bluuebunny/crossref_metadata_2025 huggingface dataset:
More than Five Decades of Proton Therapy: A Bibliometric Overview of the Scientific Literature
Authors: Vincini Maria Giulia , Zaffaroni Mattia , Schwarz Marco , Marvaso Giulia , Mastroleo Federico , Volpe Stefania , Bergamaschi Luca , Mazzola Giovanni Carlo , Corrao Giulia , Orecchia Roberto , Jereczek-Fossa Barbara Alicja , Alterio Daniela
Published: November 2023

Background: The therapeutic potential of proton therapy (PT) was first recognized in 1946 by Robert Wilson, and nowadays, over 100 proton centers are in operation worldwide, and more than 60 are under construction or planned. Bibliometric data can be used to perform a structured analysis of large amounts of scientific data to provide new insights, e.g., to assess the growth and development of the field and to identify research trends and hot topics. The aim of this study is to provide a comprehensive bibliometric analysis of the current status and trends in scientific literature in the PT field. Methods: The literature on PT until the 31st December 2022 in the Scopus database was searched, including the following keywords: proton AND radiotherapy AND cancer/tumor in title, abstract, and/or keywords. The open-source R Studio’s Bibliometrix package and Biblioshiny software (version 2.0) were used to perform the analysis. Results: A total of 7335 documents, mainly articles (n = 4794, 65%) and reviews (n = 1527, 21%), were collected from 1946 to 2022 from 1054 sources and 21,696 authors. Of these, roughly 84% (n = 6167) were produced in the last 15 years (2008–2022), in which the mean annual growth rate was 13%. Considering the corresponding author’s country, 79 countries contributed to the literature; the USA was the top contributor, with 2765 (38%) documents, of whom 84% were single-country publications (SCP), followed by Germany and Japan, with 535 and 531 documents of whom 66% and 93% were SCP. Considering the themes subanalysis (2002–2022), a total of 7192 documents were analyzed; among all keywords used by authors, the top three were radiotherapy (n = 1394, 21% of documents), intensity-modulated radiotherapy (n = 301, 5%), and prostate cancer (n = 301, 5%). Among disease types, prostate cancer is followed by chordoma, head and neck, and breast cancer. The change in trend themes demonstrated the fast evolution of hotspots in PT; among the most recent trends, the appearance of flash, radiomics, relative biological effectiveness (RBE), and linear energy transfer (LET) deserve to be highlighted. Conclusions: The results of the present bibliometric analysis showed that PT is an active and rapidly increasing field of research. Themes of the published works encompass the main aspects of its application in clinical practice, such as the comparison with the actual photon-based standard of care technique and the continuing technological advances. This analysis gives an overview of past scientific production and, most importantly, provides a useful point of view on the future directions of the research activities.
89Zr-Immuno-PET with Immune Checkpoint Inhibitors: Measuring Target Engagement in Healthy Organs
Authors: Miedema Iris H. C. , Wijngaarden Jessica E. , Pouw Johanna E. E. , Zwezerijnen Gerben J. C. , Sebus Hylke J. , Smit Egbert , de Langen Adrianus J. , Bahce Idris , Thiele Andrea , Vugts Daniëlle J. , Boellaard Ronald , Huisman Marc C. , Menke-van der Houven van Oordt C. Willemien
Published: November 2023

Introduction: 89Zr-immuno-PET (positron emission tomography with zirconium-89-labeled monoclonal antibodies ([89Zr]Zr-mAbs)) can be used to study the biodistribution of mAbs targeting the immune system. The measured uptake consists of target-specific and non-specific components, and it can be influenced by plasma availability of the tracer. To find evidence for target-specific uptake, i.e., target engagement, we studied five immune-checkpoint-targeting [89Zr]Zr-mAbs to (1) compare the uptake with previously reported baseline values for non-specific organ uptake (ns-baseline) and (2) look for saturation effects of increasing mass doses. Method: 89Zr-immuno-PET data from five [89Zr]Zr-mAbs, i.e., nivolumab and pembrolizumab (anti-PD-1), durvalumab (anti-PD-L1), BI 754,111 (anti-LAG-3), and ipilimumab (anti-CTLA-4), were analysed. For each mAb, 2–3 different mass doses were evaluated. PET scans and blood samples from at least two time points 24 h post injection were available. In 35 patients, brain, kidneys, liver, spleen, lungs, and bone marrow were delineated. Patlak analysis was used to account for differences in plasma activity concentration and to quantify irreversible uptake (Ki). To identify target engagement, Ki values were compared to ns-baseline Ki values previously reported, and the effect of increasing mass doses on Ki was investigated. Results: All mAbs, except ipilimumab, showed Ki values in spleen above the ns-baseline for the lowest administered mass dose, in addition to decreasing Ki values with higher mass doses, both indicative of target engagement. For bone marrow, no ns-baseline was established previously, but a similar pattern was observed. For kidneys, most mAbs showed Ki values within the ns-baseline for both low and high mass doses. However, with high mass doses, some saturation effects were seen, suggestive of a lower ns-baseline value. Ki values were near zero in brain tissue for all mass doses of all mAbs. Conclusion: Using Patlak analysis and the established ns-baseline values, evidence for target engagement in (lymphoid) organs for several immune checkpoint inhibitors could be demonstrated. A decrease in the Ki values with increasing mass doses supports the applicability of Patlak analysis for the assessment of target engagement for PET ligands with irreversible uptake behavior.
Chimeric Antigen Receptor T Cell Therapy Targeting Epithelial Cell Adhesion Molecule in Gastric Cancer: Mechanisms of Tumor Resistance
Authors: Yang Yanping , Louie Raymond , Puc Janusz , Vedvyas Yogindra , Alcaina Yago , Min Irene M. , Britz Matt , Luciani Fabio , Jin Moonsoo M.
Published: November 2023

