bluuebunny/crossref_metadata_2025 huggingface dataset:
Risk Assessment of Bisphenol A in the Korean General Population
Authors: Hwang Myungsil , Park Seon-Joo , Lee Hae-Jeung
Published: March 2023

Bisphenol A (BPA) is not a natural substance but is produced artificially during the manufacturing of various plastics. Exposure to (BPA) is a pervasive and growing concern. BPA has recently been classified as a substance of great concern by the European Union (EU). BPA is suspected to be associated with several chronic human health effects. In this study, the estimated total BPA exposure levels were based on biomonitoring of the general population, and exposure levels ranged from a mean of 0.031 to 0.042 µg/kg body weight (bw)/day, reaching up to 0.104 µg/kg bw/day in the high-exposure population. When comparing the exposure levels of BPA to some toxicological effects, such as immunotoxicity and thyroid function, a sufficient exposure margin was not secured in the high-exposure group. Food is considered the main source of exposure for the general population, but other sources of exposure may exist in the high-exposure group. As humans are primarily exposed to BPA through food, water, house dust, skin contact, and air, integrated risk management is required to reduce BPA exposure. In addition, it is considered necessary to develop a new methodology for human health evaluation in response to low-dose exposure to BPA.
Prevent Workers from Injuries in the Brewing Company via Using Digital Human Modelling Technology
Authors: Ji Xiaoxu , Hettiarachchige Ranuki O. , Littman Alexa L. E. , Lavery Nicole L. , Piovesan Davide
Published: March 2023

A large percentage of musculoskeletal disorder cases occur in brewing companies. The aim of this research study is to evaluate the risk of injuries for workers in the local brewing industry by integrating the actual human motion, which was captured by the Xsens MVN Awinda motion tracking system, with the JACK Siemens ergonomics tools. This proposed fusion technology greatly overcomes the time-consuming issue in the traditionally full-body simulation and the posture sensitivity issue in the current digital human modelling (DHM) technology. In this study, the subjects performed a series of daily lifting tasks utilizing 72 kg kegs. The forces exerted on the lower back of brewery workers were fully analyzed. The maximum load applied on the hands for each of the tasks was also estimated to prevent workers from injuries. Additionally, the key factors that highly correlate to lower back injuries were emphasized. Due to the heavy load applied by the kegs, large spinal forces were exerted on the lower back of workers. Moreover, reducing trunk and hip flexion is also important to prevent workers from injuries. The results of this study can greatly improve the implementation of training techniques, environmental modifications, and assistive device design, which aim to eliminate injury risk and increase the productivity of workers within the breweries.
Multilabel Text Classification with Label-Dependent Representation
Authors: Alfaro Rodrigo , Allende-Cid Héctor , Allende Héctor
Published: March 2023

Assigning predefined classes to natural language texts, based on their content, is a necessary component in many tasks in organizations. This task is carried out by classifying documents within a set of predefined categories using models and computational methods. Text representation for classification purposes has traditionally been performed using a vector space model due to its good performance and simplicity. Moreover, the classification of texts via multilabeling has typically been approached by using simple label classification methods, which require the transformation of the problem studied to apply binary techniques, or by adapting binary algorithms. Over the previous decade, text classification has been extended using deep learning models. Compared to traditional machine learning methods, deep learning avoids rule design and feature selection by humans, and automatically provides semantically meaningful representations for text analysis. However, deep learning-based text classification is data-intensive and computationally complex. Interest in deep learning models does not rule out techniques and models based on shallow learning. This situation is true when the set of training cases is smaller, and when the set of features is small. White box approaches have advantages over black box approaches, where the feasibility of working with relatively small sets of data and the interpretability of the results stand out. This research evaluates a weighting function of the words in texts to modify the representation of the texts during multilabel classification, using a combination of two approaches: problem transformation and model adaptation. This weighting function was tested in 10 referential textual data sets, and compared with alternative techniques based on three performance measures: Hamming Loss, Accuracy, and macro-F1. The best improvement occurs on the macro-F1 when the data sets have fewer labels, fewer documents, and smaller vocabulary sizes. In addition, the performance improves in data sets with higher cardinality, density, and diversity of labels. This proves the usefulness of the function on smaller data sets. The results show improvements of more than 10% in terms of macro-F1 in classifiers based on our method in almost all of the cases analyzed.
CL-MMAD: A Contrastive Learning Based Multimodal Software Runtime Anomaly Detection Method
Authors: Kong Shiyi , Ai Jun , Lu Minyan
Published: March 2023

