bluuebunny/crossref_metadata_2025 huggingface dataset:
PENGEMBANGAN MODUL AJAR PADA MATERI PERBANDINGAN DENGAN KONTEKS BUDAYA BANJAR
Authors: Illiyyin  , Fajriah Noor , Suryaningsih Yuni
Published: November 2024

Saat ini kebudayaan peserta didik, khususnya budaya Banjar belum termuat dalam bahan ajar yang digunakan oleh guru. Selain itu, banyak peserta didik yang kesulitan mengerjakan soal materi perbandingan karena belum memahami konsep perbandingan. Salah satu usaha untuk mengatasi hal tersebut adalah dengan dilakukan penelitian pengembangan modul ajar pada materi perbandingan dengan konteks budaya Banjar. Konteks budaya yang digunakan adalah makanan khas Banjar. Tujuan penelitian adalah untuk menghasilkan modul ajar yang valid pada materi perbandingan dengan konteks budaya Banjar. Metode penelitian yang digunakan adalah penelitian dan pengembangan (Researh and Development) dengan model pengembangan 4D. Model pengembangan 4D dilaksanakan dari tahap define, design, dan develop dengan menggunakan instrumen berupa lembar validasi. Berdasarkan hasil analisis dari lembar validasi tiga orang ahli, dapat diketahui modul ajar pada materi perbandingan dengan konteks budaya Banjar valid dengan persentase 84,05%. 
Currently, students' culture, especially Banjar culture, has not been included in the teaching materials used by teachers. In addition, many students have difficulty working on proportion material because they are not understand the concept of proportion. In addition, there is a lack of teaching materials that incorporate the Banjar cultural context. One effort to address this issue is conducting research to develop a teaching module for the topic proportion with the Banjar cultural context. The cultural context used is typical Banjar food. The research aims to produce a valid teaching module for the topic of proportion with the Banjar cultural context. The research method used is research and development with the 4D development model. The 4D development model is carried out from the define, design, and develop stages using instruments in the form of validation sheets. Based on the analysis of the validation sheet of three experts, the teaching module for the topic of proportion with the Banjar cultural context is determined to be valid with a percentage of 84.05%.
PENGEMBANGAN MEDIA PEMBELAJARAN INTERAKRIF BERBASIS WEB PADA MATERI BARISAN DAN DERET DENGAN METODE TUTORIAL UNTUK SISWA KELAS X
Authors: Said Muhammad , Sukmawati R. Ati , Sari Delsika Pramata
Published: November 2024

Penelitian ini bertujuan untuk (1) mengembangkan media pembelajaran interaktif berbasis web pada materi barisan dan deret dengan metode tutorial untuk siswa kelas X dan (2) mengetahui kevalidan media pembelajaran interaktif berbasis web pada materi barisan dan deret dengan metode tutorial untuk kelas X. Penelitian ini menggunakan metode  R&D (Reserch and Development) dengan model ADDIE yang sudah dibatasi analisis (analysis), desain (design), pengembangan (development), dan evaluasi (evaluation). Teknik pengumpulan data yang digunakan adalah angket. Instrumen yang digunakan angket validasi materi dan angket validasi media. Hasil dari penelitian ini adalah media pembelajaran pada materi barisan dan deret dengan metode tutorial yang dikembangkan dengan teknologi HTML, CSS, Boostrap, Javascript, JSON, Firebase, Mathjax, Balsamiq Mockups. Penelitian ini menunjukkan bahwa media pembelajaran termasuk valid, karena perolehan persentasi capaian validitas  materi sebesar 91,5% dengan kriteria tinggi dan perolehan persentasi capaian validitas media sebesar 76,4%  dengan kriteria sedang. Oleh karena itu, media pembelajaran interaktif berbasis web pada materi barisan dan deret dengan metode tutorial untuk siswa kelas X dinyatakan valid digunakan untuk uji coba.
This study aims to (1) develop web-based interactive learning media on row and series material with tutorial methods for grade X students and (2) find out the feasibility of web-based interactive learning media on row and series material with tutorial methods for class X. This research uses the R&D (Research and Development) method with the ADDIE model that has been limited namely analysis, design, development, and evaluation. The data collection techniques used are material validation questionnaires and media validation questionnaires. The result of this study is a learning medium on row and series material with tutorial methods developed with HTML, CSS, Boostrap, Javascript, JSON, Firebase, Mathjax, Balsamiq Mockups. This study shows that learning media is valid, because the percentage of material validity achievement is 91.5% with high criteria and the acquisition of media validity achievement percentage is 76.4% with medium criteria. Therefore, web-based interactive learning media on row and series material with tutorial methods for grade X students is declared valid for trials.
MEDIA PEMBELAJARAN DIGITAL UNTUK MENINGKATKAN MINAT BELAJAR SISWA PADA MATERI TRIGONOMETRI MENGGUNAKAN GOOGLE SLIDE
Authors: Umardiyah Fitri , Hisommudin Muhammad , Khotimah Khusnul
Published: November 2024

