bluuebunny/crossref_metadata_2025 huggingface dataset:
HLA-Homozygous iPSC-Derived Mesenchymal Stem Cells Rescue Rotenone-Induced Experimental Leber’s Hereditary Optic Neuropathy-like Models In Vitro and In Vivo
Authors: Tsai En-Tung , Peng Shih-Yuan , Wu You-Ren , Lin Tai-Chi , Chen Chih-Ying , Liu Yu-Hao , Tseng Yu-Hsin , Hsiao Yu-Jer , Tseng Huan-Chin , Lai Wei-Yi , Lin Yi-Ying , Yang Yi-Ping , Chiou Shih-Hwa , Chen Shih-Pin , Chien Yueh
Published: November 2023

Background: Mesenchymal stem cells (MSCs) hold promise for cell-based therapy, yet the sourcing, quality, and invasive methods of MSCs impede their mass production and quality control. Induced pluripotent stem cell (iPSC)-derived MSCs (iMSCs) can be infinitely expanded, providing advantages over conventional MSCs in terms of meeting unmet clinical demands. Methods: The potential of MSC therapy for Leber’s hereditary optic neuropathy (LHON) remains uncertain. In this study, we used HLA-homozygous induced pluripotent stem cells to generate iMSCs using a defined protocol, and we examined their therapeutic potential in rotenone-induced LHON-like models in vitro and in vivo. Results: The iMSCs did not cause any tumorigenic incidence or inflammation-related lesions after intravitreal transplantation, and they remained viable for at least nine days in the mouse recipient’s eyes. In addition, iMSCs exhibited significant efficacy in safeguarding retinal ganglion cells (RGCs) from rotenone-induced cytotoxicity in vitro, and they ameliorated CGL+IPL layer thinning and RGC loss in vivo. Optical coherence tomography (OCT) and an electroretinogram demonstrated that iMSCs not only prevented RGC loss and impairments to the retinal architecture, but they also improved retinal electrophysiology performance. Conclusion: The generation of iMSCs via the HLA homozygosity of iPSCs offers a compelling avenue for overcoming the current limitations of MSC-based therapies. The results underscore the potential of iMSCs when addressing retinal disorders, and they highlight their clinical significance, offering renewed hope for individuals affected by LHON and other inherited retinal conditions.
Synthesis, Characterization and Photophysical Properties of Mixed Ligand (η3-Allyl)palladium(II) Complexes with N,N’Aromatic Diimines
Authors: Garypidou Antonia , Ypsilantis Konstantinos , Sifnaiou Evaggelia , Manthou Maria , Thomos Dimitris , Plakatouras John C. , Tsolis Theodoros , Garoufis Achilleas
Published: November 2023

Palladium(II) complexes of the general formula [Pd(η3-C3H5)(L)](PF6), where L is 4,7-diphenyl-1,10-phenanthroline (1), 2,9-dimethyl-1,10-phenanthroline (2), 2,9-dimethyl-4,7-diphenyl-1,10-phenanthroline (3), 5-methyl-1,10-phenanthroline (4), 3,4,7,8-tetramethyl-1,10-phenanthroline (5), and 2-(2′-pyridyl) quinoxaline (6), were synthesized and characterized using high-resolution ESI-MS, NMR techniques and, in the case of (6), single-crystal X-ray diffraction methods. In addition, their photophysical properties were investigated. Complexes (1)–(6) were emitted in the greenish-blue region, with those containing methyl-substituted phenanthrolines having the higher quantum yield (≈14%) in the solid state.
Rapid Determination of Meteorolite Composition Based on X-ray Phase Contrast Imaging-Assisted Raman Spectroscopy
Authors: Wang Hongpeng , Fang Peipei , Wang Yian , Xin Yingjian , Xiong Shengjun , Liu Sicong , Xue Yanling , Zhang Liang , Wan Xiong
Published: November 2023

Returning extraterrestrial samples to Earth has become essential for future deep space exploration. Achieving a comprehensive evaluation of the physical and chemical properties of samples with minimal damage is key to analyzing extraterrestrial samples in the future, as well as to the future sampling and returning of heterogeneous solid samples. This article aims to reconstruct the three-dimensional internal structure of high-contrast objects, select sections of interest through internal structure and detail features, and then analyze the physical and chemical properties of the samples based on laser spectroscopy technology. This paper proposes a strategy based on Raman mapping and X-ray phase-contrast imaging technology to reconstruct the three-dimensional internal structure of a heterogeneous solid sample and detect the substance composition of the region of interest. This study takes meteorite samples as an example and uses X-ray phase-contrast imaging technology to distinguish and reconstruct the spatial distribution of different components in the meteorite, providing a three-dimensional visualization reference with a high spatial resolution for the spatial positioning of the region of interest. Raman spectroscopy, in combination with LIBS, was used to further identify the meteorite as pallasite and to achieve the spectral image fusion of high spatial and high spectral resolutions. The experimental results show that the unknown meteorite’s three-dimensional structure and its components’ spatial distribution can be evaluated based on Raman mapping combined with X-ray phase-contrast imaging technology. This article provides a highly valuable analytical strategy by which to analyze samples returned from deep space exploration.
Breast Cancer and Fertility Preservation in Young Female Patients: A Systematic Review of the Literature
Authors: Boutas Ioannis , Kontogeorgi Adamantia , Koufopoulos Nektarios , Dimas Dionysios T. , Sitara Kyparissia , Kalantaridou Sophia N. , Dimitrakakis Constantine
Published: November 2023

