bluuebunny/crossref_metadata_2025 huggingface dataset:
Data from The E3 Ubiquitin Ligase FBXO38 Maintains the Antitumor Function of Natural Killer Cells by Sustaining IL15R Signaling
Authors: Shi Yongjing , Zheng Xiaodong , Peng Hui , Xu Chenqi , Sun Rui , Tian Zhigang , Sun Haoyu , Wang Xianwei
Published: October 2024

Abstract
Natural killer (NK) cells are the main innate antitumor effector cells but their function is often constrained in the tumor microenvironment. It has been reported that the E3 ligase FBXO38 accelerates PD-1 degradation in tumor-infiltrating T cells to unleash their cytotoxic function. In this study, we found that the transcriptional levels of FBXO38 in intratumoral NK cells of patients with cancer and tumor-bearing mice were significantly lower than in peritumoral NK cells. Conditional knockout of FBXO38 in NK cells accelerated tumor growth and increased tumor metastasis. FBXO38 deficiency resulted in impaired proliferation and survival of tumor-infiltrating NK (TINK) cells. Mechanistically, FBXO38 deficiency enhanced TGF-β signaling, including elevating expression of Smad2 and Smad3, which suppressed expression of the transcription factor Eomes and further reduced expression of surface IL15Rβ and IL15Rγc on NK cells. Consequently, FBXO38 deficiency led to TINK cell hyporesponsiveness to IL15. Consistent with these observations, FBXO38 mRNA expression was positively correlated with the proliferation of TINK cells in multiple human tumors. To study the therapeutic potential of FBXO38, mice bearing human tumors were treated with FBXO38 overexpressed human primary NK cells and showed a significant reduction in tumor size and prolonged survival. In conclusion, our results suggest that FBXO38 sustains NK-cell expansion and survival to promote antitumor immunity and have potential therapeutic implications as they suggest FBXO38 could be harnessed to enhance NK cell–based cancer immunotherapy.
Supplementary Table 4 from SREBP-Dependent Regulation of Lipid Homeostasis Is Required for Progression and Growth of Pancreatic Ductal Adenocarcinoma
Authors: Ishida Chiaki T. , Myers Stephanie L. , Kubota Casie S. , Shao Wei , McGuire Meredith R. , Liu Chune , Ewachiw Theodore E. , Mukhopadhyay Debaditya , Ke Suqi , Wang Hao , Rasheed Zeshaan A. , Anders Robert A. , Espenshade Peter J.
Published: October 2024

Table S4. Pa03c cell microarray gene expression data
Supplementary Table 2 from SREBP-Dependent Regulation of Lipid Homeostasis Is Required for Progression and Growth of Pancreatic Ductal Adenocarcinoma
Authors: Ishida Chiaki T. , Myers Stephanie L. , Kubota Casie S. , Shao Wei , McGuire Meredith R. , Liu Chune , Ewachiw Theodore E. , Mukhopadhyay Debaditya , Ke Suqi , Wang Hao , Rasheed Zeshaan A. , Anders Robert A. , Espenshade Peter J.
Published: October 2024

Table S2. Primer sequences used for mouse tissue genotyping
Figure S3 from Exosomal Thomsen–Friedenreich Glycoantigen: A New Liquid Biopsy Biomarker for Lung and Breast Cancer Diagnoses
Authors: Hsu Chang-Chieh , Su Yafei , Rittenhouse-Olson Kate , Attwood Kristopher M. , Mojica Wilfrido , Reid Mary E. , Dy Grace K. , Wu Yun
Published: October 2024

Supplementary Figure S3. Sensing performance of SPR assay in detecting exosomal TF-Ag-α. Exosomes from A549 NSCLC cells (a) and MDA-MB-231 breast cancer cells (b) were spiked in the serum of a normal control at concentrations of 0 to 10^11 exosomes/mL. The expression of exosomal TF-Ag-α was measured using the SPR assay. For A549 cell-derived exosomes, the LOD was 5×10^9 exosomes/mL and the linear range was from 5×10^9 to 10^11 exosomes/mL. For MDA-MB-231 cell-derived exosomes, the LOD was 10^9 exosomes/mL and the linear range was from 10^9 to 10^11 exosomes/mL.
Figure S1 from Exosomal Thomsen–Friedenreich Glycoantigen: A New Liquid Biopsy Biomarker for Lung and Breast Cancer Diagnoses
Authors: Hsu Chang-Chieh , Su Yafei , Rittenhouse-Olson Kate , Attwood Kristopher M. , Mojica Wilfrido , Reid Mary E. , Dy Grace K. , Wu Yun
Published: October 2024

Supplementary Figure S1. SPR assay detects exosomal TF-Ag-α for cancer diagnosis. Representative SPR curves for the detection of exosomal TF-Ag-α in serum samples from (a) a Stage II lung cancer patient using a biochip modified with IgG3 negative control antibodies; (b) a Stage 0 breast cancer patient using a biochip modified with IgG3 negative control antibodies, (c) a male normal control using a biochip modified with JAA-F11 antibodies, (d) a male normal control using a biochip modified with IgG3 negative control antibodies, (e) a female normal control using a biochip modified with JAA-F11 antibodies, and (f) a female normal control using a biochip modified with IgG3 negative control antibodies.
Supplementary Figure 2 from SREBP-Dependent Regulation of Lipid Homeostasis Is Required for Progression and Growth of Pancreatic Ductal Adenocarcinoma
Authors: Ishida Chiaki T. , Myers Stephanie L. , Kubota Casie S. , Shao Wei , McGuire Meredith R. , Liu Chune , Ewachiw Theodore E. , Mukhopadhyay Debaditya , Ke Suqi , Wang Hao , Rasheed Zeshaan A. , Anders Robert A. , Espenshade Peter J.
Published: October 2024