Trigonometri merupakan salah satu mata pelajaran yang berat bagi sebagian besar siswa.  Rendahnya minat belajar, menjadi faktor utama yang menyebabkan masalah tersebut muncul. Salah satu upaya yang dapat dilakukan peneliti adalah mengembangkan media pembelajaran meng­gunakan Google Slide dengan tujuan meningkatkan minat belajar siswa pada materi Trigonometri. Penelitian ini dilakukan di MA Balongrejo Sumobito Jombang kelas X-IPA pada tahun ajaran 2022-2023 semester genap. Metode penelitian yang digunakan adalah penelitian dan pengembangan (Research and Development) menggunakan model pengembangan ADDIE. Dari hasil penelitian didapatkan bahwa: 1) Hasil analisis pengisian angket respon minat siswa, terhadap pembelajaran Matematika sebelum menggunakan media pembelajaran Google Slide, didapatkan persentase sebesar 45%. 2) Hasil analisis pengisian angket respon minat siswa, terhadap pembelajaran Matematika setelah menggunakan media pembelajaran Google Slide, didapatkan persentase sebesar 63,19%. Berdasarkan hasil penelitian tersebut, menunjukkan bahwa adanya peningkatan minat siswa dalam pembelajaran Matematika pada materi Trigonometri. Selain itu, didukung dengan hasil tes dan respon siswa terhadap penggunaan media pembelajaran, dengan persentase sebesar 75,39% yang menunjukkan respon baik dan tertarik.
Trigonometry is a tough subject for most students. Low interest in learning is the main factor that causes these problems to arise. One effort that can be done by researchers is to develop learning media using Google Slides with the aim of increasing student interest in Trigonometry material. This research was conducted at MA Balongrejo Sumobito Jombang class X-IPA in the 2022-2023 academic year, even semester. The research method used is research and development (Research and Development) with the ADDIE development model. From the results of the study it was found that: 1) The results of the analysis of filling in the questionnaire on student interest responses, towards learning Mathematics before using Google Slides learning media, obtained a percentage of 45%. 2) The results of the analysis of filling in the questionnaire on students' interest in learning Mathematics after using Google Slides learning media, obtained a percentage of 63.19%. Based on the results of this study, it shows that there is an increase in students' interest in learning Mathematics on Trigonometry material. In addition, it is supported by test results and student responses to the use of instructional media, with a percentage of 75.39% showing good and interested responses.
Harnessing Multi-Source Data and Deep Learning for High-Resolution Land Surface Temperature Gap-Filling Supporting Climate Change Adaptation Activities
Authors: Kustura Katja , Conti David , Sammer Matthias , Riffler and Michael
Published: November 2024

Addressing global warming and adapting to the impacts of climate change is a primary focus of Climate Change Adaptation strategies at both European and national levels. Land surface temperature (LST) is a widely used proxy for investigating the climate-change-induced phenomena, providing insights into the surface radiative properties of different land cover types and the impact of urbanization on local climate characteristics. Accurate and continuous estimation across large spatial regions is crucial for the implementation of LST as an essential parameter in climate change mitigation strategies. Here, we propose a deep-learning-based methodology for LST estimation using multi-source data, including Sentinel-2 imagery, land cover, and meteorological data. We develop a regression-based Convolutional Neural Network model, trained on ECOSTRESS mission data, which performs pixelwise LST predictions using 5×5 image patches, capturing contextual information around each pixel. This method not only preserves ECOSTRESS's native resolution but also fills data gaps, enhances spatial and temporal coverage, and provides LST predictions with at least 80% of all pixel errors that fall within a ±3°C range. Unlike traditional satellite-based techniques, our model leverages high-temporal-resolution meteorological data to capture diurnal variations, allowing for more robust LST predictions across different regions and time periods. The model's performance demonstrates the potential for integrating land surface temperature into urban planning, climate resilience strategies, and near-real-time heat stress monitoring, providing a valuable resource to assess and visualize the impact of urban development and land use and land cover changes.
Very High-Energy Electron Therapy Toward Clinical Implementation: A Review Study
Authors: Panaino Costanza Maria Vittoria , Piccinini Simona , Andreassi Maria Grazia , Bandini Gabriele , Borghini Andrea , Borgia Marzia , Di Naro Angelo , Labate Luca Umberto , Maggiulli Eleonora , Portaluri Maurizio Giovanni Agostino , Gizzi Leonida Antonio
Published: November 2024

The use of Very High Energy Electron (VHEE) beams, with energies between 50 and 400 MeV, has drawn considerable interest in radiotherapy due to their deep tissue penetration, sharp beam edges, and low sensitivity to tissue density. VHEE beams can be precisely steered with magnetic components, positioning VHEE therapy as a cost-effective option between photon and proton therapies. However, clinical implementation of VHEE Therapy (VHEET) requires advances in several areas: developing compact, stable, and efficient accelerators; creating sophisticated treatment planning software; and establishing clinically validated protocols. In addition, the perspective of VHEE to access ultra-high dose-rate regime presents a promising avenue to practical integration of FLASH radiotherapy of deep tumors and metastases with VHEET (FLASH-VHEET), enhancing normal tissue sparing while maintaining the inherent dosimetric advantages of VHEET. However, FLASH-VHEET systems require validation of time-dependent dose parameters, thus introducing additional technological challenges. Here we discuss recent progress in VHEET research, focusing on both conventional and FLASH modalities, and covering key aspects including dosimetric properties, radioprotection, accelerator technology, beam focusing, radiobiological effects, and clinical outcomes. Furthermore, we provide a comprehensive analysis of initial VHEET in silico studies on coverage across various tumor sites.
Sign Language Visual Semiotic Decoder Using Deep Learning Approach
Authors: Rodríguez-Rodríguez Juan José , Alfonso-Morales Wilfredo
Published: November 2024