Epithelial cell adhesion molecule (EpCAM) is a tumor-associated antigen that is frequently overexpressed in various carcinomas. We have developed chimeric antigen receptor (CAR) T cells specifically targeting EpCAM for the treatment of gastric cancer. This study sought to unravel the precise mechanisms by which tumors evade immune surveillance and develop resistance to CAR T cell therapy. Through a combination of whole-body CAR T cell imaging and single-cell multiomic analyses, we uncovered intricate interactions between tumors and tumor-infiltrating lymphocytes (TILs). In a gastric cancer model, tumor-infiltrating CD8 T cells exhibited both cytotoxic and exhausted phenotypes, while CD4 T cells were mainly regulatory T cells. A T cell receptor (TCR) clonal analysis provided evidence of CAR T cell proliferation and clonal expansion within resistant tumors, which was substantiated by whole-body CAR T cell imaging. Furthermore, single-cell transcriptomics showed that tumor cells in mice with refractory or relapsing outcomes were enriched for genes involved in major histocompatibility complex (MHC) and antigen presentation pathways, interferon-γ and interferon-α responses, mitochondrial activities, and a set of genes (e.g., CD74, IDO1, IFI27) linked to tumor progression and unfavorable disease prognoses. This research highlights an approach that combines imaging and multiomic methodologies to concurrently characterize the evolution of tumors and the differentiation of CAR T cells.
Clinical and Biological Differences between Upper Tract Carcinoma and Bladder Urothelial Cancer, Including Implications for Clinical Practice
Authors: Lefort Félix , Rhanine Yasmine , Larroquette Mathieu , Domblides Charlotte , Heraudet Luc , Sionneau Baptiste , Lambert Simon , Lasserre Matthieu , Robert Grégoire , Ravaud Alain , Gross-Goupil Marine
Published: November 2023

Upper tract urothelial carcinoma (UTUC) is a rare disease included, along with the much more frequent urothelial bladder cancer (BUC), in the family of urothelial carcinomas (UCs). However, while UTUCs and BUCs share several features, their epidemiological, clinical, pathological, and biological differences must be considered to establish an optimal therapeutic strategy. This review examines the clinical differences between UTUC and BUC, as well as the main results obtained by molecular screening of the two diseases. The findings of clinical trials, performed in peri-operative and metastatic settings and assessing systemic treatments in UC, are summarised. A comparison of the data obtained for UTUC and BUC suggests improved therapeutic approaches, both in regards to routine practice and future drug development.
A Fluidizable Catalyst for N-Butane Oxidative Dehydrogenation under Oxygen-Free Reaction Conditions
Authors: Bin Sulayman Abdulhamid , Torres Brauer Nicolas , de Lasa Hugo
Published: November 2023

This study evaluates the effectiveness of fluidizable VOx/MgO-γAl2O3 catalysts for C4-olefin production via n-butane oxidative dehydrogenation (BODH). Catalysts were prepared via vacuum incipient wetness impregnation and then characterized by employing several techniques such as BET (Brunauer–Emmett–Teller) method, XRD (X-ray diffraction), LRS (laser Raman spectroscopy), XPS (X-ray photoelectron spectroscopy), TPR/TPO (temperature-programmed reduction/temperature-programmed oxidation), NH3-TPD (temperature-programmed desorption), NH3 -desorption kinetics and pyridine-FTIR. The BET analysis showed the prepared catalysts’ mesoporous structure and high surface areas. The XRD, LRS and XPS established the desirable presence of amorphous VOx phases. The TPR/TPO analyses corroborated catalyst stability over repeated reduction and oxidation cycles. The NH3-TPD and NH3 desorption kinetics showed that the catalysts had dominant moderate acidities and weak metal-support interactions. In addition, Pyridine-FTIR showed the critical influence of Lewis acidity. The VOx/MgO-γAl2O3 catalysts were evaluated for BODH using a fluidized CREC Riser Simulator, operated under gas-phase oxygen-free conditions, at 5 to 20 s reaction times, and at 450 °C to 600 °C temperatures. The developed VOx/MgO-γAl2O3 catalysts demonstrated performance stability throughout multiple injections of butane feed. Catalyst regeneration was also conducted after six consecutive BODH runs, and the coke formed was measured using TOC (Total Organic Carbon). Regarding the various BODH catalyst prepared, the 5 wt% V-doped MgO-γAl2O3 yielded in a fluidized CREC Riser Simulator the highest selectivity for C4-olefins, ranging from 82% to 86%, alongside a butane conversion rate of 24% to 27%, at 500 °C and at a 10 s reaction time.
High-Performance ε-Ga2O3 Solar-Blind Photodetectors Grown by MOCVD with Post-Thermal Annealing
Authors: Fei Zeyuan , Chen Zimin , Chen Weiqu , Luo Tiecheng , Chen Shujian , Liang Jun , Wang Xinzhong , Lu Xing , Wang Gang , Pei Yanli
Published: November 2023