FIGURE S2 – Mouse genotyping methods and survival study data
Data from Combined Oxygen-Enhanced MRI and Perfusion Imaging Detect Hypoxia Modification from Banoxantrone and Atovaquone and Track Their Differential Mechanisms of Action
Authors: O’Connor James P.B. , Tessyman Victoria , Little Ross A. , Babur Muhammad , Forster Duncan , Latif Ayşe , Cheung Susan , Lipowska-Bhalla Grazyna , Higgins Geoff S. , Asselin Marie-Claude , Parker Geoff J.M. , Williams Kaye J.
Published: October 2024

Abstract
Oxygen-enhanced MRI (OE-MRI) has shown promise for quantifying and spatially mapping tumor hypoxia, either alone or in combination with perfusion imaging. Previous studies have validated the technique in mouse models and in patients with cancer. Here, we report the first evidence that OE-MRI can track change in tumor oxygenation induced by two drugs designed to modify hypoxia. Mechanism of action of banoxantrone and atovaquone were confirmed using in vitro experiments. Next, in vivo OE-MRI studies were performed in Calu6 and U87 xenograft tumor models, alongside fluorine-18–fluoroazomycin arabinoside PET and immunohistochemistry assays of hypoxia. Neither drug altered tumor size. Banoxantrone reduced OE-MRI hypoxic fraction in Calu6 tumors by 52.5% ± 12.0% (P = 0.008) and in U87 tumors by 29.0% ± 15.8% (P = 0.004) after 3 days treatment. Atovaquone reduced OE-MRI hypoxic fraction in Calu6 tumors by 53.4% ± 15.3% (P = 0.002) after 7 days therapy. PET and immunohistochemistry provided independent validation of the MRI findings. Finally, combined OE-MRI and perfusion imaging showed that hypoxic tissue was converted into necrotic tissue when treated by the hypoxia-activated cytotoxic prodrug banoxantrone, whereas hypoxic tissue became normoxic when treated by atovaquone, an inhibitor of mitochondrial complex III of the electron transport chain. OE-MRI detected and quantified hypoxia reduction induced by two hypoxia-modifying therapies and could distinguish between their differential mechanisms of action. These data support clinical translation of OE-MRI biomarkers in clinical trials of hypoxia-modifying agents to identify patients demonstrating biological response and to optimize treatment timing and scheduling.

Significance: For the first time, we show that hypoxic fraction measured by oxygen-enhanced MRI (OE-MRI) detected changes in tumor oxygenation induced by two drugs designed specifically to modify hypoxia. Furthermore, when combined with perfusion imaging, OE-MRI hypoxic volume distinguished the two drug mechanisms of action. This imaging technology has potential to facilitate drug development, enrich clinical trial design, and accelerate clinical translation of novel therapeutics into clinical use.
Electronic system to speckle phenomenon characterization for random movement on fiber optics
Authors: Ortega Galicio Orlando Adrian , Calvo Jinmi Lezama , Diaz Leyva Teodoro Neri , Saavedra Melina Machaca , Sanchez Lopez Simon Alejandro , Baldárrago Alexandra Chávez , Atalaya Omar Chamorro
Published: October 2024

Peru is a country located in a telluric area. The early detection of earthquakes will alert the population and avoid human losses. There are different methods to detect it, mainly on mechanical movements and electronic sensors, which are currently used. This article presents the analysis and implementation of a repetitive motion generation and detection system based on the study of the speckle phenomenon through an optical fiber. The analysis is calculated by the technique of averaged difference that allows obtaining the intensity variation of two consecutive frames, as the speckle pattern changes and occupies different positions. Several tests are carried out that show the relationship of the controlled random movement and speckle characteristics obtained, the test system that can be used for the detection of random movements similar to P and S earthquakes waves.
Deep-SFER: deep convolutional neural network and MFCC an effective speech and face emotion recognition
Authors: Gummula Ravi , Arumugam Vinothkumar , Aranganathan Abilasha
Published: October 2024

There has been a lot of progress in recent years in the fields of expert systems, artificial intelligence (AI) and human machine interface (HMI). The use of voice commands to engage with machinery or instruct it to do a certain task is becoming more common. Numerous consumer electronics have SIRI, Alexa, Cortana, and Google Assistant built in. In the field of human-device interaction, emotion recognition from speech is a complex research subject. We can't imagine modern life without machines, so naturally there's a need to create a more robust framework for human-machine communication. A number of academics are now working on speech emotion recognition (SER) in an effort to improve the interaction between humans and machines. We aimed to identify four fundamental emotions: angry, unhappy, neutral and joyful from speech in our experiment. As you can hear below, we trained and tested our model using audio data of brief Manipuri speeches taken from films. This task makes use of convolutional neural networks (CNNs) to extract functions from speech in order to recognize different moods using the Mel-frequency cepstral coefficient (MFCC).
Design and development of coastal marine water quality monitoring based on IoT in achieving implementation of SDGs
Authors: Kustija Jaja , Fahrizal Diki , Nasir Muhamad , Setiawan Deny , Surya Irgi
Published: October 2024