Introduction: Breast cancer affects almost 1.5 million women worldwide below the age of 45 years each year. Many of these women will be advised to undergo adjuvant chemotherapy to minimize the risk of death or recurrence of the tumor. For these patients, chemotherapy is a known cause of infertility, as it can damage primordial follicles, which can lead to early menopause or premature ovarian insufficiency. This systematic review aims to synthesize the current evidence of the most suitable treatments for fertility preservation. Methodology: This review was performed following the PRISMA guidelines. The authors conducted an extensive search from the last 15 years. Relevant studies were pursued in PubMed, Embase, and the Cochrane Library up until 31 July 2023. A total of seven eligible studies were identified. Results: From the reviewed literature, ovarian suppression with gonadotropin-releasing hormone agonists showed promising results in preserving fertility for breast cancer patients undergoing chemotherapy. Additionally, oocyte and embryo cryopreservation demonstrated successful outcomes, with embryo cryopreservation being the most effective option. Notably, the slow-freezing and vitrification methods were both effective in preserving embryos, with vitrification showing superior results in clinical-assisted reproductive technologies. Ovarian tissue cryopreservation emerged as a viable option for prepubertal girls and those unable to undergo conventional ovarian stimulation. The potential of in vitro maturation (IVM) as an alternative method presents a promising avenue for future fertility preservation research. Discussion: The most suitable treatments for fertility preservation in young patients is the temporary suppression with luteinizing hormone-releasing analogs, while the patient undergoes chemotherapy and cryopreservation. For cryopreservation, the physicians might deem it necessary to either cryopreserve ovarian tissue taken from the patient before any treatment or cryopreserve embryos/oocytes. Cryopreservation of oocytes and/or embryos is the most effective solution for fertility preservation in women of reproductive age, who have a sufficient ovarian reserve and are diagnosed with breast cancer, regardless of the histological type of the tumor. Because approximately 50% of young breast cancer patients are interested in becoming pregnant right after completion of therapy, the evolution and development of fertility preservation techniques promise to be very exciting.
The Relevance of Process Parameter Optimization and Geometric Figure for Direct Laser Deposition of Inconel 738 Alloy and Its Theoretical Modeling
Authors: Qi Kun , Wu Wenxing , Chen Pinghu , Liu Hao , Qiu Changjun
Published: November 2023

In order to minimize the gaps between the direct laser deposition channels and improve the quality and performance of the formed parts, the process of direct laser deposition is utilized in laser additive manufacturing to create sequential, single- and double-channel deposition layers on 304 stainless steel plates. Under the premise of keeping the layer rate and defocusing amount unchanged, this study investigates the effects of laser power, scanning speed, and powder feeding rate on the morphology and inclusions of single- and double-channel deposited layers. The aim is to determine the optimal process parameter values for direct laser deposition of single-layer, single-channel Inconel 738. The effects of the three process parameters on the response values were investigated using a multi-factor, multi-level experimental design. The evaluation indexes for the analysis included the deposited layer wetting angle and aspect ratio. The analysis involved one-way extreme analysis and ANOVA analysis. The optimal process parameters are a laser power of 550~750 W, a scanning speed of 7~13 mm/s, and the powder feeding rate was 2.1~4.33 g/min. At the same time, the relationship between surface tension and gravity was integrated with the spherical coronal model and Young’s equation to develop a mathematical model of the direct laser deposition process at a theoretical level. The mathematical model of the direct laser deposition process was utilized to analyze the correlation between the geometric parameters of the cross-section of the deposited layer. This analysis provides a valuable data reference for future Inconel 738 direct laser deposition.
Antifungal Properties of Ozone Treatment against P. citrinum and R. stolonifera in Fresh-Peeled Garlic
Authors: Liu Hong , Fan Xiangfeng , Cao Lin , Wang Xiaomin , Liu Xiaocui , Huang Jie , Xing Yage , Luo Wanmin
Published: November 2023

Garlic is susceptible to decay and presents a potential vehicle for foodborne disease transmission. Ozone treatment has proven to be an effective and innoxious method to provide bacterial resistance in this globally popular pungent vegetable. This study was conducted to evaluate the effects of different ozone treatments (differing in terms of concentration, treatment time and temperature) against Penicillium citrinum and Rhizopus stolonifer in spoiled fresh-peeled garlic. The results have shown that the most inhibitory conditions for in vitro treatments were achieved with an ozone concentration, treatment time and temperature of 6 ppm, for 20 min at 20 °C, respectively, on the P. citrinum and 8 ppm, for 20 min at 25 °C, respectively, on the R. stolonifer. The optimum in vivo ozone treatment conditions for fresh-peeled garlic inoculated with the same two kinds of spoilage molds remained the same for both, consisting of an ozone concentration of 6 ppm, a time of 15 min and a temperature of 20 °C. Following these ozone treatments, the total number of colonies of yeast and mold, as well as the incidence, lesion diameter and depth of spoilage in the fresh-peeled garlic was significantly reduced during storage, with improved bactericidal inhibition effects. In conclusion, this study showed that ozone treatment effectively inhibits the growth of spoilage molds, destroys cell structures, and affects the metabolic and physiological processes of P. citrinum and R. stolonifer. Thus, it provides a protective shield and extends the shelf life of fresh-peeled garlic.
Influence of the Ambient Relative Humidity on the Very-Long-Term DEF
Authors: Houndonougbo Thierry , Nedjar Boumediene , Divet Loic , Torrenti Jean-Michel
Published: November 2023