High-temperature annealing has been regarded as an effective technology to improve the performance of Ga2O3-based solar-blind photodetectors (SBPDs). However, as a metastable phase, ε-Ga2O3 thin film may undergo phase transformation during post-annealing. Therefore, it is necessary to investigate the effect of the phase transition and the defect formation or desorption on the performance of photodetectors during post-annealing. In this work, the ε-Ga2O3 thin films were grown on c-plane sapphire with a two-step method, carried out in a metal-organic chemical vapor deposition (MOCVD) system, and the ε-Ga2O3 metal-semiconductor-metal (MSM)-type SBPDs were fabricated. The effects of post-annealing on ε-Ga2O3 MSM SBPDs were investigated. As a metastable phase, ε-Ga2O3 thin film undergoes phase transition when the annealing temperature is higher than 700 °C. As result, the decreased crystal quality makes an SBPD with high dark current and long response time. In contrast, low-temperature annealing at 640 °C, which is the same as the growth temperature, reduces the oxygen-related defects, as confirmed by X-ray photoelectron spectroscopy (XPS) measurement, while the good crystal quality is maintained. The performance of the SBPD with the post-annealing temperature of 640 °C is overall improved greatly compared with the ones fabricated on the other films. It shows the low dark current of 0.069 pA at 10 V, a rejection ratio (Rpeak/R400) of 2.4 × 104 (Rpeak = 230 nm), a higher photo-to-dark current ratio (PDCR) of 3 × 105, and a better time-dependent photoresponse. These results indicate that, while maintaining no phase transition, post-annealing is an effective method to eliminate point defects such as oxygen vacancies in ε-Ga2O3 thin films and improve the performance of SBPDs.
The Influence of Lyophobicity and Lyophilicity of Film-Forming Systems on the Properties of Tin Oxide Films
Authors: Dmitriyeva Elena , Lebedev Igor , Bondar Ekaterina , Fedosimova Anastasia , Temiraliev Abzal , Murzalinov Danatbek , Ibraimova Sayora , Nurbaev Bedebek , Elemesov Kasym , Baitimbetova Bagila
Published: November 2023

In this work, the effects of lyophobicity and lyophilicity of film-forming systems on the properties of thin nanostructured films was studied. Systematic series of experiments were carried out with lyophilic film-forming systems: SnCl4/EtOH, SnCl4/EtOH/NH4F, SnCl4/EtOH/NH4OH and lyophobic systems: SnO2/EtOH and SnO2/EtOH/NH4F. Film growth mechanisms are determined depending on the type of film-forming system. The surface of the films was studied using a scanning electron microscope and an optical microscope. The spectrophotometric method is used to study the transmission spectra and the extinction coefficient. The surface resistance of the films was determined using the four-probe method. The quality factor and specific conductivity of the films are calculated. It was found that the addition of a fluorinating agent (NH4F) to a film-forming system containing SnO2 in the form of a dispersed phase does not lead to an increase in the specific conductivity of the films. X-ray diffraction analysis proved the incorporation of fluorine ions into the structure of the film obtained from the SnCl4/EtOH/NH4F system by the presence of SnOF2 peaks. In films obtained from SnO2/EtOH/NH4F systems, there are no SnOF2 peaks. In this case, ammonium fluoride crystallizes as a separate phase and decomposes into volatile compounds.
Determination of Critical Velocity of Cold-Sprayed NiCoCrAlY Coating via Arbitary Lagrangian-Eulerian (ALE) Method of Finite Element Simulation
Authors: Wu Qian , Su Jiahui , Zhao Weiling , Li Jiaxue , Zhang Ke , Wang Liang
Published: November 2023

NiCoCrAlY coatings are commonly used as bond-coat in thermal barrier coatings due to their excellent high-temperature oxidation resistance and suitable thermal expansion coefficient between the superalloy substrate and ceramic top layer. Previous studies have shown that the NiCoCrAlY coatings prepared by cold spray (CS) exhibit excellent comprehensive properties. In the process of cold spray, the solid particles impact onto the substrate with a high velocity, powder particles and the substrate undergo plastic deformation, and the coating is deposited finally. When the velocity of the impacted particles reaches a certain value (critical velocity), the particles can be effectively deposited on the substrate. Due to the short impact time and large plastic deformation of the cold spray process, the process is difficult to be observed in the actual experiment process in real time. Therefore, the current work has used the explicit dynamics method in finite element numerical simulation to simulate the deposition behavior of the particle during the cold spray process. By changing the impact velocities and sizes of particles, the changes in temperature (TEMP), equivalent plastic strain (PEEQ), deformation characteristics of the particle and substrate after particles being completely deposited on the substrate have been obtained. The critical velocity of particle deposition is about 600 m/s, and the larger the particle, the easier it is to deposit. And the current modeling and simulation work provided the theory instruction for the preparation of NiCoCrAlY coatings with excellent performance via cold spray.
NiO/Ga2O3 Vertical Rectifiers of 7 kV and 1 mm2 with 5.5 A Forward Conduction Current
Authors: Li Jian-Sian , Wan Hsiao-Hsuan , Chiang Chao-Ching , Yoo Timothy Jinsoo , Ren Fan , Kim Honggyu , Pearton Stephen J.
Published: November 2023

