bluuebunny/crossref_metadata_2025 huggingface dataset:
Current Trends in Antimicrobial Resistance Patterns in Bacterial Pathogens among Adult and Pediatric Patients in the Intensive Care Unit in a Tertiary Care Hospital in Kolkata, India
Authors: Chakraborty Mandira , Sardar Sayani , De Rituparna , Biswas Malabika , Mascellino Maria Teresa , Miele Maria Claudia , Biswas Silpak , Mitra Anita Nandi
Published: February 2023

Nosocomial infections by multidrug-resistant (MDR) bacteria are among the main causes of morbidity and death in patients hospitalized in intensive care units (ICUs) worldwide. Antibiotic resistance has become a major concern for treating the patients with nosocomial infections. The aim of this study was to describe the antibiotic resistance patterns of pathogens causing infections in adult and pediatric patients in the ICUs of a tertiary care hospital in Kolkata, India. A cross-sectional, retrospective study was conducted from January 2022 to October 2022 on a total of 139 adult and 146 pediatric patients. Depending on clinical symptoms of the patients, samples were collected and subjected to antibiotic sensitivity testing. The culture and sensitivity pattern of clinical isolates from blood, urine, sputum, endotracheal tube (ET) aspirate, and central line catheter insertion site swabs were analyzed. A total of 695 and 556 specimens were obtained from adult and pediatric ICU, respectively. Culture positivity rate among adults and pediatric patients were 37% and 40%, respectively. The most commonly isolated organisms were Gram-negative Enterobacterales and non-fermenters. Most of the bacterial isolates showed very high resistance against multiple antibiotics. Escherichia coli from adult and pediatricpatients were found to be resistant to second generation cephalosporins (95% and 96%, respectively), beta-lactams (95% and 63%, respectively), fluoroquinolones (95% and 81%, respectively), and cotrimoxazole (85% and 78%, respectively). Klebsiella spp. from adult patients were found to be resistant to aminoglycosides (75%), second generation cephalosporins (100%), beta-lactams (94%), fluoroquinolones (92%), carbapenems (88%), and cotrimoxazole (83%). Proteus spp., Acinetobacter baumannii, and Pseudomonas spp. werefound to be resistant to multiple antibiotics. Enterococcus spp. from ICUs showed more than 90% resistance against ampicillin and more than 75% resistance against fluoroquinolones. MDR bacterial infections are increasing in both adult and pediatric ICUs, leading to significant therapeutic challenges. A frequent study of antimicrobial resistance patterns is imperative for antibiotic stewardshipin combatting the deadly effect of the MDR bacteria in critically ill patients.
Clinical Resistant Strains of Enterococci and Their Correlation to Reduced Susceptibility to Biocides: Phenotypic and Genotypic Analysis of Macrolides, Lincosamides, and Streptogramins
Authors: Abu Lila Amr Selim , Alharby Tareq Nafea , Alanazi Jowaher , Alanazi Muteb , Abdallah Marwa H. , Rizvi Syed Mohd Danish , Moin Afrasim , Khafagy El-Sayed , Tabrez Shams , Al Balushi Abdullah Ali , Hegazy Wael A. H.
Published: February 2023

Enterococci are troublesome nosocomial, opportunistic Gram-positive cocci bacteria showing enhanced resistance to many commonly used antibiotics. This study aims to investigate the prevalence and genetic basis of antibiotic resistance to macrolides, lincosamides, and streptogramins (MLS) in Enterococci, as well as the correlation between MLS resistance and biocide resistance. From 913 clinical isolates collected from King Khalid Hospital, Hail, Saudi Arabia, 131 isolates were identified as Enterococci spp. The susceptibility of the clinical enterococcal isolates to several MLS antibiotics was determined, and the resistance phenotype was detected by the triple disk method. The MLS-involved resistance genes were screened in the resistant isolates. The current results showed high resistance rates to MLS antibiotics, and the constitutive resistance to all MLS (cMLS) was the most prevalent phenotype, observed in 76.8% of resistant isolates. By screening the MLS resistance-encoding genes in the resistant isolates, the erythromycin ribosome methylase (erm) genes that are responsible for methylation of bacterial 23S rRNA were the most detected genes, in particular, ermB. The ereA esterase-encoding gene was the most detected MLS modifying-encoding genes, more than lnuA (adenylation) and mphC (phosphorylation). The minimum inhibitory concentrations (MICs) of commonly used biocides were detected in resistant isolates and correlated with the MICs of MLS antibiotics. The present findings showed a significant correlation between MLS resistance and reduced susceptibility to biocides. In compliance with the high incidence of the efflux-encoding genes, especially mefA and mefE genes in the tolerant isolates with higher MICs to both MLS antibiotics and biocides, the efflux of resistant isolates was quantified, and there was a significant increase in the efflux of resistant isolates with higher MICs as compared to those with lower MICs. This could explain the crucial role of efflux in developing cross-resistance to both MLS antibiotics and biocides.
Antimicrobial Consumption from 2017 to 2021 in East Trinidad and Tobago: A Study in the English-Speaking Caribbean
Authors: Nagassar Rajeev P. , Jalim Narin , Mitchell Arianne , Harrinanan Ashley , Mohammed Anisa , Dookeeram Darren K. , Marin Danini , Giangreco Lucia , Lichtenberger Paola , Marin Gustavo H.
Published: February 2023

