bluuebunny/crossref_metadata_2025 huggingface dataset:
Nesting Ecology of European Hedgehogs (Erinaceus europaeus) in Urban Areas in Southeast Spain: Nest Habitat Use and Characteristics
Authors: Marco-Tresserras Jana , López-Iborra Germán M.
Published: July 2023

Appropriate nesting sites are needed for the presence of European hedgehogs (Erinaceus europaeus) in an area, along with food availability. However, little attention has been paid to them in the literature. This study aimed at analysing, for the first time, the environmental characteristics of nesting sites chosen by hedgehogs, their spatial distribution and the effect of sex and season on them in two types of urban parks in southeastern Spain. A total of 31 hedgehogs were equipped with GPS devices, and 130 hedgehog nests were located and described. Both sexes had a similar number of nests; however, the spatial distribution of the male nests was larger, and they changed nests more frequently than females. The environment around the nests and hosting structures used also differed between the sexes, with males using a higher variety of nesting structures available and females being more selective. The differences in topography and habitat composition of the two urban parks also affected hedgehog nesting ecology, especially in reference to artificial elements like cat feeders. Further studies of nest locations at a microhabitat level are needed to contribute to a better understanding of a hedgehog’s requirements, fostering the design of more effective conservation strategies.
Influence of Sex of Stranger on Responses of Shelter Dogs during Canine Behavioral Evaluations
Authors: McGuire Betty , Song Andrew
Published: July 2023

In many situations, domestic dogs display greater uneasiness with unfamiliar men than unfamiliar women. However, little is known about whether the sex of an unfamiliar person is a risk factor for stranger-directed aggression, especially with respect to behaviors less intense than biting. We analyzed data collected by behavioral staff over a 27-month period (n = 283 dogs) at a New York shelter to determine whether the sex of an unfamiliar person influenced behaviors assessed during the Stranger test of the canine behavioral evaluation. Scores ranged from 1 (calm and friendly) to 5 (will not approach stranger or unsafe to allow an approach). No concerning behaviors (scores 1–3) were assessed for 19.2% of 26 undersocialized dogs from one home and 89.9% of the remaining 257 dogs. Within the group of 257, those tested with a male stranger had significantly higher scores than those tested with a female stranger; the effect size was small to moderate. Thus, we found that dogs responded differently to male and female strangers during this testing situation, but from a practical standpoint, our findings do not warrant adjustments in how shelters conduct or interpret tests for stranger-directed aggression. Our findings also highlight the importance of early exposure to different people and situations for dogs.
Factors Affecting Toxic and Essential Trace Element Concentrations in Cow’s Milk Produced in the State of Pernambuco, Brazil
Authors: Oliveira Filho Emanuel Felipe de , López-Alonso Marta , Vieira Marcolino Guilherme , Castro Soares Pierre , Herrero-Latorre Carlos , Lopes de Mendonça Carla , de Azevedo Costa Nivaldo , Miranda Marta
Published: July 2023

The aim of this study was to provide information on the levels of toxic (Cd and Pb) and essential (Cu, Fe, and Zn) elements in cow’s milk produced in the State of Pernambuco (Brazil). A total of 142 samples of raw milk were collected, and the concentrations of essential and toxic elements were determined using inductively coupled plasma-optical emission spectrometry. In almost 30% of the samples analyzed, the Pb content exceeded the maximum level established in the Brazilian legislation (0.05 mg/L). By contrast, in all the samples, the Cd content was below the maximum allowable level (0.02 mg/L). The essential trace elements Cu, Fe, and Zn were generally present at lower concentrations than reported in other studies and can be considered within the deficient range for cow’s milk. Statistical and chemometric procedures were used to evaluate the main factors influencing the metal concentrations (proximity to major roads, presence of effluents, and milking method). The study findings demonstrate that the proximity of the farms to major roads influences the concentrations of Cd, Pb, and Cu and that this is the main factor explaining the Pb content of milk. In addition, the presence of effluents influenced the concentrations of Cu, while no relationship between the metal content and the milking method was observed. Thus, in accordance with the study findings, the consumption of cow’s milk produced in the region can be considered a risk to public health due to the high concentrations of Pb and the low concentrations of other essential minerals such as Cu, Zn, and Fe in some of the milk samples.
Detection of Leishmania spp. in Cats: Analysis of Nasal, Oral and Conjunctival Swabs by PCR and HRM
Authors: Alves-Martin Maria Fernanda , Bertozzo Thainá Valente , Aires Isabella Neves , Manzini Suzane , Paixão-Marques Mirian dos Santos , Guiraldi Lívia Maísa , dos Santos Wesley José , Sánchez Gabriela Pacheco , Curci Vera Cláudia Lorenzetti Magalhães , Richini-Pereira Virgínia Bodelão , Lucheis Simone Baldini
Published: July 2023