In this study, we present the fabrication and characterization of vertically oriented NiO/β polymorph n-Ga2O3/n+ Ga2O3 heterojunction rectifiers featuring a substantial area of 1 mm2. A dual-layer SiNX/SiO2 dielectric field plate edge termination was employed to increase the breakdown voltage (VB). These heterojunction rectifiers exhibit remarkable simultaneous achievement of high breakdown voltage and substantial conducting currents. In particular, the devices manifest VB of 7 kV when employing a 15 µm thick drift layer doping concentration of 8.8 × 1015 cm−3, concurrently demonstrating a forward current of 5.5 A. The thick drift layer is crucial in obtaining high VB since similar devices fabricated on 10 µm thick epilayers had breakdown voltages in the range of 3.6–4.0 kV. Reference devices fabricated on the 15 µm drift layers had VB of 5 kV. The breakdown is still due to leakage current from tunneling and thermionic emission and not from avalanche breakdown. An evaluation of the power figure-of-merit, represented by VB2/RON, reveals a value of 9.2 GW·cm−2, where RON denotes the on-state resistance, measuring 5.4 mΩ·cm2. The Coff was 4 nF/cm2, leading to an RON × Coff of 34 ps and FCO of 29 GHz. The turn-on voltage for these rectifiers was ~2 V. This exceptional performance surpasses the theoretical unipolar one-dimensional (1D) limit of both SiC and GaN, underscoring the potential of β-Ga2O3 for forthcoming generations of high-power rectification devices.
Comparative Analysis of Bacteria, Fungi, and Arbuscular Mycorrhizal Fungi in Medicinal Plants Lippia alba and Petiveria alliacea in Colombia
Authors: Vélez-Martínez Glever Alexander , Duque-Zapata Juan Diego , Reyes-Ardila Wendy Lorena , Muñoz Flórez Jaime Eduardo , Díaz Gallo Sergio Alberto , Díaz Ariza Lucia Ana , López-Álvarez Diana
Published: November 2023

Medicinal plants maintain structures and diversities of bacteria, fungi, and arbuscular mycorrhizal fungi (AMF) that can interact to promote growth and therapeutic properties. Therefore, the purpose of this research was to evaluate the microbiome of Lippia alba and Petiveria alliacea, species known for their high potential for medicinal benefits in Colombia. To achieve this, rhizosphere soils and roots were sampled from five departments in Colombia: Boyacá, Cundinamarca, Tolima, Putumayo, and Valle del Cauca. The results revealed that the dominant bacterial groups in both plants were primarily Proteobacteria, Acidobacteriota, and Actinobacteriota, with the first phylum showing the highest number of differentially abundant genera between the sampling points. In fungi, Ascomycota tended to dominate in most of the sampled locations, while Mortierellomycota was particularly abundant in roots of P. alliacea in Valle. Furthermore, the study of AMF indicated differentiation in the colonization for both plants, with the genera Glomus and Paraglomus being predominant. Differences in the Shannon diversity index were recorded between sampling types within these sampling points, possibly influenced by local and environmental factors. Our findings reveal that the microbiomes of both medicinal plants exhibit distinct community assemblies, which could be a significant factor for their future therapeutic use.
Relaxation–Diffusion T2–ADC Correlations in Breast Cancer Patients: A Spatiotemporally Encoded 3T MRI Assessment
Authors: Otikovs Martins , Nissan Noam , Furman-Haran Edna , Anaby Debbie , Agassi Ravit , Sklair-Levy Miri , Frydman Lucio
Published: November 2023

Quantitative correlations between T2 and ADC values were explored on cancerous breast lesions using spatiotemporally encoded (SPEN) MRI. To this end, T2 maps of patients were measured at more than one b-value, and ADC maps at several echo time values were recorded. SPEN delivered quality, artifact-free, TE-weighted DW images, from which T2-ADC correlations could be obtained despite the signal losses brought about by diffusion and relaxation. Data confirmed known aspects of breast cancer lesions, including their reduced ADC values vs. healthy tissue. Data also revealed an anticorrelation between the T2 and ADC values, when comparing regions with healthy and diseased tissues. This is contrary to expectations based on simple water restriction considerations. It is also contrary to what has been observed in a majority of porous materials and tissues. Differences between the healthy tissue of the lesion-affected breast and healthy tissue in the contralateral breast were also noticed. The potential significance of these trends is discussed, as is the potential of combining T2- and ADC-weightings to achieve an enhanced endogenous MRI contrast about the location of breast cancer lesions.
CMP-UNet: A Retinal Vessel Segmentation Network Based on Multi-Scale Feature Fusion
Authors: Gu Yanan , Cao Ruyi , Wang Dong , Lu Bibo
Published: November 2023