Software plays a critical role in the infrastructure of modern society. Due to the increasing complexity, it suffers runtime reliability issues. Online anomaly detection can detect partial failures within the program based on manifestations exhibited internally or externally before serious failures occur in the software system, thus enabling timely intervention by operation and maintenance staff to avoid serious losses. This paper introduces CL-MMAD, a novel anomaly detection method based on contrastive learning using multimodal data sources. CL-MMAD uses ResNet-18 to learn the comprehensive feature spaces of software running status. MSE loss is used as the objective to guide the training process and is taken as the anomaly score. Empirical results highlight the superiority of MSE loss over InfoNCE loss and demonstrate CL-MMAD’s effectiveness in detecting both functional failures and performance issues, with a greater ability to detect the latter.
Analytical Model of Gear Thermal Stiffness Based on the Potential Energy Method
Authors: He Yanbing , Yang Haibo
Published: March 2023

Stiffness is the main internal excitation of gear system vibration and is the basis for studying the static and dynamic characteristics of the system. Based on the relationship between thermal stress and strain in plane strain theory and combined with the potential energy method, a novel gear thermal stiffness model is proposed in this paper. The model deduces more general analytical formulas for gear thermal mesh stiffness and time-varying thermal mesh stiffness. The results show that the gear thermal stiffness decreases as a whole compared with the elastic stiffness. The decrease in stiffness is nonuniform along the line of action. The effects of temperatures, temperature distributions and thermal stress on gear thermal stiffness are investigated. The accuracy of the model is verified by comparisons with existing methods and references.
A Data-Science Approach for Creation of a Comprehensive Model to Assess the Impact of Mobile Technologies on Humans
Authors: Garvanova Magdalena , Garvanov Ivan , Jotsov Vladimir , Razaque Abdul , Alotaibi Bandar , Alotaibi Munif , Borissova Daniela
Published: March 2023

Mobile technologies are an essential part of people’s everyday lives since they are utilized for a variety of purposes, such as communication, entertainment, commerce, and education. However, when these gadgets are misused, the human body is exposed to continuous radiation from the electromagnetic field created by them. The communication services available are improving as mobile technologies advance; however, the problem is becoming more severe as the frequency range of mobile devices expands. To solve this complex case, it is necessary to propose a comprehensive approach that combines and processes data obtained from different types of research and sources of information, such as thermal imaging, electroencephalograms, computer models, and surveys. In the present article, a complex model for the processing and analysis of heterogeneous data is proposed based on mathematical and statistical methods in order to study the problem of electromagnetic radiation from mobile devices in-depth. Data science selection/preprocessing is one of the most important aspects of data and knowledge processing aiming at successful and effective analysis and data fusion from many sources. Special types of logic-based binding and pointing constraints are considered for data/knowledge selection applications. The proposed logic-based statistical modeling method provides both algorithmic as well as data-driven realizations that can be evolutionary. As a result, non-anticipated and collateral data/features can be processed if their role in the selected/constrained area is significant. In this research, the data-driven part does not use artificial neural networks; however, this combination was successfully applied in the past. It is an independent subsystem maintaining control of both the statistical and machine-learning parts. The proposed modeling applies to a wide range of reasoning/smart systems.
Study of the Behavior of Excavations and Support Systems for an Alternative Construction Model of the Tunnels in Mexico City
Authors: Alonso-Jiménez Antonio , Suárez-Fino José Francisco , Regueiro y González-Barros Manuel , López-Jimeno Carlos
Published: March 2023

The project proposal for the Desierto de los Leones tunnels in the Poetas Highway in Mexico aims to improve the excavation and support systems and evaluate the safety levels during construction. The design is based on geological and geotechnical information from the original project and can be approached using various methods, including numerical simulations. The article includes a summary of the lithological characteristics, a reinterpretation of geotechnical properties, a description of new construction procedures, and the results of calculations using the finite data differences method. The proposed project aims to improve the safety and stability of the tunnels through advanced construction technologies and adapted procedures, generated by a redesign and the scientific application of new techniques developed for this project.
Hydrothermal Leaching of Silver and Aluminum from Waste Monocrystalline and Polycrystalline Photovoltaic Panels
Authors: Kastanaki Eleni , Lagoudakis Emmanouel , Kalogerakis Georgios , Giannis Apostolos
Published: March 2023

