bluuebunny/crossref_metadata_2025 huggingface dataset:
Characterization of Distinct Chondrogenic Cell Populations of Patients Suffering from Microtia Using Single-Cell Micro-Raman Spectroscopy
Authors: Zielinska Dominika , Yosef Hesham K. , Zollitsch Tilo , Kern Johann , Jakob Yvonne , Gvaramia David , Rotter Nicole , Pontiggia Luca , Moehrlen Ueli , Biedermann Thomas , Klar Agnes S.
Published: September 2023

Microtia is a congenital condition of abnormal development of the outer ear. Tissue engineering of the ear is an alternative treatment option for microtia patients. However, for this approach, the identification of high regenerative cartilage progenitor cells is of vital importance. Raman analysis provides a novel, non-invasive, label-free diagnostic tool to detect distinctive biochemical features of single cells or tissues. Using micro-Raman spectroscopy, we were able to distinguish and characterize the particular molecular fingerprints of differentiated chondrocytes and perichondrocytes and their respective progenitors isolated from healthy individuals and microtia patients. We found that microtia chondrocytes exhibited lower lipid concentrations in comparison to healthy cells, thus indicating the importance of fat storage. Moreover, we suggest that collagen is a useful biomarker for distinguishing between populations obtained from the cartilage and perichondrium because of the higher spectral contributions of collagen in the chondrocytes compared to perichondrocytes from healthy individuals and microtia patients. Our results represent a contribution to the identification of cell markers that may allow the selection of specific cell populations for cartilage tissue engineering. Moreover, the observed differences between microtia and healthy cells are essential for gaining better knowledge of the cause of microtia. It can be useful for designing novel treatment options based on further investigations of the discovered biochemical substrate alterations.
Comparative Clinical Study of Suprathel® and Jelonet® Wound Dressings in Burn Wound Healing after Enzymatic Debridement
Authors: Heitzmann Wolfram , Mossing Mitja , Fuchs Paul Christian , Akkan Jan , Seyhan Harun , Grieb Gerrit , Opländer Christian , Schiefer Jennifer Lynn
Published: September 2023

Following the enzymatic debridement of deep dermal burns, the choice of wound dressing is crucial for providing an adequate environment and suitable conditions for rapid wound healing. As Suprathel® and fatty gauze (Jelonet®) are the most commonly used dressings in burn centers, the aim of this study is to compare Suprathel® and Jelonet® in the treatment of deep dermal burns after enzymatic debridement with respect to wound healing, patient comfort, and pain. A total of 23 patients with deep dermal burns of the hand or foot (mean total body surface area of 4.31%) were included in this prospective, unicentric, open, comparative, and intra-individual clinical study. After enzymatic debridement, wounds were divided into two areas: one was treated with Suprathel® and the other with Jelonet®. Suprathel® remained on the wounds without dressing changes while Jelonet® was regularly changed. Wound healing, infection, bleeding, exudation, time for dressing changes, and pain were documented (from days 2 to 48) after injury. Satisfactory results were obtained in 22 cases; only one patient had to undergo a second debridement followed by skin grafting. No significant difference in time to final wound healing could be observed (18–19 d). Patients reported significantly less pain during the dressing changes for Suprathel® compared to Jelonet®. Furthermore, the wound areas treated with Suprathel® showed significantly less exudation and bleeding. Wound infections rarely occurred in both groups. In conclusion, the authors found that both wound dressings could be used to achieve safe and rapid wound healing after the enzymatic debridement of deep dermal burns of the hands and feet. However, compared to Jelonet®, Suprathel® showed superior results in terms of patient comfort and pain reduction.
Genes Involved by Dexamethasone in Prevention of Long-Term Memory Impairment Caused by Lipopolysaccharide-Induced Neuroinflammation
Authors: Shishkina Galina T. , Kalinina Tatyana S. , Lanshakov Dmitriy A. , Bulygina Veta V. , Komysheva Natalya P. , Bannova Anita V. , Drozd Ulyana S. , Dygalo Nikolay N.
Published: September 2023

Inflammatory activation within the brain is linked to a decrease in cognitive abilities; however, the molecular mechanisms implicated in the development of inflammatory-related cognitive dysfunction and its prevention are poorly understood. This study compared the responses of hippocampal transcriptomes 3 months after the striatal infusion of lipopolysaccharide (LPS; 30 µg), resulting in memory loss, or with dexamethasone (DEX; 5 mg/kg intraperitoneal) pretreatment, which abolished the long-term LPS-induced memory impairment. After LPS treatment, a significant elevation in the expression of immunity/inflammatory-linked genes, including chemokines (Cxcl13), cytokines (Il1b and Tnfsf13b), and major histocompatibility complex (MHC) class II members (Cd74, RT1-Ba, RT1-Bb, RT1-Da, and RT1-Db1) was observed. DEX pretreatment did not change the expression of these genes, but significantly affected the expression of genes encoding ion channels, primarily calcium and potassium channels, regulators of glutamate (Slc1a2, Grm5, Grin2a), and GABA (Gabrr2, Gabrb2) neurotransmission, which enriched in such GO biological processes as “Regulation of transmembrane transport”, “Cognition”, “Learning”, “Neurogenesis”, and “Nervous system development”. Taken together, these data suggest that (1) pretreatment with DEX did not markedly affect LPS-induced prolonged inflammatory response; (2) DEX pretreatment can affect processes associated with glutamatergic signaling and nervous system development, possibly involved in the recovery of memory impairment induced by LPS.
Mometasone Furoate Inhibits the Progression of Head and Neck Squamous Cell Carcinoma via Regulating Protein Tyrosine Phosphatase Non-Receptor Type 11
Authors: Qiu Lin , Gao Qian , Tao Anqi , Jiang Jiuhui , Li Cuiying
Published: September 2023