Indonesia, an archipelagic nation with about 70% ocean territory, relies on oceanographic data for efficient marine environment monitoring and natural resource sustainability. Current data collection is limited by tools measuring only single parameters and lengthy data collection times. This study proposes a marine coastal water quality monitoring tool based on the internet of things (IoT), capable of simultaneously measuring temperature, electrical conductivity, pH, and dissolved oxygen. Utilizing an Atmega328 and a battery lasting up to 119 hours, this system offers a cost-effective solution for real-time oceanographic data collection. Employing the ADDIE methodology, the results demonstrate high measurement accuracy compared to traditional methods, with accuracy of 90.5% for temperature, 93.50% for electrical conductivity, 93.67% for pH, and 96.82% for dissolved oxygen. The development of this tool aims to reduce costs and labor in capturing oceanographic data integrated with IoT, facilitate access and monitoring of water data, and make a significant contribution to achieving SDGs targets. The main focus on the goals of addressing climate change and life underwater, especially in the aspects of water resources management and protection of marine ecosystems in Indonesian.
Energy baseline model enhanced based on artificial neural network in industrial buildings
Authors: Ouaomar Younes , Benkachcha Said , Kaddiri Mourad
Published: October 2024

In this article, a new energy-efficient reference model has been established for a plastic injection molding plant. However, the proposed model handled difficulties due to the lack of robust and complete data, such as production mix and cooling degree-days. In addition, the proposed model applies three distinct enhanced modeling methodologies, including regression modeling, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Furthermore, these performance parameters were established to assess the accuracy of each model in this work. Moreover, the numerical results show that among the methodologies used in this work, the ANN demonstrated effective performance despite uncertainties in the measured input variables. The ANN numerical results in this paper highlight the ability to accurately assess baseline consumption in the industrial sector, providing a practical tool for decision-makers to improve energy efficiency.
Electrical circuit approaches to modeling brain chaos: insights into neural dynamics
Authors: Selmi Kaouther , Bachta Kods , Bouallegue Kais
Published: October 2024

This paper investigates the simulation of brain chaos dynamics using a combination of the Chua circuit and diode tunnel mechanisms, aiming to examine chaotic behavior in brain networks. Leveraging the inherent chaotic properties of the Chua circuit, Fitzhugh-Nagumo (FHN) function and the nonlinear characteristics of diode tunneling, our model offers a platform to mimic the intricate synaptic interactions observed in the brain. By subjecting the model to various stimuli and perturbations, we analyze the emergence and evolution of chaotic patterns, shedding light on the underlying mechanisms of cerebral chaos. Through numerical simulations and experimental validation, we demonstrate the effectiveness of our approach in replicating key features of brain chaos and highlight its potential implications for understanding neurological disorders and cognitive processes. This research contributes to the broader effort of leveraging computational models to explore the complex dynamics of the brain and their implications for neuroscience and microengineering.
Probing the depths: assessing the efficacy of the two-tier deception-driven security model
Authors: Gamilla Anazel P. , Palaoag Thelma D. , Naagas Marlon A.
Published: October 2024

In the age characterized by relentless cyber threats, the need for innovative and proactive security measures has never been more important. Deception is defined as the deliberate structure of tricks, traps, and false information to mislead and discourage threats, while providing timely warning signals and useful information to defenders. The two-tier deception-driven security model's implementation focuses on applying deception security techniques to deceive potential attackers and protect network resources, with an emphasis on a proactive defense approach. The study emphasized the deployment and deep testing of the model, which aims to assess its efficacy and feasibility in real-time practice. The study shows that the two-layered approach effectively defends the network within the multiple layers using a combination of decoys, honeypots, and deceptive network segments. The deception security model effectively prevents and confuses potential threats, improving the network's overall resilience and threat defense capabilities. The findings suggest that integrating deception techniques into cybersecurity frameworks can provide a robust layer of protection against evolving cyber threats. Furthermore, this research contributes to the ongoing discourse on proactive cybersecurity strategies and offers practical insights for improving network defense mechanisms.
Fusion algorithms on identifying vacant parking spots using vision-based approach
Authors: Adi Ginanjar Suwasono , Nugroho Hertog , Rahmatullah Griffani Megiyanto , Fadhlan Muhammad Yusuf , Mutamaddin Dinan
Published: October 2024