Relative humidity is a key parameter for the development of delayed ettringite formation (DEF). Here, new results of very-long-term experiments (10 years) are presented. It is observed that for a relative humidity of 96%, swelling could occur after several years but with a slower kinetics. A model coupling the kinetics of swelling with the internal relative humidity is presented. It is shown that this model can reproduce the experimental behavior.
Rapid and Accurate Diagnosis of Dermatophyte Infections Using the DendrisCHIP® Technology
Authors: Anton Aurore , Plinet Mathilde , Peyret Thomas , Cazaudarré Thomas , Pesant Stéphanie , Rouquet Yannick , Tricoteaux Marie-Andrée , Bernier Matthieu , Bayette Jérémy , Fournier Remi , Marguerettaz Mélanie , Rolland Pierre , Bayol Thibaud , Abbaoui Nadia , Berry Antoine , Iriart Xavier , Cassaing Sophie , Chauvin Pamela , Bernard Elodie , Fabre Richard , François Jean-Marie
Published: November 2023

Dermatophytosis is a superficial fungal infection with an ever-increasing number of patients. Culture-based mycology remains the most commonly used diagnosis, but it takes around four weeks to identify the causative agent. Therefore, routine clinical laboratories need rapid, high throughput, and accurate species-specific analytical methods for diagnosis and therapeutic management. Based on these requirements, we investigated the feasibility of DendrisCHIP® technology as an innovative molecular diagnostic method for the identification of a subset of 13 pathogens potentially responsible for dermatophytosis infections in clinical samples. This technology is based on DNA microarray, which potentially enables the detection and discrimination of several germs in a single sample. A major originality of DendrisCHIP® technology is the use of a decision algorithm for probability presence or absence of pathogens based on machine learning methods. In this study, the diagnosis of dermatophyte infection was carried out on more than 284 isolates by conventional microbial culture and DendrisCHIP®DP, which correspond to the DendrisCHIP® carrying oligoprobes of the targeted pathogens implicated in dermatophytosis. While convergence ranging from 75 to 86% depending on the sampling procedure was obtained with both methods, the DendrisCHIP®DP proved to identify more isolates with pathogens that escaped the culture method. These results were confirmed at 86% by a third method, which was either a specific RT-PCR or genome sequencing. In addition, diagnostic results with DendrisCHIP®DP can be obtained within a day. This faster and more accurate identification of fungal pathogens with DendrisCHIP®DP enables the clinician to quickly and successfully implement appropriate antifungal treatment to prevent the spread and elimination of dermatophyte infection. Taken together, these results demonstrate that this technology is a very promising method for routine diagnosis of dermatophytosis.
The Visual Patient Avatar ICU Facilitates Information Transfer of Written Information by Visualization: A Multicenter Comparative Eye-Tracking Study
Authors: Viautour Julie , Naegeli Lukas , Braun Julia , Bergauer Lisa , Roche Tadzio , Tscholl David , Akbas Samira
Published: November 2023

Patient monitoring is crucial in critical care medicine. Perceiving and interpreting multiple vital signs requires a high workload that can lead to decreased situation awareness and consequently inattentional blindness, defined as impaired perception of unexpectedly changing data. To facilitate information transfer, we developed and validated the Visual-Patient avatar. Generated by numerical data, the animation displays the status of vital signs and patient installations according to a user-centered design to improve situation awareness. As a surrogate parameter for information transfer in patient monitoring, we recorded visual attention using eye-tracking data. In this computer-based study, we compared the correlation of visually perceived and correctly interpreted vital signs between a Visual-Patient-avatar ICU and conventional patient monitoring. A total of 50 recruited study participants (25 nurses, 25 physicians) from five European study centers completed five randomized scenarios in both modalities. Using a stationary eye tracker as the primary endpoint, we recorded how long different areas of interest of the two monitoring modalities were viewed. In addition, we tested for a possible association between the length of time an area of interest was viewed and the correctness of the corresponding question. With the conventional monitor, participants looked at the installation site the longest (median 2.13–2.51 s). With the Visual-Patient-avatar ICU, gaze distribution was balanced; no area of interest was viewed for particularly long. For both modalities, the longer an area was viewed, the more likely the associated question was answered incorrectly (OR 0.97, 95% CI 0.95–0.99, p = 0.008). The Visual-Patient-avatar ICU facilitates and improves information transfer through its visualizations, especially with written information. The longer an area of interest was viewed, the more likely the associated question was answered incorrectly.
Nomogram Based on Dual-Layer Spectral Detector CTA Parameter for the Prediction of Infarct Core in Patients with Acute Ischemic Stroke
Authors: Gu Yan , Shi Dai , Shen Hao , Wang Yeqing , Xu Dandan , Xiao Aoqi , Jin Dan , Lu Kuan , Cai Wu , Xu Liang
Published: November 2023