Mometasone furoate (MF) is a kind of glucocorticoid with extensive pharmacological actions, including inhibiting tumor progression; however, the role of MF in head and neck squamous cell carcinoma (HNSCC) is still unclear. This study aimed to evaluate the inhibitory effect of MF against HNSCC and investigate its underlying mechanisms. Cell viability, colony formation, cell cycle and cell apoptosis were analyzed to explore the effect of MF on HNSCC cells. A xenograft study model was used to investigate the effect of MF on HNSCC in vivo. The core targets of MF for HNSCC were identified using network pharmacology analysis, TCGA database analysis and real-time PCR. Molecular docking was performed to determine the binding energy. Protein tyrosine phosphatase non-receptor type 11 (PTPN11)-overexpressing cells were constructed, and then, the cell viability and the expression levels of proliferation- and apoptosis-related proteins were detected after treatment with MF to explore the role of PTPN11 in the inhibitory effect of MF against HNSCC. After cells were treated with MF, cell viability and the number of colonies were decreased, the cell cycle was arrested and cell apoptosis was increased. The xenograft study results showed that MF could inhibit cell proliferation via promoting cell apoptosis in vivo. PTPN11 was shown to be the core target of MF against HNSCC via network pharmacology analysis, TCGA database analysis and real-time PCR. The molecular docking results revealed that PTPN11 exhibited the strongest ability to bind to MF. Finally, MF could attenuate the effects of increased cell viability and decreased cell apoptosis caused by PTPN11 overexpression, suggesting that MF can inhibit the progression of HNSCC by regulating PTPN11. MF targeted PTPN11, promoting cell cycle arrest and cell apoptosis, and consequently exerting effective anti-tumor activity.
New Glycosalen–Manganese(III) Complexes and RCA120 Hybrid Systems as Superoxide Dismutase/Catalase Mimetics
Authors: Lanza Valeria , Vecchio Graziella
Published: September 2023

Reactive oxygen species are implicated in several human diseases, including neurodegenerative disorders, cardiovascular dysfunction, inflammation, hereditary diseases, and ageing. MnIII–salen complexes are superoxide dismutase (SOD) and catalase (CAT) mimetics, which have shown beneficial effects in various models for oxidative stress. These properties make them well-suited as potential therapeutic agents for oxidative stress diseases. Here, we report the synthesis of the novel glycoconjugates of salen complex, EUK-108, with glucose and galactose. We found that the complexes showed a SOD-like activity higher than EUK-108, as well as peroxidase and catalase activities. We also investigated the conjugate activities in the presence of Ricinus communis agglutinin (RCA120) lectin. The hybrid protein–galactose–EUK-108 system showed an increased SOD-like activity similar to the native SOD1.
Dielectric Properties of Compacts Sintered after High-Pressure Forming of Lithium Fluoride
Authors: Ctibor Pavel , Straka Libor , Sedláček Josef , Lukáč František
Published: September 2023

High-pressure forming at 300 MPa and room temperature was applied before the sintering of a lithium fluoride (LiF) powder. The as-fired samples were tested as dielectrics and showed very interesting characteristics. The best sample, sintered at 750 °C for 8 h, had a relative permittivity of 12.1 and a loss tangent of 0.0006, both of them frequency-independent and temperature-independent up to at least 150 °C, and moreover, the volume DC resistivity was 27.4 × 1012 Ωm at room temperature. These parameters are comparable with oxide ceramics, processed at temperatures over 1300 °C, as for example, aluminum dioxide (Al2O3) or Y3Al5O12 (YAG). LiF material is advantageous because of its very low sintering temperature, which is only about one-half of typical oxide ceramic dielectrics.
Research Progress on Bionic Recognition and Biosensors for the Detection of Biomarkers of Diabetic Nephropathy
Authors: Tian Ye , Gao Lili , Abdussalam Abubakar , Xu Guobao
Published: September 2023

