our metric offers a dual assessment of drought severity, taking into account both the intensity of the drought and its duration. We employ this evaluation in the primary paddy cultivation region of Sri Lanka, with the aim of shedding light on the prevailing drought conditions affecting paddy crops due to insufficient water supply and prolonged periods ofelevated temperatures. Additionally, we calibrate our metric by aligning it with historical drought records and subsequently compare the outcomes with those derived from the conventional SPI index.
Whole Genome Sequencing and Assembly of Bangladeshi Native Cattle, Mirkadim
Authors: Ahammad Ishtiaque , Amin U.S. Mahzabin , Ahsan Tamim , Bhattacharjee Arittra , Chowdhury Zeshan Mahmud , Rahman Md. Hadisur , Jahan Nusrat , Hossain Mohammad Uzzal , Das Keshob Chandra , Salimullah Md 
Published: September 2023

Abstract
Mirkadim breed of Bos indicus is a cattle native to Bangladesh. For whole genome sequencing of Mirkadim, a sample was collected from the Munshiganj district of Bangladesh. It was subjected to short read sequencing using the NextSeq 2000 Illumina platform with 2x150-bp paired-end reads configuration at the National Institute of Biotechnology, Ganakbari, Ashulia, Savar, Dhaka, Bangladesh. Approximately 62.2 Gb of raw data in compressed format were generated which was subsequently assembled into a draft genome spanning 2,234,532,856 bp, comprising 1,007,556 contigs. The genome data has been deposited under the National Center for Biotechnology Information (NCBI) BioProject number PRJNA947975.
Fabrication Method of Carbon-based Materials in CH4/N2 Plasma by RF-PECVD and Annealing Treatment for Laser Diodes
Authors: Abbas Arwa Saud , Hiazaa Abdulrhman Faraj M , Jalalah Abdullah , Alkhamisah Mohammed , Alrasheed Rasheed , Alfadhl Fadhl S , Aljalham Ghadeer H , Basem Fatimah 
Published: September 2023

The present research addresses the synthesis of carbon materials thin films by RF-PECVD in N2/CH4 gas mixture. Carbon materials film was formed at 40/48 sccm of CH4/N2 of the total gas flow rate ratio CH4/CH4+N2 = 0.45 and 200/100 W HF/LF power at a deposition temperature of 350 oC and 1000 mTorr pressure. Then, post-annealing of carbon materials film took place at 400 oC by means of RTA under N2 flow. The formation of carbon nanostructures was investigated by scanning electron microscopy, energy dispersive X-ray, Raman spectroscopy, and atomic force microscopy, respectively. AFM shows that the films consisted of nanocrystalline grains. The surface morphology and structural characteristics of materials were studied as a gas flow function and substrate temperature. EDX results indicated the carbon presence, and Raman spectroscopy analysis revealed two broad bands: D-band 1381.64 cm−1 and G-band 1589.42 cm−1. The temperature-dependent post-annealing of carbon materials plays a key role in the graphite crystallites growth at high substrate temperatures. Our results indicate carbon materials incorporation for laser diode applications.
Contamination, ecological, and human health risks of heavy metals in water from a Pb–Zn–F mining area, North Eastern Nigeria
Authors: Adewumi Adeniyi JohnPaul , Laniyan Temitope Ayodeji 
Published: September 2023

Abstract

In Nigeria, artisanal mining has become a serious issue. In the Nigerian mining region of Arufu Pb–Zn–F, this study assessed the level of pollution, ecological hazards, and health risks related to the presence of metals in the water. In the dry and rainy seasons, 36 water samples (20 from the ground, 10 from the surface, and six from the mine) were gathered. Samples were examined for the presence of heavy metals such as Cr, Co, Ni, Cu, Zn, As, Cd, and Pb. Other than Cu, Zn, As, Cd, Sb, and Cd (surface water, dry season), which were below the acceptable norm, all water samples had metals over the suggested limits. Heavy metals from nearby mining activities polluted the water, according to contamination evaluations utilizing the contamination factor (CF). Metals in the water may pose very significant ecological dangers, according to ecological risk assessments. The evaluation of human health risks revealed that both adults and children in the region are susceptible to carcinogenic and non-carcinogenic health hazards since the hazard index (HI) values for both indices were above 1 × 10−5 and above 1, respectively. This report emphasizes the need for monitoring mining operations in the nation to safeguard public health.
Comprehensive quantitative analysis of single-molecule proteins using ribosome fusion nanopore technology
Authors: Uemura Sotaro 
Published: September 2023