(1) Background: Acute ischemic stroke (AIS) is time-sensitive. The accurate identification of the infarct core and penumbra areas in AIS patients is an important basis for formulating treatment plans, and is the key to dual-layer spectral detector computed tomography angiography (DLCTA), a safer and more accurate diagnostic method for AIS that will replace computed tomography perfusion (CTP) in the future. Thus, this study aimed to investigate the value of DLCTA in differentiating infarct core from penumbra in patients with AIS to establish a nomogram combined with spectral computed tomography (CT) parameters for predicting the infarct core and performing multi-angle evaluation. (2) Methods: Data for 102 patients with AIS were retrospectively collected. All patients underwent DLCTA and CTP. The patients were divided into the non-infarct core group and the infarct core group, using CTP as the reference. Multivariate logistic regression analysis was used to screen predictors related to the infarct core and establish a nomogram model. The receiver operating characteristic (ROC) curve, the calibration curve, and decision curve analysis (DCA) were used to evaluate the predictive efficacy, accuracy, and clinical practicability of the model, respectively. (3) Results: Multivariate logistic analysis identified three independent predictors: iodine density (OR: 0.022, 95% CI: 0.003–0.170, p < 0.001), hypertension (OR: 7.179, 95% CI: 1.766–29.186, p = 0.006), and triglycerides (OR: 0.255, 95% CI: 0.109–0.594, p = 0.002). The AUC–ROC of the nomogram was 0.913. Calibration was good. Decision curve analysis was clinically useful. (4) Conclusions: The spectral CT parameters, specifically iodine density values, effectively differentiate between the infarct core and penumbra areas in patients with AIS. The nomogram, based on iodine density values, showed strong predictive power, discrimination, and clinical utility to accurately predict infarct core in AIS patients.
Investigating Online versus Face-to-Face Course Dropout: Why Do Students Say They Are Leaving?
Authors: Hachey Alyse C. , Wladis Claire , Conway Katherine M.
Published: November 2023

Despite more focused attention in the wake of the COVID-19 pandemic, high online attrition remains both a concern and a mystery; gaps in our knowledge exist as to why students so often do not complete online courses. Pre-pandemic, and using a sample of 780 students who dropped out of fully online courses (or the same course face-to-face) from a large university system in the Northeast U.S., students were explicitly asked about their specific reasons for course withdrawal. All students enrolled in a fully online course (or a face-to-face section of the same course) at the City University of New York (CUNY) in fall 2015 were invited to take the online survey from which this study data was taken. Results indicate that there were distinct differences in the patterns of reasons given by online and face-to-face students: although the perceived quality of the instructor/instruction was deemed important to student persistence in both modalities, it seemed to be of greater importance face-to-face than online. Furthermore, issues related to time were found to be more prominent reasons for dropping for online learners than face-to-face learners. Findings from this study shed new light on the impetus for online attrition, with implications for online policy and course design in a post-pandemic era.
The Missing Piece in the CASEL Model: The Impact of Social–Emotional Learning on Online Literature Teaching and Learning
Authors: Levin Orna , Segev Yael
Published: November 2023

The use of social–emotional learning (SEL) practices in online literature teaching has not yet been sufficiently researched. This study addresses this lacuna by identifying SEL practices mentioned by lecturers and preservice teachers (PSTs) as they reported on their respective experiences of teaching and learning in online literature lessons. Data were collected using three research tools: questionnaires were completed by 28 lecturers from four different teacher education colleges and 90 PSTS; semi-structured interviews were held with 12 of the literature PSTs; and a focus group was held with six lecturers. A data analysis revealed six major SEL-related themes mentioned by lecturers and PSTs as essential practices of online learning and teaching: building relationships, working collaboratively, emotional involvement, effective communication, dealing with conflicting feelings, and techno-pedagogic skills. These findings contribute to our understanding of online learning and teaching versatility and complexity. Considering these findings in light of existing theoretical models demonstrates that while five themes coincide with the skills included in the CASEL model, the sixth theme regarding techno-pedagogical skills is not part of the original model. These findings expand the applicability of the CASEL model from its original face-to-face learning context to the interaction between learners and lecturers in an online platform.
Student Ratings: Skin in the Game and the Three-Body Problem
Authors: Dziuban Charles , Moskal Patsy , Reiner Annette , Cohen Adysen , Carassas Christina
Published: November 2023

To capture the student voice, university researchers examined the high-stakes Student Perception of Instruction form, administered online to students each semester, allowing them anonymous feedback on their courses. A total of 2,171,565 observations were analyzed for all courses each semester from fall 2017 through fall 2022. The results indicated that 68% of students responded identically to each of the protocol’s 9 Likert scale items, essentially straight-lining their rating of instruction and casting doubt on the validity of their engagement with the process. Student responses by various University demographics are presented. We discuss the potential influences of students’ reactions and present a possible model for effective teaching and evaluation.
Characteristics of Filipino Online Learners: A Survey of Science Education Students’ Engagement, Self-Regulation, and Self-Efficacy
Authors: Briones Mary Rose , Prudente Maricar , Errabo Denis Dyvee
Published: November 2023