An antimicrobial consumption (AMC) study was performed in Trinidad and Tobago at the Eastern Regional Health Authority (ERHA). A retrospective, cross-sectional survey was conducted from 1 November 2021 to 30 March 2022. Dosage and package types of amoxicillin, azithromycin, co-amoxiclav, cefuroxime, ciprofloxacin, levofloxacin, moxifloxacin, nitrofurantoin and co-trimoxazole were investigated. Consumption was measured using the World Health Organization’s Antimicrobial Resistance and Consumption Surveillance System methodology version 1.0, as defined daily doses (DDD) per 1000 population per day (DID). They were also analyzed using the ‘Access’, ‘Watch’ and ‘Reserve’ classifications. In the ERHA, AMC ranged from 6.9 DID to 4.6 DID. With regards to intravenous formulations, the ‘Watch’ group displayed increased consumption, from 0.160 DID in 2017 to 0.238 DID in 2019, followed by a subsequent drop in consumption with the onset of the COVID-19 pandemic. Oral co-amoxiclav, oral cefuroxime, oral azithromycin and oral co-trimoxazole were the most highly consumed antibiotics. The hospital started off as the higher consumer of antibiotics, but this changed to the community. The consumption of ‘Watch’ group antibiotics increased from 2017 to 2021, with a drop in consumption of ‘Access’ antibiotics and at the onset of COVID-19. Consumption of oral azithromycin was higher in 2021 than 2020.
Impact of NG-Test CTX-M MULTI Immunochromatographic Assay on Antimicrobial Management of Escherichia coli Bloodstream Infections
Authors: Boattini Matteo , Bianco Gabriele , Ghibaudo Davide , Comini Sara , Corcione Silvia , Cavallo Rossana , De Rosa Francesco Giuseppe , Costa Cristina
Published: February 2023

Rapid detection of extended-spectrum-β-lactamase (ESBL) is of paramount importance to accelerate clinical decision-making, optimize antibiotic treatment, and implement adequate infection control measures. This study was aimed at assessing the impact of direct detection of CTX-M ESBL-producers on antimicrobial management of Escherichia coli bloodstream infections over a 2-year period. This study included all E. coli bloodstream infection (BSI) events that were serially processed through a rapid workflow with communication to the clinicians of direct detection of CTX-M ESBL-producers and conventional culture-based workflow. Antimicrobial management was retrospectively analyzed to assess the contribution of the rapid test result. A total of 199 E. coli BSI events with a report of direct detection of CTX-M ESBL production results were included. Of these, 33.7% (n = 67) and 66.3% (n = 132) were reported as positive and negative CTX-M producers, respectively. Detection of CTX-M positive results induced more antibiotic therapy modifications (mainly towards carbapenem-containing regimens, p < 0.01), and antimicrobial susceptibility testing results of CTX-M ESBL-producing E. coli isolates induced more antibiotic escalations towards carbapenem-containing regimens (p < 0.01). Direct detection of CTX-M ESBL-producing E. coli resulted in a remarkable rate of antibiotic optimizations on the same day of blood culture processing. Observing antibiotic management following the availability of antimicrobial susceptibility testing results, additional early optimizations in escalation could probably have been made if the rapid test data had been used. Detection of CTX-M negative results resulted in few therapeutic changes, which could have probably been higher, integrating epidemiological and clinical data.
Designs of Particle-Swarm-Optimization-Based Intelligent PID Controllers and DC/DC Buck Converters for PEM Fuel-Cell-Powered Four-Wheeled Automated Guided Vehicle
Authors: Demir Mehmet Hakan , Demirok Mehmet
Published: February 2023

For automatic guided vehicles (AGVs), maximizing the operating time with maximum energy efficiency is the most important factor that increases work efficiency. In this study, the fuel-cell-powered AGV (FCAGV) system was modeled and optimized control and design were carried out to obtain high tracking performance with minimum power consumption. Firstly, a full mathematical model of FCAGV, which involves the AGV, the fuel cell, DC/DC converters and motors, was obtained. Then, particle swarm optimization (PSO)-based intelligent PID and I controllers were developed for maximizing the route-tracking performance of AGV and voltage-tracking performance of the DC/DC converter with reduced power consumption. PSO was used to determine the optimal parameters of controllers and the values of DC/DC converters’ components. The performance of the full AGV system was analyzed for different paths. The results show that the sufficient path-tracking and voltage-tracking performance was obtained for AGV and DC/DC converters, respectively. The average tracking errors according to global coordinate system are 0.0061 m at the x axis, 0.0572 m at the y axis and 0.0228 rad at rotational axis. The obtained average voltage-tracking errors for each DC/DC converters were approximately 0.8033 V. These results indicate that the developed controllers with optimal coefficients work successfully with small voltage and path-tracking errors. During this motion, the average consumed power from the fuel cell was observed as 58.2675 W. These results show that the designed optimized intelligent controllers have sufficient performance with high energy efficiency and maximum route tracking.
Federated Learning-Based Analysis of Human Sentiments and Physical Activities in Natural Disasters
Authors: Amin Muhammad Sadiq , Loh Woong-Kee
Published: February 2023