Human communication is essential for social and cognitive development, but not everyone has equal access due to sensory disabilities, such as hearing impairments. These affect 25% of people with sensory disabilities, and it is projected that by 2050, the number of people with hearing loss will reach 2.5 billion. Sign language becomes a crucial tool for the social integration of deaf individuals, although its regional variability and limited inclusion in essential services pose challenges. This study proposes a new sign language translation methodology using a deep learning architecture that models gestures through semiotic analysis, overcoming limitations observed in other approaches. The system, tested with 10, 30, 100, 150, 220, 320, 480, 700, and 1171 synthetic gestures of representative sign language vocabulary, showed an accuracy of 99.65% in Cohen’s Kappa coefficient and Matthews Correlation Coefficient, highlighting its potential to improve real-time communication between deaf and hearing people.
Effect of a Plant-Based Nootropic Supplement on Perceptual Decision-Making and Brain Network Interdependencies: A Randomised, Double-Blinded and Placebo-Controlled Study
Authors: O' Reilly David , Bolam Joshua , Delis Ioannis , Utley Andrea
Published: November 2024

Natural nootropic compounds are evidenced to restore brain function in clinical and older populations and are purported to enhance cognitive abilities in healthy cohorts. This study aimed to provide a neurocomputational explanation for the discrepancies between the remarkable self-reports and growing interest in nootropics among healthy adults and the inconclusive performance enhancing effects found in the literature. Towards this end, we devised a randomised, double-blinded, and placebo-controlled study where participants performed a visual categorisation task prior to and following 60 days of supplementation with a plant-based nootropic while electroencephalographic (EEG) signals were concurrently captured. We found that although no improvements in choice accuracy or reaction times were observed, the application of multivariate information-theoretic measures to the EEG source space showed broadband increases in similar and complementary interdependencies across brain networks of various spatial scales. These changes not only resulted in localised increases in the redundancy among brain network interactions but also more significant and widespread increases in synergy, especially within the delta frequency band. Thus, our findings suggest that natural nootropics can improve overall brain network cohesion and energetic efficiency, demonstrating computationally the beneficial effects of natural nootropics on brain health. However, these effects could not be related to enhanced rapid perceptual decision-making performance in a healthy adult sample. Future research investigating these specific compounds as cognitive enhancers in healthy populations should focus on complex cognition in deliberative tasks (e.g. creativity, learning) and over longer supplementation durations.
Formulation of a Biological Sunscreen from Calendula arvensis Capitula Extracts: Antioxidant, Anti Ageing, Surface Tension and UVB Protection Properties Assessed
Authors: El-otmani Najlae , Zeouk Ikrame , Zahidi Ahmed
Published: November 2024

Skin protection against ultraviolet (UV) radiation has long been crucial due to its role in photoaging, sunburn, and wrinkles. This study focuses on developing a bio-based sunscreen from Calendula arvensis capitula extract. Various extraction methods (maceration, sonication, infusion) and solvents (EtOH, EtOH-H2O, H2O) were explored to identify the most effective extract for sunscreen formulation. Each extract was analyzed for phenolic content, with antioxidant activities assessed through DPPH, CAT, and FRAP assays. Enzyme inhibition assays on tyrosinase, elastase, and collagenase highlighted the hydroethanolic extract for its low IC50 values. Furthermore, the in vitro sun protection factor (SPF) against UVB radiation was measured using ultraviolet spectrophotometry. Phytochemical analysis showed phenolic levels between 8 and 27 mg GAE/g, flavonoid concentrations of 7-13 mg QE/g, and tannin levels of 1.15-1.68 mg/mL, alongside moderate antioxidant activity. The ethanol maceration extract reduced interfacial tension to 2.15 mN/m in 600 seconds, outperforming the conventional emulsifier polysorbate 20. The sonicated hydroethanolic extract demonstrated remarkable SPF efficacy (SPF = 193.65 ± 0.02), far exceeding standard zinc oxide (SPF = 11.88 ± 0.03). The formulations meet COSMOS standards, suggesting potential certification as a biological product. Further clinical and in vivo studies are necessary to confirm its safety and commercial viability.
Next Generation Risk Assessment of Cosmetics to Address Disease-Related Vulnerability. A Proof of Concept for the Sunscreen Octocrylene
Authors: Fernández-Martín María-Elena , Tarazona Jose V.
Published: November 2024