Online education allows learners to develop knowledge and skills flexibly and conveniently—an observation made among students whose characteristics involve student engagement, self-regulation, and self-efficacy. However, studies characterizing Filipino online learners seem to be lacking. This study aimed to characterize science education tertiary students in the Philippines concerning their online student engagement (OSE), self-regulated learning (SRL), and online learning self-efficacy (OLSE). The unprecedented events brought about by the COVID-19 pandemic also urged the implementation of online modalities, yet there is still no available information on students’ online learning profiles. Hence, we conducted survey research using an ex post facto approach to determine the effects of demographic profiles on OSE, SRL, and OLSE. The survey was completed by N = 373 respondents who answered the questionnaire, with informed consent administered via Google Forms. The results revealed that OSE indicators moderately characterized the students, while SRL and OLSE indicators accurately described them, as substantiated by the overall means of M = 3.85 (SD = 0.90), M = 3.86 (SD = 0.92), and M = 3.14 (SD = 0.73), respectively. Also, multivariate tests showed no significant effects among the independent groups (p > 0.05), except for a gender and OLSE interaction (p < 0.05), so only for OLSEE was a significant difference found in terms of gender. In conclusion, Filipino online learners have moderate characteristics across the aspects of student engagement, self-regulation, and self-efficacy.
Separator Materials for Lithium Sulfur Battery—A Review
Authors: Mori Ryohei
Published: November 2023

In the recent rechargeable battery industry, lithium sulfur batteries (LSBs) have demonstrated to be a promising candidate battery to serve as the next-generation secondary battery, owing to its enhanced theoretical specific energy, economy, and environmental friendliness. Its inferior cyclability, however, which is primarily due to electrode deterioration caused by the lithium polysulfide shuttle effect, is still a major problem for the real industrial usage of LSBs. The optimization of the separator and functional barrier layer is an effective strategy for remedying these issues. In this article, the current progress based on the classification and modification of functional separators is summarized. We will also describe their working mechanisms as well as the resulting LSB electrochemical properties. In addition, necessary performance for separators will also be mentioned in order to gain optimized LSB performance.
SPICE-Aided Nonlinear Electrothermal Modeling of an IGBT Module
Authors: Górecki Krzysztof , Górecki Paweł
Published: November 2023

The paper proposes a compact electrothermal model of the IGBT module in the form of a subcircuit for SPICE. This model simultaneously takes into account electrical phenomena occurring in the module components and thermal phenomena occurring in this module. In the description of electrical phenomena, the previously formulated models of the IGBT and the diode are used, whereas the description of thermal phenomena is original. While describing thermal phenomena, self-heating in each component, mutual thermal couplings between each pair of these components, and the influence of the dissipated power on the efficiency of heat removal are taken into account. The form of the proposed model and the results of its experimental verification for the IGBT module operating under different cooling conditions are presented. The DC characteristics of the module and the characteristics of the half-bridge converter containing the considered module are presented.
Knowledge Distillation for Enhancing a Lightweight Magnet Tile Target Detection Model: Leveraging Spatial Attention and Multi-Scale Output Features
Authors: Huang Qinyuan , Yang Kun , Zhu Yuzhen , Chen Long , Cao Lijia
Published: November 2023

Accurate and efficient sorting of diverse magnetic tiles during manufacturing is vital. However, challenges arise due to visual similarities among types, necessitating complex computer vision algorithms with large sizes and high computational needs. This impedes cost-effective deployment in the industry, resulting in the continued use of inefficient manual sorting. To address this issue, we propose an innovative lightweight magnetic tile detection approach that improves knowledge distillation for a compressed YOLOv5s model. Incorporating spatial attention modules into different feature extraction stages of YOLOv5s during the knowledge distillation process can enhance the ability of the compressed model to learn the knowledge of intermediate feature extraction layers from the original large model at different stages. Combining different outputs to form a multi-scale output, the multi-scale output feature in the knowledge refinement process enhances the capacity of the compressed model to grasp comprehensive target knowledge in outputs. Experimental results on our self-built magnetic tile dataset demonstrate significant achievements: 0.988 mean average precision, 0.5% discrepancy compared to the teacher’s network, and an 85% model size reduction. Moreover, a 36.70% boost in inference speed is observed for single image analysis. Our method’s effectiveness is also validated by the Pascal VOC dataset results, showing potential for broader target detection scenarios. This approach offers a solution to magnetic tile target detection challenges while being expected to expand to other applications.
Impact of the Stimulus Presentation Structure on Subjective Video Quality Assessment
Authors: Konaszyński Tomasz , Juszka Dawid , Leszczuk Mikołaj
Published: November 2023