Background and objectives: Feline leishmaniasis (FeL) is caused by several species of parasites of the genus Leishmania. The disease can occur with the presence or absence of clinical signs, similar to those observed in other common infectious diseases. In endemic regions for FeL, the infection has been associated with dermatological lesions. Therefore, considering the search for less invasive and more effective diagnostic techniques, we aimed to investigate the presence of Leishmania spp. in domestic cats through Polymerase Chain Reaction (PCR) and high-resolution melting (HRM) analyses of conjunctival, oral, and nasal epithelial cells, and we detected the presence of anti-Leishmania IgG antibodies from serological techniques of the Immunofluorescent Antibody Test (IFAT) and ELISA. Methods: The PCR and HRM for detection of Leishmania spp. were performed on 36 samples of epithelial cells from the conjunctiva of male and female cats, collected using sterile swabs. The serological tests IFAT and ELISA were also performed. Results: The prevalence of Leishmania donovani infection was 11.1% (4/36) by PCR assay, and those results were confirmed for Leishmania species using the HRM technique. Twenty-four cats (24/36 = 66.7%) were reactive to the IFAT and twenty-two cats were reactive by the ELISA technique (22/36 = 61.1%). Interpretation and Conclusions: The use of conjunctival swabs was shown to be a non-invasive, practical, and easy-to-perform technique, and in addition to the genetic sequencing and HRM, it was able to identify the parasitic DNA of L. donovani in cats. This technique can be used for screening diagnosis in future epidemiological surveys of FeL and can be used as a complement to clinical and/or serological tests, as well as associating the clinical history of the animal, for the diagnostic conclusion.
Effect of Dietary Orange Peel Meal and Multi-Enzymes on Productive, Physiological and Nutritional Responses of Broiler Chickens
Authors: Abd El Latif Maha A. , Abdel-Wareth Ahmed A. A. , Daley Milton , Lohakare Jayant
Published: July 2023

The objective of the study was to evaluate the impact of various concentrations of orange (Citrus sinensis) peel meal (OPM), with or without the supplementation of multi-enzymes, on the growth performance, nutrient digestibility, antioxidant properties, and blood metabolic profile of broiler chickens. The experiment was conducted on 240 one-day-old Arbor Acres broiler chicks, assigned to eight dietary treatments with 30 broilers per treatment group. Four dietary orange peel meal (OPM) concentrations were supplemented, namely, the control (without OPM), and with 80, 160, and 240 g/kg of the diet. To each of these diets was added two concentrations of multi-enzyme inclusion (0 or 0.6 g as a combination of 0.5 g of Nutrikem and 0.1 g Optiphos per kg diet) in a completely randomized design in a 4 × 2 factorial arrangement. The experiment lasted until 42 days of age. Body weight gain (BWG) was influenced during the grower period (22–42 days) and the overall period (0–42 days), and the feed conversion ratio (FCR) was significantly improved by supplementations of OPM compared with the control for 22–42 days and overall (0–42 days) periods. Moreover, BWG, FCR during the grower and overall periods, and crude fiber digestibility were improved (p < 0.01) by multi-enzyme supplementation compared to the non-supplemented groups. Broilers with diets supplemented with OPM had considerably lower abdominal fat (p < 0.01) than the control. In addition, when compared to the non-supplemented enzyme group, serum T3 and T3/T4 ratios were significantly improved in response to enzyme addition. When compared to the control group, superoxide dismutase (SOD) was significantly higher in the OPM groups, showing the largest improvement in antioxidant response. Interaction effects were observed only for serum SOD levels. Based on our findings, it is recommended that OPM be used as a feed supplement for raising broilers, and adding 0.6 m g/kg of multi-enzymes could provide additional benefits to the performance of broilers.
Ethical Concerns of the Veterinarian in Relation to Experimental Animals and In Vivo Research
Authors: Kiraga Łukasz , Dzikowski Andrzej
Published: July 2023

Animal experiments, despite their controversial nature, play an indispensable role in scientific advancement and led to numerous significant discoveries. The supervision of veterinarians in the realm of in vivo research holds immense importance. However, this particular aspect of veterinary medicine, distinct from their other activities, can pose ethical challenges. Veterinarians are entrusted with the prevention of diseases, healing, and pain elimination, yet in the case of animal experiments, they witness intentional suffering and death. This article evaluates the ethical and professional deontological aspects of this issue. It explores the historical evolution of human–animal (including experimental) relationships and discusses how deontology stems from the definition of ethics. The article also examines codes of ethics for veterinarians, providing illustrative examples. It highlights that the actions of veterinarians in this domain align with their deontology and emphasises the role of veterinarians in in vivo research as viewed within current legal frameworks. In conclusion, the veterinarian’s participation in animal research is both ethically and deontologically justified, and it is also a legal requirement.
Diagnosis and Treatment of Paraneoplastic Neurologic Syndromes
Authors: Chiu Daniel , Rhee John , Gonzalez Castro L. Nicolas
Published: July 2023

Paraneoplastic antibody syndromes result from the anti-tumor antibody response against normal antigens ectopically expressed by tumor cells. Although this antibody response plays an important role in helping clear a nascent or established tumor, the engagement of antigens expressed in healthy tissues can lead to complex clinical syndromes with challenging diagnosis and management. The majority of known paraneoplastic antibody syndromes have been found to affect the central and peripheral nervous system. The present review provides an update on the pathophysiology of paraneoplastic neurologic syndromes, as well as recommendations for their diagnosis and treatment.
Anticariogenic Activity of Celastrol and Its Enhancement of Streptococcal Antagonism in Multispecies Biofilm
Authors: Li Hao , Niu Chenguang , Luo Junyuan , Huang Zhengwei , Zhou Wei
Published: July 2023