In densely populated cities, parking space scarcity results in issues like traffic congestion and difficulty finding parking spots. Recent advancements in computer vision have introduced methods to address parking lot management challenges. The availability of public image datasets and rapid growth in deep learning technology has led to vision-based parking management studies, offering advantages over sensor-based systems in comprehensive area coverage, cost reduction, and additional functionalities. This study presents an innovative fusion algorithm that integrates object detection with occupancy state algorithms to accurately identify vacant parking spaces. The employment of the YOLOv7 framework for vehicle instance segmentation, combined with three occupancy algorithms Euclidean distance (ED), intersection over reference (IoR), and intersection over union (IoU) are compared to determine the occupancy state of observed areas. The proposed method is evaluated using the CNRPark-EXT dataset, and its performance is compared with state-of-the-art methods. As a result, the proposed approach demonstrates robustness under varying conditions. It outperforms existing methods in terms of system evaluation performance, achieving accuracies of 98.88%, 97.99%, and 90.04% for ED, IoR, and IoU, respectively. This fusion detection method enhances adaptability and addresses occlusions, emphasizing YOLOv7’s advantages and accurate shape approximation for slot annotation. This study contributes valuable insights for effective parking management systems and has potential usage in the real-world implementation of intelligent transportation systems.
Survey of IoT and AI applications: future challenges and opportunities in agriculture
Authors: Elhattab Kamal , Elatar Said
Published: October 2024

The internet of things (IoT) connects physical objects through sensors, software, and communication technologies, enabling efficient data collection and sharing. This interconnection promotes automation, real-time monitoring, and improved decision-making across various sectors. In agriculture, the integration of IoT with artificial intelligence (AI) is revolutionizing resource management by providing farmers with real-time information on crop health, climate conditions, and soil quality. This paper explores how IoT and AI are transforming traditional agricultural practices to enhance both efficiency and sustainability. Through an in-depth analysis of existing literature and practical applications in the sector, this study identifies significant advancements in crop management, reduction of losses, and resource optimization. Additionally, it highlights persistent challenges such as data security and interoperability. The aim is to address these challenges and propose innovative solutions to optimize agricultural processes. The results indicate that while IoT and AI offer substantial benefits, further advancements and solutions are needed to fully leverage these technologies for sustainable agricultural development.
Digital learning using ChatGPT in elementary school mathematics learning: a systematic literature review
Authors: Listyaningrum Prabandari , Retnawati Heri , Harun Harun , Ibda Hamidulloh
Published: October 2024

Digital learning with ChatGPT in elementary school mathematics learning is urgently implemented. Several studies have explored digital learning with ChatGPT in elementary school mathematics learning, but studies using SLR are minimal. This article presents the 2022-2024 SLR study on digital learning with ChatGPT in elementary school mathematics learning. This SLR and PRISMA method is supported by Publish or Perish 8, VOSviewer version 1.6.20, Mendeley version 1.19.8, and ATLAS.ti version 7.5.16. The search results obtained 1,259 Scopus articles, which were filtered to 40 and analyzed using ATLAS.ti, then the results were described according to the research question. Digital learning with ChatGPT is a learning approach using the synchronous-asynchronous mode, virtual classrooms, distance, use of interactive digital tools, digital methods and media, innovation, digital modeling, use of robotics and AI ChatGPT for children with the principle of collaboration digital, and problem-solving with the support of digital resources. ChatGPT features multilingual, natural language, advanced AI, 24/7 availability, answering math questions, recurring training, and helping students with various math tasks. Implementation of digital learning with ChatGPT in elementary school mathematics learning for problem-solving, geometry, function limits in algebra, the material on flat shapes, geometric shapes, integrated PjBL, online, mixed and flipped classes.
Development of image extraction using the centerline method in the identification of appendicitis in ultrasonography
Authors: Rizki Syafrika Deni , Yuhandri Yuhandri , Fitri Iskandar
Published: October 2024

Appendicitis is a disease that refers to inflammation of the appendix caused by obstruction, or blockage, in the lumen of the appendix. We investigated that this disease can be detected early through medical imaging such as ultrasonography (USG). However, the role of ultrasound in these cases is still limited due to the low visualization rate of the visible appendix. Based on this, this research aims to develop an image extraction process using the Centerline method in the process of identifying appendicitis in ultrasound images. The development of the extraction process is presented in the performance of the centerline and boundary extraction (CBE) algorithm which can represent image objects as boundaries that limit and separate one area from other areas. The research dataset used was 2097 ultrasound images sourced from 90 patients at the West Sumatra Lung Hospital. Based on the tests that have been carried out, it has been proven that it can reduce the width of the image object iteratively until the object is represented as a center line or the thinnest representation. The performance of the CBE algorithm in the identification process is sufficient to provide accuracy results of 92%. These results can be a new extraction concept that can provide accuracy in the identification process.
A robust method for detecting fake news using both machine and deep learning algorithms
Authors: Alikhashashneh Enas Ahmad , Nahar Khalid M.O. , Abual-Rub Mohammed , Alkhaldy Hedaya M.
Published: October 2024

Spreading fake news and false information on social media is very common and can be done effortlessly due to the huge number of users of each of the various social media platforms. Another reason for having such a speedy spread of fake news (which makes about 40% of the information published on social media platforms) is the inability of these platform to verify the authenticity of the news before allowing it to be published. This research will use information technology to detect fake news/ false information and change this kind of technology from being the cause of the problem to a tool to solve it. This research provides a method that uses both machine learning (ML) and deep learning (DL) algorithms to detect fake information versus real information and compare the performance of the algorithms. The results of this research indicate that the algorithms that use term frequency inverse document frequency (TF-IDF) have achieved better results than the algorithms that use Word2Vec. Long short-term memory (LSTM) algorithm, however, has achieved the best performance; of 99% accuracy -when using TF-IDF, and 94% -when using Word2Vec.
Hesitant fuzzy clustering with convolutional spiking neural network for movie recommendations
Authors: Shrivastava Vineet , Kumar Suresh
Published: October 2024

