bluuebunny/crossref_metadata_2025_split huggingface dataset:
Editorial
Authors: Gütl Christian 
Published: February 2024

Dear Readers,&nbsp;
It gives me great pleasure to announce the second regular issue of 2024. I would like to thank all the authors for their sound research and the editorial board for the extremely valuable reviews and suggestions for improvement. These contributions together with the generous support of the consortium members enable us to run our journal and maintain its quality.&nbsp;
I would still like to expand our editorial board: If you are a tenured associate professor or above with a good publication record, please apply to join our editorial board. We are also interested in high-quality proposals for special issues on new topics and emerging trends. And finally, we are still looking for some financial support for 2024 to cover all our expenses. We would be very grateful if your library or institution can support us. We would then be happy to add it to our consortium list.&nbsp;
In this regular issue, I am very pleased to present 6 accepted papers by 19 authors from 9 countries: Canada, China, Croatia, India, Kazakhstan, M&eacute;xico, Sri Lanka, Ukraine, and Vietnam.&nbsp;
Petra Grd, Igor Tomi&#269;i&#263;, and Ena Bar&#269;i&#263; from Croatia address in their article a multi-step methodology for face shape classification that is based on the potential of transfer learning and a pretrained EfficientNetV2S neural network.&nbsp;
Lizbeth Alejandra Hern&aacute;ndez-Gonz&aacute;lez, Ulises Ju&aacute;rez-Mart&iacute;nez, Jezreel Mej&iacute;a, and Alberto Aguilar-Laserre from M&eacute;xico focus their research on applying the naturalistic programming paradigm within a software development process using a naturalistic software development method.&nbsp;
In a joint research, Shanshan Jia from China, Gaukhar A. Kamalova from the Republic of Kazakhstan, and Dmytro Mykhalevskiy from Ukraine report on a mobile handover technique aligning with the neighbour discovery paradigm in 6LoWPAN.&nbsp;
Ajay Kumar from India is investigating a mechanism to assess machine learning approaches for software effort estimation (SEE) modeling in the context of accuracy measures, specifically exploring machine learning techniques for SEE modeling as a multi-criteria decision making (MCDM) problem.&nbsp;
Anne Perera and Amitha Caldera from Sri Lanka conduct a comprehensive review on sentiment analysis in the context of mix of languages, phonetic typing and lexical borrowing in web communication.&nbsp;
And last but not least, in a collaboration between researchers from Vietnam and Canada, Tien Quang Dam, Nghia Thinh Nguyen, Trung Viet Le, Tran Duc Le, Sylvestre Uwizeyemungu, and Thang Le-Dinh look into malware detection methods, specifically leveraging machine learning to encode critical information from portable executable (PE) headers into visual representations of ransomware samples.&nbsp;
Enjoy Reading!&nbsp;
Cordially,&nbsp;
Christian G&uuml;tl, Managing Editor-in-Chief
Graz University of Technology, Graz, Austria
Diagnosis and Treatment of Vernal Keratoconjunctivitis: A Qualitative Study of Caregiver, Patient, and Clinician Experience in the United States
Authors: Rose Heath , Bielory Leonard 
Published: February 2024

Purpose:
To better understand the patient journey and challenges in the diagnosis and treatment of patients with vernal keratoconjunctivitis (VKC).

Methods:
This qualitative study assessed the experience of caregivers of children with VKC (n = 7) and of clinicians who treat VKC (n = 16) in the United States. The structured interviews were conducted to identify key “pain points”, obstacles, and trends on the path to diagnosis.

Results:
Like an earlier study conducted in the United Kingdom, this study found low awareness of the nature and severity of VKC among U.S. caregivers and non-specialist providers, and a tendency among young patients and their caregivers to downplay initial symptoms. Medical intervention was delayed as caregivers treated symptoms with over-the-counter medications; 88% (14 of 16) of specialists reported frequent misdiagnosis and mistreatment by pediatricians and primary care providers who were initial points of care. Time to appropriate referral ranged from 1 to 2 weeks to 3 months, in part due to convoluted referral pathways that were universal points of frustration for caregivers and specialists.