Dental caries is a chronic disease resulting from dysbiosis in the oral microbiome. Antagonism of commensal Streptococcus sanguinis and Streptococcus gordonii against cariogenic Streptococcus mutans is pivotal to keep the microecological balance. However, concerns are growing on antimicrobial agents in anticaries therapy, for broad spectrum antimicrobials may have a profound impact on the oral microbial community, especially on commensals. Here, we report celastrol, extracted from Traditional Chinese Medicine’s Tripterygium wilfordii (TW) plant, as a promising anticaries candidate. Our results revealed that celastrol showed antibacterial and antibiofilm activity against cariogenic bacteria S. mutans while exhibiting low cytotoxicity. By using a multispecies biofilm formed by S. mutans UA159, S. sanguinis SK36, and S. gordonii DL1, we observed that even at relatively low concentrations, celastrol reduced S. mutans proportion and thereby inhibited lactic acid production as well as water-insoluble glucan formation. We found that celastrol thwarted S. mutans outgrowth through the activation of pyruvate oxidase (SpxB) and H2O2-dependent antagonism between commensal oral streptococci and S. mutans. Our data reveal new anticaries properties of celastrol that enhance oral streptococcal antagonism, which thwarts S. mutans outgrowth, indicating its potential to maintain oral microbial balance for prospective anticaries therapy.
Microbiological Assessment of the FilmArray Blood Culture Identification 2 Panel: Potential Impact in Critically Ill Patients
Authors: Mauri Carola , Consonni Alessandra , Briozzo Elena , Giubbi Chiara , Meroni Elisa , Tonolo Silvia , Luzzaro Francesco
Published: July 2023

Rapid pathogen detection and characterization from positive blood cultures are crucial in the management of patients with bloodstream infections (BSI) and in achieving their improved outcomes. In this context, the FilmArray Blood Culture Identification (BCID2) panel is an FDA approved molecular test, which can quickly identify different species and resistance determinants, thus making an impact in antimicrobial practice. In this study, we analyzed 136 positive blood cultures collected from septic critically ill patients from April 2021 to March 2023 by using the FilmArray BCID2 panel, and results obtained by fast molecular analysis were compared to those obtained by routine protocols. Overall, the BCID2 panel showed a strong concordance with conventional methods, particularly in the case of monomicrobial samples, whereas some discrepancies were found in 10/32 polymicrobial samples. Of note, this technique allowed us to identify a significant number of yeasts (37/94 samples) and to unravel the presence of several resistance markers, including both Gram-positive and Gram-negative organisms. These findings strongly support the potential use of the BCID2 panel as an adjunct to the conventional microbiology methods for the management of critically ill septic patients, thus accelerating blood pathogen and resistance genes identification, focusing antibiotic therapy, and avoiding inappropriate and excessive use of drugs.
Antimicrobial Resistance, Genetic Lineages, and Biofilm Formation in Pseudomonas aeruginosa Isolated from Human Infections: An Emerging One Health Concern
Authors: Silva Adriana , Silva Vanessa , López María , Rojo-Bezares Beatriz , Carvalho José , Castro Ana , Sáenz Yolanda , Igrejas Gilberto , Poeta Patrícia
Published: July 2023

Pseudomonas aeruginosa (PA) is a leading nosocomial pathogen and has great versatility due to a complex interplay between antimicrobial resistance and virulence factors. PA has also turned into one the most relevant model organisms for the study of biofilm-associated infections. The objective of the study focused on analyzing the antimicrobial susceptibility, resistance genes, virulence factors, and biofilm formation ability of thirty-two isolates of PA. PA isolates were characterized by the following analyses: susceptibility to 12 antimicrobial agents, the presence of resistance genes and virulence factors in PCR assays, and the quantification of biofilm production as evaluated by two distinct assays. Selected PA isolates were analyzed through multilocus sequence typing (MLST). Thirty PA isolates have a multi-resistant phenotype, and most of the isolates showed high levels of resistance to the tested antibiotics. Carbapenems showed the highest prevalence of resistance. Various virulence factors were detected and, for the quantification of biofilm production, the effectiveness of different methods was assessed. The microtiter plate method showed the highest accuracy and reproducibility for detecting biofilm-producing bacteria. MLST revealed four distinct sequence types (STs) in clinical PA, with three of them considered high-risk clones of PA, namely ST175, ST235, and ST244. These clones are associated with multidrug resistance and are prevalent in hospitals worldwide. Overall, the study highlights the high prevalence of antibiotic resistance, the presence of carbapenemase genes, the diversity of virulence factors, and the importance of biofilm formation in PA clinical isolates. Understanding these factors is crucial for effective infection control measures and the development of targeted treatment strategies.
Targeting Homocysteine and Hydrogen Sulfide Balance as Future Therapeutics in Cancer Treatment
Authors: Majumder Avisek
Published: July 2023