The detection and analysis of proteins is important for science and medicine and methods for sequencing and synthesising proteins have been developed to assist with this. The analysis of single molecules provides more detailed and targeted information and the development of single-molecule techniques has helped to advance molecular research. Professor Sotaro Uemura, The University of Tokyo, Japan, has over 20 years experience in this field of research, with a focus on singling out and measuring single-molecule proteins using optical tweezers, fluorescence imaging and other techniques. Labelling is a key technology that facilitates the detection of target molecules and molecular sorting by the labelling process provides numerous advantages. However, there are restrictions to this technique, leading to Uemura's involvement in utilising label-free technology to assist in the detection and measurement of single molecules. Nanopore measurement is interesting, especially in its use as a DNA sequencer but, using this method, it isn't possible to pinpoint which molecule each signal comes from. Uemura is interested in using Artificial Intelligence (AI) as an additional analysis method that can link the signals. He is working with collaborators to use machine learning to determine which molecules are producing the signals identified by nanopore measurement.	Single-molecule detection, biological target samples, antibodies, ribosome fusion nanopore technology, quantitative analyses, single molecule research, molecular motors, protein synthesis, optical tweezers, fluorescence imaging technologies, biomolecular functions, DNA sequencing, machine learning, Artificial Intelligence.
Differing Behaviors Around Adult Nonmedical Use of Prescription Stimulants and Opioids: Latent Class Analysis
Authors: Rockhill Karilynn M , Olson Richard , Dart Richard C , Iwanicki Janetta L , Black Joshua C 
Published: September 2023

Background
The availability of central nervous system stimulants has risen in recent years, along with increased dispensing of stimulants for treatment of, for example, parent-reported attention-deficit/hyperactivity disorder in children and new diagnoses during adulthood. Typologies of drug use, as has been done with opioids, fail to include a sufficient range of behavioral factors to contextualize person-centric circumstances surrounding drug use. Understanding these patterns across drug classes would bring public health and regulatory practices toward precision public health.

Objective
The objective of this study was to quantitatively delineate the unique behavioral profiles of adults who currently nonmedically use stimulants and opioids using a latent class analysis and to contrast the differences in findings by class. We further evaluated whether the subgroups identified were associated with an increased Drug Abuse Screening Test-10 (DAST-10) score, which is an indicator of average problematic drug use.

Methods
This study used a national cross-sectional web-based survey, using 3 survey launches from 2019 to 2020 (before the COVID-19 pandemic). Data from adults who reported nonmedical use of prescription stimulants (n=2083) or prescription opioids (n=6127) in the last 12 months were analyzed. A weighted latent class analysis was used to identify the patterns of use. Drug types, motivations, and behaviors were factors in the model, which characterized unique classes of behavior.

Results
Five stimulant nonmedical use classes were identified: amphetamine self-medication, network-sourced stimulant for alertness, nonamphetamine performance use, recreational use, and nondiscriminatory behaviors. The drug used nonmedically, acquisition through a friend or family member, and use to get high were strong differentiators among the stimulant classes. The latter 4 classes had significantly higher DAST-10 scores than amphetamine self-medication (P<.001). In addition, 4 opioid nonmedical use classes were identified: moderate pain with low mental health burden, high pain with higher mental health burden, risky behaviors with diverse motivations, and nondiscriminatory behaviors. There was a progressive and significant increase in DAST-10 scores across classes (P<.001). The potency of the opioid, pain history, the routes of administration, and psychoactive effect behaviors were strong differentiators among the opioid classes.

Conclusions
A more precise understanding of how behaviors tend to co-occur would improve efficacy and efficiency in developing interventions and supporting the overall health of those who use drugs, and it would improve communication with, and connection to, those at risk for severe drug outcomes.