A study of video quality perceived by user (Quality of Experience, QoE) was conducted with an examination of whether and how the order and structure of the video sequence presentation affects subjective assessment. For this purpose, the influence of content variability/repeatability, the quality of the preceding sequence, and the sequence order were analyzed. Observations on the correlation of QoE with the micro-structure of sequence presentation are described, which can be the basis for hypotheses of the dependence of QoE assessment on the abovementioned factors. The observed relationships regarding the influence of the number of video repetitions and the impact of the predecessor’s quality on subjective evaluation are consistent with research work on the influence of the order/arrangement and structure of research stimuli on the results of subjective evaluations. Areas for further research are indicated, including relating obtained results in the area of QoE to other cognitive sciences, such as psychological, medical, and economic sciences.
Energy-Aware Framework for Underwater Mine Detection System Using Underwater Acoustic Wireless Sensor Network
Authors: Al-Ahmadi Saad A.
Published: November 2023

Underwater mines are considered a major threat to aquatic life, submarines, and naval activities. Detecting and locating these mines is a challenging task, due to the nature of the underwater environment. The deployment of underwater acoustic sensor networks (UWASN) can provide an efficient solution to this problem. However, the use of these self-powered sensors for intensive data sensing and wireless communication is often energy-scaring and might call into question the viability of their application. One attractive solution to extend the underwater wireless sensor network will be the adoption of cluster-based communication, since data processing and communication loads are distributed in a timely manner over the members of the cluster. In this context, this study proposes an energy-efficient solution for high-accuracy underwater mine detection based on the adequate clustering approach. The proposed scheme uses a processing approach based on wavelet transformation to extract relevant features to efficiently distinguish mines from other objects using the Naïve Bayes algorithm for classification. The main novelty of this approach is the design of a new low-complexity scheme for efficient sensor-based acoustic object detection that outperforms most of the existing solutions. It consumes a low amount of energy, while ensuring 95.12% target detection accuracy.
Delving Deep into Reverse Engineering of UEFI Firmwares via Human Interface Infrastructure
Authors: Chen Siyi , Tan Yu-An , Qiu Kefan , Zhang Zheng , Li Yuanzhang , Zhang Quanxin
Published: November 2023

The Unified Extensible Firmware Interface (UEFI) provides a specification of the software interface between an OS and its underlying platform firmware. UEFI UI is an interactive interface that allows users to configure and manage UEFI settings, which is closely related to HII (Human Interface Infrastructure). In practice, HII provides a mechanism that allows developers to create UI elements with HII-related protocols. In this paper, we provide a comprehensive analysis of the UEFI combined with a case study. We proposed a protocol-centered static analysis method to obtain UEFI’s password policy, using HII-related protocols to find password implementation. Existing static analyses are ineffective in detecting such password policy in stripped UEFI firmware images. By reverse-engineering the IFR (Internal Forms Representation) in HII, we located where much sensitive information is stored. Lastly, we studied hardware port configurations, using Secure Boot as a case in point. We analyzed how UEFI uses the HII protocol to set relevant information in the UEFI UI. This paper is the first to offer a reverse-engineering systematic analysis of exploring UEFI via HII, providing valuable insights into its structure and potential enhancements for firmware security.
Distributed Multi-Agent Approach for Achieving Energy Efficiency and Computational Offloading in MECNs Using Asynchronous Advantage Actor-Critic
Authors: Khan Israr , Raza Salman , Khan Razaullah , Rehman Waheed ur , Rahman G. M. Shafiqur , Tao Xiaofeng
Published: November 2023

Mobile edge computing networks (MECNs) based on hierarchical cloud computing have the ability to provide abundant resources to support the next-generation internet of things (IoT) network, which relies on artificial intelligence (AI). To address the instantaneous service and computation demands of IoT entities, AI-based solutions, particularly the deep reinforcement learning (DRL) strategy, have been intensively studied in both the academic and industrial fields. However, there are still many open challenges, namely, the lengthening convergence phenomena of the agent, network dynamics, resource diversity, and mode selection, which need to be tackled. A mixed integer non-linear fractional programming (MINLFP) problem is formulated to maximize computing and radio resources while maintaining quality of service (QoS) for every user’s equipment. We adopt the advanced asynchronous advantage actor-critic (A3C) approach to take full advantage of distributed multi-agent-based solutions for achieving energy efficiency in MECNs. The proposed approach, which employs A3C for computing offloading and resource allocation, is shown through numerical results to significantly reduce energy consumption and improve energy efficiency. This method’s effectiveness is further shown by comparing it to other benchmarks.
Blockchain Technology for Access and Authorization Management in the Internet of Things
Authors: Polychronaki Maria , Kogias Dimitrios G. , Leligkou Helen C. , Karkazis Panagiotis A.
Published: November 2023

The Internet of Things (IoT) continues to suffer from security issues, even after 20 years of technological evolution and continuing efforts. While the decentralization of the IoT seems to be a solution for improved resource management and scalability, most of the services remain centralized, exposing IoT systems to malicious attacks. As a result, this leads to functionality failures and endangers user and data integrity. Identity and Access Management (IAM) has the ability to provide defense against a great number of security threats. Additionally, blockchain is a technology which can natively support decentralization, as well as access and authorization management techniques, using the corresponding programmable logic and leveraging cryptographic mechanisms for privacy and security. Using standardized frameworks (e.g., Decentralized Identifiers and Verifiable Credentials), a blockchain-based access and authorization solution can present the basis for a uniform decentralized IAM framework for the IoT. To this end, this paper presents a proof-of-concept design and implementation of an IAM solution based on Solidity smart contracts, targeting two areas: firstly, supporting the fact that blockchain can seamlessly provide the basis for a decentralized IAM framework, while secondly (and most importantly) exploring the challenge of integrating within existing IoT systems, avoiding redesigning and redeveloping on behalf of IoT manufacturers.
Polarization Direction of Arrival Estimation for Vector Array of Unmanned Aerial Vehicle Swarm
Authors: Lan Xiaoyu , Wang Kunming , Dong Ming , Wang Ershen , Tian Ye
Published: November 2023