A high level of homocysteine (Hcy) is associated with oxidative/ER stress, apoptosis, and impairment of angiogenesis, whereas hydrogen sulfide (H2S) has been found to reverse this condition. Recent studies have shown that cancer cells need to produce a high level of endogenous H2S to maintain cell proliferation, growth, viability, and migration. However, any novel mechanism that targets this balance of Hcy and H2S production has yet to be discovered or exploited. Cells require homocysteine metabolism via the methionine cycle for nucleotide synthesis, methylation, and reductive metabolism, and this pathway supports the high proliferative rate of cancer cells. Although the methionine cycle favors cancer cells for their survival and growth, this metabolism produces a massive amount of toxic Hcy that somehow cancer cells handle very well. Recently, research showed specific pathways important for balancing the antioxidative defense through H2S production in cancer cells. This review discusses the relationship between Hcy metabolism and the antiapoptotic, antioxidative, anti-inflammatory, and angiogenic effects of H2S in different cancer types. It also summarizes the historical understanding of targeting antioxidative defense systems, angiogenesis, and other protective mechanisms of cancer cells and the role of H2S production in the genesis, progression, and metastasis of cancer. This review defines a nexus of diet and precision medicine in targeting the delicate antioxidative system of cancer and explores possible future therapeutics that could exploit the Hcy and H2S balance.
The Comparison with Commercial Antioxidants, Effects on Colour, and Sensory Properties of Green Tea Powder in Butter
Authors: Çakmakçı Songül , Gülçin İlhami , Gündoğdu Engin , Ertem Öztekin Hatice , Taslimi Parham
Published: July 2023

Oxidation is one of the most important factors limiting shelf life and is a major deterioration process affecting both the sensory and nutritional quality of food. The high oxidation stability of lipids, which can be improved by the addition of antioxidants, is important for health protection, food quality, and economic reasons. In recent years, research on plant-derived antioxidants for use in human health and food has steadily increased. The aim of this study was to compare the antioxidant effects of green tea powder (GTP) in butter with those of commercial antioxidants (BHA, BHT, α-tocopherol, and Trolox). In addition, the effects on colour, sensory, gross physicochemical properties, and β-carotene content were investigated in butter. After the separation of butter into five pieces, the first part was chosen as the control sample without GTP; the second part has 100 mg/kg of BHT added to it; and the third, fourth, and fifth parts had 1, 2, and 3% of GTP added in the samples. They were stored at 4 ± 1 °C. Analysis was performed at intervals of 15 days. According to the iron reduction, CUPRAC and FRAP methods were performed, and parallel results were observed. Using the radical elimination methods (ABTS, DPPH•, and DMPD•+), IC50 values were calculated for the samples. According to the IC50 values, the GTP-containing samples were good antioxidants. The total phenolic andf β-carotene contents increased as the GTP addition increased. The addition of GTP had an antioxidant capacity equal to or higher than that of the BHT-added sample. For the production of a sensory-pleasing, greenish-coloured, new functional butter, the 1% GTP addition showed the most positive results.
Voltage Control in LV Distribution Grid Using AC Voltage Compensator with Bipolar AC/AC Matrix Choppers
Authors: Sztajmec Elżbieta , Szcześniak Paweł
Published: July 2023

The modern low-voltage distribution system is exposed to frequent changes in voltage amplitude due to the presence of high-power receivers with variable operating characteristics as well as distributed renewable energy sources whose generation levels depend on weather conditions. Maintaining the appropriate parameters related to power quality in the context of permissible voltage levels more and more often requires the use of additional voltage regulators. These are static systems in which the values of taps on transformers are changed, or dynamic compensators using power electronic converters. This article presents the continuation of research on one of the proposals of AC voltage compensators based on a bipolar AC/AC converter. The general properties of the presented system are reviewed and an analysis of the range of generated output voltages depending on the phase shift of the compensating voltage are presented. The next part of the article presents the formulas for calculating the duty cycle factors for the control functions of individual converters. The verification of the determined dependencies is presented on the basis of simulation tests of the proposed system. At the end of this article, the disadvantages of the proposed open-loop control system regulation and a proposal for further research are indicated.
Control of a Novel Parallel Mechanism for the Stabilization of Unmanned Aerial Vehicles
Authors: Chamas Mohamad Haidar , Amine Semaan , Gazo Hanna Eddie , Mokhiamar Ossama
Published: July 2023

The use of delivery drones is currently hindered by the inability of transported objects to maintain a steady position, which can result from roll-, pitch-, and heave-induced vibrations. This paper proposes a novel parallel manipulator for stabilizing the platform of unmanned aerial vehicles. The proposed mechanism builds upon an existing study of a 3-SRR/SRU parallel stabilizing mechanism by incorporating the dynamical properties of the system into the control model. The resultant control technique is then applied to both the 3-RRS and 3-SRR mechanisms, and a comparative study is conducted to identify the most reliable stabilizer for regulating the platform’s orientation. The results demonstrate that the 3-SRR mechanism exhibits superior robustness and stability characteristics compared to the other two mechanisms. Additionally, the 3-SRR mechanism is controlled using artificial neural networks, which significantly improves the accuracy and stability of the system. Overall, this research presents a novel and effective solution for stabilizing the platform of unmanned aerial vehicles, with significant implications for the development of delivery drone technology.
We Do Not Anthropomorphize a Robot Based Only on Its Cover: Context Matters too!
Authors: Dubois-Sage Marion , Jacquet Baptiste , Jamet Frank , Baratgin Jean
Published: July 2023