Retinal vessel segmentation plays a critical role in the diagnosis and treatment of various ophthalmic diseases. However, due to poor image contrast, intricate vascular structures, and limited datasets, retinal vessel segmentation remains a long-term challenge. In this paper, based on an encoder–decoder framework, a novel retinal vessel segmentation model called CMP-UNet is proposed. Firstly, the Coarse and Fine Feature Aggregation module decouples and aggregates coarse and fine vessel features using two parallel branches, thus enhancing the model’s ability to extract features for vessels of various sizes. Then, the Multi-Scale Channel Adaptive Fusion module is embedded in the decoder to realize the efficient fusion of cascade features by mining the multi-scale context information from these features. Finally, to obtain more discriminative vascular features and enhance the connectivity of vascular structures, the Pyramid Feature Fusion module is proposed to effectively utilize the complementary information of multi-level features. To validate the effectiveness of the proposed model, it is evaluated on three publicly available retinal vessel segmentation datasets: CHASE_DB1, DRIVE, and STARE. The proposed model, CMP-UNet, reaches F1-scores of 82.84%, 82.55%, and 84.14% on these three datasets, with improvements of 0.76%, 0.31%, and 1.49%, respectively, compared with the baseline. The results show that the proposed model achieves higher segmentation accuracy and more robust generalization capability than state-of-the-art methods.
Risk-Aware Deep Reinforcement Learning for Robot Crowd Navigation
Authors: Sun Xueying , Zhang Qiang , Wei Yifei , Liu Mingmin
Published: November 2023

Ensuring safe and efficient navigation in crowded environments is a critical goal for assistive robots. Recent studies have emphasized the potential of deep reinforcement learning techniques to enhance robots’ navigation capabilities in the presence of crowds. However, current deep reinforcement learning methods often face the challenge of robots freezing as crowd density increases. To address this issue, a novel risk-aware deep reinforcement learning approach is proposed in this paper. The proposed method integrates a risk function to assess the probability of collision between the robot and pedestrians, enabling the robot to proactively prioritize pedestrians with a higher risk of collision. Furthermore, the model dynamically adjusts the fusion strategy of learning-based and risk-aware-based features, thereby improving the robustness of robot navigation. Evaluations were conducted to determine the effectiveness of the proposed method in both low- and high-crowd density settings. The results exhibited remarkable navigation success rates of 98.0% and 93.2% in environments with 10 and 20 pedestrians, respectively. These findings emphasize the robust performance of the proposed method in successfully navigating through crowded spaces. Additionally, the approach achieves navigation times comparable to those of state-of-the-art methods, confirming its efficiency in accomplishing navigation tasks. The generalization capability of the method was also rigorously assessed by subjecting it to testing in crowd environments exceeding the training density. Notably, the proposed method attains an impressive navigation success rate of 90.0% in 25-person environments, surpassing the performance of existing approaches and establishing itself as a state-of-the-art solution. This result highlights the versatility and effectiveness of the proposed method in adapting to various crowd densities and further reinforces its applicability in real-world scenarios.
AM/AM and AM/PM Characterization of a GaN Phase and Amplitude Setting Circuit
Authors: Colangeli Sergio , Das Anjeeshnu , Longhi Patrick Ettore , Ciccognani Walter , De Angelis Enzo , Bolli Filippo , Limiti Ernesto
Published: November 2023

This contribution presents the AM/AM and AM/PM characteristics of a 6-bit Phase and Amplitude Setting Circuit realized in Gallium Nitride technology and operating at the Ku band. A test bench, based on three vector receivers and an absolute power reference, has been purposely devised to capture the deviation with respect to the linear behavior (known by the S-parameters) for both the magnitude and the phase of the vector response. The complete 64-state constellation is reported up to a 37 dBm of input power level, at which the effects of the static AM/AM and AM/PM distortion become evident, with about 3 dB of gain compression and 2.7 deg of phase conversion. The key figure of merit of the proposed test bench is the capability of operating with very high driving power levels (potentially up to 41 dBm), with possible applications in phased arrays, AESAs, and other signal conditioning systems.
IoV Vulnerability Classification Algorithm Based on Knowledge Graph
Authors: Wang Jiuru , Wang Yifang , Song Jingcheng , Cheng Hongyuan
Published: November 2023

With the rapid development of smart technologies, the Internet of Vehicles (IoV) is revolutionizing transportation and mobility. However, the complexity and interconnectedness of IoV systems lead to a growing number of security incidents caused by vulnerabilities. Current vulnerability classification algorithms often struggle to address the low occurrence frequency and incomplete information associated with IoV vulnerabilities, resulting in decreased precision and recall rates of classifiers. To address these challenges, an effective vulnerability classification algorithm (KG-KNN), is proposed, designed to handle imbalanced sample data. KG-KNN integrates the vulnerability information of IoV and the association relationship between features by constructing a feature knowledge graph to form a complete knowledge system. It adds the correlation relationship between features to the similarity calculation, calculates vulnerability similarity from multiple dimensions, and improves the prediction performance of the classifier. The experimental results show that compared to the k-NearestNeighbor (KNN), Support Vector Machine (SVM), Deep Nueral Network (DNN) and TFI-DNN classification algorithms, KG-KNN can effectively deal with imbalanced sample data and has different degrees of improvement in precision, recall, and the F1 score.
Quantitative Evaluation of Neurite Morphology Using Graph Structure
Authors: Chikudo Fumiya , Baar Stefan , Ota Ayaka , Kuragano Masahiro , Tokuraku Kiyotaka , Watanabe Shinya
Published: November 2023