Conclusions:
Limited awareness of VKC remains a barrier to timely identification and management of this rare but disruptive ocular surface disease. Caregivers underestimate symptom severity, pediatricians and primary care providers often misdiagnose VKC as allergy or infection, and referrals to appropriate specialists are delayed until symptoms are severe. Early identification is essential to improving the diagnostic journey and treatment of VKC.

[
J Pediatr Ophthalmol Strabismus
. 2024;61(4):252–256.]
A Comparative Analysis of Surgical Outcomes for Infantile Esotropia With and Without Prior Botulinum Toxin A Injection
Authors: Yigit Didem Dizdar , Kockar Alev , Gurez Ceren , Inal Aslı , Gokyigit Birsen 
Published: February 2024

Purpose:
To compare the surgical outcomes in patients who had a failed botulinum toxin A injection before surgery versus those who had surgery as primary treatment (primary surgery) for infantile esotropia.

Methods:
The files of patients who had strabismus surgery in the Strabismus Unit of Beyoglu Eye Training and Research Hospital between January 2012 and March 2022 were reviewed. This study included 104 eyes of 52 patients with infantile esotropia. The angle of deviation before and 1, 3, and 6 months after botulinum toxin A injection or surgery, complications, pattern deviations, family history, abnormal head position, history of prematurity, intensive care unit admission, and consanguineous marriage were noted. A successful outcome was defined as ocular alignment of 10 prism diopters (PD) or less.

Results:

The study population consisted of 52 patients: 27 (52%) boys and 25 (48%) girls. In the botulinum toxin A group (n = 26), the mean age at admission was 14.0 ± 6.8 months, whereas the mean preoperative near and far angle of deviation were 41.92 ± 12.2 and 41.3 ± 13.0 PD, respectively. The mean age at the time of surgery was 40.6 ± 18.1 months. In the primary surgery group (n = 26), the mean age at admission was 34.0 ± 15.9 months. The mean preoperative near and far angle of deviation were 37.3 ± 8.0 and 35.3 ± 10.5 PD, respectively. The mean age at the time of surgery was 48.1 ± 18.5 months. The success rates 6 months after treatment in the botulinum toxin A group and the primary surgery group were 76.9% and 88.5% in near (
P
> .05) and 80.8% and 88.5% in far (
P
> .05), respectively. Three patients had transient ptosis and one had consecutive exotropia after botulinum toxin A injection.

Conclusions:
In infantile esotropia treatment, strabismus surgery after failed botulinum toxin A injection compared to primary surgery has statistically comparable surgical success rates.

[
J Pediatr Ophthalmol Strabismus
. 2024;61(4):245–251.]
Sensory Function of Children With Strabismus After Epiblepharon Surgery
Authors: You Heejeong , Lew Helen 
Published: February 2024

Purpose:
To investigate the effect of epiblepharon surgery on motor and sensory function in children with epiblepharon and concomitant strabismus.

Methods:
Among 564 patients who underwent epiblepharon correction surgery from May 2016 to April 2023, 45 children who were examined for strabismus were included in the study. Preoperative and postoperative visual acuity, refraction, and angle of deviation were analyzed. For those with significant angle of deviation, additional stereopsis, sensory fusion, and returning time were retrospectively investigated. The angle of deviation was examined by one examiner at distance and near.

Results:
The mean age of 45 children (18 boys and 27 girls) was 6.8 ± 2.9 years. Fifteen had significant strabismus at the time of epiblepharon surgery. Visual acuity improved in both the total and strabismus groups. Improvement in stereopsis and fusion was also observed in the strabismus group. The angle of deviation showed no difference in both groups.