The increasing presence of robots in our society raises questions about how these objects are perceived by users. Individuals seem inclined to attribute human capabilities to robots, a phenomenon called anthropomorphism. Contrary to what intuition might suggest, these attributions vary according to different factors, not only robotic factors (related to the robot itself), but also situational factors (related to the interaction setting), and human factors (related to the user). The present review aims at synthesizing the results of the literature concerning the factors that influence anthropomorphism, in order to specify their impact on the perception of robots by individuals. A total of 134 experimental studies were included from 2002 to 2023. The mere appearance hypothesis and the SEEK (sociality, effectance, and elicited agent knowledge) theory are two theories attempting to explain anthropomorphism. According to the present review, which highlights the crucial role of contextual factors, the SEEK theory better explains the observations on the subject compared to the mere appearance hypothesis, although it does not explicitly explain all the factors involved (e.g., the autonomy of the robot). Moreover, the large methodological variability in the study of anthropomorphism makes the generalization of results complex. Recommendations are proposed for future studies.
Implementation of Machine Learning and Deep Learning Techniques for the Detection of Epileptic Seizures Using Intracranial Electroencephalography
Authors: Kołodziej Marcin , Majkowski Andrzej , Rysz Andrzej
Published: July 2023

The diagnosis of epilepsy primarily relies on the visual and subjective assessment of the patient’s electroencephalographic (EEG) or intracranial electroencephalographic (iEEG) signals. Neurophysiologists, based on their experience, look for characteristic discharges such as spikes and multi-spikes. One of the main challenges in epilepsy research is developing an automated system capable of detecting epileptic seizures with high sensitivity and precision. Moreover, there is an ongoing search for universal features in iEEG signals that can be easily interpreted by neurophysiologists. This article explores the possibilities, issues, and challenges associated with utilizing artificial intelligence for seizure detection using the publicly available iEEG database. The study presents standard approaches for analyzing iEEG signals, including chaos theory, energy in different frequency bands (alpha, beta, gamma, theta, and delta), wavelet transform, empirical mode decomposition, and machine learning techniques such as support vector machines. It also discusses modern deep learning algorithms such as convolutional neural networks (CNN) and long short-term memory (LSTM) networks. Our goal was to gather and comprehensively compare various artificial intelligence techniques, including both traditional machine learning methods and deep learning techniques, which are most commonly used in the field of seizure detection. Detection results were tested on a separate dataset, demonstrating classification accuracy, sensitivity, precision, and specificity of seizure detection. The best results for seizure detection were obtained with features related to iEEG signal energy (accuracy of 0.97, precision of 0.96, sensitivity of 0.99, and specificity of 0.96), as well as features related to chaos, Lyapunov exponents, and fractal dimension (accuracy, precision, sensitivity, and specificity all equal to 0.95). The application of CNN and LSTM networks yielded significantly better results (CNN: Accuracy of 0.99, precision of 0.98, sensitivity of 1, and specificity of 0.99; LSTM: Accuracy of 0.98, precision of 0.96, sensitivity of 1, and specificity of 0.99). Additionally, the use of the gradient-weighted class activation mapping algorithm identified iEEG signal fragments that played a significant role in seizure detection.
Influence of Tissue Thermophysical Characteristics and Situ-Cooling on the Detection of Breast Cancer
Authors: Al Husaini Mohammed Abdulla Salim , Habaebi Mohamed Hadi , Suliman F. M. , Islam Md Rafiqul , Elsheikh Elfatih A. A. , Muhaisen Naser A.
Published: July 2023

This article presents a numerical simulation model using COMSOL software to study breast thermophysical properties. It analyzes tumor heat at different locations within the breast, records breast surface temperatures, investigates the effects of factors such as blood perfusion, size, depth, and thermal conductivity on breast size, and applies Pennes’ bioheat formula to illustrate thermal distribution on the breast skin surface. An analysis was conducted to examine how changes in tumor location depth, size, metabolism, blood flow, and heat conductivity affect breast skin surface temperature. The simulation model results showed that the highest variations in skin temperatures for breasts with tumors and without tumors can range from 2.58 °C to 0.274 °C. Further, large breast size with a large surface area consistently reduces the temperature variations on the skin and might have difficulty in yielding observable temperature contrast. For small breast sizes, however, heat from tumor sizes below 0.5 cm might be quite difficult to detect, while tumors located deep within the breast layers could not produce observable temperature variations. Motivated by the above interesting results, an emulation experiment was conducted to enhance the observable heat and temperature background contrast, using situ-cooling gel applied to silicon breasts, while the tumor source was emulated using LEDs. The experiment was used to evaluate the effectiveness of adding situ-cooling to the breast surface area, and to study the modulated effect of tumor size and depth. Experimental results showed that situ-cooling enhances thermal contrast in breast thermal images. For example, for a tumor location at a depth of 10 cm, a difference of 6 °C can still be achieved with situ-cooling gel applied, a feat that was not possible in the simulation model. Furthermore, changes in tumor size and location depth significantly impacted surface temperature distribution.
An Improved YOLO Model for Traffic Signs Small Target Image Detection
Authors: Han Tianxin , Sun Lina , Dong Qing
Published: July 2023

