bluuebunny/crossref_metadata_2025 huggingface dataset:
From Single Shot to Structure: End-to-End Network based Deflectometry for Specular Free-Form Surface Reconstruction
Authors: Ngcobo Kwanele , Bhengu Sandiswa , Mudau Ambani , Thango (Y2-rated Researcher) Bonginkosi , Matshaka Lerato
Published: September 2024

Deflectometry is a key component in the precise measurement of specular (mirrored) surfaces, however traditional methods often lack an end-to-end approach that performs 3D reconstruction in a single shot with high accuracy and generalizes across different free-form surfaces. This paper introduces a novel deep neural network (DNN)-based approach for end-to-end 3D reconstruction of free-form specular surfaces using single-shot deflectometry. Our proposed network, VUDNet, innovatively combines discriminative and generative components to accurately interpret orthogonal fringe patterns and generate high-fidelity 3D surface reconstructions. By leveraging a hybrid architecture integrating a Variational Autoencoder (VAE) and a modified U-Net, VUDNet excels in both depth estimation and detail refinement, achieving superior performance in challenging environments. Extensive data simulation using Blender, leading to a dataset which we are making available, ensures robust training and enables the network to generalize across diverse scenarios. Experimental results demonstrate the strong performance of VUDNet, setting a new standard for 3D surface reconstruction. The implementation of VUDNet is available on GitHub at https://github.com/hadious/VUDNet.
Sustainability Consciousness and Awareness of Sustainable Development Goals among Future Engineers
Authors: Shaik Salma , A Varun
Published: September 2024

Since engineering discipline is at the forefront of latest innovations and developments, there is an increasing emphasis on the critical responsibilities of engineers towards sustainability. There are several studies investigating the incorporation of sustainability principles into engineering curriculum but studies analyzing the knowledge, attitude, and behavior of engineering students are lacking. A deeper understanding of the current state of students’ knowledge, attitude, and behavior towards sustainability is crucial to determine the type of pro-sustainability trainings, strategies and pedagogical activities needed. Hence, the current study is aimed at assessing the level of sustainability cognizance of Mechanical Engineering students via an online Sustainability Consciousness Questionnaire. The final sample size was 134 students from a Higher Education Institute in India with 90% male responders and 52% from urban areas. Results indicate that 70% of the respondents have heard of the Sustainable Development Goals, only around 32% of the students demonstrate a positive behavior and attitude towards sustainability whereas 30% of the students do not hold any opinion towards sustainability principles. The current exploratory study contributes to the knowledge base about sustainability awareness and consciousness and advocates for measures to insert its principles in engineering courses.
Circular Design for Natural Fibers: A Literature Review on Life Cycle Evaluation Approaches for Environmental, Social and Economic Sustainability
Authors: Thiebat Francesca , Fregonara Elena , Masoero Alice , Morselli Fiamma , Senatore Chiara , Giordano Roberto
Published: September 2024

The textile and clothing (T&C) industry, despite its significant contribution to the European economy, is one of the most polluting industries globally. Therefore, the EU's Green Deal has identified the T&C sector as a strategic area for allocating public funding and transitioning towards a circular economy (CE). In this context, the “Circular Design for Natural Fibers” project, financed by the PNRR MICS “Made in Italy Circolare e Sostenibile” programme, focuses on enhancing the use of natural fibrous material, related by-products and waste from local production chains. In the research, a crucial aspect is the evaluation of the effectiveness of circularity through life cycle approaches, such as LCA, LCC, S-LCA and LCSA, for environmental, economic and social sustainability evaluation, respectively. A systematic review was carried out, to provide a synthesis of the available scientific evidence regarding the life cycle approaches in the international/national context within the production, processing, and post-consumer supply chains of natural fibers. The study identifies gaps in the literature on life cycle evaluation approaches for natural fibers. Specifically, the LCA approach appears preponderant compared to the others. The paper offers insights for researchers and professionals interested in improving the sustainability of natural fiber production processes and the evaluation of their overall sustainability.
Enhanced Ocular Drug Delivery of Dexamethasone Using Chitosan-Coated Soluplus® Based Mixed Micellar System
Authors: Adwan Samer , Al-Akayleh Faisal , Qasmieh Madeha , Obeidi Teiba
Published: September 2024

Background: This study introduces a novel dexamethasone (DEX) mixed micellar system (DEX-MM) using Soluplus® and Pluronic F-127 (PF127) to enhance ocular drug delivery. The enhancement of ocular application properties was achieved by creating a chitosan-coated DEX-MM (DEX-CMM), which promotes better adherence to the ocular surface, thereby improving drug absorption; Methods: Using the solvent evaporation method, a formulation was developed with a Soluplus® to drug ratio of 1:10, enhanced with 0.25% PF127. After dispersing in water, 1% chitosan (CS) was added. The stability and integrity of DEX within the micelles were verified using attenuated total reflection-fourier transform infrared spectroscopy (ATR-FTIR) and differential scanning calorimetry (DSC). Additionally, in vitro and ex vivo drug release studies were con-ducted; Results: DEX-CMM (F6) demonstrated a particle size of 151.9±1 nm and a polydispersity index (PDI) of 0.168±0.003, suggesting uniformity and high electrostatic stability with a zeta po-tential of +35.96±2.13 mV. The non-Fickian drug release mechanism indicated prolonged drug retention. Comparative analyses showed DEX-CMM outperforming a standard DEX suspension in drug release and ocular tissue permeation, with flux measurements significantly higher than the DEX suspension; Conclusion: The study confirmed the efficacy of DEX-CMM in enhancing drug delivery to ocular tissues, evidenced by improved permeability. Safety evaluations using the HET-CAM test demonstrated that DEX-CMM was non-irritant, supporting its potential for effective ocular drug delivery.
Wound Healing Efficacy of Cellulose Hydrogel on ICR Mice: A Morphoanatomical, Histological and Genomic Study
Authors: Castro Abigail , Domingo Alvin , Cariaga Jayson , Gamboa Francis , Castro Aira Cassandra , Pascua Aira Nadine , Fabia Jimmbeth Zenila , Gann Peter James , Santos Bjorn , Agrupis Shirley
Published: September 2024