In federated learning (FL), in addition to the training and speculating capacities of the global and local models, an appropriately annotated dataset is equally crucial. These datasets rely on annotation procedures that are error prone and laborious, which require personal inspection for training the overall dataset. In this study, we evaluate the effect of unlabeled data supplied by every participating node in active learning (AL) on the FL. We propose an AL-empowered FL paradigm that combines two application scenarios and assesses different AL techniques. We demonstrate the efficacy of AL by attaining equivalent performance in both centralized and FL with well-annotated data, utilizing limited data images with reduced human assistance during the annotation of the training sets. We establish that the proposed method is independent of the datasets and applications by assessing it using two distinct datasets and applications, human sentiments and human physical activities during natural disasters. We achieved viable results on both application domains that were relatively comparable to the optimal case, in which every data image was manually annotated and assessed (criterion 1). Consequently, a significant improvement of 5.5–6.7% was achieved using the active learning approaches on the training sets of the two datasets, which contained irrelevant images.
Strain Prediction Using Deep Learning during Solidification Crack Initiation and Growth in Laser Beam Welding of Thin Metal Sheets
Authors: Huo Wenjie , Bakir Nasim , Gumenyuk Andrey , Rethmeier Michael , Wolter Katinka
Published: February 2023

The strain field can reflect the initiation time of solidification cracks during the welding process. The traditional strain measurement is to first obtain the displacement field through digital image correlation (DIC) or optical flow and then calculate the strain field. The main disadvantage is that the calculation takes a long time, limiting its suitability to real-time applications. Recently, convolutional neural networks (CNNs) have made impressive achievements in computer vision. To build a good prediction model, the network structure and dataset are two key factors. In this paper, we first create the training and test sets containing welding cracks using the controlled tensile weldability (CTW) test and obtain the real strain fields through the Lucas–Kanade algorithm. Then, two new networks using ResNet and DenseNet as encoders are developed for strain prediction, called StrainNetR and StrainNetD. The results show that the average endpoint error (AEE) of the two networks on our test set is about 0.04, close to the real strain value. The computation time could be reduced to the millisecond level, which would greatly improve efficiency.
Age Prediction in Healthy Subjects Using RR Intervals and Heart Rate Variability: A Pilot Study Based on Deep Learning
Authors: Lee Kyung Hyun , Byun Sangwon
Published: February 2023

Autonomic cardiac regulation is affected by advancing age and can be observed by variations in R-peak to R-peak intervals (RRIs). Heart rate variability (HRV) has been investigated as a physiological marker for predicting age using machine learning. However, deep learning-based age prediction has rarely been performed using RRI data. In this study, age prediction was demonstrated in a healthy population based on RRIs using deep learning. The RRI data were extracted from 1093 healthy subjects and applied to a modified ResNet model to classify four age groups. The HRV features were evaluated using this RRI dataset to establish an HRV-based prediction model as a benchmark. In addition, an age prediction model was developed that combines RRI and HRV data. The adaptive synthetic algorithm was used because of class imbalance and a hybrid loss function that combined classification loss and mean squared error functions was implemented. Comparisons suggest that the RRI model can perform similarly to the HRV and combined models, demonstrating the potential of the RRI-based deep learning model for automated age prediction. However, these models showed limited efficacy in predicting all age groups, indicating the need for significant improvement before they can be considered reliable age prediction methods.
Improving the Creep Resistance of Hardened Cement Paste through the Addition of Wollastonite Microfibers: Evaluation Using the Micro-Indentation Technique
Authors: Doner Sami , Villada Jonathan T. , Das Sumanta
Published: February 2023

This paper evaluates the creep behavior of wollastonite-microfiber-reinforced hardened cement paste. A parametric study was performed for cementitious mixtures with partial replacement of cement using wollastonite. The samples were subjected to uniaxial compression and microindentation experiments. The compressive strength significantly improved by up to 30% for the mixture containing 10% wollastonite fiber. Microindentation experiments were performed to evaluate the creep response and time-dependent performance of both the control and the wollastonite-fiber-reinforced samples. Approximately 36% increase in creep modulus was observed with 10% wollastonite fiber content. This indicates a significant improvement in creep behavior, which can be attributed to the micro-reinforcing effect of the fibers. No significant impact was observed for time characteristic of creep with the incorporation of wollastonite fibers. Overall, the study establishes a cost-effective, sustainable, and efficient route to enhance the creep behavior of hardened cement paste for a wide range of infrastructural applications.
Capacitive Angle Sensor Research Using COMSOL Multiphysics
Authors: Wu Jianwen , Meng Zhen , Zhang Xingcheng , Mi Wei , Yan Yuepeng
Published: February 2023