Diabetic nephropathy (DN) refers to kidney damage caused by diabetes and is one of the major microvascular complications of diabetes. This disease has a certain degree of concealment in the early stage, with clinical symptoms appearing later and a higher mortality rate. Therefore, the detection of early biomarkers for DN is of great importance in reducing kidney function damage. The common biomarkers for DN mainly include glomerular and tubular lesion markers. At present, clinical diagnosis often uses a combination of multiple indicators and symptoms, and the development of a simple, efficient, and sensitive multi-marker detection platform is particularly important for the early diagnosis of DN. In recent years, with the vigorous development of various biomimetic molecular recognition technologies, biomimetic recognition biosensors (BRBS) have many advantages, such as easy preparation, low cost, high stability, and repeatability under harsh environmental conditions, and have great application potential in the analysis of DN biomarkers. This article reviews the research progress of molecularly imprinted polymers (MIPs) construction technology and aptamers assembly technology developed in the field of biomimetic sensor research in recent years, as well as the detection of DN biomarkers based on BRBS, and prospects for their development.
A Novel Nomogram Based on Quantitative MRI and Clinical Features for the Prediction of Neonatal Intracranial Hypertension
Authors: Qin Yan , Liu Yang , Cao Chuanding , Ouyang Lirong , Ding Ying , Wang Dongcui , Zheng Mengqiu , Liao Zhengchang , Yue Shaojie , Liao Weihua
Published: September 2023

Intracranial hypertension (ICH) is a serious threat to the health of neonates. However, early and accurate diagnosis of neonatal intracranial hypertension remains a major challenge in clinical practice. In this study, a predictive model based on quantitative magnetic resonance imaging (MRI) data and clinical parameters was developed to identify neonates with a high risk of ICH. Newborns who were suspected of having intracranial lesions were included in our study. We utilized quantitative MRI to obtain the volumetric data of gray matter, white matter, and cerebrospinal fluid. After the MRI examination, a lumbar puncture was performed. The nomogram was constructed by incorporating the volumetric data and clinical features by multivariable logistic regression. The performance of the nomogram was evaluated by discrimination, calibration curve, and decision curve. Clinical parameters and volumetric quantitative MRI data, including postmenstrual age (p = 0.06), weight (p = 0.02), mode of delivery (p = 0.01), and gray matter volume (p = 0.003), were included in and significantly associated with neonatal intracranial hypertension risk. The nomogram showed satisfactory discrimination, with an area under the curve of 0.761. Our results demonstrated that decision curve analysis had promising clinical utility of the nomogram. The nomogram, incorporating clinical and quantitative MRI features, provided an individualized prediction of neonatal intracranial hypertension risk and facilitated decision making guidance for the early diagnosis and treatment for neonatal ICH. External validation from studies using a larger sample size before implementation in the clinical decision-making process is needed.
Anaphylaxis after Shrimp Intake in a European Pediatric Population: Role of Molecular Diagnostics and Implications for Novel Foods
Authors: Miraglia del Giudice Michele , Dinardo Giulio , Klain Angela , D’Addio Elisabetta , Bencivenga Chiara Lucia , Decimo Fabio , Indolfi Cristiana
Published: September 2023

(1) Background: Tropomyosin is a major cause of shellfish allergy and anaphylaxis triggered by food. It acts as a pan-allergen, inducing cross-reactivity in insects, dust mites, crustaceans, and mollusks. Our study investigates anaphylaxis in children with asthma or atopic diseases after consuming tropomyosin-containing food. (2) Methods: We analyzed the molecular sensitization profiles of pediatric patients at the University of Campania ‘Luigi Vanvitelli’ from 2017 to 2021, with conditions such as allergic rhinitis, asthma, atopic dermatitis, urticaria, and food allergies. (3) Results: Out of a total of 253 patients aged 1 to 18 years (167 males, 86 females), 21 patients (8.3%) experienced anaphylaxis after shrimp ingestion. All 21 (100%) were sensitized to various tropomyosins: Pen m 1 (100%), Der p 10 (90.5%), Ani s 3 (81%), and Bla g 7 (76.2%). Clinical symptoms included allergic asthma (76.2%), atopic dermatitis (61.9%), urticaria (38.1%), and allergic rhinitis (38.1%). (4) Conclusions: Crustaceans and mollusks are major allergens in Italy and Europe, requiring mandatory declaration on food labels. Italian pediatric patients demonstrated significant anaphylaxis after consuming shrimp, often accompanied by multiple atopic disorders such as asthma, rhinitis, and atopic dermatitis. Considering the cross-reactivity of tropomyosin among various invertebrates and the emergence of ‘novel foods’ containing insect flours in Europe, there is ongoing debate about introducing precautionary labeling for these products.
6-Hydroxy-2,2,4-trimethyl-1,2,3,4-tetrahydroquinoline Alleviates Oxidative Stress and NF-κB-Mediated Inflammation in Rats with Experimental Parkinson’s Disease
Authors: Kryl’skii Evgenii D. , Razuvaev Grigorii A. , Popova Tatyana N. , Medvedeva Svetlana M. , Shikhaliev Khidmet S.
Published: September 2023

A study was conducted to investigate the effects of different doses of 6-hydroxy-2,2,4-trimethyl-1,2,3,4-tetrahydroquinoline (HTHQ) on motor coordination scores, brain tissue morphology, the expression of tyrosine hydroxylase, the severity of oxidative stress parameters, the levels of the p65 subunit of nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) factor, and the inflammatory response in rats during the development of rotenone-induced Parkinsonism. The findings indicate that HTHQ, with its antioxidant attributes, reduced the levels of 8-isoprostane, lipid oxidation products, and protein oxidation products. The decrease in oxidative stress due to HTHQ led to a reduction in the mRNA content of proinflammatory cytokines and myeloperoxidase activity, accompanying the drop in the expression of the factor NF-κB. These alterations promoted an improvement in motor coordination scores and increased tyrosine hydroxylase levels, whereas histopathological changes in the brain tissue of the experimental animals were attenuated. HTHQ exhibited greater effectiveness than the comparative drug rasagiline based on the majority of variables.
Influence of Temperature on Shear Behavior of Lightweight Reinforced Concrete Beams Using Pozzolana Aggregate and Expanded Polystyrene Beads
Authors: Abdel-Jaber Mu’tasim , Shatarat Nasim , Katkhuda Hasan , Al-zu’bi Hebah , Al-Nsour Rawand , Alhnifat Rouzan , Al-Qaisia Ahmad
Published: September 2023