The movie recommender system is one of the most influential and practical tools for aiding individuals in quickly selecting films to watch. Despite numerous academic efforts to employ recommender systems for various purposes, such as movie-watching and book-buying, many studies have overlooked user-specific movie recommendations. This paper introduces a novel approach for movie recommendations that combines the hesitant fuzzy clustering with a convolutional spiking neural network movie recommender system. The initial step involves acquiring input data from benchmark datasets like MovieLens 100K and MovieLens 1M. Further, content-based features are extracted from the dataset using ternary pattern and discrete wavelet transforms. After that hesitant fuzzy linguistic Bi-objective clustering (HFLBC) is applied for cluster selection based on the extracted features. Subsequently, a movie recommender scheme utilizing a convolutional spiking neural network is introduced to predict user film preferences. The efficiency of the proposed model is compared to existing methods such as multi-modal trust-dependent recommender scheme and graph-dependent hybrid recommendation scheme. The results show a significant improvement, with the proposed model achieving 13.79% and 16.47% higher accuracy than the existing methods. The findings highlight the potential of proposed system in enhancing the accuracy and personalization of movie recommendations.
Android malware detection using the random forest algorithm
Authors: El Attaoui Anas , El Hami Norelislam , Koulou Younes
Published: October 2024

The rapid growth in Android device usage has resulted in a significant increase in malware targeting this platform, posing serious threats to user security and privacy. This research tackles the challenge of Android malware detection by leveraging advanced machine learning techniques, with a particular emphasis on the random forest (RF) algorithm. Our primary objective is to accurately identify and classify malicious applications to enhance the security of Android devices. In this study, we employed the RF algorithm to analyze a comprehensive dataset of Android applications, where the classification of each application as either malware or benign is known. The method was rigorously tested, yielding impressive results: an average accuracy of 98.47%, a sensitivity of 98.60%, and an F-score of 98.60%. These metrics underscore the effectiveness of our approach. Moreover, we conducted a comparative analysis of the RF algorithm against other malware detection methods. The results demonstrate that the RF algorithm outperforms these alternative methods, offering superior detection capabilities and contributing to more robust Android security measures.
Privacy-preserving data mining optimization for big data analytics using deep reinforcement learning
Authors: Utomo Wiranto Herry , Rosalina Rosalina , Afriyadi Afriyadi
Published: October 2024

The rapid growth of big data analytics has heightened concerns about data privacy, necessitating the development of advanced privacy-preserving techniques. This research addresses the challenge of optimizing privacy-preserving data mining (PPDM) for big data analytics through the innovative application of deep reinforcement learning (DRL). We propose a novel framework that integrates DRL to dynamically balance privacy and utility, ensuring robust data protection while maintaining analytical accuracy. The framework employs a reinforcement learning agent to adaptively select optimal privacy-preserving strategies based on the evolving data environment and user requirements, while ensuring compliance with the latest security and privacy standards such as ISO/IEC 27001:2023. Experimental results demonstrate significant improvements in both privacy protection and data utility, surpassing traditional PPDM methods. Our findings highlight the potential of DRL in enhancing privacy-preserving mechanisms, offering a scalable and efficient solution for secure big data analytics.
Space vector pulse width modulation realization for three-phase voltage source inverter
Authors: Palanisamy Ramasamy , Santhakumari Valarmathi Thangamani , Venkatarajan Shanmugasundaram , Hemalatha Selvaraj , Hepzibah Albert Alice , Ramkumar Ravindran , Sugavanam Vidyasagar
Published: October 2024

This paper presents the implementation of space vector pulse width modulation (SVPWM) for a three-phase voltage source inverter (VSI). SVPWM is a technique used to control the output voltage of VSIs with improved efficiency and precision. The abstract outlines the key steps involved in implementing SVPWM, including reference signal clarification, sector identification, determination of voltage vectors, and switching state calculation. This proposed system provides improved output voltage of the inverter, minimized voltage stress across the switches and reduced total harmonic distortion and electromagnetic interference. The proposed implementation aims to enhance the performance of three-phase VSIs in various applications, such as motor drives, renewable energy systems, and power converters. The simulation results of proposed system are verified using MATLAB Simulink.
Competitive Peacekeeping Interventions, State Factionalisation and the Escalation of Organised Violence: the Case of Syria
Authors: Alkhayer Talip , Kivimäki Timo
Published: October 2024

This study investigates the paradox of peacekeeping interventions exacerbating conflict intensity, particularly when executed by various competing powers rather than the UN, through the lens of the Syrian conflict. By tracing the causal pathway from intervention to state factionalisation and subsequent violence escalation, we utilise novel data highlighting the impact of corruptive state factionalism on conflict dynamics in Syria. Through qualitative and quantitative analyses, we show that competitive interventions create credible threats and mobilise violence, thus intensifying the conflict. This research underscores the necessity for non-partisan, UN-led peacekeeping efforts to mitigate the adverse effects of competitive interventions in fragile states.
Pentacentrus rectangulus sp. nov., the twelfth Pentacentrus species recorded in China (Orthoptera: Gryllidae; Pentacentrinae)
Authors: DAI JIN-HONG , XIE CAN-HONG , MA LI-BIN
Published: October 2024