This paper presents a method of simulating a capacitive sensor using COMSOL Multiphysics. A petal-form sensitive electrode structure applied to a capacitive angle sensor was simulated using COMSOL software; the angle was calculated using sines and cosines with capacitance value to fill in the simulation of the capacitive angle sensor. First, according to the construction principle of the sensor structure, a GeoGebra graphing calculator with theoretical analysis and expression of function was used to obtain the two-dimensional structure of the target petal-form sensitive structure. Second, the holistic structural model was completed by importing it into COMSOL Multiphysics and setting the electrical and mechanical rotation parameters, which realized the simulation of the rotation process. Finally, because errors were found, the structure design was improved by introducing an angle between the partition domain, and the simulation errors were reduced. The resulting curves and calculated angles were consistent with the theory; the simulation results showed that the maximum angle difference value was 0.0175° and the minimum angle difference value was 0.0018° of a single cycle structure.
Integrating Prior Knowledge into Attention for Ship Detection in SAR Images
Authors: Pan Yin , Ye Lei , Xu Yingkun , Liang Junyi
Published: February 2023

Although they have achieved great success in optical images, deep convolutional neural networks underperform for ship detection in SAR images because of the lack of color and textual features. In this paper, we propose our framework which integrates prior knowledge into neural networks by means of the attention mechanism. Because the background of ships is mostly water surface or coast, we use clustering algorithms to generate the prior knowledge map from brightness and density features. The prior knowledge map is later resized and fused with convolutional feature maps by the attention mechanism. Our experiments demonstrate that our framework is able to improve various one-stage and two-stage object detection algorithms (Faster R-CNN, RetinaNet, SSD, and YOLOv4) on two benchmark datasets (SSDD, LS-SSDD, and HRSID).
Determining the Quality of a Dataset in Clustering Terms
Authors: Rachwał Alicja , Popławska Emilia , Gorgol Izolda , Cieplak Tomasz , Pliszczuk Damian , Skowron Łukasz , Rymarczyk Tomasz
Published: February 2023

The purpose of the theoretical considerations and research conducted was to indicate the instruments with which the quality of a dataset can be verified for the segmentation of observations occurring in the dataset. The paper proposes a novel way to deal with mixed datasets containing categorical and continuous attributes in a customer segmentation task. The categorical variables were embedded using an innovative unsupervised model based on an autoencoder. The customers were then divided into groups using different clustering algorithms, based on similarity matrices. In addition to the classic k-means method and the more modern DBSCAN, three graph algorithms were used: the Louvain algorithm, the greedy algorithm and the label propagation algorithm. The research was conducted on two datasets: one containing on retail customers and the other containing wholesale customers. The Calinski–Harabasz index, Davies–Bouldins index, NMI index, Fowlkes–Mallows index and silhouette score were used to assess the quality of the clustering. It was noted that the modularity parameter for graph methods was a good indicator of whether a given set could be meaningfully divided into groups.
DTGCF: Diversified Tag-Aware Recommendation with Graph Collaborative Filtering
Authors: Zuo Yi , Liu Shengzong , Zhou Yun
Published: February 2023

In tag-aware recommender systems, users are strongly encouraged to utilize arbitrary tags to mark items of interest. These user-defined tags can be viewed as a bridge linking users and items. Most tag-aware recommendation models focus on improving the accuracy by introducing ingenious design or complicated structures to handle the tagging information appropriately. Beyond accuracy, diversity is considered to be another important indicator affecting the user satisfaction. Recommending more diverse items will provide more interesting items and commercial sales. Therefore, we propose a diversified tag-aware recommendation model based on graph collaborative filtering. The proposed model establishes a generic graph collaborative filtering framework tailored for tag-aware recommendations. To promote diversity, we adopt two modules: personalized category-boosted negative sampling to select a certain proportion of similar but negative items as negative samples for training, and adversarial learning to make the learned item representation category-free. To improve accuracy, we conduct a two-way TransTag regularization to model the relationship among users, items, and tags. Blending these modules into the proposed framework, we can optimize both the accuracy and diversity concurrently in an end-to-end manner. Experiments on Movielens datasets show that the proposed model can provide diverse recommendations while maintaining a high level of accuracy.
Selective Fragmentation of Lignocellulosic Biomass with ZnCl2·4H2O Using a Dissolution/Precipitation Method
Authors: Lara-Serrano Marta , Sboiu Daniela M. , Morales-delaRosa Silvia , Campos-Martin Jose M.
Published: February 2023

Lignocellulosic biomass dissolution in an inorganic salt hydrate (ZnCl2·4H2O) and its subsequent precipitation with water for the separation of its main compounds were investigated. For this purpose, different dissolution times and temperatures were studied, where 24 h and 70 °C were found to be the optimal choice. Three solids were obtained, which were analyzed and identified by XRD, SEM, NMR, and FTIR spectroscopy. Solid I is the undissolved part of the starting material, and it consists of lignin, which does not react with the inorganic salt hydrate and the unreacted cellulose. Solid II is a cellulose-rich solid with a low portion of hemicellulose and lignin, and Solid III is mainly pure lignin as the characterization results showed. Hemicellulose is mainly dissolved and hydrolyzed in the dissolution treatment and the amount present in all solids was very small. The reactivity of Solid I and Solid II in a hydrolysis reaction was tested (0.2 M/L H2SO4, 5 h, and 140 °C), where a significant improvement in the conversion and the yield of sugars was obtained with respect to the untreated samples in both cases. Solid II yields a large amount of total reducing sugars, with a % selectivity of 78–88%, depending on the starting biomass.
Conceptual Design of a Semi-Dual Polarized Monopulse Antenna by Computer Simulation
Authors: Monebi Ayodeji Matthew , Otgonbat Delger , Ahn Bierng-Chearl , Lee Chan-Soo , Ahn Jae-Hyeong
Published: February 2023