The innovation inherent to employing expanded polystyrene (EPS) beads lies in its transformative impact on traditional concrete practices. Through the incorporation of EPS beads in concrete mixtures, a novel approach emerges that significantly alters the material’s characteristics, and opens up new avenues for construction and design. Studying the shear behavior of RC beams made with EPS beads is essential for advancing knowledge, improving design practices, ensuring structural integrity, and promoting the effective and responsible use of innovative materials in construction. This research experimentally investigated the effect of using EPS beads and pozzolana aggregate (PA) on the shear behavior of the RC beams. A total of 27 simply supported rectangular beams were cast, using three novel distinct mix designs, and were subjected to two-point load testing until failure. These three mixes were categorized as follows: a control mix, a mix with only EPS, and a mix with EPS, along with an additive. The ultimate failure load was experimentally recorded for all specimens, and the influence of the temperature (300 °C and 600 °C) on the RC beams made with EPS was examined. The findings revealed a reduction in the concrete compressive strength and density in the beams containing EPS and EPS with superplasticizers of (21.7%, 24.9%) and (11.3%, 16.2%), respectively. Additionally, EPS played a significant role in diminishing the ultimate shear capacity of the beams, compared to the control beams, by about 19.4%. However, the addition of a superplasticizer along with the EPS helped to maintain the beam capacity, to some extent. Conversely, the beams exposed to a temperature of 300 °C exhibited an almost similar capacity to that of the control beams without heating. Nevertheless, at 600 °C, the beams displayed a noticeable decrease in the ultimate load capacity, compared to the unheated control beams.
Heat Recovery Potential in a Semi-Closed Greenhouse for Tomato Cultivation
Authors: Labihi Abdelouhab , Byrne Paul , Meslem Amina , Collet Florence , Prétot Sylvie
Published: September 2023

This study first presents the development and the experimental validation of a numerical model of a semi-closed greenhouse using a dynamic thermal simulation. The second objective was to identify the influential parameters on the indoor climate and to calculate the heating demand of the greenhouse. The model reproduced the behavior of a full-scale experimental greenhouse in Carquefou (France). The comparison with experimental measurements recorded over an entire season of tomato cultivation validated the numerical model. The result of the simulated energy consumption was 310 kWh/m2/year with a relative error of 3.5%. The parametric study identified that the evapotranspiration power and ventilation rate were the most influential input variables, accounting for 50% and 32%, respectively, of the heating demand. The most sensitive output variable was indoor humidity. The presence of a thermal buffer zone all around the greenhouse reduced the energy consumption by 48%, and thermal/shading screens reduced it by 30%. The final objective was to assess the amount of heat recovery potential over the year and each week, depending on the energy storage strategy. Around 43 kWh/m2/year can be recovered over the year, leading to a potential energy savings of 24%.
Effects of Bias Voltage and Target Current on Microstructure and Load Measurement Performance of ZnO Piezoelectric Coatings Applied to Bolt in Transformer
Authors: Kou Hanpeng , Wang Fuyuan , Nie Dayu , Ning Zhaojun , Li Qiaoqiao , Deng Jiangang , Lan Zhenbo , Xu Zhuolin
Published: September 2023

Electrical accidents caused by bolt looseness in transformers have been frequently reported in recent years. The monitoring, and warning of, axial force as an indicator of looseness is one of the key issues affecting the operation and maintenance of transformers. Traditional ultrasonic testing and a patch-type ultrasonic method, using piezoelectric probes and coupling agents, showed poor repeatability and accuracy in detecting the bolt pre-tightening force, because of the uncertainty of the contact interface produced via manual operation. A permanent thin-film pressure sensor (PMTS), which provides accurate and in-situ stress detection, is more suitable for bolts, to reveal the pretightening force. The key is depositing a nano-zinc oxide (ZnO) piezoelectric film with an excellent measurement performance, which could be tuned using deposition parameters. This paper investigates the effects of the current and bias voltage on the crystal structure and performance of ZnO piezoelectric films. The results show that the crystallinity degree and resistance decrease with the increase in bias voltage, while the target current could increase the crystallinity. However, a high current also brings large particles in the coating surface, which greatly decrease the resistance. The cause is expected to be related to the ion energy, which could be affected by the bias voltage and current. The PMTS deposited with an optimized bias voltage and current revealed excellent measurement performance, and is expected to be applied to the bolt, to detect the pretightening force.
Chemical Composition and Antioxidant Activity of Artemisia argyi Essential Oil and Hydrolate
Authors: Chizzola Remigius , Gansinger Doris
Published: September 2023