Chronic wounds remain a major challenge in healthcare, with conventional dressings often leading to infections and lacking advanced healing capabilities. This study evaluated the wound healing potential of NIMO-CH, a cellulose hydrogel derived from nipa fronds and enriched with indigenous microorganisms, particularly Lacticaseibacillus paracasei BCRC-16100. Using ICR mice with excisional wounds, three treatments were compared: no treatment, NIMO-CH, and DuoDERM. Results showed that NIMO-CH and DuoDERM both achieved 100% wound closure, with NIMO-CH-treated wounds showing complete healing with hair growth by day 18-20, while untreated wounds healed by day 20. Minimal scarring was observed in both NIMO-CH and DuoDERM groups. Histological analysis confirmed similar healing processes, including granulation tissue formation and moderate inflammatory responses, with no significant differences in collagen fiber orientation. Furthermore, genome sequencing revealed that Lacticaseibacillus paracasei BCRC-16100 contained genes sodA and gsiC, responsible for coding enzymes necessary for the wound healing process. These results suggest that NIMO-CH could be an effective alternative for wound care applications.
Descriptive Analysis of Routine Laboratory Testing for Neuraxial Blocks: A Real-world Retrospective Study
Authors: Moon Sung ho , Oh Daeseok
Published: September 2024

Background/Objectives: The necessity and clinical utility of routine pre-procedural blood tests (PBTs) before neuraxial blockade remain controversial. This study evaluates the effectiveness of PBTs in identifying clinically significant conditions in an outpatient setting. Methods: This single-center retrospective study involved patients who received neuraxial blockade from January 2020 to August 2023. We extracted medical information and laboratory data from the electronic medical records during the pre-procedural period. Through multivariate regression analysis, we identified patient factors associated with abnormal laboratory results. Results: Advanced age (OR, 1.021; p = 0.026) and a history of cancer (OR, 2.359; p = 0.016) were significantly associated with elevated CRP levels (>0.30 mg/dL). Severe hyperglycemia (≥200 mg/dL) was found in 24 patients (3.88%), with a history of cancer being a strong predictor (OR, 6.764; p < 0.001). No significant abnormalities were observed in PT or PTT. Reduced eGFR (<60 mL/min/1.73m²) was detected in 8.62% of patients, despite no prior history of renal dysfunction. Multivariate analysis revealed that advanced age, hypertension, cancer, and coronary artery disease were significant predictors of abnormal PBT results, highlighting the importance of selective testing in high-risk patients. Conclusions: Routine PBTs are not universally required for all patients undergoing neuraxial blockade but can provide crucial information in high-risk populations. A selective testing approach based on individual risk factors is recommended to optimize patient safety and resource utilization. Future studies should aim to establish clear guidelines for targeted PBT use.
Individual and Synergistic Effects of the Acetonic Extract of Jatropha curcas L. (Euphorbiaceae) Seeds and the Enzymatic Extract of Serratia marcescens Strain 81 on Spodoptera frugiperda (J.E. Smith)
Authors: Valdez-Ramirez Armando , de la Torre-Hernandez Maria E. , Figueroa-Brito Rodolfo , Flores-Macias Antonio , Nuñez-Valdez Maria E. , Ramos-Lopez Miguel A. , Rodriguez del Leon Eloy , Hernandez-Carreño Pavel E. , Gutierrez-Grijalva Erick P. , Lizarraga-Verdugo Erik , Gutierrez-Arzapalo Perla Y. , Diaz Daniel
Published: September 2024

We evaluated the individual and combined insecticidal and insectistatic activities of the acetonic extract of Jatropha curcas seeds (JcAE) and the enzymatic extract of Serratia marcescens (SmEE) against the fall armyworm Spodoptera frugiperda. An ingestion bioassay was performed using 0 ppm, JcAE (100, 250, 1000, and 2500 ppm), SmEE (40, 80, 150, and 300 ppm), or JcAE+SmEE (1000+150, 1000+300, 2500+150, and 2500+300 ppm). We determined the weight, development, and mortality of S. frugiperda larvae and pupae, identified the major components of JcAE, and evaluated the chitinolytic activity of SmEE. Mortality was compared via Fisher’s exact test, whereas continuous variables were compared via the Kruskal‒Wallis test followed by Dunn’s test. The percentage difference was also evaluated using 0 ppm as a baseline. The two highest concentrations of JcAE (1000 and 2500 ppm) and SmEE (150 and 300 ppm) resulted in significant insecticidal and insectistactic activity because they increased the cumulative larval and pupal mortality between 77.33% and 86.67%, decreased the weights of the larvae (range, -63.02% to -80.23%) and pupae (range, -8.45% to -22.72%), and retarded their development for up to 10 and 4 days, respectively. Additionally, 1000+300 and 2500+300 ppm JcAE+SmEE increased the cumulative mortality by 93.33% and 90.0%, reduced larval and pupal weights, and prolonged larval development. Seven major fatty acids were identified as the main components of JcAE, and SmEE showed exo- and endochitinase activity. JcAE and SmEE, either individually or in combination, are alternatives for sustainable biological control of S. frugiperda.
Cervical Mesonephric Adenocarcinoma Treated with Neoadjuvant Chemotherapy. A Case Report and a Literature Review
Authors: Ishida Hiroaki , Manrai Megumi , Egashira Hiroki , Nonaka Mizuki , Hiruta Nobuyuki , Watanabe Reiko , Takashima Akiko
Published: September 2024