Presented in this paper is a conceptual design by computer simulation of a monopulse reflector antenna with dual-circularly polarized sum patterns and linearly polarized azimuth and elevation difference patterns, which can be called a semi-dual polarized antenna. The proposed antenna consists of a five-element monopulse feed and a prime-focus parabolic reflector. The novelty of the proposed antenna is a monopulse feed consisting of a dual-circularly polarized square waveguide sum channel radiator and linearly polarized rectangular waveguide azimuth and elevation difference channel radiators. The separation of dual circular polarization is realized by a septum polarizer. The difference pattern is obtained by feeding two rectangular waveguides in opposite directions using a coaxial probe. The proposed monopulse feed geometry requires only two power combiners for a monopulse comparator network while providing dual-polarized performance comparable to the full dual-polarized sum and difference channel monopulse scheme. The concept of the proposed antenna is shown in a conceptual design by computer simulation. The monopulse feed is designed first, and then combined with a parabolic reflector. The designed monopulse reflector antenna operates at 14.5–16.0 GHz, and shows excellent sum and difference pattern characteristics: 36.1–36.7 dBc sum channel directivity with 0.65 dB boresight axial ratio and 32.6–32.9 dBi difference channel directivity with 1.56–1.66° crossover angle.
Predictability of Maxillary Dentoalveolar Expansion Using Clear Aligners in Different Types of Crossbites
Authors: Nogal-Coloma Ana , Yeste-Ojeda Fara , Rivero-Lesmes Juan Carlos , Martin Conchita
Published: February 2023

The aim of this study was to determine the efficacy and predictability of maxillary dentoalveolar expansion clear aligners in unilateral, bilateral, and single-tooth types of crossbite. This prospective analytical study enrolled adults with dentoalveolar posterior crossbite. Orthoanalyzer™ 1.7 software was used to analyze stl files of digital models before treatment (T1), predicted post-treatment outcome (T2), and observed outcome (T3). Changes in intercanine width, first and second interpremolar widths, and intermolar width were compared using a mixed ANOVA model for repeated measures (α = 0.05). The study included 46 patients aged 20–60 years, 15 with unilateral, 15 bilateral, and 16 single-tooth crossbite. In all crossbite groups, expansion was largest at the second premolar level (unilateral: 2.54 mm; bilateral:, 4.86 mm; single-tooth: 3.41 mm) (ANOVA p < 0.001) and smallest at the canine level. Expansion predictability was 90% at the first premolar level in the single-tooth crossbite group, 86% at the second premolar level in the bilateral crossbite group, and 79% at the second premolar level in the unilateral crossbite group. No significant difference was found between the predicted and observed expansion in any crossbite group. Dentoalveolar expansion using differential anchorage techniques with clear aligners is highly predictable, although the treatment plan should consider overcorrection of the expansion movement to achieve the planned outcome.
Required Visibility Level for Reliable Object Detection during Nighttime Road Traffic in Non-Urban Areas
Authors: Erkan Anil , Hoffmann David , Kreß Nikolai , Vitkov Tsoni , Kunst Korbinian , Peier Markus Alexander , Khanh Tran Quoc
Published: February 2023

Motor vehicle headlamps are the only light sources that create visibility conditions for the driver in nighttime non-urban traffic. Therefore, a suitable design of these lighting systems is of the highest relevance to allow the driver an early detection of obstacles so that an appropriate reaction is possible. In order to provide a design basis for the headlamps, this article deals with the determination of the minimum required lighting conditions for reliable object detection. For this purpose, studies with drivers were conducted in a light tunnel on a closed test site, and the Visibility Level (VL) required for reliable object detection was considered. Gray cards with a reflectance of 4% were positioned on different positions of measurement grids, and the intensity of the low beam and high beam of a test vehicle was increased step by step until the drivers had detected the gray card. The results demonstrate that a Visibility Level of at least 13.35 is required in non-urban areas in order to reliably detect objects. In addition, the required Visibility Level depends on the eccentricity angle. Thus, the required Visibility Level reaches its maximum value in the foveal area of the field of view and decreases in a Gaussian shape in the periphery.
Synthesis of New 2D-π-2A Chromophores Based on Tetraphenyl Fulvene and Investigation of Their Optical Properties
Authors: Ng Yoke Mooi , Coluccini Carmine
Published: February 2023