Risk assessment for cosmetics in the European Union (EU) are triggered by a ban on animal testing and concerns on endocrine disruption (ED). The risk assessment focuses on healthy populations, and for potential ED includes specific developmental stages as vulnerable due to specific concerns on endocrine effects. However, the assessment focuses on healthy individuals and does not consider that some pathologies may increase dermal absorption and even vulnerability to endocrine disruptors. Data from the EU risk assessment, human pharmacokinetic studies and ToxCast bioactivity were combined in hypothesis driven Next Generation Risk Assessment, to identify possible risk drivers for vulnerable populations including oncological patients and atopic dermatitis. In vitro effects are observed at concentration in the order of measured plasmatic levels under normal use patterns. Induction of hepatic enzymes is the most relevant bioactivity endpoint, in line with animal findings. The information on endocrine potential is unconclusive, the possibility for skin effects and endocrine mechanism linked to tumor induction require further elucidation. The information on octocrylene (CAS number: 6197-30-4) bioactivity is limited, lacking information on the metabolites and the immunotoxicity potential, particularly relevant for oncological patients.
The Space-Time Membrane Model: Unifying Quantum Mechanics and General Relativity through Elastic Membrane Dynamics
Authors: Swann Paul
Published: November 2024

The unification of Quantum Mechanics (QM) and General Relativity (GR) remains a major challenge in theoretical physics. This study introduces the Space-Time Membrane (STM) Model, describing spacetime as a continuous, four-dimensional elastic membrane with intrinsic elastic properties. By modelling photons as composite particle-antiparticle pairs oscillating transversely, the STM model derives key equations, including the Einstein Field Equations, and replicates gravitational phenomena like time dilation and light bending, while also explaining quantum interference through persistent waves. It addresses the cosmological constant problem by linking vacuum energy density to membrane elasticity, offering insights into dark energy and the universe’s accelerated expansion. The model also resolves black hole singularities with quantised energy levels and proposes a solution to the information loss paradox using black hole standing waves. Additionally, it aligns with Hawking radiation. The study details the derivation of force functions between particles and mirror particles, integrating the cosmological constant and emphasising quantum fluctuations' time averaging. Although the STM model is a preliminary attempt to unify QM and GR, it presents new possibilities and encourages further exploration and empirical testing to assess its potential, inviting collaboration within the scientific community.
Attentional Convolutional Neural Network based on Distinction Enhancement and Information Fusion for FDIA Detection in Power System
Authors: Luo Chuan , Zhou Zhiyong , Jiang Rui , Zheng Bochuan
Published: November 2024

False data injection attack (FDIA) is one of the major threats to power systems, and identifying false data is critical to the stable operation of power systems. However false data that closely resemble normal data hinder the accuracy of existing detection methods, and their performance further declines when exposed to ambient noise. To address these challenges, this paper proposes an attentional convolutional neural network based on distinction enhancement and information fusion (DEIF-ACNN) for FDIA detection. Firstly, by minimizing the loss of reconstruction and discrimination, this paper designed an autoencoder with a discriminator for normal data (NAE), which had the characteristic of producing small loss for normal data. Secondly, the trained NAE is utilized to compute the feature correlation matrix between the original and reconstructed data to enhance the distinction between normal and false data. Finally, to enhance feature extraction and suppress ambient noise interference in detection, DEIF-ACNN incorporates a convolutional block attention module (CBAM) to emphasize key feature channels and highlight crucial regions in the feature matrix. Experimental results show that DEIF-ACNN outperforms other FDIA detection methods on IEEE-9, IEEE-14 and IEEE-118 bus power systems. In addition, the method exhibits the best robustness under different noise environments.
UAV-Based Survey of the Earth Pyramids in Kuklica Geosite (North Macedonia)
Authors: Milevski Ivica , Aleksova Bojana , Dragicevic Slavoljub
Published: November 2024

This paper presents methods for a UAV-based survey of the site "Kuklica" near Kratovo, North Macedonia. Kuklica is a rare natural complex with earth pyramids, and because of its exceptional scientific, educational, tourist, and cultural significance, it was proclaimed as a Natural Monument in 2008. However, after the proclamation, the interest in visiting this site and the threats of its potential degradation rapidly grew, increasing the need for a detailed survey and monitoring. Given the site's small size (0.5 km2), the freely available satellite images and digital elevation models do not allow comprehensive analysis and monitoring, especially of the individual forms. Instead, new tools are increasingly used, including UAV (Unmanned Aircraft Vehicles) and LiDAR (Light Detection and Ranging). Since professional LiDAR is very expensive and still poorly available, we used a low-cost UAV (DJI Mini 4 Pro) for a detailed survey. First, the flight path, the altitude of the UAV, the camera angle, and photo recording intervals were precisely planned and defined. Also, the ground markers (checkpoints) were carefully selected. Then, the recorded drone photos are aligned and processed through the Agisoft Metashape software, producing a digital elevation model and orthophoto imagery with very high (sub-decimeter) resolution. Following this procedure, more than 120 earth pyramids are delineated, ranging in height from 1-2 m to 30 m of the highest. In this stage, a very accurate UAV-based 3D model of the most remarkable earth pyramids is made (accuracy is checked with the iPhone 14 Pro LiDAR module), and their morphometrical properties are calculated. Also, the site's erosion rate and flash flood potential are calculated, showing high susceptibility. The final goal is to follow the changes and to minimize the degradation of the unique land-scape, thus protecting the geosite and its values much better.
Patient-Reported Outcomes Measurement Information System as a Clinical Tool for Capturing the Patient Perspective in Pediatric Inflammatory Bowel Disease: A Narrative Review
Authors: Azevedo Sara , Lopes Ana Isabel
Published: November 2024