Cervical mesonephric ductal adenocarcinoma (MA) is a HPV-independent adenocarcinoma that originates from remnants of the wolffian duct in the male reproductive system during fetal development and occurs in middle-aged women. MA is a rare disease that accounts for less than 1% of all cervical adenocarcinomas. We report a case of MA in which abdominal radical hysterectomy(ARH) was performed after neoadjuvant chemotherapy (NAC). The patient was a 66-year-old woman with abnormal genital bleeding. A colposcopy examination revealed macroscopic invasive cancer. A pelvic MRI scan revealed a 53 × 26 mm tumor in the cervix, and the histological diagnosis of the cervix was endometrioid carcinoma, with the diagnosis being cervical adenocarcinoma cT1b3N0M0. One course of NAC with paclitaxel-carboplatin (PC) was administered to shrink the tumor and stop the bleeding, and ARH was performed. Postoperative histopathological diagnosis was MA. The surgical margins of the resected specimen were negative, and NAC had been effective, so the patient underwent five courses of PC therapy after surgery. There has been no recurrence 12 months after surgery. There is no established standard treatment, but there are reports that PC therapy is effective. It is necessary to search for effective treatments by following up and accumulating further cases.
Exploring the Interactome of the Queuine Salvage Protein DUF2419 in Entamoeba histolytica
Authors: Ye Jun , Trebicz-Geffen Meirav , Ankri Serge
Published: September 2024

Entamoeba histolytica causes amebiasis, a significant global health issue with millions affected annually, especially in developing countries. EhDUF2419, an important protein involved in E. histolytica's queuine salvage pathway, shares homology with DNA glycosylase. However, its in-teraction network remains unclear. To explore this, we transfected E. histolytica trophozoites with a plasmid encoding Myc-tagged EhDUF2419 and achieved successful overexpression. Through immunoprecipitation with Myc antibody followed by mass spectrometry, we identified 335 proteins interacting with Myc-tagged EhDUF2419, including over 100 ribosomal proteins, along with translation initiation and elongation factors, and aminoacyl-tRNA synthetases. Ribosome purification revealed the presence of EhDUF2419 in ribosomal protein-enriched fractions. Treatment with queuosine (Q) significantly reduced EhDUF2419 protein levels and decreased Q-modified tRNA in Myc-tagged EhDUF2419 overexpressing trophozoites. This effect which is Q-dependent was not observed in strains carrying an empty vector control or overexpressing a truncated form of EhDUF2419 lacking catalytic activity. The reduction in EhDUF2419 protein levels is regulated by proteasome-mediated degradation, as evidenced by reduced degradation in the presence of MG132, a proteasome inhibitor. Our study uncovers the novel interaction of EhDUF2419 with ribosomal proteins and its regulation by the proteasome machinery, providing new insights into its role in E. histolytica and potential therapeutic strategies.
High-Fat Diet-Induced Blood–Brain Barrier Dysfunction: Impact on Allodynia and Motor in Rats
Authors: Ubaldo-Reyes Laura M. , Espitia-Bautista Estefania , Barajas-Martínez Antonio , Martínez-Tapia Ricardo , Rodríguez-Mata Verónica , Noriega-Navarro Roxana , Escalona Rene , Castillo-Hernández Jesús , Pérez-Torres Armando , Navarro Luz
Published: September 2024

The associations among increased pain sensitivity, obesity, and systemic inflammation have not been described as a direct link between BBB dysfunctions. To analyze the metabolic, behavioral, and inflammatory effects of a HFD and ultrastructural modifications in brain regions, we used an experimental model. Adult male Wistar rats were randomly assigned to one of two conditions, an ad libitum control group, or a high-fat-diet (60%) group (HFD-fed), for eight weeks. At the end of the protocol, glucose, and insulin tolerance tests were performed. Additionally, we analyzed the response to a normally innocuous mechanical stimulus and changes in motor coordination. At the end of the protocol, HFD-fed individuals presented increased HOMA-IR and metabolic syndrome (MetS) prevalence. HFD-fed rats also developed an increased nociceptive response to mechanical stimuli and neurological injury, resulting in impaired motor function. Hypothalamus and cerebellum neurons from HFD-fed presented with nuclear swelling, an absence of nucleoli, and karyolysis. These results reveal that HFD consumption affecting vital brain structures such as the cerebellum, hippocampus, and hypothalamus. This, in turn, causes neuronal damage, impairs cellular communication, and alters motricity and pain sensitivity. Understanding the link between BBB impairment and sensory and motor changes could lead to targeted therapeutic strategies.
Enhanced Outer Membrane Vesicle Production in Escherichia coli: from Metabolic Network Model to Designed Strain Lipidomic Profile
Authors: Ruiz-Moreno Héctor Alejandro , Valderrama-Rincon Juan Daniel , Cala Monica Patricia , Fernández-Niño Miguel , Gonzalez Barrios Andrés Fernando
Published: September 2024