Emitting organic molecules can find application in Light-Emitting Diodes and as Biosensors. The new generation of organic emitters are full conjugated molecules exhibiting conformational freedom that can gain emission intensity when accumulate. We synthesized new tetraphenyl fulvene (TPF) derivatives by connecting the phenyl rings 3 and 4 with electron donor groups and the fulvene carbon 6 with two electron-withdrawing groups. We analyzed the optical properties, UV–vis absorption, and emission in different solvents with different polarities. The compound with π-donor thiophene and π-acceptor methyl malonate, named 5mT, displays the highest emission intensity compared to unsubstituted TPF, the compounds with a weak electron π-donor group on phenyl rings 3 and 4, and the weak π-withdrawing group on carbon 6 of the fulvene core. The same compound exhibits emission frequencies in solutions that vary from 435 to 495 nm in the different solvents, while the emission frequency is 435 nm independently of the solvent for the other TPF derivatives. We demonstrated that D-π-A TPF derivatives with high dipolar moments display a fluorescence that is strongly influenced by conformational status and intermolecular interactions.
Coupling RetinaFace and Depth Information to Filter False Positives
Authors: Nanni Loris , Brahnam Sheryl , Lumini Alessandra , Loreggia Andrea
Published: February 2023

Face detection is an important problem in computer vision because it enables a wide range of applications, such as facial recognition and an analysis of human behavior. The problem is challenging because of the large variations in facial appearance across different individuals and lighting and pose conditions. One way to detect faces is to utilize a highly advanced face detection method, such as RetinaFace or YOLOv7, which uses deep learning techniques to achieve high accuracy in various datasets. However, even the best face detectors can produce false positives, which can lead to incorrect or unreliable results. In this paper, we propose a method for reducing false positives in face detection by using information from a depth map. A depth map is a two-dimensional representation of the distance of objects in an image from the camera. By using the depth information, the proposed method is able to better differentiate between true faces and false positives. The method proposed by the authors is tested on a dataset of 549 images, which includes 614 upright frontal faces. The outcomes of the evaluation demonstrate that the method effectively minimizes false positives without compromising the overall detection rate. These findings suggest that incorporating depth information can enhance the accuracy of face detection.
Feasibility of Magneto-Encephalography Scan under Color-Tailored Illumination
Authors: Weerasuriya Charitha , Ng Soon Hock , Woods William , Johnstone Tom , Vitta Pranciškus , Hugrass Laila , Juodkazis Saulius
Published: February 2023

Color plays an important part in human activities, and it also affects circadian cycle and the decision making process. Therefore, it is important to investigate human judgment under different color of illumination, especially because color perception is subjective. In this study, we developed required instrumentation to control the red (R), green (G), blue (B), and amber (A) colored light emitting diode (LED) lamp for carrying out magneto-encephalography (MEG) brain scans. We developed a software to generate all the colors in the visual spectrum, predefined white light combinations and saturation of an illuminated objects by using RGBA color pallet. The lamp is required to control from outside of the MEG electromagnetically shielded room with the LED lamp located inside the MEG room. Hence, USB is used for the communication mode. The feasibility of using LED lamp with MEG brain scanner has been validated for bio-medical and psychological MEG experiments.
The Relevance of General Spatial Anticipation Skills for Basketball Referees
Authors: Schrödter Robin , Schwarting Alessa , Fasold Frowin , Schul Karsten , Klatt Stefanie
Published: February 2023

In recent years, research has shown a positive correlation between sport-specific cognitive abilities and the performance of referees in different sports. However, it is still uncertain how much of the expertise exhibited by referees results from acquired sport-specific skills versus pre-existing general abilities. This study aimed to determine if the relationship between sport-specific abilities and expertise previously observed also applies to general visual abilities. In cooperation with the German basketball federation, basketball referees were recruited for the study, resulting in the final sample of 86 participants who were grouped as elite basketball referees, amateur referees, or novices. These participants completed five tests within the Vienna Test System: time and spatial anticipation, perceptual speed, visual orientation, and spatial working memory. Five separate ANOVAs reported a significant effect of the factor expertise on the performance in spatial anticipation. Post hoc tests revealed that elite referees performed significantly better than amateur referees and novices in spatial anticipation. The superior performance of elite referees in spatial anticipation tasks suggests that their expertise may not be solely limited to acquired skills in basketball but may also be partly attributed to better general spatial anticipatory abilities that might reflect a required skill for refereeing.
GIS-Based Optimal Route Selection of Submarine Cables Considering Potential Seismic Fault Zones
Authors: Makrakis Nikolaos , Psarropoulos Prodromos N. , Tsompanakis Yiannis
Published: February 2023

Submarine lifelines (pipelines and cables) often cross areas characterized by earthquake-related geohazards (tectonic faulting, landslides and seabed liquefaction). Avoiding geologically hazardous areas increases the length (i.e., cost), whereas a potential crossing may detrimentally affect the structural performance of the infrastructure, requiring more sophisticated design approaches and/or more costly and probably impractical deep sea condition-mitigation measures. Under such adverse conditions, a cost-effective and resilient lifeline route is deemed necessary. The current paper presents a smart decision-support tool for the optimal route selection of submarine cables, assessing whether the proposed routing could effectively cross a (seismically) geologically hazardous area. The GIS-based tool is based on an efficient methodology that combines a least-cost path analysis with a multi-criteria decision method. Accordingly, several routes can be derived for user-defined scenarios, by assigning different weight factors in the adopted design criteria and hazards. When crossing fault zones, the problem of fault-cable intersection is quantitatively assessed in a realistic manner via advanced numerical models. The optimal route can be selected by considering the potential cable distress (i.e., exceedance of allowable cable strains). This tool can be efficiently implemented for deriving the optimal route of energy and telecommunication offshore cables, as it is described in the examined real case studies.
Deep Clustering Efficient Learning Network for Motion Recognition Based on Self-Attention Mechanism
Authors: Ru Tielin , Zhu Ziheng
Published: February 2023