Inflammatory bowel disease (IBD) is an immune-mediated chronic disease with a significant impact on quality of life. In pediatric patients, diagnosing and managing IBD is particularly challenging, and IBD often presents as a more severe and progressive disease.
Patient-reported outcomes (PROs) are measures of treatment and disease management outcomes reported by patients and/or caregivers. These measures evaluate several aspects of disease management from the patient/caregiver perspective, emphasizing the patient’s real-life experience with the disease and its treatment. PROs represent a model of patient-centered care, facilitating better-informed healthcare decisions.
The Patient-Reported Outcomes Measurement Information System (PROMIS) was developed to promote the use of PROs among patients with chronic conditions. Its primary objective is to provide PROs for research and clinical practice throughout the lifespan. The PROMIS includes no disease-specific instruments for both adults and pediatric patients assessing domains of physical, psychological and social health, as well as quality of life (QoL). These instruments are designed to be applicable to a wide range of chronic diseases. Despite the initial expectation concerning PROs in assessing pediatric IBD outcomes, objective data in this area have only recently begun to emerge.
This narrative review, based on a selection of reliable articles recognized by PubMed and Cochrane Library, aimed to identify and summarize previously published evidence of the usefulness of PROs, particularly PROMIS, in IBD patients and in the pediatric population. We present an updated perspective, including identification of their general applications and most relevant previous studies, in the mentioned areas and identifying knowledge gaps.
Digitalizing Material Knowledge: A Practical Framework for Ontology-Driven Knowledge Graphs in Process Chains
Authors: García Trelles Elena , Schweizer Christoph , Thomas Akhil , von Hartrott Philipp , Janka-Ramm Marina
Published: November 2024

This paper proposes a robust methodology for integrating process-specific data and domain expert knowledge into linked knowledge graphs. These graphs utilize an ontology that provides a standardized vocabulary for material science and facilitates the creation of semantic models for various processes along the digital process chain. A generic template for structuring processes is proposed, simplifying subsequent data retrieval. The templates of specific processes are designed collaboratively by domain and ontology experts aided by a proposed interview template that bridges the knowledge gap. Following the digitalization of material data through semantic modeling, machine-readable data with contextual metadata is stored in a graph database, which can be efficiently queried using the SPARQL language, enabling seamless integration into data pipelines. To demonstrate this approach, a knowledge graph is developed to represent the process chain of AlSi10Mg objects manufactured via permanent mold casting, capturing their complete history from the initial manufacturing step to final non-destructive testing and mechanical characterization. This methodology enhances data interoperability and accessibility while providing context-rich data for training AI models, potentially accelerating new knowledge discovery in material science.
Study of the Effect of Nitrite and Temperature on the Autotrophic Denitrifying Activity of Anammox Granular Biomass from a Partial Nitritation-Anammox Reactor of a Wastewater Treatment Plant
Authors: Ibarra Benjamín , Lesty Yves , Pastur Mateo , Castro Celia , Girard Chloe , Chamy Rolando
Published: November 2024

In this study, the effect of nitrite in the short and long term on the anaerobic ammonium oxidation (ANAMMOX) was investigated using granules from a full-scale SBR reactor in operation. In the short term, maximum activity was achieved at 100 mg N-NO2-/L, with higher concentrations being inhibitory. It was determined that the biomass behavior is well interpreted (R2=0.955) by a non-competitive substrate inhibition model (Andrews model) with a KS of 55.6 mg N-NO2-/L and a KI of 116.7 mg N-NO2-/L, while well interpreted too by the Edwards model (R2=0.957) with a KS of 36 mg N-NO2-/L and a KI of 287 mg N-NO2-/L were achieved. In the long term, the biomass retained its Anammox activity at 15 mg N-NO2-/L over a 3 TRH horizon; however, at 30 mg N-NO2-/L, Anammox activity decreased by 50% at the end of the experiment. Finally, the effect of temperature on the activity of the Anammox granules of a different source was studied, revealing that the activity increases with temperature within the range of 25 - 35 °C.
Optimizing Predictive and Prescriptive Maintenance Using Unified Namespace (UNS) for Industrial Equipment
Authors: Surendran Pillai Renjithkumar , Denny Patrick , O'connell Eoin
Published: November 2024

The document "UNS-Predictive_maintenance-_NW.docx" explores the integration of Unified Namespace (UNS) in predictive and prescriptive maintenance strategies within industrial settings. UNS serves as a centralized data architecture that consolidates real-time data from various machines, devices, and systems, facilitating seamless communication and data exchange. This integration enhances the accuracy and efficiency of maintenance processes by providing a single source of truth for operational data. The document highlights the evolution of maintenance strategies from reactive and preventive to predictive and prescriptive approaches. Predictive maintenance leverages real-time data, sensor technology, and machine learning to predict equipment failures, allowing for timely interventions. Prescriptive maintenance goes a step further by recommending optimal actions based on predictive insights, thus optimizing equipment performance and extending its lifespan. Key benefits of UNS include improved data accessibility, reduced unplanned downtime, enhanced operational efficiency, and cost savings. The document also addresses challenges such as data standardization, cybersecurity, and integration with legacy systems. Case studies from companies like Nestlé and Coca-Cola demonstrate the practical applications and effectiveness of UNS in industrial maintenance. Overall, the integration of UNS in maintenance strategies represents a significant advancement in industrial asset management, promoting higher performance, reliability, and sustainability.
On the Method for Proving the RH Using the Alcantara-Bode Equivalence
Authors: Adam Dumitru
Published: November 2024