Artemisia argyi, Chinese mugwort, is a plant widely used in China for various purposes from traditional medicine to food. The plant is less known in Europe. From plants grown in Austria, essential oils and their respective hydrolates were obtained, and their compositions were studied. Oxidized monoterpenes 1,8-cineole (32–42%), camphor (12–14%), and borneol (10–12%) were the main components present in both the essential oils and hydrolates. The essential oils also contained 6.6–10.5% monoterpene hydrocarbons such as β-caroyphyllene, camphene, and p-cymene. The hydrolate volatile fractions were devoid of hydrocarbons because of the low solubility of these compounds in water. Neointermedeol (selin-11-en-4-α-ol), a rather rare compound, and caryophyllene oxide were the major oxidized sesquiterpenes in the essential oils and were also present in low levels in the hydrolate volatiles. Furthermore, small amounts of eugenol were in the hydrolate volatiles. The essential oils and hydrolates showed some antioxidant activities in the DPPH and FRAP assays. Essential oils diluted 1:100 corresponded to gallic acid equivalents of 212–274 µg/mL in the FRAP assay and 26.1–30.7 µg/mL in the DPPH assay, while the ranges of activity for the hydrolates corresponded to gallic acid equivalents of 109–597 and 10.5–31.7 µg/mL for FRAP and DPPH assays, respectively.
Evaluation of Diverse Sorghum for Leaf Dhurrin Content and Post-Anthesis (Stay-Green) Drought Tolerance
Authors: Hayes Chad , Emendack Yves , Sanchez Jacobo , Burke John , Pugh N. Ace , Xin Zhanguo , Rooney William
Published: September 2023

Post-flowering drought tolerance (stay-green) in grain sorghum (Sorghum bicolor (L.) Moench) is an important agronomic trait in many arid and semiarid environments throughout the world. Stay-green has been associated with increased grain yields, as well as resistance to lodging and charcoal rot disease. Nonetheless, the relative effects of genotype, environment, and genotype × environment interactions are not well understood for this trait; similarly, the relationship between various leaf sugars and stay-green has not been sufficiently evaluated in diverse germplasm. Thus, the goals of this study were to determine the genotype, environment, and genotype by environment (GxE) effects for leaf dhurrin, sugars, and stay-green in ten diverse grain sorghum breeding lines, to evaluate the Pearson’s correlation coefficients (r) between these traits, and to determine entry-mean repeatability (R) for each of these traits. Of the compositional traits studied, we determined that leaf dhurrin had the highest correlation with the stay-green phenotypes (r = −0.62). We found that stay-green sorghum lines contained approximately 2–3 times as much dhurrin as non-stay-green lines, with B1778 containing the highest concentration of dhurrin (84.8 µg/cm2) and Tx7000 containing the least (20.9 µg/cm2). The differences between the environments for several of the traits were high, and all the traits examined had high repeatability (R = 0.89–0.92). These data demonstrate a relationship between leaf dhurrin and the stay-green phenotypes in sorghum, and further study will allow researchers to determine the causal effect that dhurrin has on post-flowering drought tolerance in sorghum.
Time-Domain Identification Method Based on Data-Driven Intelligent Correction of Aerodynamic Parameters of Fixed-Wing UAV
Authors: Yang Dapeng , Zang Jianwen , Liu Jun , Liu Kai
Published: September 2023

In order to overcome the influence of complex environmental disturbance factors such as nonlinear time-varying characteristics on the dynamic control performance of small fixed-wing UAVs, the nonlinear expression relationship of neural networks (NNs) is combined with the recursive least squares (RLSs) identification algorithm. This paper proposes a hybrid aerodynamic parameter identification method based on NN-RLS offline network training and online learning correction. The simulation results show that compared with the real value of the identification value obtained by this algorithm, the residual error of the moment coefficient is reduced by 69%, and the residual error of the force coefficient is reduced by 89%. Under the same identification accuracy, the identification time is shortened from the original 0.1 s to 0.01 s. Compared with traditional identification algorithms, better estimation results can be obtained. By using this algorithm to continuously update the NN model and iterate repeatedly, iterative learning for complex dynamic models can be realized, providing support for the optimization of UAV control schemes.
Reconfiguration for UAV Formation: A Novel Method Based on Modified Artificial Bee Colony Algorithm
Authors: Yang Zipeng , Yang Futing , Mao Tianqi , Xiao Zhenyu , Han Zhu , Xia Xianggen
Published: September 2023

The flight formation of unmanned aerial vehicles (UAVs) needs to be reconfigured whenever necessary to cope with complex environments and varying tasks. However, the continuity, nonlinearity and high dimensionality of the UAV formation control parameters bring significant challenges to the efficiency and safety of UAV formation reconfiguration. To this end, this paper proposes a reconfiguration strategy of the UAV formation based on a modified Artificial Bee Colony (ABC) algorithm, which ensures superior efficiency and safety level simultaneously. Specifically, we first formulate the formation reconfiguration problem minimizing the time consumed for reconfiguration under the constraints of safety and connection. Then the continuous optimization problem is discretized by using the control parameterization and time discretization (CPTD) method. Finally, we use a modified ABC algorithm to find the solution of formation reconfiguration. Extensive performance evaluations are conducted to verify the superiority of the proposed method. It is concluded that the proposed algorithm achieves a better performance than the existing approaches in literature in solving the problem of 3-D formation reconfiguration.
MobileRaT: A Lightweight Radio Transformer Method for Automatic Modulation Classification in Drone Communication Systems
Authors: Zheng Qinghe , Tian Xinyu , Yu Zhiguo , Ding Yao , Elhanashi Abdussalam , Saponara Sergio , Kpalma Kidiyo
Published: September 2023