Bacterial structures formed from the outer membrane and the periplasm components carry biomolecules to expel cellular material and interact with other cells. These outer membrane vesicles (OMVs) can encapsulate bioactive content, which gives OMVs a high potential as alternative drug delivery vehicles or as a platform for novel vaccine development. Single-gene mutants of the hypervesiculating strain Escherichia coli JC8031 were engineered to further enhance OMV production based on metabolic network modelling and in silico gene knockout design (ΔpoxB, ΔsgbE, ΔgmhA, and ΔallD). Mutants were experimentally obtained by genome editing using CRISPR-Cas9 and tested for OMVs recovery observing an enhanced OMV production in all of them. Lipidomic analysis through LC-ESI-QTOF-MS was performed for OMVs obtained from each engineered strain and compared to the wild type E. coli JC8031 strain. The lipid profile of OMVs from the wild type E. coli JC8031 did not change significantly when compared to the mutant strains. The obtained results suggest that the vesicle production can be further improved while the obtained vesicles are not altered in their composition, allowing further study for stability and integrity for use in therapeutic settings.
Management of Postoperative Left-Sided Chylothorax Using Indocyanine Green Fluorescence-Guided VATS
Authors: Lione Luigi , Busetto Alberto , Bonis Alessandro , Verzeletti Vincenzo , Faccioli Eleonora , Comacchio Giovanni Maria , Battistel Michele , Rebusso Alessandro , Nicotra Samuele , Rea Federico
Published: September 2024

Chylothorax is a rare complication occurring after cardio-thoracic surgical procedures. This condition presents challenges about diagnosis and treatment. Operative ductal ligation is the method of choice for relapsing or refractory cases, and it can be performed through the aid of IGC injection for the identification of chylous leakage. Our report presents the use of ICG fluorescence during VATS to successfully identify and treat a left-sided post-surgical chylothorax. The patient underwent a pulmonary wedge resection for a suspect malignant lesion and developed chylous leakage in the early postoperative period. On postoperative day 7 the patient underwent a revision thoracoscopy for hemostasis and thoracic duct ligation. ICG injections have been performed through bilateral inguinal lymph nodes and approximately 15 minutes after we performed the re-thoracoscopy with effective identification and ligation of the chyle leakage. ICG fluorescence-guided VATS is a valuable and effective method for managing postoperative chylothorax, especially for left-sided leaks.

The video of the procedure can be found at: https://zenodo.org/records/13786288
Deep Learning Models for White Blood Cell Image Classification
Authors: Youssef Noureldin Said , Emam Omar Sameh , Elmaghraby Adel Said
Published: September 2024

White blood cells are crucial for immune defense in the human body, and fluctuations in their levels can lead to severe health issues. This research introduces a system designed for the localization and classification of white blood cells. The dataset utilized comprises two segments: one for localization consisting of 364 annotated images, and another for classification with 12,444 labeled images. The purpose of this study is to develop and evaluate a robust system for the accurate localization and classification of white blood cells using deep learning techniques. The study explores two localization methods—a traditional technique and a deep learning-based approach—and evaluates five deep learning architectures for classification, including three pretrained and two custom models. Upon testing these models on the dataset, the study identifies and evaluates the most effective approach. The localization process achieved an average Intersection over Union (IoU) of 71%, and the classification process reached an accuracy of 92%. The robustness of the model was further assessed by introducing different types of noise to test its resilience. The performance of this system demonstrates high accuracy and resilience to noise, which can be attributed to the use of a large dataset, our developed architectures, and our proposed methodology.
Assessment of Particulate Matter (PM2.5) and Air Quality Index (AQI) in Eight Locations of Lagos State, Nigeria
Authors: Suriano Domenico , Akinnusotu Akinyinka , Abulude Francis Olawale , Oluwagbayide Samuel Dare
Published: September 2024

Air pollution, particularly fine particulate matter (PM2.5), poses a significant threat to public health, especially in rapidly urbanizing areas like Lagos State. Despite the severity of the issue, there is a lack of comprehensive data on PM2.5 levels and the Air Quality Index (AQI) in Lagos. This study leverages data from AirQo, an innovative air quality monitoring network using low-cost sensors, to provide a detailed assessment of PM2.5 concentrations and AQI across eight locations in Lagos State, Nigeria. This approach offers real-time, continuous monitoring, filling a critical data gap in the region's air quality management efforts. This study uses AirQo data to assess the concentration levels and spatial distribution of PM2.5 and AQI across the selected eight locations. Data on PM2.5 concentrations were obtained from AirQo sensors deployed across the eight strategic locations in Lagos. The spatial distribution of PM2.5 levels and AQI were analyzed, and the results were compared with national and international air quality standards. The findings revealed that PM2.5 levels (minimum – 6.28 µg/m3 (Ikeja) and maximum - 204.68 µg/m3 (Banana Island) in many areas of Lagos exceed annual WHO and national air quality guidelines, indicating significant health risks. The spatial analysis identified pollution hotspots, particularly in densely populated and industrial regions. The data suggested vehicular emissions and industrial activities as major contributors to high PM2.5 levels. This study underscores the severe air quality issues in Lagos State, emphasizing the need for immediate and targeted interventions to mitigate PM2.5 pollution. The use of AirQo sensors proved effective in providing accurate and timely data for air quality assessment. It is recommended that Lagos State implements stricter emission controls, promotes cleaner technologies, and enhances public transportation systems to reduce traffic-related pollution. Additionally, expanding the air quality monitoring network and incorporating more environmental data will provide a more comprehensive understanding of air pollution impacts, thereby facilitating better policy and public health decisions.
Detection of Apple Proliferation Disease by Hyperspectral Sensors and Machine Learning Based Image Analysis
Authors: Knauer Uwe , Warnemünde Sebastian , Menz Patrick , Thielert Bonito Steffen , Klein Lauritz , Holstein Katharina , Runne Miriam , Jarausch Wolfgang
Published: September 2024