Following the Beurling equivalence ([4], 1955) of the Riemann Hypothesis (RH, 1859), the Alcantara-Bode equivalence ([3], 1993) reduced RH to a concrete problem of the injectivity of a certain Hilbert-Smith integral operator on $L^2(0,1)$. In this article we provided the theory and the methods for the investigation of the linear, bounded operators injectivity on separable Hilbert spaces. The Theorem 1 shows that a linear, bounded operator on a separable Hilbert space strict positive on a dense set, is injective. Choosing the dense set the union of including subspaces built on the indicator interval functions in $L^2(0,1)$, in order to compensate the unbounded positivity parameters sequence of the operator on the finite dimension subspaces, we separated the methods in two versions: one, considering the restrictions of the operator involving its adjoint and another, taking the finite rank operator approximations and compensating this time the strict positivity of the operator with its compactness property. The related theorem shows that, an Hilbert-Schmidt integral operator on a separable Hilbert space having the finite rank approximations with the sequence of the positivity parameters inferior bounded, is injective. Using the methods introduced, we proved the injectivity of the integral operator, injectivity that is the equivalent formulation of RH obtained by Alcantara-Bode in [3]. Follows, RH holds i.e.: the non trivial zeros of the Riemann Zeta function are all on the vertical line $\sigma$ = 1/2.
Hydrogen Embrittlement in Aviation Components: An Incident Based Overview and Analysis
Authors: Karaveli Kadir Kaan , Kalaycioglu Mete , Bal Burak , Saraçyakupoğlu Tamer
Published: November 2024

Hydrogen embrittlement (HE) poses a significant risk in the aviation industry, leading to unexpected failures in high-strength metals and alloys used in critical components. This paper investigates multiple aviation incidents where hydrogen embrittlement contributed, notably in helicopter drive systems and fixed-wing aircraft crankshaft bolts. The failures were attributed mainly to hydrogen introduced during manufacturing processes, particularly electroplating and surface treatments, which left residual hydrogen despite dehydrogenation procedures. These incidents led to the issuance of Airworthiness Directives (ADs), which mandated stricter manufacturing controls and post-processing treatments, such as baking, to eliminate residual hydrogen and prevent future embrittlement. While the paper briefly covers traditional mitigation strategies like cathodic protection and surface coatings, its primary focus is on the need for improved quality control in manufacturing processes to reduce hydrogen-related failures, thereby enhancing the safety and reliability of aviation systems.
A Narrative Review on the Neurocognitive Profiles in Eating Disorders and Higher Weight Individuals: Insights for Targeted Interventions
Authors: Krug Isabel , Dang An Binh , Lu Evonne , Ooi Wenn , Portingale Jade , Miles Stephanie
Published: November 2024

Background/Objectives: Recent research has increasingly focused on the neurocognitive characteristics associated with eating disorders (EDs), including, anorexia nervosa (AN), bulimia nervosa (BN), binge eating disorder (BED), other specified feeding or eating disorders (OSFED) and individuals of higher weight (HW). This critical narrative review summarizes recent findings on neurocognitive processes across the weight spectrum, emphasizing implications for diagnosis and treatment.
Results: Our review highlights that individuals with AN, especially the restrictive subtype of AN, exhibit reduced cognitive flexibility and increased attention to detail. In BN and BED, cognitive profiles are also marked by cognitive rigidity as well as impaired decision-making and impulsivity, with dysregulated reward processing and attentional bias toward food-related signals and reward sensitivity. OSFED presents a diverse neurocognitive profile, necessitating more focused research to delineate its specific characteristics. Finally, neurocognitive studies in individuals with HW highlight deficits in cognitive flexibility, increased impulsivity, and altered reward processing. These cognitive impairments may contribute to the intricate interplay of factors underlying overeating behaviours and subsequent weight gain.

Conclusions: Comparing these profiles across the weight spectrum underscores unique and shared impairments, emphasizing the complexity of neurocognitive mechanisms in relation to body mass index (BMI) and eating behaviours. Future research should prioritize longitudinal and intervention studies to elucidate causal relationships and explore cognitive training, virtual reality, and neuromodulation techniques, ultimately informing more personalized treatment strategies for EDs.
Hybrid Artificial Neural Network Approach for Modelling the Behavior of Polyethylene Terephthalate (PET) at Conditions Applicable to Stretch Blow Molding
Authors: Teng Fei , Menary Gary , Yan Shiyong , Stevens John Boyet
Published: November 2024