Multi-person behavior event recognition has become an increasingly challenging research field in human–computer interaction. With the rapid development of deep learning and computer vision, it plays an important role in the inference and analysis of real sports events, that is, given the video frequency of sports events, when letting it analyze and judge the behavior trend of athletes, often faced with the limitations of large-scale data sets and hardware, it takes a lot of time, and the accuracy of the results is not high. Therefore, we propose a deep clustering learning network for motion recognition under the self-attention mechanism, which can efficiently solve the accuracy and efficiency problems of sports event analysis and judgment. This method can not only solve the problem of gradient disappearance and explosion in the recurrent neural network (RNN), but also capture the internal correlation between multiple people on the sports field for identification, etc., by using the long and short-term memory network (LSTM), and combine the motion coding information in the key frames with the deep embedded clustering (DEC) to better analyze and judge the complex behavior change types of athletes. In addition, by using the self-attention mechanism, we can not only analyze the whole process of the sports video macroscopically, but also focus on the specific attributes of the movement, extract the key posture features of the athletes, further enhance the features, effectively reduce the amount of parameters in the calculation process of self-attention, reduce the computational complexity, and maintain the ability to capture details. The accuracy and efficiency of reasoning and judgment are improved. Through verification on large video datasets of mainstream sports, we achieved high accuracy and improved the efficiency of inference and prediction. It is proved that the method is effective and feasible in the analysis and reasoning of sports videos.
MFAMNet: Multi-Scale Feature Attention Mixture Network for Short-Term Load Forecasting
Authors: Yang Shengchun , Zhu Kedong , Li Feng , Weng Liguo , Cheng Liangcheng
Published: February 2023

Short-term load forecasting is an important prerequisite for smart grid controls. The current methods are mainly based on the convolution neural network (CNN) or long short-term memory (LSTM) model to realize load forecasting. For the multi-factor input sequence, the existing methods cannot obtain multi-scale features of the time series and the important parameters of the multi-factor, resulting in low accuracy and robustness. To address these problems, a multi-scale feature attention hybrid network is proposed, which uses LSTM to extract the time correlation of the sequence and multi-scale CNN to automatically extract the multi-scale feature of the load. This work realizes the integration of features by constructing a circular network. In the proposed model, a two-branch attention mechanism is further constructed to capture the important parameters of different influencing factors to improve the model’s robustness, which can make the network to obtain effective features at the curve changes. Comparative experiments on two open test sets show that the proposed multi-scale feature attention mixture network can achieve accurate short-term load forecasting and is superior to the existing methods.
Design of a Quick-Pressing and Self-Locking Temporary Fastener for Easy Automatic Installation and Removal
Authors: Tang Wei , He Jie , Xiao Yunya , Qu Weiwei , Ye Jiying , Long Hui , Liang Chaolin
Published: February 2023

In the traditional pre-joining technology of aircraft panels, bolts are generally employed for pre-joining. Due to the length and width of panels, bilateral manual operations are required to operate bolts. In this case, there are problems such as low work efficiency, unstable quality, cumbersome operation, and inconvenient installation-removal. This paper takes a temporary fastener with one-side installation-removal as a research object and conducts in-depth research on three levels of quick-pressing: unloading, stable self-locking, and easy automatic installation. Firstly, by coordinating the ratchet and the spring, the restoring force of the spring is used to make the cylindrical top-rod rotary and realize the telescopic function to achieve quick loading and unloading of fasteners; subsequently, through the cooperation between the buckle and the spring, loading and unloading self-locking is attained; afterwards, through the threaded joining and the same cylinder design between the external profile components, the convenience of fasteners for automatic transportation is realized. When assembling two thin-walled parts of the aircraft, only continuous one-side pressing of fasteners is needed to carry out the tightening and unloading work, namely, one-pressing installation and one-pressing removal, which could solve the problems caused by the bilateral operation of traditional bolts and part tolerances. After the application of the fasteners into the pre-joining process of aircraft panels, the experiment results have shown that this temporary fastener provided a good clamping effect, could be quickly and efficiently installed and removed by continuous one-pressing, and avoided the problems of complexity and high cost for pre-joining processes.
Predicting Location of Tweets Using Machine Learning Approaches
Authors: Alsaqer Mohammed , Alelyani Salem , Mohana Mohamed , Alreemy Khalid , Alqahtani Ali
Published: February 2023

Twitter, one of the most popular microblogging platforms, has tens of millions of active users worldwide, generating hundreds of millions of posts every day. Twitter posts, referred to as “tweets”, the short and the noisy text, bring many challenges with them, such as in the case of some emergency or disaster. Predicting the location of these tweets is important for social, security, human rights, and business reasons and has raised noteworthy consideration lately. However, most Twitter users disable the geo-tagging feature, and their home locations are neither standardized nor accurate. In this study, we applied four machine learning techniques named Logistic Regression, Random Forest, Multinomial Naïve Bayes, and Support Vector Machine with and without the utilization of the geo-distance matrix for location prediction of a tweet using its textual content. Our extensive experiments on our vast collection of Arabic tweets From Saudi Arabia with different feature sets yielded promising results with 67% accuracy.
Energy Efficient Routing Protocol for an IoT-Based WSN System to Detect Forest Fires
Authors: Pedditi Ramesh Babu , Debasis Kumar
Published: February 2023