Apple proliferation is among the most important diseases in European fruit production. Early and reliable detection enables farmers to respond appropriately and to prevent further spreading of the disease. Traditional phenotyping approaches by human observers consider multiple symptoms, but these are difficult to measure automatically in the field. Therefore, we investigated the potential of hyperspectral imaging in combination with data analysis by machine learning algorithms to detect the symptoms solely based on the spectral signature of collected leaf samples. In the growing seasons 2019 and 2020, we collected a total of 1,160 leaf samples. Hyperspectral imaging with a dual camera setup in spectral bands from 400 nm to 2500 nm was accompanied with subsequent PCR analysis of the samples to provide reference data for the machine learning approaches. Data processing consists of preprocessing for segmentation of the leaf area, feature extraction, classification and subsequent analysis of relevance of spectral bands. Results show, that imaging multiple leaves of a tree enhances detection results, that spectral indices are a robust means to detect the diseased trees, and that the potentials of the full spectral range can be exploited using machine learning approaches.
Ustekinumab Biosimilars
Authors: Carmona-Rocha Elena , Puig Lluís
Published: September 2024

Ustekinumab is a fully human IgG1k monoclonal antibody that binds with high affinity and specificity to the p40 subunit of interleukins (IL-) 12 and 23, inhibiting their activity by preventing binding to their receptors. The European extension of the patent (Supplementary Protection Certificate) of ustekinumab expired on July 20, 2024. Biosimilar alternatives to ustekinumab are now an additional option for treating patients. The efficacy data for this drug in moderate-to-severe psoriasis, obtained both from clinical trials and indirect comparisons through meta-analyses, are superior to those of etanercept and adalimumab, and its safety profile is more favorable than that of tumor necrosis factor (TNF) inhibitors.
Several ustekinumab biosimilars have already been approved by regulatory agencies: between October 2023 and July 2024, Wezlana® (Amgen ABP 654), Uzpruvo® (Alvotech AVT04) and Pyzchiva® (Samsung/Bioepis SB17) have been approved by both the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA). SteQeyma® (Celltrion Healthcare CT-P43) has been approved by the EMA in August 2024. Several other potential biosimilar candidates are under development, including FYB202 (Formycon), BAT2206 (Bio-Thera), DMB-3115 (Dong-A ST), QX001S (Qyuns Therapeutic), BFI-751 (BioFactura), NeuLara (Neuclone), ONS3040 (Oncobiologics), and BOW090 (Epirus Biopharmaceuticals). In most cases, these monoclonal antibodies are expressed in cell lines (e.g. Chinese Hamster Ovary, CHO) different from those used for the originator (Sp2/0 spleen cell murine myeloma); of note, the cell line of origin is not a requirement for biosimilarity in the totality-of-evidence comparison exercise and may facilitate the production and reduce the immunogenicity of biosimilars originated in CHO cultures.
This narrative review will summarize the available data on characteristics of the full comparability exercises and comparative clinical trials of these drugs.
Creating and Validating a Ground Truth Dataset of UML Diagrams Using Deep Learning Techniques
Authors: Torcal Javier , Moreno Valentín , Llorens Juan , Granados Ana
Published: September 2024

UML (Unified Modeling Language) diagrams are graphical representations used in software engineering which play a vital role in the design and development of software systems and various engineering processes. Large, good-quality datasets containing UML diagrams are essential for different areas in the industry, research and teaching purposes, however few exist in the literature and it is common to find duplicate elements in the existing datasets. This might affect the evaluation of the models obtained when using these datasets. This paper addresses the challenge of creating a ground truth dastaset of UML diagrams, including semi-automated inspection to remove duplicates and ensure the correct labeling of all UML diagrams contained in the dataset. In particular, a dataset of six UML diagram classes has been assembled, comprising a total of 2,626 images (426 activity diagrams, 636 class diagrams, 352 component diagrams, 357 deployment diagrams, 435 sequence diagrams, and 420 use case diagrams). Importantly, unlike other existing datasets, ours contains no duplicate elements and all diagrams are correctly labeled. Our curated dataset is a valuable and unique resource for the research community because it serves as a foundation for training and evaluating various models. In this paper, we demonstrate this by training and testing several deep learning models using our dataset, achieving highly satisfactory results compared to those presented in other works in the literature. Additionally, our experimental results highlight the potential of Visual Transformers for UML diagram classification, setting our approach apart from others that predominantly used Convolutional Neural Networks for similar tasks.
On Deadlock Analysis and Characterization of Labeled Petri Nets with Undistinguishable and Unobservable Transitions
Authors: Zaghdoud Amal , Li Zhiwu
Published: September 2024