Conclusions:
Epiblepharon could affect ocular manifestations of strabismus as shown in this study. The authors suggest strabismus treatment be continued after epiblepharon surgery, with improved sensory function. Further evaluation with larger groups is needed in the future.

[
J Pediatr Ophthalmol Strabismus
. 2024;61(4):257–261.]
Efficient Photoelectrochemical Hydrogen Production Utilizing Advanced Integrated Heterostructures Silicon Solar Cells and 2D-Material
Authors: Alarawi Abeer. A. 
Published: February 2024

Abstract
To avoid air pollution and global warming correlated with utilize utilizing fossil fuels to produce hydrogen. Developing new renewable technologies for generating H2, such as photoelectrochemical water splitting, is undoubtedly important for the future of this clean energy carrier. In fact, most of the H2 today is produced from the steam-reforming methane, which produces environmentally, and health hazard gases, such as CO2. To address this critical challenge, researchers tend to generate H2 by utilizing a promising alternative method to the current CO2-emitting fossil fuel, such as electrolysis of water. A Photoelectrochemical (PEC) unit consists of three essential components: a light absorber that generates electron-hole pairs upon illumination and a co-catalyst combined as a photocathode. This photocathode's structure facilitates charge transfer and reduces the resultant overpotential for gas production. In addition to the reference electrode (made of metal such as Pt) and electrolyte (acid-based solution). The Si-heterojunction solar cell (Si-HJ) is applied as a photocathode due to its high open-circuit voltages (VOC), high-circuit current density (JSC), and excellent photon management to avoid recombination by the surface structuring. Moreover, the Si-HJ cell has high efficiency due to its periodic micro-pyramid (MP) structure, which has the omnidirectional broadband ability to trap light. Moreover, co-catalyst materials possess characteristics such as a chemical match between Si-HSC/co-catalyst and high stability during the operation. In addition, an earth-abundant, inexpensive, non-toxic, and highly efficient catalytic characteristic toward H2 generating can be integrated into photocathodes. Several characterization methods were utilized to investigate the performance of developed Si-HSC/co-catalyst based PEC. We demonstrate Si-HSC/co-catalyst as an efficient photocathode for PEC H2 production. The Si-HSC/co-catalyst photocathode shows an ability to address the shortcoming of each component. We have achieved a half cell solar-to-hydrogen (STH) conversion efficiency of 5.57% under AM 1.5G illumination with a maximum photocurrent density of 36.33 mA/cm2 and an onset potential of 0.5 V vs. RHE. Besides, the electrochemical impedance spectroscopy (EIS) elucidates that the integration of co-catalyst significantly reduced the charge transfer resistance and thereby enhanced the PEC H2 production performance of Si-HSC/co-catalyst based PEC. Accordingly, the integration of Si (excellent light harvesting) and co-catalyst (H2 catalytic ability, and chemical protection) results in fabricating of an earth-abundant catalyst coupled photocathode that has an efficient and stable PEC H2 production characteristics.
Graphene-Infused Zinc-Rich Epoxy Coating for Corrosion Protection Enhancement in Oil and Gas Steel Structures
Authors: Othman N. H. , Samsudin M. , Nor A. Mohammed , Zakaria M. 
Published: February 2024