Pentacentrus rectangulus sp. nov. is the twelfth Pentacentrus species we have found in China. This new species has a uniformly colored head (without longitudinal yellow stripe on the occiput, and the color of the scape and peduncle segments is the same as that of the first three flagellar segments), and significantly curved ectoparamere. It has a similar coloration (coloration of the forewings and hind feet) to that of both Pentacentrus bituberus Liu & Shi, 2011 and Pentacentrus medogensis Zong, Qiu & Liu, 2017, but the morphology of the male genitalia differs markedly (the ventral inner margin of the epiphallus of the new has tufts of bristles, which is not present in either P. bituberus or P. medogensis). Here, we describe and illustrate the new species, and we also provide a key to the Chinese species of the genus.
Maternal/paternal helicopter parenting and school adjustment in adolescents: The mediating roles of internal and external academic locus of control
Authors: Güler Öztekin Gülçin , Ata Samet , Coşgun Demirdağ Merve
Published: October 2024

There is little evidence regarding the impact of helicopter parenting (HP) on adolescents’ school adjustment (SA). This study examined gender differences in maternal/paternal HP, internal/external academic locus of control (ALC) and SA, and the mediating effect of internal/external ALC in the relationship between maternal/paternal HP and SA in adolescents. The participants of the study were 447 adolescents. The results revealed no significant gender differences in study variables except for external ALC. Adolescent boys indicated significantly higher levels of external ALC than girls. HP predicted ALC and SA, and internal/external ALC had a mediating role in the relationship between HP and SA. This study provides important insights for researchers and practitioners seeking to understand the effects of maternal/paternal HP on adolescent SA. The results of our study suggest that HP may be associated with lower SA among adolescents and that internal ALC may be a key mechanism in this relationship.
Evaluating the performance of six 1970s off-gas deep retrofit bungalows
Authors: Perisoglou Emmanouil , Patterson Joanne , Ionas Miltiadis
Published: October 2024

This paper presents the energy and environmental performance of whole house energy systems implemented in six 1970s bungalows in South Wales, owned by Swansea Council. The objective was to reduce energy demand and carbon emissions, maximise renewable supply whilst ensuring a comfortable and affordable home for the residents. The whole house energy system for each home involved the installation of a combination of passive and active low carbon solutions. Detailed monitoring was carried out for a year before and for more than 2 years after the work, annual figures have been validated and normalised for weather. Analysis of monitored data confirms that Standard Assessment Performance ratings improved from 12 to 95. The average annual energy consumption across the six bungalows was reduced from 16,117 to 4560 kWh. 2418 kWh was provided by the PV panels and battery, with 1963 kWh of excess electricity that could be sold back to the grid. Real-life average Ground Source Heat Pump CoP was monitored at 3.3. Embodied carbon for retrofitting each house is estimated at 22,980 KgCO2e +/−20%, approximately 5-years carbon payback. Indoor conditions have been improved with internal temperature and relative humidity achieving standards with residents reporting levels of improved comfort satisfaction.

Practical Application: A whole house energy system demonstrates the benefits of a holistic, fabric and systems retrofit approach. The work informs how gathering data using appropriate methods assists modelling predictions and decisions throughout the retrofit stages. The study demonstrates extensive monitoring as a vital tool to evaluate the performance of the individual components and the whole system within the home, as used by the residents. Performance evaluation is critical in identifying issues, allowing resolutions to be made both immediately on-site as well for longer term, follow on retrofit programmes. Real-life performance evaluation and visualisation methods are transferable across global building industry.
Machine learning and radiomics for ventricular tachyarrhythmia prediction in hypertrophic cardiomyopathy: insights from an MRI-based analysis
Authors: Durmaz Emine Sebnem , Karabacak Mert , Ozkara Burak Berksu , Kargın Osman Aykan , Demir Bilal , Raimoglou Damla , Aygun Ahmet Atil , Adaletli Ibrahim , Bas Ahmet , Durmaz Eser
Published: October 2024

Background

Myocardial fibrosis is often detected in patients with hypertrophic cardiomyopathy (HCM), which causes left ventricular (LV) dysfunction and tachyarrhythmias.

Purpose

To evaluate the potential value of a machine learning (ML) approach that uses radiomic features from late gadolinium enhancement (LGE) and cine images for the prediction of ventricular tachyarrhythmia (VT) in patients with HCM.

Material and Methods

Hyperenhancing areas of LV myocardium on LGE images were manually segmented, and the segmentation was propagated to corresponding areas on cine images. Radiomic features were extracted using the PyRadiomics library. The least absolute shrinkage and selection operator (LASSO) method was employed for radiomic feature selection. Our model development employed the TabPFN algorithm, an adapted Prior-Data Fitted Network design. Model performance was evaluated graphically and numerically over five-repeat fivefold cross-validation. SHapley Additive exPlanations (SHAP) were employed to determine the relative importance of selected radiomic features.