Traffic sign detection significantly reduces traffic accidents, but in real-world scenarios, the considerable distance between traffic signs and in-vehicle cameras means only a small proportion of traffic signs are captured in the images. This makes far-off traffic sign detection a small object detection problem, and with fewer details in small sign objects, detection becomes increasingly challenging. In this paper, we specifically address poor localization, low accuracy, and missed detections when using You Only Look Once Version 5 (YOLOv5) for detecting small traffic sign objects. Firstly, we incorporate a decoupled head into YOLOv5’s detection algorithm, which serves to improve detection accuracy and accelerate network convergence. Then, to handle low-resolution targets better, we substitute the network’s original convolution layers with Space-to-Depth Convolution (SPD-Conv) modules. This modification enhances the model’s capacity to extract features from low-resolution traffic sign objects. Lastly, we integrate the Context Augmentation Module (CAM) into YOLOv5 by employing variable rate extended convolution. This module extracts context information from multiple receptive fields, thus providing essential supplementary information and significantly enhancing detection accuracy. Empirical results demonstrate the efficacy of our algorithm, shown by a substantial increase in object detection precision rate to 95.0%, a recall rate of 91.6%, and an average precision of 95.4%. These results represent improvements of 2.1%, 4.8% and 3.7%, respectively, when compared to the original YOLOv5 algorithm. Furthermore, when tested against other state-of-the-art methods, our proposed methodology shows superior performance.
Enhancing Phishing Email Detection through Ensemble Learning and Undersampling
Authors: Qi Qinglin , Wang Zhan , Xu Yijia , Fang Yong , Wang Changhui
Published: July 2023

In real-world scenarios, the number of phishing and benign emails is usually imbalanced, leading to traditional machine learning or deep learning algorithms being biased towards benign emails and misclassifying phishing emails. Few studies take measures to address the imbalance between them, which significantly threatens people’s financial and information security. To mitigate the impact of imbalance on the model and enhance the detection performance of phishing emails, this paper proposes two new algorithms with undersampling: the Fisher–Markov-based phishing ensemble detection (FMPED) method and the Fisher–Markov–Markov-based phishing ensemble detection (FMMPED) method. The algorithms first remove benign emails in overlapping areas, then undersample the remaining benign emails, and finally, combine the retained benign emails with phishing emails into a new training set, using ensemble learning algorithms for training and classification. Experimental results have demonstrated that the proposed algorithms outperform other machine learning and deep learning algorithms, achieving an F1-score of 0.9945, an accuracy of 0.9945, an AUC of 0.9828, and a G-mean of 0.9827.
An Improved YOLOv5s-Based Helmet Recognition Method for Electric Bikes
Authors: Huang Bingqiang , Wu Shanbao , Xiang Xinjian , Fei Zhengshun , Tian Shaohua , Hu Haibin , Weng Yunlong
Published: July 2023

This paper proposes an improved model based on YOLOv5s, specifically designed to overcome the challenges faced by current target detection algorithms in the field of electric bike helmet detection. In order to enhance the model’s ability to detect small targets and densely populated scenes, a specialized layer dedicated to small target detection and a novel loss function called Normalized Wasserstein Distance (NWD) are introduced. In order to solve the problem of increasing model parameters and complexity due to the inclusion of a small target detection layer, a Cross-Stage Partial Channel Mixing (CSPCM) on top of Convmix is designed. The collaborative fusion of CSPCM and the Deep Feature Consistency (DFC) attention mechanism makes it more suitable for hardware devices. In addition, the conventional Nearest Upsample technology is replaced with the advanced CARAFE Upsample module, further improving the accuracy of the model. Through rigorous experiments on carefully constructed datasets, the results show significant improvements in various evaluation indicators such as precision, recall, mAP.5, and mAP.95. Compared with the unmodified YOLOv5s algorithm, the proposed enhanced model achieves significant improvements of 1.1%, 8.4%, 5.2%, and 8.6% on these indicators, respectively, and these enhancements are accompanied by a reduction of 778,924 parameters. The experimental results on our constructed dataset demonstrate the superiority of the improved model and elucidate its potential applications. Furthermore, promising improvements for future research are suggested. This study introduces an efficient approach for improving the detection of electric bike helmets and verifies the effectiveness and practicality of the model through experiments. Importantly, the proposed scheme has implications for other target detection algorithms, especially in the field of small target detection.
RoboTwin Metaverse Platform for Robotic Random Bin Picking
Authors: Tsai Cheng-Han , Hernandez Eduin E. , You Xiu-Wen , Lin Hsin-Yi , Chang Jen-Yuan
Published: July 2023