Around a third of the world’s surface area is covered with forests. This is about 4–5 billion hectares of land. We are losing an additional 3 million hectares of forest cover each year than we did in 2001. Hence, researchers worldwide have been working on the identification and prevention of forest fires more aggressively. The wireless sensor network technology for forest fire detection was established as a result of the shortcomings of standard forest fire detection techniques in real time. This paper proposes an energy-aware Internet of Things (IoT) based Wireless Sensor Network (WSN) model with an efficient clustering and routing technique for forest fire detection. The proposed model is named Energy Efficient Routing Protocol (EERP). The model decreases the energy utilization in sensor nodes by minimizing idle listening in cluster heads. EERP also minimizes the transmission of redundant data by allowing only the sensor nodes close to an event to report the event. Furthermore, the model ensures that sensor nodes with low energy levels do not become cluster heads. EERP uses multi-hop routes to send data from source nodes to the Base Station. The performance of EERP is compared with that of some existing Medium Access Control (MAC) protocols in various scenarios. The simulation results prove that the proposed model reduces energy consumption in sensor nodes significantly.
Special Issue “Advances in Multi-Agent Systems”: Editorial
Authors: Mariani Stefano , Omicini Andrea
Published: February 2023

Multi-agent systems (MAS) are collections of autonomous computational entities (the agents) capable of pro-actively pursuing goals and re-actively adapting to environment change. Agents in MAS exploit their social abilities, interacting with peers, and their situated capabilities as well, by perceiving and acting on the world around them. From distributed computing to intelligent systems, the relevance of agents and MAS as software abstractions is steadily growing as they are extensively and increasingly used to model, simulate, and build heterogeneous systems across a huge variety of diverse application scenarios and business domains, ranging from industrial manufacturing to robotics, from social simulation to applications, and more. The recent, renewed popularity of AI techniques has further spread the adoption of MAS, focusing in particular on the cognitive capabilities of agents, so that intelligent systems can be modelled and built as MAS. Along those lines, this Special Issue gathers five contributions that well represent the many diverse advancements that are currently ongoing in the MAS field.
Intelligent Model for the Reliability of the Non-Intrusive Continuous Sensors Used for the Detection of Fouling-Layer in Heat Exchanger System
Authors: Aikhuele Daniel Osezua , Ighravwe Desmond E. , Sorooshian Shahryar
Published: February 2023

Faults in this sensor must be detected on time to ensure the functionality of the entire system’s architecture and to maintain system balance, which will keep false positive rates low during the system’s operational period. False positives reduce diagnostic confidence and necessitate unnecessary and costly mitigation actions, lowering system productivity. It is on this basis that this study proposes a clustering model algorithm (K-mean clustering) to investigate and manage the reliability and performance of the sensors. The results from the implementation of the K-mean clustering method show that the running of the algorithm fits the model correctly, both for the training of the dataset and for the prediction of the cluster in each of the datasets considered. A reasonable grouping was found for the two and three clusters considered, which are represented by the colors (blue, orange, and green). These colors indicate the fault state, non-fault state, normal state, and abnormal state of the non-intrusive continuous sensor. The simulated results show the fault state in the blue region and the non-fault state in the orange region for the two clusters, while the normal state is in the blue region and the abnormal state is in the orange and green regions for the three clusters considered.
Comparative Phenolic Profiles of Monovarietal Wines from Different Croatian Regions
Authors: Kropek Matija , Štefan Maja Bival , Rajkovača Katarina , Petković Tea , Cvetnić Matija , Bolanča Tomislav , Vladimir-Knežević Sanda
Published: February 2023

The phenolic profile of wines is often used to evaluate their quality and authenticity. The phenolic composition of twenty-five commercial wines produced in different Croatian regions from eight red and nine white grape varieties was studied. A total of twenty-four polyphenols were analyzed using HPLC-DAD and classified into five groups based on their structure: phenolic acids, flavan-3-ols, anthocyanins, flavonoids, and stilbenes. The red wines contained higher concentrations of phenolic constituents than the white wines, of which gallic acid (11.8–90.3 mg/L), procyanidin B1 (13.7–63.8 mg/L), and catechin (10.5–34.5 mg/L) were the most abundant. In contrast to the white wines, great variability was observed in the red wines, with the autochthonous Plavac Mali and Babić showing the most specific phenolic profiles. The most representative phenolic components in the studied Croatian wines showed strong antioxidant activity. Gallic acid proved to be the most effective DPPH (IC50 = 0.33 µg/mL) and NO scavenger (IC50 = 12.36 µg/mL), while myricetin was the most potent inhibitor of lipid peroxidation (IC50 = 1.68 µg/mL). Our research has contributed to the characterization and varietal differentiation of Croatian wines, highlighting those rich in certain polyphenols as potential nutraceuticals.