Abstract
This study aims to investigate the impact of incorporating graphene into zinc-rich epoxies, with the primary objective of extending the cathodic protection of steel plates against corrosion and reducing the zinc content in the paint formulation. To achieve this, graphene-modified zinc-rich epoxy coatings (G-ZRE) were developed, containing varying concentrations of 0.05 wt% and 0.2 wt% graphene in a matrix of 65 wt% zinc-rich epoxy. Electrochemical impedance spectroscopy (EIS) was employed to assess the effect of graphene on the corrosion protection capabilities of the G-ZREs. Additionally, Open Circuit Potential (OCP) measurements were utilized to gain deeper insights into the fundamental mechanism underlying the protective characteristics of the composite coating. Salt spray tests were conducted for 3000 hours, and the creep age was calculated to validate and corroborate the findings derived from the EIS and OCP measurements. The results demonstrated that the addition of 0.05 wt% graphene in the 65 wt% zinc-rich epoxy coatings increased the duration of cathodic protection from 558 hours to 850 hours. The analysis from EIS and OCP indicated that the primary protective mechanism for G-ZREs involved a combination of barrier and sacrificial modes of protection. In contrast, the control coating without graphene exhibited solely sacrificial protection, resulting in accelerated failure compared to the G-ZREs. Furthermore, the salt spray tests revealed that the creepage area of the 0.05 wt% G-ZREs was the smallest, further confirming the excellent barrier protection offered by the composite coating. Therefore, these findings could help reduce the reliance on zinc particles as pigments in zinc-rich coatings, thereby mitigating the environmental impact associated with the potential leaching of zinc particles into the surrounding environment. Overall, incorporating graphene into zinc-rich epoxy coatings in the petroleum industry offers enhanced corrosion resistance, extending the lifespan of equipment and infrastructure. This reduces maintenance costs, improves operational efficiency, and contributes to the advancement of knowledge while benefiting the petroleum industry.
Efficient Spectral Wave Fatigue Assessment of Modules: Utilizing Response Amplitude Operators and Generic Stress Concentration Factors
Authors: Winmar Abrogena P. E. , Christopher Al Basilio , Johanna Czarina Tan 
Published: February 2024

Abstract
Fatigue failure occurs due to repeated loading, and it is important to consider cyclic loading from wave action in the design. This paper describes an efficient spectral fatigue procedure that utilizes response amplitude operators (RAO), generic stress concentration factors (SCF), and structure analysis computer system (SACS) using its sub-programs, called Tow, Fatigue and Joint Mesher. Hot spot stress spectral fatigue analysis of topside modules, consisting of non-tubular joints, is usually complex and cannot be easily performed. An integrated model of the topside module and floating production, storage, and offloading (FPSO) vessel can be used in fatigue analysis. The hull and topside module engineers work with independent models due to contractual obligations. The hull engineer analyzes the FPSO vessel with a finite element (FE) model. The topside module engineer uses beam elements without meshing. The topsides module engineer can utilize RAO information provided by the naval architect. This was considered as an efficient alternative to developing an integrated model.
In a hot spot stress approach, a large portion of the labor hours for fatigue analysis were normally allocated in generating the SCF. A default SCF equal to six is typically assumed for axial, in-plane bending, and out-of-plane bending in fatigue screening of non-tubular joints. This default SCF is not effective when there are many modules to analyze, and a local FE model is needed to further evaluate joints. In this paper, an efficient approach is proposed. The approach is using generic SCF of typical joint for the module. The approach has been applied on FPSO topside modules, which were planned to be installed in the North Sea and Gulf of Mexico. An example application of the procedure for a module is discussed. It also includes other considerations when performing fatigue analysis of modules.
The usage of RAO information instead of an integrated model, and generic SCF to derive hot-spot stresses allows convenient application of different modules or projects that are based on typical or standard joint details. It increases the efficiency of resources, i.e. labor, computation, in performing spectral fatigue life assessment. The approach appears to be an improvement compared to using a default SCF equals to six.
Intelligent Prediction and Geomechanical Analysis of Well Leakage in High Temperature and High Pressure Wells in the South China Sea
Authors: Zhao Yuhang , Yang Jin , Yang KaiDie , Xu DongSheng , Zhu Hong , Zhang Xun , Han ZengCheng , Wen ZhiLiang 
Published: February 2024