Although vision-guided robotic picking systems are commonly used in factory environments, achieving rapid changeover for diverse workpiece types can still be challenging because the manual redefinition of vision software and tedious collection and annotation of datasets consistently hinder the automation process. In this paper, we present a novel approach for rapid workpiece changeover in a vision-guided robotic picking system using the proposed RoboTwin and FOVision systems. The RoboTwin system offers a realistic metaverse scene that enables tuning robot movements and gripper reactions. Additionally, it automatically generates annotated virtual images for each workpiece’s pickable point. These images serve as training datasets for an AI model and are deployed to the FOVision system, a platform that includes vision and edge computing capabilities for the robotic manipulator. The system achieves an instance segmentation mean average precision of 70% and a picking success rate of over 80% in real-world detection scenarios. The proposed approach can accelerate dataset generation by 80 times compared with manual annotation, which helps to reduce simulation-to-real gap errors and enables rapid line changeover within flexible manufacturing systems in factories.
A Systematic Parameter Analysis of Cloud Simulation Tools in Cloud Computing Environments
Authors: Shahid Muhammad Asim , Alam Muhammad Mansoor , Su’ud Mazliham Mohd
Published: July 2023

To provide various applications in various domains, a large-scale cloud data center is required. Cloud computing enables access to nearly infinite computing resources on demand. As cloud computing grows in popularity, researchers in this field must conduct real-world experiments. Configuring and running these tests in an actual cloud environment is costly. Modeling and simulation methods, on the other hand, are acceptable solutions for emulating environments in cloud computing. This research paper reviewed several simulation tools specifically for cloud computing in the literature and presented the most effective simulation methods in this research domain, as well as an analysis of a variety of cloud simulation tools. Cloud computing tools such as CloudSim, CloudSim Plus, CloudAnalyst, iFogSim, and CloudReports were evaluated. Furthermore, a parametric evaluation of cloud simulation tools is presented based on the identified parameters. Several 5-parameter tests were performed to demonstrate the capabilities of the cloud simulator. These results show the value of our proposed simulation system. CloudSim, CloudSim Plus, CloudAnalyst, iFogSim, and CloudReports are used to evaluate host processing elements, virtual machine processing elements, cloudlet processing elements, userbase average, minimum, and maximum, and cloudlet ID Start Time, Finish Time, Average Start, and Average Finish for each simulator. The outcomes compare these five simulator metrics. After reading this paper, the reader will be able to compare popular simulators in terms of supported models, architecture, and high-level features. We performed a comparative analysis of several cloud simulators based on various parameters. The goal is to provide insights for each analysis given their features, functionalities, and guidelines on the way to researchers’ preferred tools.
Variational Autoencoders for Data Augmentation in Clinical Studies
Authors: Papadopoulos Dimitris , Karalis Vangelis D.
Published: July 2023

Sample size estimation is critical in clinical trials. A sample of adequate size can provide insights into a given population, but the collection of substantial amounts of data is costly and time-intensive. The aim of this study was to introduce a novel data augmentation approach in the field of clinical trials by employing variational autoencoders (VAEs). Several forms of VAEs were developed and used for the generation of virtual subjects. Various types of VAEs were explored and employed in the production of virtual individuals, and several different scenarios were investigated. The VAE-generated data exhibited similar performance to the original data, even in cases where a small proportion of them (e.g., 30–40%) was used for the reconstruction of the generated data. Additionally, the generated data showed even higher statistical power than the original data in cases of high variability. This represents an additional advantage for the use of VAEs in situations of high variability, as they can act as noise reduction. The application of VAEs in clinical trials can be a useful tool for decreasing the required sample size and, consequently, reducing the costs and time involved. Furthermore, it aligns with ethical concerns surrounding human participation in trials.
Single-Switch Non-Isolated Resonant DC-DC Converter for Single-Input Dual-Output Applications
Authors: Díaz-Martín Cristian , Durán Eladio , Litrán Salvador P. , Álvarez José Luis , Semião Jorge
Published: July 2023

This paper describes a new configuration of Cuk and SEPIC (Single-Ended Primary Converter) ZVS-QR (zero-voltage switching quasi-resonant) combination DC-DC converter for bipolar output with a single switch. The proposed topology employs a single ground-referenced power switch, which simplifies the gate drive design with a single L-C resonant network and provides a bipolar output voltage with good regulation, acceptable efficiency and a step-down/up conversion ratio. This configuration provides dual-output voltage by switching the power switch to zero voltage, which is an interesting alternative for many applications where small size, light weight and high power density are very important aspects. In order to verify its performance, a SEPIC–Cuk Combination ZVS-QR prototype with a cost-effective commercial resonant controller was designed and tested. The experimental results show that the proposed combined topology is suitable for Single-Input Dual-Output (SIDO) applications.
Fast Calibration Method for Base Coordinates of the Dual-Robot Based on Three-Point Measurement Calibration Method
Authors: Mao Jincheng , Xu Ruidi , Ma Xiaojie , Hu Shun , Bao Xiulan
Published: July 2023