Nowadays, automatic modulation classification (AMC) has become a key component of next-generation drone communication systems, which are crucial for improving communication efficiency in non-cooperative environments. The contradiction between the accuracy and efficiency of current methods hinders the practical application of AMC in drone communication systems. In this paper, we propose a real-time AMC method based on the lightweight mobile radio transformer (MobileRaT). The constructed radio transformer is trained iteratively, accompanied by pruning redundant weights based on information entropy, so it can learn robust modulation knowledge from multimodal signal representations for the AMC task. To the best of our knowledge, this is the first attempt in which the pruning technique and a lightweight transformer model are integrated and applied to processing temporal signals, ensuring AMC accuracy while also improving its inference efficiency. Finally, the experimental results—by comparing MobileRaT with a series of state-of-the-art methods based on two public datasets—have verified its superiority. Two models, MobileRaT-A and MobileRaT-B, were used to process RadioML 2018.01A and RadioML 2016.10A to achieve average AMC accuracies of 65.9% and 62.3% and the highest AMC accuracies of 98.4% and 99.2% at +18 dB and +14 dB, respectively. Ablation studies were conducted to demonstrate the robustness of MobileRaT to hyper-parameters and signal representations. All the experimental results indicate the adaptability of MobileRaT to communication conditions and that MobileRaT can be deployed on the receivers of drones to achieve air-to-air and air-to-ground cognitive communication in less demanding communication scenarios.
Psychological Evaluation of a Residential Children’s Burns Camp Programme: A Ten-Year Evaluation
Authors: Thomlinson Alison M. , Farrell Niamh R. , Shah Mamta , Gaskell Sarah L.
Published: September 2023

Residential burns camp programmes provide help and support to children with burn injuries by providing activities designed to build their confidence and self-esteem. Our regional burns service has been running camps for over 20 years and evaluation is an important part of assessing their effectiveness. In this study, we report both qualitative and quantitative data from 10 consecutive years of burns camps. Qualitative feedback was gathered using Likert scales and free-response questionnaires at the end of camp and six weeks post-camp. Three quantitative outcome measures, the Paediatric Quality of Life Inventory v4, the Children’s Revised Impact of Event Scale (CRIES8) and the Satisfaction with Appearance scale (SWAP), were completed before and six weeks after camp. Both children and their parents/carers reported that attending the burns camp was helpful for them/their child; meeting other children with burn injuries and developing confidence and self-esteem were cited as reasons. Parents/carers also reported improvements in their child’s physical and psychological functioning post-camp in some years, although these results were not as clear as the qualitative findings. These findings confirm the importance of providing burns camp programmes for children with burn injuries as part of their post-burn rehabilitation.
Assessing the Usability of ChatGPT for Formal English Language Learning
Authors: Shaikh Sarang , Yayilgan Sule Yildirim , Klimova Blanka , Pikhart Marcel
Published: September 2023

Recently, the emerging technologies have been constantly shaping the education domain, especially the use of artificial intelligence (AI) for language learning, which has attracted significant attention. Many of the AI tools are being used for learning foreign languages, in both formal and informal ways. There are many studies that have explored the potential of the recent technology “ChatGPT” for education and learning languages, but none of the existing studies have conducted any exploratory study for assessing the usability of ChatGPT. This paper conducts an assessment for usability of ChatGPT for formal English language learning. The study uses a standard questionnaire-based approach to ask participants about their feedback for usefulness and effectiveness of ChatGPT. The participants were asked for their feedback after performing series of tasks related to formal English language learning with ChatGPT. A variety of student participants were selected for this study with diverse English language proficiency levels, education levels, and nationalities. The quantitative analysis of the participant responses shed light on their experience with regards to the usability of ChatGPT for performing different English language learning tasks such as conversation, writing, grammar, and vocabulary. The findings from this study are quite promising and indicate that ChatGPT is an effective tool to be used for formal English language learning. Overall, this study contributes to the fast-growing research domain on using emerging technologies for formal English language learning by conducting in-depth assessment of usability for ChatGPT in formal English language learning.
A Deep Learning-Enhanced Stereo Matching Method and Its Application to Bin Picking Problems Involving Tiny Cubic Workpieces
Authors: Yoshizawa Masaru , Motegi Kazuhiro , Shiraishi Yoichi
Published: September 2023