Aiming at the problem of the excessive error of direction of arrival (DOA) estimation caused by the position disturbance of a UAV swarm during flight, a robust polarization-DOA estimation method based on sparse Bayesian learning (SBL) is proposed. First, the algorithm decomposes the covariance matrix of the received data of the UAV swarm vector array and then constructs the determination matrix of the UAV position coordinates by exploiting the orthogonality of the eigenvalues and eigenvectors. Then, the optimal solution of the semi-positive definite programming (SDP) problem is solved using the constrained global least square method, and the exact self-positioning coordinates of UAVs are obtained. Second, we construct a spatially discrete grid to model the received data of the UAV group vector array. The SBL theory is then applied to obtain the posterior probability distribution of the sparse signal matrix. The sparsity of the signal matrix is controlled with a hyperparameter, and the estimation of the DOA is conducted using a fixed-point iteration to obtain the maximum posterior estimate of the signal matrix. Finally, according to the estimated DOA, the polarization parameter is obtained from the constructed objective function of the polarization parameter estimation. The simulation results show that the proposed algorithm achieves higher accuracy and robustness than the traditional 2D DOA estimation algorithm in the direction-finding system for UAV swarm vector arrays.
Multi-Agent Multi-Target Pursuit with Dynamic Target Allocation and Actor Network Optimization
Authors: Han Baoqiang , Shi Lin , Wang Xueyuan , Zhuang Lihua
Published: November 2023

In this paper, we consider the cooperative decision-making problem for multi-target tracking in multi-agent systems using multi-agent deep reinforcement learning algorithms. Multi-agent multi-target pursuit has faced new challenges in practical applications, where pursuers need to plan collision-free paths and appropriate multi-target allocation strategies to determine which target to track at the current time for each pursuer. We design three feasible multi-target allocation strategies from different perspectives. We compare our allocation strategies in the multi-agent multi-target pursuit environment that models collision risk and verify the superiority of the allocation strategy marked as POLICY3, considering the overall perspective of agents and targets. We also find that there is a significant gap in the tracking policies learned by agents when using the multi-agent reinforcement learning algorithm MATD3. We propose an improved algorithm, DAO-MATD3, based on dynamic actor network optimization. The simulation results show that the proposed POLICY3-DAO-MATD3 method effectively improves the efficiency of completing multi-agent multi-target pursuit tasks.
BRAIN: Blockchain-Based Record and Interoperability Network
Authors: López-Sorribes Sergi , Rius-Torrentó Josep , Solsona-Tehàs Francesc
Published: November 2023

In today’s highly competitive landscape, effective information sharing is crucial for distinguishing successful entities from the rest. However, achieving seamless collaboration in this digital era remains challenging due to distrust among entities and the absence of a secure data exchange medium. These limitations hinder interactions, reducing competitiveness, especially for resource-constrained entities. To address these challenges, we introduce the Blockchain-based Record and Interoperability Network (BRAIN), a modular framework utilizing blockchain technology. BRAIN offers an innovative solution to foster collaboration, trust, and competitiveness. By leveraging blockchain’s cryptographic techniques, it ensures secure and transparent data exchange, eliminating the need for intermediaries and thereby enhancing productivity and collaboration. BRAIN serves as a configurable foundation for domain-specific applications. BRAIN’s key features include flexibility, efficiency, security, robustness, and scalability. It adapts to diverse applications, integrates seamlessly with existing systems, and ensures secure information sharing. In conclusion, BRAIN provides a foundational platform for entities to assert data ownership and grant access permission. Built on blockchain technology, it offers access traceability and data security and eliminates the need for trusted third parties. Its flexibility and scalability make it a valuable tool for enhancing data sharing. Future enhancements aim to address weaknesses and expand its use cases, promising a robust and versatile solution for secure and efficient data exchange.
Designing an Electro-Optical Tunable Racetrack Microring Resonator on a Diamond–Lithium Niobate Thin-Film Hybrid Platform
Authors: Yang Fan , Wu Yuhao , Cai Changlong , Fang Hong
Published: November 2023

This study proposes and simulates a numerical analysis of a diamond racetrack microring resonator on a lithium niobate thin film, operating at a 1.55 µm wavelength. The single-mode conditions, transmission losses, and waveguide dispersions are systematically examined. The microring resonator’s radius and gap size are computed and optimized. The designed racetrack microring resonator exhibits a high quality factor (Q-factor) and a high coupling efficiency of approximately 6100 and 95%, respectively, for the transverse TE mode in the C-band. This study achieves a resonant tunability of 1.84 pm/V near the 1.55 μm wavelength by harnessing the electro-optical effect of lithium niobate.
Enhancing Dimensional Emotion Recognition from Speech through Modulation-Filtered Cochleagram and Parallel Attention Recurrent Network
Authors: Peng Zhichao , Zeng Hua , Li Yongwei , Du Yegang , Dang Jianwu
Published: November 2023