Recently, the analysis of cellular images, particularly the assessment of neurite activity, has gained increasing significance in the study of neurodegenerative diseases, including Alzheimer’s disease. This study introduces an automated analysis approach that focuses on neurite activity through the application of cellular segmentation techniques to bright-field images of neurons. This study proposes a method for treating individual cell instances as graphs consisting of nodes and edges. Furthermore, this study suggests a quantitative assessment for precisely identified neurites through the definition of several evaluation metrics. This approach enables the fast and objective automated analysis of bright-field images focused on neurons. In a variety of experiments, the precision of our proposed method was verified through a comparative analysis by comparing the results to manual analysis data using ImageJ for measuring the neurite length of rat adrenal pheochromocytoma PC12 cells. The findings revealed that the average discrepancy in the length of neurites is only 4.387 μm, highlighting the high level of accuracy in our method’s ability to detect neurites, which is almost on par with manual analysis. This observation holds significance in analytical applications pertinent to Alzheimer’s disease.
Intelligent Scheduling Based on Reinforcement Learning Approaches: Applying Advanced Q-Learning and State–Action–Reward–State–Action Reinforcement Learning Models for the Optimisation of Job Shop Scheduling Problems
Authors: Momenikorbekandi Atefeh , Abbod Maysam
Published: November 2023

Flexible job shop scheduling problems (FJSPs) have attracted significant research interest because they can considerably increase production efficiency in terms of energy, cost and time; they are considered the main part of the manufacturing systems which frequently need to be resolved to manage the variations in production requirements. In this study, novel reinforcement learning (RL) models, including advanced Q-learning (QRL) and RL-based state–action–reward–state–action (SARSA) models, are proposed to enhance the scheduling performance of FJSPs, in order to reduce the total makespan. To more accurately depict the problem realities, two categories of simulated single-machine job shops and multi-machine job shops, as well as the scheduling of a furnace model, are used to compare the learning impact and performance of the novel RL models to other algorithms. FJSPs are challenging to resolve and are considered non-deterministic polynomial-time hardness (NP-hard) problems. Numerous algorithms have been used previously to solve FJSPs. However, because their key parameters cannot be effectively changed dynamically throughout the computation process, the effectiveness and quality of the solutions fail to meet production standards. Consequently, in this research, developed RL models are presented. The efficacy and benefits of the suggested SARSA method for solving FJSPs are shown by extensive computer testing and comparisons. As a result, this can be a competitive algorithm for FJSPs.
Semi-Supervised Alert Filtering for Network Security
Authors: Shon Hyeon gy , Lee Yoonho , Yoon MyungKeun
Published: November 2023

Network-based intrusion detection systems play a pivotal role in cybersecurity, but they generate a significant number of alerts. This leads to alert fatigue, a phenomenon where security analysts may miss true alerts hidden among false ones. To address alert fatigue, practical detection systems enable administrators to divide alerts into multiple groups by the alert name and the related Internet Protocol (IP) address. Then, some groups are deliberately ignored to conserve human resources for further analysis. However, the drawback of this approach is that the filtering basis is so coarse-grained that some true alerts are also ignored, which may cause critical security issues. In this paper, we present a new semi-supervised and fine-grained filtering method that uses not only alert names and IP addresses but also semi-supervised clustering results from the alerts. We evaluate our scheme with both a private dataset from a security operations center and a public dataset from the Internet. The experimental results demonstrate that the new filtering scheme achieves higher accuracy and saves more human resources compared to the current state-of-the-art method.
Enhancing Underwater Image Quality Assessment with Influential Perceptual Features
Authors: Liu Feifei , Huang Zihao , Xie Tianrang , Hu Runze , Qi Bingbing
Published: November 2023

In the multifaceted field of oceanic engineering, the quality of underwater images is paramount for a range of applications, from marine biology to robotic exploration. This paper presents a novel approach in underwater image quality assessment (UIQA) that addresses the current limitations by effectively combining low-level image properties with high-level semantic features. Traditional UIQA methods predominantly focus on either low-level attributes such as brightness and contrast or high-level semantic content, but rarely both, which leads to a gap in achieving a comprehensive assessment of image quality. Our proposed methodology bridges this gap by integrating these two critical aspects of underwater imaging. We employ the least-angle regression technique for balanced feature selection, particularly in high-level semantics, to ensure that the extensive feature dimensions of high-level content do not overshadow the fundamental low-level properties. The experimental results of our method demonstrate a remarkable improvement over existing UIQA techniques, establishing a new benchmark in both accuracy and reliability for underwater image assessment.
Solar Photovoltaic Home Systems in Malaysia: A Comprehensive Review and Analysis
Authors: Sarker Md Tanjil , Haram Mohammed Hussein Saleh Mohammed , Ramasamy Gobbi , Al Farid Fahmid , Mansor Sarina
Published: November 2023