Results

Our cohort consisted of 60 patients with HCM (73.3% male; median age = 51.5 years), among whom 17 had documented VT during the follow-up. A total of 1612 radiomic features were extracted for each patient. The LASSO algorithm led to a final selection of 18 radiomic features. The model achieved a mean area under the receiver operating characteristic curve of 0.877, demonstrating good discrimination, and a mean Brier score of 0.119, demonstrating good calibration.

Conclusion

Radiomics-based ML models are promising for predicting VT in patients with HCM during the follow-up period. Developing predictive models as clinically useful decision-making tools may significantly improve risk assessment and prognosis.
Branch retinal artery occlusion in a patient with COVID-19 infection: A case report
Authors: Jiang Zhijian , Ji Huiying , Zhang Hui , Dong Ziyi , Dong Jianhong
Published: October 2024

We report here, a case of branch retinal artery occlusion (BRAO) in the left eye of a 76-year-old man that involved three arteries which was considered to be related to a COVID-19 infection due to high levels of blood cytokines and coagulation factors. Although the patient had hypertension and atherosclerosis, his hypertension had been well controlled for the past five years by regular antihypertensive medication. Twenty-five days after starting treatment with anti-inflammatory, anticoagulant and conservative therapy, the patient’s biomarkers of inflammation and coagulation returned to normal and his vision improved. However, some visual field defects remained and were probably a consequence of low oxygen saturation.
A reflection on faces seen under mirror reversal
Authors: Suchow Jordan , McDowell Malerie , Huang Jessica , Haberman Jason
Published: October 2024

Much of our visual experience of faces, including our own, is mediated by technology, for example when a digital photo depicts a mirror reversal of reality. How does this difference in visual experience affect judgments about appearance? Here, we asked participants to view their likeness in photographs that were reversed (as when viewed in a mirror) or not reversed (as when viewed directly). Observers also perceptually adapted (or not) to the reversed or non-reversed images in a 2 × 2 design. Observers then rated how much each photograph resembled them and how much they liked their appearance in the photograph, later repeating the procedure for images of close friends. We found that non-reversed images are perceived as more “unlike” one's self and less pleasant than reversed images; the pattern disappears when evaluating close friends, where the non-reversed image is the more familiar, with adaptation having asymmetric effects. Experiment 1A was fully replicated seven years later. These results are likely driven by a strong, albeit malleable, visual representation of self, born of technology mediated experience and activated when an unfamiliar perspective exposes facial asymmetries. We conclude by considering the downstream effects of these preferences on consumer and social behavior.
Experimental and simulation study on lean combustion characteristics of passive pre-combustion chamber ignition boosted GDI engine
Authors: Yin Congbo , Zhu Minmin , Zhao Xiaowei , Zhang Zhendong
Published: October 2024

In order to verify the potential application of passive pre-combustion chamber technology in commercial GDI engines, the efficiency characteristics, combustion characteristics (CA50, Knock Index and COV) and emission characteristics of a pre-combustion chamber ignition engine in a boosted environment were studied by three-stage injection strategy to form a lean burn environment (λ = 1.3). A 3D simulation model of pre-combustion chamber ignition engine is established. Combined with the engine bench test results, the process of intake, mixture formation, combustion and emission generation under different working conditions is simulated by simulation method. The development process of TKE (turbulent kinetic energy), air-fuel ratio, temperature and combustion emissions in the main/pre-combustor is studied. It is found that under high load conditions in boosted pre-combustion chamber engine, the average knock index can be controlled between 2 and 3.4 bar, and the COV can be controlled between 3% and 3.5%. The indicated thermal efficiency reaches over 40% within the load range of IMEP from 10 to 14 bar. When the load increases, NOx and soot emissions show a downward trend, the HC and CO emissions slight increase. The pre-combustion chamber engine exhibits a tumble intake pattern around the cylinder axis perpendicular to the cylinder axis. The TKE reaches its maximum value 20 CAD before the top dead center and then rapidly decreases. The use of a three-injection strategy can form a uniform stratified mixture at the ignition moment. The fuel air equivalence ratio in the ignition area of the pre-combustion chamber is 0.8–0.9, slightly higher than the average fuel air equivalence ratio of 0.75.
A study of the effects of housing tongue design on radial turbine performance
Authors: Li Chao , Chen Hua , Wang Yu , Wu Guitao
Published: October 2024

The nozzleless volute housings can generate non-uniform flow distributions to the downstream radial turbine rotors, which not only affects turbine aerodynamic performance but also poses a threat to the fatigue life of the rotors. It is believed that the housing tongue is the main factor in generating this non-uniformity. Tongue design is based on experience and the geometric parameters of tongue, such as the distance between the tongue and rotor, the thickness of the tongue, and tongue shape are to be specified in the design. A few studies on housing tongue can be found in the literature, but in these investigations of the influences of tongue geometric parameters, factors such as mass flow rate and volute A/R distribution were not kept constant, thus introducing additional factors and reducing the persuasiveness of the results. Therefore, three new housings with different tongue-rotor distances are designed with a two-dimensional method for comparative CFD studies. The results indicate that when the mass flow of the turbines is kept, the distance between the tongue and the rotor has little impact on the turbine efficiency (about 0.1%), while the blade excitation forces from the housing increase with the decrease of the distance by more than 50%. Two further new housings are then created by modifying the housing inlet pipe to minimize tongue thickness, one of the housings is more practical with a short and thin tongue. CFD results indicate 0.2%∼0.4% improvement of turbine efficiency and reduction of housing excitation forces by more than 50% using these two thin tongue housings.
Personality in Alcohol and Opioid Use Disorder:  A Multiphasic Personality Questionnaire Study
Authors: Rana Devender Kumar , Malhotra Anil , Kumar Krishan , Verma Abhishek , Basu Debasish , Mattoo Surendra K. , Malhotra Rama , Sharma Rajni
Published: October 2024