Dimensional emotion can better describe rich and fine-grained emotional states than categorical emotion. In the realm of human–robot interaction, the ability to continuously recognize dimensional emotions from speech empowers robots to capture the temporal dynamics of a speaker’s emotional state and adjust their interaction strategies in real-time. In this study, we present an approach to enhance dimensional emotion recognition through modulation-filtered cochleagram and parallel attention recurrent neural network (PA-net). Firstly, the multi-resolution modulation-filtered cochleagram is derived from speech signals through auditory signal processing. Subsequently, the PA-net is employed to establish multi-temporal dependencies from diverse scales of features, enabling the tracking of the dynamic variations in dimensional emotion within auditory modulation sequences. The results obtained from experiments conducted on the RECOLA dataset demonstrate that, at the feature level, the modulation-filtered cochleagram surpasses other assessed features in its efficacy to forecast valence and arousal. Particularly noteworthy is its pronounced superiority in scenarios characterized by a high signal-to-noise ratio. At the model level, the PA-net attains the highest predictive performance for both valence and arousal, clearly outperforming alternative regression models. Furthermore, the experiments carried out on the SEWA dataset demonstrate the substantial enhancements brought about by the proposed method in valence and arousal prediction. These results collectively highlight the potency and effectiveness of our approach in advancing the field of dimensional speech emotion recognition.
Analysis of Training Data Augmentation for Diabetic Foot Ulcer Semantic Segmentation
Authors: Kairys Arturas , Raudonis Vidas
Published: November 2023

Deep learning model training and achieved performance relies on available data. Diabetic foot ulcers and other image processing applications in the medical domain add another layer of complexity to training data collection. Data collection is troublesome and data annotation requires medical expertise. This problem is usually solved by employing training data augmentation. Although in previous research augmentation was facilitated in various ways, it is rarely evaluated or reported how much it contributes to achieved performance. The current research seeks to answer this question by applying individual photometric and geometric augmentation techniques and comparing the model performance achieved for semantic segmentation of diabetic foot ulcers. It was found that geometric augmentation techniques help achieve a better model performance when compared with photometric techniques. The model trained using an augmented dataset and applying a shear technique was found to improve segmentation results the most; the benchmark dice score was increased by 6%. An additional improvement over the benchmark was observed (a total of 6.9%) when the model was trained using data combining image sets generated by the three best-performing augmentation techniques. The highest test dice score achieved was 91%.
Graph- and Machine-Learning-Based Texture Classification
Authors: Ali Musrrat , Kumar Sanoj , Pal Rahul , Singh Manoj K. , Saini Deepika
Published: November 2023

The analysis of textures is an important task in image processing and computer vision because it provides significant data for image retrieval, synthesis, segmentation, and classification. Automatic texture recognition is difficult, however, and necessitates advanced computational techniques due to the complexity and diversity of natural textures. This paper presents a method for classifying textures using graphs; specifically, natural and horizontal visibility graphs. The related image natural visibility graph (INVG) and image horizontal visibility graph (IHVG) are used to obtain features for classifying textures. These features are the clustering coefficient and the degree distribution. The suggested outcomes show that the aforementioned technique outperforms traditional ones and even comes close to matching the performance of convolutional neural networks (CNNs). Classifiers such as the support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) are utilized for the categorization. The suggested method is tested on well-known image datasets like the Brodatz texture and the Salzburg texture image (STex) datasets. The results are positive, showing the potential of graph methods for texture classification.
FLIBD: A Federated Learning-Based IoT Big Data Management Approach for Privacy-Preserving over Apache Spark with FATE
Authors: Karras Aristeidis , Giannaros Anastasios , Theodorakopoulos Leonidas , Krimpas George A. , Kalogeratos Gerasimos , Karras Christos , Sioutas Spyros
Published: November 2023

In this study, we introduce FLIBD, a novel strategy for managing Internet of Things (IoT) Big Data, intricately designed to ensure privacy preservation across extensive system networks. By utilising Federated Learning (FL), Apache Spark, and Federated AI Technology Enabler (FATE), we skilfully investigated the complicated area of IoT data management while simultaneously reinforcing privacy across broad network configurations. Our FLIBD architecture was thoughtfully designed to safeguard data and model privacy through a synergistic integration of distributed model training and secure model consolidation. Notably, we delved into an in-depth examination of adversarial activities within federated learning contexts. The Federated Adversarial Attack for Multi-Task Learning (FAAMT) was thoroughly assessed, unmasking its proficiency in showcasing and exploiting vulnerabilities across various federated learning approaches. Moreover, we offer an incisive evaluation of numerous federated learning defence mechanisms, including Romoa and RFA, in the scope of the FAAMT. Utilising well-defined evaluation metrics and analytical processes, our study demonstrated a resilient framework suitable for managing IoT Big Data across widespread deployments, while concurrently presenting a solid contribution to the progression and discussion surrounding defensive methodologies within the federated learning and IoT areas.