This paper proposes a stereo matching method enhanced by object detection and instance segmentation results obtained through the use of a deep convolutional neural network. Then, this method is applied to generate a picking plan to solve bin picking problems, that is, to automatically pick up objects with random poses in a stack using a robotic arm. The system configuration and bin picking process flow are suggested using the proposed method, and it is applied to bin picking problems, especially those involving tiny cubic workpieces. The picking plan is generated by applying the Harris corner detection algorithm to the point cloud in the generated three-dimensional map. In the experiments, two kinds of stacks consisting of cubic workpieces with an edge length of 10 mm or 5 mm are tested for bin picking. In the first bin picking problem, all workpieces are successfully picked up, whereas in the second, the depths of the workpieces are obtained, but the instance segmentation process is not completed. In future work, not only cubic workpieces but also other arbitrarily shaped workpieces should be recognized in various types of bin picking problems.
Comparison of 2L + 2M and 6L SVPWM for Five-Phase Inverter to Reduce Common Mode Voltage
Authors: Tawfiq Kotb B. , Mansour Arafa S. , Sergeant Peter
Published: September 2023

Multiphase drives have received a lot of interest because of their several features over traditional three-phase systems for high-power applications. Pulse-width modulation (PWM) approaches are necessary to regulate the supply for multiphase ac drives. As a result, it is vital to continually improve the modulation and control approaches used to upgrade output power converters’ quality. This paper offers a comparative analysis of the 2L + 2M and 6L space vector pulse-width modulation (SVPWM) techniques applied to a five-phase two-level voltage source inverter (VSI) fed an inductive (R-L) load. The comparative evaluation is based on measuring the inverter switching losses, the total harmonic distortion (THD) values, and the common mode voltage (CMV) under different operation scenarios. A system model is carried out by MATLAB/Simulink. An experimental prototype is constructed in the lab to validate the theoretical analysis. Simulation results for the system based on the two SVPWM techniques are obtained at different modulation indices and different output frequencies and are confirmed by the experimental results. It has been found that the peak-to-peak CMV of the 6L method is 80% lower than that of the 2L + 2M method. Moreover, 6L SVPWM offers better DC-link utilization compared to 2L + 2M SVPWM.
A Study on Webtoon Generation Using CLIP and Diffusion Models
Authors: Yu Kyungho , Kim Hyoungju , Kim Jeongin , Chun Chanjun , Kim Pankoo
Published: September 2023

This study focuses on harnessing deep-learning-based text-to-image transformation techniques to help webtoon creators’ creative outputs. We converted publicly available datasets (e.g., MSCOCO) into a multimodal webtoon dataset using CartoonGAN. First, the dataset was leveraged for training contrastive language image pre-training (CLIP), a model composed of multi-lingual BERT and a Vision Transformer that learnt to associate text with images. Second, a pre-trained diffusion model was employed to generate webtoons through text and text-similar image input. The webtoon dataset comprised treatments (i.e., textual descriptions) paired with their corresponding webtoon illustrations. CLIP (operating through contrastive learning) extracted features from different data modalities and aligned similar data more closely within the same feature space while pushing dissimilar data apart. This model learnt the relationships between various modalities in multimodal data. To generate webtoons using the diffusion model, the process involved providing the CLIP features of the desired webtoon’s text with those of the most text-similar image to a pre-trained diffusion model. Experiments were conducted using both single- and continuous-text inputs to generate webtoons. In the experiments, both single-text and continuous-text inputs were used to generate webtoons, and the results showed an inception score of 7.14 when using continuous-text inputs. The text-to-image technology developed here could streamline the webtoon creation process for artists by enabling the efficient generation of webtoons based on the provided text. However, the current inability to generate webtoons from multiple sentences or images while maintaining a consistent artistic style was noted. Therefore, further research is imperative to develop a text-to-image model capable of handling multi-sentence and -lingual input while ensuring coherence in the artistic style across the generated webtoon images.
Towards Privacy-Preserving Federated Neuromorphic Learning via Spiking Neuron Models
Authors: Han Bing , Fu Qiang , Zhang Xinliang
Published: September 2023

Federated learning (FL) has been broadly adopted in both academia and industry in recent years. As a bridge to connect the so-called “data islands”, FL has contributed greatly to promoting data utilization. In particular, FL enables disjoint entities to cooperatively train a shared model, while protecting each participant’s data privacy. However, current FL frameworks cannot offer privacy protection and reduce the computation overhead at the same time. Therefore, its implementation in practical scenarios, such as edge computing, is limited. In this paper, we propose a novel FL framework with spiking neuron models and differential privacy, which simultaneously provides theoretically guaranteed privacy protection and achieves low energy consumption. We model the local forward propagation process in a discrete way similar to nerve signal travel in the human brain. Since neurons only fire when the accumulated membrane potential exceeds a threshold, spiking neuron models require significantly lower energy compared to traditional neural networks. In addition, to protect sensitive information in model gradients, we add differently private noise in both the local training phase and server aggregation phase. Empirical evaluation results show that our proposal can effectively reduce the accuracy of membership inference attacks and property inference attacks, while maintaining a relatively low energy cost. blueFor example, the attack accuracy of a membership inference attack drops to 43% in some scenarios. As a result, our proposed FL framework can work well in large-scale cross-device learning scenarios.
Optimizing Long Short-Term Memory Network for Air Pollution Prediction Using a Novel Binary Chimp Optimization Algorithm
Authors: Baniasadi Sahba , Salehi Reza , Soltani Sepehr , Martín Diego , Pourmand Parmida , Ghafourian Ehsan
Published: September 2023