This paper presents a thorough review and analysis of solar photovoltaic (PV) home systems in Malaysia, offering a comprehensive exploration of their implementation, challenges, benefits, and future potential. As a nation striving to embrace sustainable and renewable energy solutions, Malaysia’s adoption of solar PV systems at the residential level is of paramount importance. The study delves into the current state of solar PV deployment, government initiatives, and policy frameworks that influence adoption trends. It examines the economic, environmental, and societal advantages of solar PV home systems, outlining their contribution to reducing electricity costs, carbon emissions, and energy dependence. Addressing barriers to wider acceptance, the paper scrutinizes challenges encompassing cost, awareness, intermittency, and regulatory constraints. Furthermore, the study evaluates the socio-economic implications of solar PV home systems, including potential job creation and energy independence. Technological advancements, such as enhanced efficiency and smart grid integration, are also explored for their role in surmounting obstacles. Drawing insights from practical case studies, the paper underscores successful installations and the lessons they offer. In examining policy and regulatory frameworks, it analyzes existing incentives while suggesting improvements to accelerate adoption. Finally, the paper offers a visionary perspective on the future trajectory of solar PV home systems in Malaysia, envisioning increased affordability, advanced energy storage solutions, and seamless integration with emerging technologies. This holistic analysis contributes valuable insights to propel Malaysia’s sustainable energy transition and inform strategic decision making for stakeholders across sectors.
Analyzing the Differences in the Quantitative and Spatial Characteristics of Inter-Provincial Embodied Carbon Transfers in China Induced via Various Demand Factors
Authors: Li Qinghua , Chen Cong
Published: November 2023

The development of human society has led to the growing consumption of industrial products, which generates significant amounts of carbon emissions. However, relatively few in-depth studies have been conducted on the influence of different demand factors (e.g., household consumption, government consumption, export, and capital formation) on carbon emissions, which hinders the development of targeted industrial policies. To address this issue, an analytical framework based on input–output theory, the hypothesis extraction method, and complex network analysis was established to estimate the intrinsic influence of different demand factors on the embodied carbon transfer between provinces in China. The key findings can be summed up as follows: (1) The macro direction of China’s embodied carbon transfer runs from resource-rich northern provinces to industrially developed southern provinces. (2) From the perspective of different demand factors, capital formation is the most significant contributor to China’s embodied carbon transfer, with the construction industry being the most important driver. In contrast, government consumption causes the least embodied carbon transfer, but it has the highest average carbon emission intensity. (3) According to complex network theory, the carbon transfer networks via provinces and industries caused by exports are the most concentrated, with the manufacture of electrical machinery and electronic equipment serving as the main source of demand. In contrast, the carbon transfer network resulting from household consumption exhibits a high level of decentralization, with dominant sectors including electric power, gas and water production, and supply and other services. Based on these findings, this study is expected to contribute targeted suggestions with which provinces and industries can formulate demand-side carbon reduction policies for different demand factors, which will contribute to the achievement of “carbon peaking and carbon neutrality”.
Decadal Fire Effects on the Structure, Composition, Diversity, and Aboveground Carbon Stocks of a Neotropical Savanna
Authors: Neri Sarah Cristine Martins , Bomfim Barbara , Pereira Reginaldo Sérgio , dos Santos Pâmela Virgilio , Tetto Alexandre França
Published: November 2023

Fire is a common disturbance in the Brazilian savanna (the cerrado), wherein high-frequency fires drive the vegetation structure, composition, function, and dynamics of savanna ecosystems. Under climate change pressure, further understanding of fire–vegetation relationships and interactions can provide new approaches for establishing integrated fire management strategies and can promote post-fire savanna vegetation recovery. To understand how 15 years of annually manipulated burning has affected vertical and horizontal structures of the vegetation, species composition and diversity metrics (species richness, Shannon’s diversity, and Pielou’s evenness), and aboveground carbon stocks, we surveyed all woody plant species with a diameter greater than three centimeters, in 15 plots of a typical Brazilian savanna (cerrado stricto sensu) at an experimental research station in central Brazil (the cerrado biome). The 15 plots (five plots per treatment) had been differently affected by fire events over a decade, and comprised three treatments: (i) annual fire, (ii) legacy fire (>15 years since the last fire event), and (iii) control (not burned in the past 30 years). A non-metric multidimensional scaling (NMDS) analysis indicated that fire had a significant effect on the species composition among the treatments; some species benefited from fire, such as Erythroxylum suberosum, whereas other species propagated better without fire, such as Roupala montana and Dalbergia miscolobium. Over a decade of annual fire events have led to decreases in Shannon’s diversity, species richness, and stem density, which were significantly lower in the annual fire treatment than in the control and legacy fire treatments. Stem density by diameter and height size classes (except for the 1–2 m class and above 8 m class) was higher in the control than that in the annual fire treatment, but the number of dead trees did not differ between the control and the annual fire treatment. Our results also showed that fire was a factor in changes in the evaluated parameters, such as in the annual fire treatment, where fire reduced the amount of biomass, and therefore the carbon stocks. This study suggests that, if burned yearly, typical Brazilian savannas can become less biodiverse in terms of woody plant species which negatively affects their fire resilience. Therefore, fire management practices should focus on determining the frequency of fire disturbances from which these ecosystems may benefit the most.
Influence of Festival Activities on Enterprise Production Behavior: A Case Study of the China Bamboo Culture Festival
Authors: Li Nan , Li Jiang , Zhang Ke , Chen Jiaojiao , Wang Peng
Published: November 2023