Multi-robot systems can perform more complex tasks with high precision and large loads than single-robot systems. The calibration of the base coordinate system is the basis and premise to ensure the collaborative operation between the dual-robot. In this study, a dual-robot calibration system based on calibration tooling components was established, which could quickly and accurately obtain the relative position between dual robots. Based on three reference measurement points of the orthogonal distribution in the calibration tool coordinate system, a mapping relationship between the distance from the reference measurement point to the robot end point and the parameters of the robot base system was established, and a fast dual-robot base system parameter calibration solution method based on "three-point measurement calibration" was proposed. The experimental results show that this method can quickly and accurately obtain the transformation relationship between the dual robot base coordinate systems. The accuracy and efficiency of the calibration have been greatly improved. It is of great significance for meeting the requirements of high efficiency, low cost, and easy operation in the factory application process.
Mesh-Free Analysis of a Vertical Axis Wind Turbine Using Lattice Boltzmann Method and Various Turbulence Models
Authors: Laloglu Cinar , Alpman Emre
Published: July 2023

This study aims to investigate the aerodynamic analysis of a Darrieus-type vertical axis wind turbine (VAWT) using the Lattice Boltzmann Method (LBM). The objective is to assess the accuracy and performance of the meshless LBM approach in predicting torque coefficients, velocity, turbulence intensity, and vorticity distributions for VAWT aerodynamic analysis. Two turbulence modelling approaches, Large Eddy Simulation (LES) and Reynolds-Averaged Navier-Stokes (RANS), are employed to model the flow domain. The central problem revolves around comparing the performance of different turbulence models based on their agreement with experimental results for power and torque coefficients. The findings demonstrate the effectiveness of the WALE turbulence model in achieving the best agreement with experimental data. Overall, the study provides valuable insights into applying LBM in VAWT aerodynamic analysis and highlights the advantages of the meshless approach compared to traditional CFD methods.
Surface-Modified Compounds Improve the Detection Sensitivity of Terahertz Metasurface Biosensors
Authors: Hu Junrong , Liu Quanjun , Chen Jie , Sun Peng , Lin Shangjun , Hu Fangrong
Published: July 2023

Some trace elements in the human body, such as proteins and metabolites, are closely related to human health. Changes in the concentration of proteins can usually be used as indicators for the diagnosis of diseases. On the other side, some metabolites such as lactic acid and uric acid are closely related to human motor function. A large part of cancer markers are proteins, and their concentration changes in human serum and body fluid are usually closely related to cancer diagnosis, staging and prognosis. It has always been the goal of researchers to efficiently and sensitively detect the relevant trace elements in human body. Due to the characteristics of low energy, coherence and fingerprint, terahertz (THz) waves have been widely used in the detection of substances in recent years. Metasurface sensor is a new type of sensor with unique electromagnetic characteristics based on artificial design. The emergence of THz metasurface sensors provides a new technical means for the detection of trace elements proteins. Based on the above background, the research objects in this paper are three common protein cancer markers: carcinoembryonic antigen (CEA), neuron-specific enolase (NSE) and squamous epithelial cell carcinoma antigen (SCC), in order to study the detection performance of THz metasurface sensor and its improvement effect after modification. In the experiment, the cancer marker is dropped onto the surface of sensor, and the spectrum is detected by THz time-domain system (TDS), and the frequency shift is calculated by comparing with the resonance frequency of the blank sensor. Then the experiment was repeated by changing the concentration and class of the marker, and the sensitivity of sensors was calculated by fitting the relationship between the frequency shift and the concentration of markers. After that, two compounds (halloysite nanotube and Tungsten disulfide) were used to modify the metasurface sensor, and compared with the unmodified metasurface sensor. The experimental results show that for the detection of three markers, the metasurface sensor modified with HNT has significantly improved the detection sensitivity compared with the sensor modified with WS2 and blank sensor. This provides a new means for THz metasurface sensors in the detection of biomarkers, which is suitable for biomedicine, human health monitoring and other fields.
Universal Adversarial Training Using Auxiliary Conditional Generative Model-Based Adversarial Attack Generation
Authors: Dingeto Hiskias , Kim Juntae
Published: July 2023

While Machine Learning has become the holy grail of modern-day computing, it has many security flaws that have yet to be addressed and resolved. Adversarial attacks are one of these security flaws, in which an attacker appends noise to data samples that machine learning models take as input with the aim of fooling the model. Various adversarial training methods have been proposed that augment adversarial examples in the training dataset for defense against such attacks. However, a general limitation exists where a robust model can only protect itself against adversarial attacks that are known or similar to those it was trained on. To address this limitation, this paper proposes a Universal Adversarial Training algorithm using adversarial examples generated by an Auxiliary Classifier Generative Adversarial Network (AC-GAN) in parallel with other data augmentation techniques, such as the mixup method. This method builds on a previously proposed technique, Adversarial Training, in which adversarial examples produced by gradient-based methods are augmented and added to the training data. Our method improves the AC-GAN architecture for adversarial example generation to make it more suitable for adversarial training by updating different loss terms and testing its performance against various attacks compared to other robust adversarial models. In this way, it becomes apparent that generative models are better suited for boosting adversarial robustness through adversarial training. When tested using various attack types, our proposed model had an average accuracy of 97.48% on the MNIST dataset and 94.02% on the CelebA dataset, proving that generative models have a higher chance of boosting adversarial security through adversarial training.