ABSTRACT
A series of drilling problems are faced during the development of high-temperature and high-pressure oil and gas fields in the South China Sea, which seriously restricts the safe and efficient development of oil and gas fields. Under the influence of geological environment, drilling technology and other factors, well leakage accidents are easy to occur. In order to prevent high temperature and high pressure well leakage and reduce accidental losses, an improved sparrow search algorithm (ISSA) optimized support vector machine for well leakage prediction is proposed. Firstly, the inverse learning strategy is introduced to enhance the global search ability of the algorithm; secondly, the learning strategy of particle swarm algorithm is borrowed to improve the finder position updating formula to improve the search speed and optimization accuracy of the algorithm; finally, Levy flights are introduced into the warning position updating formula to avoid local optimality. Comparison experiments are done between the Sparrow Search Algorithm (SSA), Particle Swarm Optimization Algorithm (PSO), Differential Evolutionary Algorithm (DE), Gray Wolf Algorithm (GWO), Whale Optimization Algorithm (WOA), and the Improved Sparrow Search Algorithm (ISSA) on eight benchmark test functions. The experimental results show that the improved Sparrow Search Algorithm (ISSA) is better than the other algorithms in terms of optimization accuracy, stability, etc. Finally, the improved Sparrow Search Algorithm (ISSA) is compared with the other algorithms. Finally, the improved sparrow search algorithm is used to optimize the support vector machine (ISSA-SVM), and ISSA-SVM models with and without geomechanical parameters are constructed to analyze the influence of geomechanical parameters on leakage, and the weights of the geomechanical parameters are analyzed by using the hierarchical analysis method (AHP). By controlling the input parameters of ISSA-SVM model, the reasonableness of the results of weighting analysis was verified. The results show that the prediction accuracy of ISSA-SVM is 98. 1135%, and the convergence speed is fast, the number of iterations is less, it can predict the well leakage accidents efficiently and quickly, and improve the drilling efficiency and reliability, and the influence of rupture pressure is the most obvious among the six important geomechanical factors affecting the occurrence of well leakage.
Temporal Deep Learning Image Processing Model for Natural Gas Leak Detection Using OGI Camera
Authors: Korjani Mehdi , Conley David , Smith Mark 
Published: February 2024

Abstract
Natural gas extraction systems often encounter manufacturing defects or develop defects over time, leading to gas leaks. These leaks pose challenges, causing revenue losses and environmental pollution. Detecting gas leaks in the vast array of extraction, transfer, and storage equipment within these systems can be arduous, allowing leaks to persist unnoticed. Additionally, natural gas leaks are not visible to naked eyes, further complicating their detection.
We developed a novel deep learning image processing model that utilizes videos captured by a specialized Optical Gas Imaging (OGI) camera to detect natural gas leaks. The temporal deep learning algorithm is designed to identify patterns associated with gas leaks and improve its performance through supervised learning. Our model incorporates algorithms to detect background environments, motion, equipment, and classify gas leaks.
Our model employs leak identification algorithms to determine the presence of gas leaks. These algorithms calculate the probability of detected motion indicating a gas leak based on long-term and short-term background subtraction, detected motion, motion duration, equipment location, and telemetry data. To minimize false positives, we have developed image segmentation and object detection models to identify known objects, such as equipment, people, and cars, within the video footage. To train our model we collect more than 10,000 short videos from real fields and include simulated data with known rate controlled gas release in different situations. Data consist of wide range of weather situations including different temperature, wind speed, humidity in sunny, rainy, and snowy fields.
We validated our model by conducting experiments involving actual footage from the field. The model achieved a 98% true positive rate, and a 100% true negative rate, correctly refraining from sending an alarm for all non-releases.
Additionally, we developed a postprocessing algorithm capable of estimating the gas leak rate based on the volume of gas leaks observed in the video footage and their distance from the camera. Our experimental results demonstrate that the detected leak rates exhibit an accuracy exceeding 78%.
By employing this deep learning image processing model, natural gas extraction systems can significantly enhance their ability to detect gas leaks promptly, reducing revenue losses and mitigating environmental impact.