Background

This study explored the relationship between personality attributes and substance use disorder (SUD). Research has identified specific personality dimensions, such as neuroticism, psychoticism, antisocial personality traits, paranoia, and anxiety, as contributing factors on the way to the initiation, continuation, and relapse of SUD.

Purpose

To explore this connection, we examined the personality profiles of subjects with alcohol use disorder (AUD) and opioid use disorder (OUD).

Methods

A total of 928 subjects, 320 with AUD, 490 with OUD and 118 healthy controls (HC), in the age range of 16–65 years were recruited in the study. The Clinical Profile Sheet and the Multiphasic Personality Questionnaire (MPQ) were administered to HC and patients who fulfil the diagnostic criteria for substance dependence as defined by ICD-10 (WHO-1992).

Results

We found significant differences in personality profiles between individuals with OUD and AUD. Patients with AUD scored higher than OUD on the MPQ subscales for depression, mania, paranoia, and antisocial personality traits. All subscales of the MPQ showed elevated scores in subjects with SUD compared to HC.

Conclusion

Elevated scores on the MPQ subscales suggest that personality factors may contribute to the pattern of substance use and misuse in this cohort.
Autoimmune Metaplastic Atrophic Gastritis (AMAG): Regional Demographics and Their Effect on Prevalence
Authors: Poveda Julio C. , Park Jason Y. , Garcia-Buitrago Monica T. , Singhi Aatur , Alruwaii Zainab , Kumar Shria , McDonald Oliver G. , Montgomery Elizabeth A.
Published: October 2024

Autoimmune metaplastic atrophic gastritis (AMAG) is a chronic immune-mediated form of gastritis characterized by damage to oxyntic cells, ultimately resulting in both iron deficiency with or without anemia and pernicious anemia. The current dogma is that AMAG is a disease of White Northern European women of advanced age. We, therefore, sought to examine the prevalence of AMAG in biopsies obtained from populations enriched for self-identified Hispanics for cross-comparison against data from previously reported populations enriched for self-identified White, non-Hispanic patients. To that end, we prospectively collected 1708 sequential gastric biopsies performed at the University of Miami Hospitals/Jackson Health Systems clinics from 1692 patients over a 1-year period as well as pertinent clinical parameters. These Florida data were then compared against data previously collected from the Baltimore population, which has far lower numbers of Hispanic patients. Self-identified race and/or ethnicity were used. From these 1692 patients, we identified 79 patients (4.6%) with AMAG. These included 60 women (76%) and 19 men (24%), with a F:M ratio of 3.1:1. Patients had a median age of 60 years (range: 15–83). Self-identified race and/or ethnicity were: 60 (76.0%) Hispanic, 9 (11.4%) Black, 9 (11.4%) White, and 1 Asian (1.2%). The median age at initial presentation was: 51 years (range: 15–83) in Hispanics, 77.2 years (range: 46–74) in Blacks, 59 years (range: 49–79) in Whites, and 58 years in the only Asian patient. The overall demographics of AMAG largely mirrored the Florida population, with an over-representation of Hispanics (Florida inhabitants self-report as 70% Hispanic). The overall 4.6% prevalence of AMAG in the Florida population differed significantly from the 1.1% in Baltimore ( p < .00001), a finding that presumably reflects the large Hispanic population. In fact, the prevalence of AMAG is far higher in Hispanic patients. Awareness of these data should increase recognition of AMAG in this population.
Recycling of PVC Supermarket Trays into Rigid Foam Extrusions
Authors: Thomas N.L. , Quirk J.P. , Cretney H.
Published: October 2024

Recycled poly(vinyl chloride) (PVC) supermarket trays have been used successfully in production-scale trials to make the foamed layer in co-extruded cladding. The extruded product had satisfactory foam density, foam structure and colour. Impact properties were better than those of the control made from virgin PVC: this is attributed to the high levels of impact modifier used in tray formulations.

These trials have demonstrated that PVC straight-on trays can be recycled into foamed extrusions for wood replacement products.
Plastic Waste Management: A Designer's Perspective Part Three: Designing With Recycled Plastics
Authors: Lintell D.T. , Smith J.S.
Published: October 2024

This is the third paper in a series concerned with a designer's role in plastic waste management. It deals with the relevant issue of limited, or non-acceptance of recycled plastics and the designer’s role in overcoming them. It presents a number of ways in which designers can overcome this reluctance and some design guidelines for designing with recycled plastics. A general conclusion sums up the pertinent issues which have been presented in this series of papers. Finally, the more general issue of sustainability and the role of design is discussed.