Elevated levels of fine particulate matter (PM2.5) in the atmosphere present substantial risks to human health and welfare. The accurate assessment of PM2.5 concentrations plays a pivotal role in facilitating prompt responses by pertinent regulatory bodies to mitigate air pollution. Additionally, it furnishes indispensable information for epidemiological studies concentrating on PM2.5 exposure. In recent years, predictive models based on deep learning (DL) have offered promise in improving the accuracy and efficiency of air quality forecasts when compared to other approaches. Long short-term memory (LSTM) networks have proven to be effective in time series forecasting tasks, including air pollution prediction. However, optimizing LSTM models for enhanced accuracy and efficiency remains an ongoing research area. In this paper, we propose a novel approach that integrates the novel binary chimp optimization algorithm (BChOA) with LSTM networks to optimize air pollution prediction models. The proposed BChOA, inspired by the social behavior of chimpanzees, provides a powerful optimization technique to fine-tune the LSTM architecture and optimize its parameters. The evaluation of the results is performed using cross-validation methods such as the coefficient of determination (R2), accuracy, the root mean square error (RMSE), and receiver operating characteristic (ROC) curve. Additionally, the performance of the BChOA-LSTM model is compared against eight DL architectures. Experimental evaluations using real-world air pollution data demonstrate the superior performance of the proposed BChOA-based LSTM model compared to traditional LSTM models and other optimization algorithms. The BChOA-LSTM model achieved the highest accuracy of 96.41% on the validation datasets, making it the most successful approach. The results show that the BChOA-LSTM architecture performs better than the other architectures in terms of the  R2 convergence curve, RMSE, and accuracy.
MultiNet-GS: Structured Road Perception Model Based on Multi-Task Convolutional Neural Network
Authors: Li Ang , Zhang Zhaoyang , Sun Shijie , Feng Mingtao , Wu Chengzhong
Published: September 2023

In order to address the issue of environmental perception in autonomous driving on structured roads, we propose MultiNet-GS, a convolutional neural network model based on an encoder–decoder architecture that tackles multiple tasks simultaneously. We use the main structure of the latest object detection model, the YOLOv8 model, as the encoder structure of our model. We introduce a new dynamic sparse attention mechanism, BiFormer, in the feature extraction part of the model to achieve more flexible computing resource allocation, which can significantly improve the computational efficiency and occupy a small computational overhead. We introduce a lightweight convolution, GSConv, in the feature fusion part of the network, which is used to build the neck part into a new slim-neck structure so as to reduce the computational complexity and inference time of the detector. We also add an additional detector for tiny objects to the conventional three-head detector structure. Finally, we introduce a lane detection method based on guide lines in the lane detection part, which can aggregate the lane feature information into multiple key points, obtain the lane heat map response through conditional convolution, and then describe the lane line through the adaptive decoder, which effectively makes up for the shortcomings of the traditional lane detection method. Our comparative experiments on the BDD100K dataset on the embedded platform NVIDIA Jetson TX2 show that compared with SOTA(YOLOPv2), the mAP@0.5 of the model in traffic object detection reaches 82.1%, which is increased by 2.7%. The accuracy of the model in drivable area detection reaches 93.2%, which is increased by 0.5%. The accuracy of the model in lane detection reaches 85.7%, which is increased by 4.3%. The Params and FLOPs of the model reach 47.5 M and 117.5, which are reduced by 6.6 M and 8.3, respectively. The model achieves 72 FPS, which is increased by 5. Our MultiNet-GS model has the highest detection accuracy among the current mainstream models while maintaining a good detection speed and has certain superiority.
The Synergy between a Humanoid Robot and Whisper: Bridging a Gap in Education
Authors: Pande Akshara , Mishra Deepti
Published: September 2023

Students may encounter problems concentrating during a lecture due to various reasons, which can be related to the educator’s accent or the student’s auditory difficulties. This may lead to reduced participation and poor performance in the class. In this paper, we explored whether the incorporation of the humanoid robot Pepper can help in improving the learning experience. Pepper can capture the audio of a person; however, there is no guarantee of accuracy of the recorded audio due to various factors. Therefore, we investigated the limitations of Pepper’s speech recognition system with the aim of observing the effect of distance, age, gender, and the complexity of statements. We conducted an experiment with eight persons including five females and three males who spoke provided statements at different distances. These statements were classified using different statistical scores. Pepper does not have the functionality to transcribe speeches into text. To overcome this problem, we integrated Pepper with a speech-to-text recognition tool, Whisper, which transcribes speech into text that can be displayed on Pepper’s screen using its service. The purpose of the study is to develop a system where the humanoid robot Pepper and the speech-to-text recognition tool Whisper act in synergy to bridge the gap between verbal and visual communication in education. This system could be beneficial for students as they will better understand the content through the visual representation of the teacher’s spoken words regardless of any hearing impairments and accent problems. The methodology involves recording the participant’s speech, followed by its transcription to text by Whisper, and then evaluation of the generated text using various statistical scores. We anticipate that the proposed system will be able to increase the student’s learning experience, engagement, and immersion in a classroom environment.