This work addresses the analysis and characterization of deadlocks in discrete-event systems modeled by labeled Petri nets (LPNs) with undistinguishable and unobservable transitions. To provide a solution for the notorious problem, it is essential to present an effective characterization such that deadlock control and synthesis are technically and methodologically possible. To this end, we introduce the notion of dangerous implicit vectors (DIVs) which implicitly threaten the system deadlock-freedom. The set of dead markings is divided into two subsets: dead basis markings (DBMs) and dangerous implicit markings (DIMs). An algorithm is designed to compute the sets of DIVs and DIMs at a given basis state of a system. Moreover, by virtue of linear algebraic equations, we formulate sufficient conditions to identify the existence of blocking markings in an LPN. Finally, an algorithm is developed to construct an observed graph that is a compact representation of the reachability graph of a Petri net with respect to the existence of dead reaches. At the end of this paper, experiment results that illustrate the correctness and effectiveness of the reported solution are presented.
Note for the P versus NP problem (II)
Authors: Vega Frank
Published: September 2024

The P versus NP problem is a fundamental question in computer science. It asks whether problems whose solutions can be quickly verified can also be quickly solved. Here, "quickly" refers to computational time that grows proportionally to the size of the input (polynomial time). While the problem's roots trace back to a 1955 letter from John Nash, its formalization is attributed to Stephen Cook and Leonid Levin. Despite extensive research, a definitive answer remains elusive. Closely tied to this is the concept of NP-completeness. If a single NP-complete problem could be solved efficiently, it would imply that all problems in NP can be solved efficiently, proving that P equals NP. Our work presents a polynomial-time algorithm for the CLOSURE problem. We implemented a Python-based solution for this polynomial time algorithm, which is available on GitHub under the username "frankvegadelgado". In addition, we propose that CLOSURE is actually an NP-complete problem, which would imply that P equals NP. This work is an expansion and refinement of the article "Note for the P versus NP problem", published in IPI Letters.
Data-Driven Sustainable Growth and Continuous Experimentation
Authors: Moraga-Díaz Roberto , Jofre Bravo Jaime
Published: September 2024

In today’s business landscape, customer retention has become a critical factor for sustainable growth. Many companies continue to focus on acquisition, which leads to high costs and constant customer turnover. This article highlights the importance of retention, proposing a shift in focus toward loyalty as the key to maximizing customer lifetime value (LTV). Improving retention not only increases LTV but also reduces acquisition costs, enhances virality, and provides financial stability. This paper introduces a scientific approach that allows companies to identify the underlying causes of low retention through observation, hypothesis formulation, and controlled experimentation. Using tools such as data analysis and A/B testing, companies can iteratively optimize their retention strategy, adjusting interventions in real-time based on the results obtained. Continuous experimentation emerges as an indispensable tool for navigating market complexity, enabling organizations to learn and adapt quickly. This data-driven approach not only improves retention but also drives long-term growth by building strong relationships with customers. In an increasingly competitive environment, companies that prioritize retention and experimentation are better positioned to achieve resilient and profitable growth.
An Insight into the Anticancer and Antibacterial Properties of New 4-(Benzylamino)benzoic Acid Derivatives: Synthesis and X-ray Crystallographic Analysis
Authors: Hijazi Ahmed K. , Sarayrah Qusai M. , Malkawi Abdelrahman W. , Abul-Futouh Hassan , Taha Ziyad A. , Abusara Osama H. , Daraosheh Ahmad Q. , Ahmad Ahmad A. L. , Nureldeen Waleed
Published: September 2024

A prominent class of reactions, reductive aminations are used extensively in research labs and industry to synthesize amines; which are applied in a variety of medicines, agrochemicals, and biomolecules. Amine compounds are essential in many aspects of life; amino groups exist in many natural and agricultural products. New 4-(benzylamino)benzoic acid derivatives with general formula of C14H12NO2R, where [R= (H), (4-Cl), (4-NMe), (4-Br), (3-NO2), (4-NO2), (2-OMe), (3-OMe), (4-OMe), (2,3-OMe), (3,4-OMe), (2-OH, 3-OMe), (3-OMe, 4-OH), (3,5-OMe, 4-OH), (2-OH, 5-Br), (3-OH), (4-SMe), (2,3-OH), (3-CF3)] (compounds 1-19) were obtained by reductive amination reaction via reacting p-amino benzoic acid with benzaldehyde derivatives and sodium borohydride. All prepared compounds has been characterized by a variety of spectroscopic techniques (HRMS, FT-IR, 1H, and 13C{1H} NMR) and elemental analysis. Furthermore, the structure of some of the synthesized derivatives were confirmed by single crystal X-ray diffraction (R = 4-Cl, 4-Br, 2-OMe, 3-OMe, 4-OMe, 2,3-OMe), compounds 2, 4, 7-9 and 11, respectively. Moreover, some of these new compounds have shown somehow moderate antibacterial activities against different strains of bacteria (gram-positive and gram-negative) with MIC values ranging from 64-256 µg/ mL. The anticancer activities of all compounds (IC50 values, µM) were evaluated against different cell lines, HGF (Fibroblasts; normal cell line), A549 (Non-Small Cell Lung Cancer cell line) and H69 (Small Cell Lung Cancer cell line). Compound 18 showed a considerable IC50 value of 90.69 against A549 and an excellent value of 32.22 and H69, respectively.
Discrete Modeling of Projectile Penetration into Calcareous Sand Subgrade Runway Pavement Structure and Genetic Algorithm Optimization for Critical Striking Velocity
Authors: Peng Chucai , Huang Jingnan , Sun Xichen , Nan Yifei , Chen Yaohui , Chen Kun , Feng Jun
Published: September 2024