A Hybrid Artificial Neural Network (ANN) based constitutive model has been developed to predict the stress-strain behaviour of PET for the Stretch Blow Moulding process (SBM). The model has been created by combining a physical based function for capturing the small strain behaviour in parallel with an ANN based model for capturing the temperature dependant large strain nonlinear viscoelastic behaviour. The architecture of the ANN has been designed to ensure stability in a load-controlled scenario thus making it suitable for use in FEA simulations of stretch blow moulding. Data for training the model has been generated by a new semi-automatic experimental rig which is able to produce 850 stress strain curves over a wide range of process conditions (temperature range 95℃-115℃ and strain rates ranging from 1/s – 100/s) directly from blowing preforms using a combination of high-speed video, digital image correlation and sensors for pressure and force. The model has already been implemented in the commercial FEA package Abaqus via a VUMAT subroutine with its performance validated by comparing the prediction of the evolution of preform shape during blowing vs high speed images.
New Frontiers of Electronic Theses and Dissertations
Authors: Schöpfel Joachim , Boock Michael , Rasuli Behrooz , Wyk Brenda van
Published: November 2024

(1] Background: Since the 1990s, theses and dissertations—a key part of scientific communication—have evolved significantly with advances in information and communication technologies. (2) Methods: This study reviews 99 publications examining these changes, drawing insights from international conferences and empirical studies in the field. (3) Results: Historically, a major challenge in managing PhD theses has been the shift to electronic formats, resulting in the creation of electronic theses and dissertations (ETDs). This shift involves four main tasks: adopting new digital formats, updating institutional workflows between departments, graduate schools, and academic libraries, implementing updated bibliographic standards (such as metadata and identifiers), and utilizing new tools and channels for distribution. With open science becoming a widespread research policy across many countries and institutions, ensuring open access for ETDs is an added challenge—though a substantial portion of ETD content remains restricted to institutional or library networks. Today, ETD management is on the brink of a new era, with advancements in data-driven science and artificial intelligence. (4) Conclusions: The development of ETDs varies significantly across different countries, regions, and institutions due to technological, organizational, and legal differences. It is essential for academic libraries and other stakeholders to address the challenges identified, while considering these variations.
Intelligent System for Cost-Effective and Energy-Sustainable Decision Making in the Welding of Steel Reinforcement for Concrete
Authors: Ferreiro-Cabello Javier , Martinez-de-Pison Francisco Javier , Fraile-Garcia Esteban , Pernia-Espinoza Alpha , Divasón Jose
Published: November 2024

In today’s industrial landscape, controlling energy costs, reducing production times, and meeting stringent environmental and quality standards are increasingly critical objectives. These demands are particularly significant in energy-intensive processes such as welding, where reducing energy costs and minimizing production times while maintaining quality is essential. This paper presents an intelligent sensor-software system for decision support in selecting optimal set-point parameters for the resistance spot welding of steel reinforcement bars, considering constraints on quality, energy consumption, and production time as specified by production managers. The system leverages machine learning models trained on process data to develop dynamic, intelligent heat maps that aid production engineers in selecting robust operational points resilient to potential perturbations. These heat maps visually guide the engineer by indicating real-time working conditions and identifying safe operating zones within the quality and efficiency thresholds defined by production management. The proposed methodology consists of the following phases: (i) identification of accurate models using small datasets, (ii) analysis of model robustness under varying conditions, and (iii) creation of interactive heat maps to visually highlight optimal working areas based on predefined optimization criteria, thereby enhancing decision-making in real-world welding applications.
“Art(s)-of-Living” in HCI: Philosophical Praxis in Human-Computer Interaction for Leading a Reciprocal Coexistence
Authors: Lee Yueh-Jung
Published: November 2024

This paper explores the integration of philosophical frameworks into Human-Computer Interaction to foster reciprocal and respectful coexistence between humans and technology. The author critiques traditional HCI’s masculine, user-centered, and anthropocentric orientations, which prioritize efficiency and control over inclusivity and mutual respect. Drawing from Eastern philosophies, feminist theories, and posthuman perspectives, the paper introduces an “arts-of-living” approach to HCI, emphasizing the ethical and aesthetic dimensions of design. Through this lens, the author advocates for “Aesthetic HCI,” which incorporates virtues, cultural awareness, and social and emotional engagement, proposing a set of heuristics to guide HCI design that respects human and non-human actors alike. Ultimately, the paper calls for a transformation of HCI into a more inclusive, values-driven practice that promotes balanced, sustainable interactions in a technologically complex world.
Operative Improvement of the Naphtha Catalytic Reforming Process as an Alternative for Reducing Environmental Impact of Benzene Fugitive Emissions from Gasoline
Authors: Velázquez-Alonso Fabiola , González-Ramírez César Abelardo , Villagómez-Ibarra José Roberto , Otazo-Sánchez Elena María , Hernández-Juárez Martín , Pérez-Villaseñor Fernando , Castro-Agüero Ángel , Alemán-Vázquez Laura Olivia , Camacho-López César , Romo-Gómez Claudia
Published: November 2024