At the present stage, China’s ecological civilization construction has entered a strategic period focusing on carbon reduction, which is also a critical period for achieving the improvement of ecological environment quality. The prosperity of bamboo culture and the development of the bamboo industry are of great significance in promoting the construction of an ecological civilization in China. Festival activities often have lasting impacts on the development of the host city. They not only promote urban development but also spur the growth of local enterprises in related industries. However, these impacts have been rarely explored by researchers. This paper analyzes how festival activities influence the behavior of host city enterprises from the perspective of stakeholders with a theoretical model. Taking the China Bamboo Culture Festival as an example, micro-data on bamboo enterprises in 1999–2015 were collected from the Chinese Industrial Enterprise Database and analyzed with a theoretical model to empirically verify the influence of the festival over the production behavior of bamboo enterprises. The research shows that the China Bamboo Culture Festival has a significant positive impact on the total output value and total factor productivity of bamboo product enterprises, which in turn affects the production behavior of the enterprises. In the host province/city, the enterprises that engaged in the festival surpassed the other enterprises by 1.0764% in terms of gross output; export enterprises were less affected by the festival than domestic market enterprises. The China Bamboo Culture Festival makes bamboo enterprises more sustainable and competitive in the industry, and the holding of festival activities enhances the sustainable development capabilities of enterprises by improving their business environment. In addition, significant differences were observed between enterprises in different geographical locations, of different ownerships, and in different subdivided industries. Enterprises in the eastern region are least affected by the China Bamboo Culture Festival, while those in the western region are the most impacted; those in the central region fall in the middle by the degree of influence. Bamboo, rattan, palm, and grass products are influenced by the China Bamboo Culture Festival, while the production of other manufacturing industries is basically unaffected.
Analysis of the Expression Patterns of 13 DREB Family Genes Related to Cone-Setting Genes in Hybrid Larch (Larix kaempferi × Larix olgensis)
Authors: Xu Daixi , Hao Junfei , Wang Chen , Zhang Lei , Zhang Hanguo
Published: November 2023

AP2/ERF is an important transcription factor family involved in physiological processes such as plant development and hormone signaling. In this study, based on the available transcriptome data of hybrid larch during floral induction, 13 DREB genes belonging to the AP2/EREBP family with complete CDS regions were identified through alignment using the NCBI website. We conducted a bioinformatics analysis on the gene sequences, examining their tissue specificity, response to hormone treatment, and response to environmental factors. The DREB genes in hybrid larch (Larix kaempferi × Larix olgensis) showed tissue-specific expression, with DREB7, DREB8, DREB10, DREB12, and DREB13 exhibiting higher expression levels in nascent buds and higher expression in female cones compared to male cones. They also showed high expression during signal convergence and floral induction, and were highly expressed in materials with good fertility, suggesting their positive role in the cone-setting process of hybrid larch. Additionally, 13 DREB genes were all induced by abscisic acid (ABA), gibberellin 3 (GA3), and indoleacetic acid (IAA), with the most pronounced expression changes observed after ABA treatment, indicating that these genes might be mainly regulated by ABA. In response to temperature and photoperiod treatments, DREB7, DREB8, DREB10, DREB12, and DREB13 showed significant responses, with increased expression levels induced by low temperature, while no clear pattern was observed after long or short-day treatments. These results of the study provide a reference for understanding the function of the DREB gene family in hybrid larch, offer a theoretical basis for inducing floral bud differentiation in hybrid larch, and contribute to a better understanding of the molecular mechanisms underlying cone-setting in hybrid larch.
An Artificial Neural Network Autoencoder for Insider Cyber Security Threat Detection
Authors: Saminathan Karthikeyan , Mulka Sai Tharun Reddy , Damodharan Sangeetha , Maheswar Rajagopal , Lorincz Josip
Published: November 2023

The COVID-19 pandemic made all organizations and enterprises work on cloud platforms from home, which greatly facilitates cyberattacks. Employees who work remotely and use cloud-based platforms are chosen as targets for cyberattacks. For that reason, cyber security is a more concerning issue and is now incorporated into almost every smart gadget and has become a prerequisite in every software product and service. There are various mitigations for external cyber security attacks, but hardly any for insider security threats, as they are difficult to detect and mitigate. Thus, insider cyber security threat detection has become a serious concern in recent years. Hence, this paper proposes an unsupervised deep learning approach that employs an artificial neural network (ANN)-based autoencoder to detect anomalies in an insider cyber security attack scenario. The proposed approach analyzes the behavior of the patterns of users and machines for anomalies and sends an alert based on a set security threshold. The threshold value set for security detection is calculated based on reconstruction errors that are obtained through testing the normal data. When the proposed model reconstructs the user behavior without generating sufficient reconstruction errors, i.e., no more than the threshold, the user is flagged as normal; otherwise, it is flagged as a security intruder. The proposed approach performed well, with an accuracy of 94.3% for security threat detection, a false positive rate of 11.1%, and a precision of 89.1%. From the obtained experimental results, it was found that the proposed method for insider security threat detection outperforms the existing methods in terms of performance reliability, due to implementation of ANN-based autoencoder which uses a larger number of features in the process of security threat detection.