As an important civil and military infrastructure, airport runway pavement is faced up with threats from cluster munitions, since it is vulnerable to projectile impact with internal explosion. Aiming at the damage assessment of island airport runway pavement under impact, this work dealt with discrete modeling of rigid projectile penetration into concrete pavement and calcareous sand subgrade multi-layer structure. First, Discrete Element Method (DEM) is introduced to model concrete and calcareous sand granular material features like cohesive fracture and strain hardening due to compression, with mesoscale constitutive laws governing the normal and shear interactions between adjacent particles. Second, sequent DEM simulations of uniaxial and triaxial compression were performed to calibrate the DEM parameters for pavement concrete as well as subgrade calcareous sand. Piror to multi-layer structure investigations, penetration into sole concrete or calcareous sand is validated in terms of projectile deceleration and depth of penetration (DOP) with relative error
Exploring the Features and Trends of Industrial Products E-Commerce in China by Using Text Mining Approaches
Authors: Sun Zhaoyang , Mao Yuxin , Zong Qi , Wu Gongxing
Published: September 2024

Industrial products e-commerce refers to the specific application of the e-commerce concept in the field of industrial products transactions. It enables industrial enterprises to conduct transactions via Internet platforms and reduce circulation and operating costs. Industry literature such as policies, reports and standards related to industrial products e-commerce contains abundant and crucial information. Through systematical analysis of the literature information, it is beneficial to explore and grasp the development characteristics and trends of the industrial products e-commerce. To study the characteristics and development status of industrial products e-commerce in China, literature comprising 18 policy documents, 10 industrial reports and 5 standards is deeply analyzed by employing text mining methods. Firstly, natural language processing (NLP) technology is utilized to pre-process the text data related to industrial products commerce. Then, word frequency statistics and TF-IDF keyword extraction are performed, and the results of word frequency statistics are visually displayed. Subsequently, the feature set is obtained by combining the manual screening method. The original text corpus is used as the training set by employing the skip-gram model in word2vec, and the feature words are transformed into word vectors in the multi-dimensional space. The k-means algorithm is used to cluster the feature words into groups. Moreover, the latent dirichlet allocation (LDA) method is also utilized to further group and discover the features. The research results based on text mining provide supportive decisions for uncovering the development characteristics and trends of industrial products e-commerce in China.
Tracking Education Transformation: A Bibliometric Review on The Influence of Socioeconomic Factors in the Education Ecosystem
Authors: Ali Mohamad , Tamami Fauziyah Qurrota A’yun , Rahmawati Siti , Nirwana, A.N. Andri , Muthoifin Muthoifin
Published: September 2024

The flow of mapping the development and direction of research on Socioeconomic Factors in the Education Ecosystem is known through published articles with the Scopus database index. Quantitative approach is used as the article research with bibliometric data analysis techniques to explore all publications indexed in the Scopus database on Socioeconomic Factors in the Education Ecosystem from 1990 to 2023. The data obtained were analyzed using Excel and R/R-Studio. VOSViewer is used to perform visual analysis of keyword occurrence and document citations stimulantly. Researchers found 772 publications that fit the function, subject, and criteria that had been set. The annual growth rate is seen in the data obtained. The papers are analyzed in terms of publications by field of science, the number of studies published annually. The bibliometric data analysis performed was limited to Scopus data. Other national and international databases were not taken into account in the study. The limitation of this study is only taking data sets or meta data in publications indexed by Scopus. This article presents a brief overview of the literature accessible to researchers working in the field of Socioeconomic Factors in the Education Ecosystem as well as provides recommendations for future research.
High Order Difference Schemes for Space Riesz Variable-Order Nonlinear Fractional Diffusion Equations
Authors: Wang Qiuya , Lin Furong
Published: September 2024

This article is aimed at studying new finite difference methods for one-dimensional (1D) and two-dimensional (2D) space Riesz variable-order (VO) nonlinear fractional diffusion equations (SRVONFDEs). In the presented model, fractional derivatives are defined in the Riemann-Liouville type. Based on 4-point weighted-shifted-Gr\"unwald-difference (4WSGD) operators for Riemann-Liouville constant-order (CO) fractional derivatives, which have a free parameter and have at least third order accuracy, we derive 4WSGD operators for space Riesz VO fractional derivatives. In order that the fully discrete schemes have good stability and can handle the nonlinear term efficiently, we apply the implicit Euler (IE) method to discretize the time derivative, which leads to IE-4WSGD schemes for SRVONFDEs. The stability and convergence of the IE-4WSGD schemes are analysed theoretically. In addiction, a parameter selection strategy is derived for 4WSGD schemes and banded preconditioners are put forward to accelerate the GMRES methods for solving the discretization linear systems. Numerical resutls demonstrate the effectiveness of the proposed schemes and preconditioners.
Empirical Evaluation and Analysis of YOLO Models in Smart Transportation
Authors: Nguyen Lan Anh , Son Yongseok , Tran Manh Dat
Published: September 2024