Oil refining industry have as a challenge the high-octane gasoline production and its reformulation with low benzene content. This work reports a mass balance of benzene emissions from gasoline vessel transfers and transport operations in Mexico, with an estimated total maximum flowrate of 1.48 KBPD, considering evaporative losses. The aim of this work is to provide an operative proposal for minimizing benzene emissions by developing an ASPEN HYSYS ® simulation study of the Naphtha Catalytic Reforming (NCR) process in its Continuous Catalyst Regeneration (CCR) mode at different operative conditions, in order to estimating transitional operative routes (TOR) that can improve reformate quality at the time benzene content is reduced. Operative conditions comprise a Hydrogen/ Hydrocarbon (H2/HC) ratio from 2.0 to 6.0 and a reaction temperature from 450 to 525 °C as the framework to find the best TOR by applying the Surface Response Method (SRM) Objective variables were assessed by a multi-objective optimization analysis, focusing on 1.48% (v/V), of benzene content, and 89.87 for Research Octane Number (RON) of produced gasoline. Finally, simulation results were compared with experimental data from a NCR pilot plant, in semi-regenerative (SR) mode, as well as with industrial scale data, observing significant similitudes.
Patterns of Discrimination Experiences and Social Capital Among Residents of Public Rental Housing in Korea: A Latent Class Analysis
Authors: Kim Seong-Eun , Jeon Seran
Published: November 2024

This study aims to explore and classify typologies among residents of public rental housing in South Korea by examining their experiences of discrimination and social capital. Social capital refers to the network of relationships, trust, and social cohesion that facilitates collective action and mutual support within a community. Discrimination experiences encompass the unfair treatment individuals face based on various characteristics, leading to social exclusion and reduced oppor-tunities. The analysis, based on data from the 2021 Seoul Public Rental Housing Panel Survey involving 4,683 individuals, utilized latent class analysis (LCA), chi-square tests, and one-way ANOVAs to identify distinct group characteristics. Four typologies emerged from the analysis: 'Group Seeking Friendly Neighbor Relationships', 'Group Accepting Losses', 'Group with High Social Capital', and 'Group Indifferent to Neighbors'. Each group demonstrated unique residential characteristics, perceptions of social integration, and resident activities, with significant differ-ences observed in mental health outcomes. Notably, the study revealed unconventional insights that challenge existing academic discussions, such as the identification of groups with a strong inclination to support neighbors despite experiencing discrimination, and a group expressing a clear desire for neighborly interaction. The findings suggest that to effectively address discrim-ination in public rental housing, policies should incorporate comprehensive approaches that consider social, economic, and cultural elements alongside physical integration. These policy recommendations aim to foster inclusivity and reduce conflict within public rental communities. Furthermore, these insights could be valuable for other countries seeking to develop or improve public rental housing policies. By integrating social capital-building initiatives and addressing discrimination comprehensively, these strategies can contribute to enhancing community cohe-sion and reducing social tensions on a global scale.
A Novel Sensor Deployment Strategy Based on Probabilistic Perception for Industrial Wireless Sensor Network
Authors: Liu Xiaokai , Xu Fangmin , Ning LiNang , Lv Yuhan , Zhao Chenglin
Published: November 2024

With the development of Industrial Internet of Things (IIoT) technology, wireless sensor networks have become increasingly important. Sensor deployment in the Industrial Internet is a key element for achieving intelligent production and equipment monitoring. The use of the sensors efficiently and collaboratively has become a popular research topic in the IIoT field. In this paper, we focus on the sensor deployment strategy to guarantee communication quality and transmission speed. This paper introduces an innovative approach by normalizing four critical metrics: deployment cost, energy consumption, network connectivity, and sensing probability. In this way, the proposed method eliminates the differences in dimensional units between these metrics and assigns unique weights based on their relative importance. Additionally, the paper incorporates larger-scale obstacles into the model to better accommodate varying environmental and scene-specific requirements. A series of diverse simulation experiments were designed and conducted across different scenarios. The simulation results demonstrate that, when compared to other similar algorithms, the particle swarm-genetic hybrid algorithm shows superior performance in optimizing the newly proposed metric of network performance. Furthermore, the algorithm also exhibits a noticeable improvement in terms of search convergence time, contributing to its overall effectiveness and efficiency in network optimization tasks.
Ionospheric Disturbances produced by the 2019 Caribbean Meteoroid
Authors: Tarango-Yong Jorge , Rodríguez-Martínez Mario , Gutiérrez-Zalapa Raúl , Aguilar-Rodríguez Ernesto , Varela-Mendez Ariana , González-Avilés José Juan , González-Esparza Juan Américo , Romero-Hernández Esmeralda , Sergeeva Maria
Published: November 2024

Extraterrestrial impacts, ranging from minor asteroids to larger bodies, induce varying degrees of atmospheric perturbation, with potential consequences ranging from localized disruptions to global events. This study investigates the ionospheric disturbances caused by a meteoroid impact over the Caribbean Sea on June 22, 2019. Detected by U.S. government sensors and the Geostationary Lightning Mapper (GLM), the event released approximately 6 kilotons of energy, marking the most energetic meteoroid impact recorded by both databases. We used data from the UNAVCO network of GNSS stations, alongside energy estimates derived from GLM light curves and USG sensors, to identify significant variations in Total Electron Content (TEC) associated with the meteoroid's atmospheric passage. Advanced detrending techniques, including the Savitzky-Golay filter, were employed to enhance wave-like features within the TEC time series, confirming their correlation with the meteoroid event. Additionally, the analysis confirms minimal solar activity during the event, ruling out solar terminator effects as a major contributor to the observed disturbances. This study underscores the need for continued research into ionospheric perturbations from meteoroid impacts, with future work focusing on modeling TID propagation velocity and refining detection techniques using tools such as the Rate of TEC Index (ROTI).