You Only Look One (YOLO) and its variants have emerged as the most popular real-time object detection algorithms. They have been widely used in real-time smart transportation applications due to their low-latency detection and high accuracy. However, because of the diverse characteristics of YOLO models,
choosing selecting the optimal model according to various applications and environments in smart transportation is critical. In this article, we conduct an empirical evaluation and analysis study for most YOLO versions to assess their performance in smart transportation. To achieve this, we first measure the average precision of YOLO models across multiple datasets (e.g., COCO, PASCAL VOC). Second, we analyze the performance of YOLO models on multiple object categories within each dataset, focusing on classes relevant to road transportation such as those commonly used in smart transportation applications.
Third, multiple intersection-over-union (IoU) thresholds are considered in our performance measurement and analysis. By examining the performance of various YOLO models across datasets, IoU thresholds, and object classes, we make six observations on these three aspects while aiming to identify optimal models for road transportation scenarios. Thus, YOLOv5 and YOLOv8 outperform other models in all three aspects due to their novel performance features. For instance, YOLOv5 achieves stable performance thanks to its cross-stage partial darknet-53 (CSPDarknet53) backbone, auto-anchor mechanism, and efficient loss functions including IoU loss, complete IoU loss, focal loss, gradient harmonizing mechanism loss. Similarly, YOLOv8 outperforms others with its upgraded CSPDarknet53 backbone, anchor-free mechanism, and efficient loss functions like complete IoU loss and distribution focal loss.
Approaches of Critical Infrastructure Companies to Recover from Cyber-Attack: Insights from Internal Specialists and External In-Formation Security Auditors
Authors: Leroy Iryna , Zolotaryova Iryna , Semenov Serhii
Published: September 2024

Companies operating in the PayTech and online e-commerce sectors play a crucial role in critical infrastructure, functioning within the dynamic digital landscape. This study focuses on the recovery process after cyber-attacks and examines the contrasting perspectives of internal and external professionals. The research reveals notable differences in the perceptions of recovery strategies between internal stakeholders such as investor relations, reputation management, and Chief Information Security Officers, representing companies belonging to critical infrastructure and external auditors, who provide just and emergency support and perform specific tasks. Importantly, the study underscores the current attitudes towards future information security strategies and their influence on the financial recovery and reputation of reliable companies following cyber incidents. This research contributes to the existing knowledge by shedding light on the perspectives of both a company's internal and external specialists involved in the recovery process and cyber resilience strategies in critical infrastructure sectors.
Adverse Effects of Aβ1-42 Oligomers: Impaired Contextual Memory and Altered Intrinsic Properties of CA1 Pyramidal Neurons
Authors: Kaung-Wint-Mon Min , Kida Hiroyuki , Kanehisa Itsuki , Kurose Masahiko , Ishikawa Junko , Sakimoto Yuya , Thein-Oo Paw-Min , Kimura Ryoichi , Mitsushima Dai
Published: September 2024

Aβ1-42 (amyloid beta) oligomers, the major neurotoxic culprits in Alzheimer's disease, initiate early pathophysiological events, including neuronal hyperactivity, that underlie aberrant network activity and cognitive impairment. Although several synaptotoxic effects have been extensively studied, neuronal hyperexcitability which may also contribute to cognitive deficits is not fully understood. Here, we found several adverse effects of in vivo injection of Aβ1-42 oligomers on contextual memory and intrinsic properties of CA1 pyramidal neurons. Male rats underwent behavioral and electrophysiological studies 1 week after microinjections into the dorsal CA1 region, followed by histological analysis. After 1 week, Aβ1-42 oligomers impaired contextual learning without affecting basic physiological functions and triggered training-induced neuronal excitability. Furthermore, riluzole, a persistent sodium current (INaP) blocker, dose-dependently reduced Aβ1-42 oligomer-induced hyperexcitability. Congo red staining, which detects insoluble amyloid deposits, further identified labeling of CA1 pyramidal neurons while immunohistochemistry with lecanemab, which detects soluble Aβ oligomers, revealed immunoreactivity of both pyramidal and non-pyramidal cells in the target area. Therefore, our study suggests that a single injection of Aβ1-42 oligomers resulted in contextual memory deficits along with concomitant neuronal hyperexcitability and amyloid deposition in the CA1 region after 1 week.
Investigating Inherited Heart Diseases Using Human Induced Pluripotent Stem Cells
Authors: Wang Brian Xiangzhi
Published: September 2024

Inherited heart diseases (IHDs) are caused by genetic mutations that disrupt the physiological structure and function of the heart. Understanding the mechanisms behind these diseases is crucial for developing personalised interventions in cardiovascular medicine. Development of induced pluripotent stem cells, which can then be differentiated to any nucleated adult cell type, has enabled the creation of personalised single-cell and multicellular models, providing unprecedented insights into the pathophysiology of IHDs. This review provides a comprehensive overview of recent advancements in human iPSC models used to dissect the molecular and genetic underpinnings of common IHDs. We examine multicellular models and tissue engineering approaches, such as cardiac organoids, engineered heart tissue, and multicellular co-culture systems, which simulate complex intercellular interactions within heart tissue. Recent advancements in stem cell models offer a more physiologically relevant platform to study disease mechanisms, enabling researchers to observe cellular interactions, study disease progression, and identify therapeutic strategies. By leveraging these innovative models, we can gain deeper insights into the molecular and cellular mechanisms underlying IHDs, ultimately paving the way for more effective diagnostic and therapeutic strategies.