The aim of this study was to investigate the hydrothermal leaching of silver and aluminum from waste monocrystalline silicon (m-Si) and polycrystalline silicon (p-Si) photovoltaic panels (PV) from both cells and metal ribbons using mild HNO3 solutions. Prior to leaching, pretreatment was applied to remove the fluoropolymer backsheet and thermally degrade the ethyl vinyl acetate (EVA) polymer. Several hydrothermal parameters were investigated, such as the liquid-to-solid (L/S) ratio, HNO3 concentration (N), time (t) and temperature (T). Based on preliminary tests, the HNO3 concentration was set in the range of 1–2 N to reduce hazardous waste effluents. The response surface methodology (RSM) was applied to optimize the hydrothermal leaching parameters. It was found that processing time was the most important factor for Ag leaching, followed by HNO3 concentration and L/S ratio, while the processing temperature (100–140 °C) was not a statistically significant factor. Aluminum leaching was efficient under most hydrothermal conditions. For comparison, leaching was also applied at lower temperatures of 25–45 °C for prolonged times; however, lower efficiencies were observed. Under the optimal hydrothermal conditions, Ag can be completely leached, while Al dissolution was favored at hydrothermal conditions compared with lower temperature leaching. Silver leaching efficiency was 100% under hydrothermal conditions; however, under conventional lower temperature conditions, it was 80.7–85.3% for m-Si and p-Si waste panels. Under conventional lower temperature conditions, Al leaching efficiency was 56.6–61.3% for p-Si and m-Si waste panels.
Mechanical Properties of NiTi Rotary Files Fabricated through Gold-Wire, CM-Wire, T-Wire, and R-Phase Heat Treatment
Authors: Oh Soram , Kim Tae-Hwan , Chang Seok Woo
Published: March 2023

Although nickel–titanium (NiTi) rotary files are invaluable tools in dentistry, the relationship between their properties and performance has not been elucidated. Herein, the performance of gold heat-treated (ProTaper Gold and ProfaTaper Gold), controlled memory (CM)-wire (HyFlex CM and V Taper 2H), T-wire (2Shape), and R-phase heat-treated (K3XF) NiTi files was evaluated; the non-heat-treated ProFile was used as the control. The bending, buckling, cyclic fatigue, and torsional resistances of the NiTi files were determined, and their phase transformation behavior was studied through differential scanning calorimetry (DSC). The angle of rotation until fracture (ARF), ultimate torsional strength (UTS), and stiffness were evaluated via torsional resistance testing. One-way analysis of variance (ANOVA) and post hoc analyses were conducted using the Games–Howell test and Tukey’s test. ProFile displayed the highest buckling resistance (8 N), CM-wire NiTi files exhibited the lowest bending resistance (0.660–0.758 N cm), and HyFlex CM displayed the highest cyclic fatigue resistance. ProTaper Gold exhibited high UTS and low ARF. K3XF demonstrated high bending resistance and the lowest cyclic fatigue resistance. The CM-wire NiTi files were the most suitable for use in curved canals, while the gold-wire NiTi files were ideal for constricted canals. ProFile was recommended for use in re-treatment cases.
Estimating the Soundscape Structure and Dynamics of Forest Bird Vocalizations in an Azimuth-Elevation Space Using a Microphone Array
Authors: Suzuki Reiji , Hayashi Koichiro , Osaka Hideki , Matsubayashi Shiho , Arita Takaya , Nakadai Kazuhiro , Okuno Hiroshi G.
Published: March 2023

Songbirds are one of the study targets for both bioacoustic and ecoacoustic research. In this paper, we discuss the applicability of robot audition techniques to understand the dynamics of forest bird vocalizations in a soundscape measured in azimuth and elevation angles with a single 16-channel microphone array, using HARK and HARKBird. First, we evaluated the accuracy in estimating the azimuth and elevation angles of bird vocalizations replayed from a loudspeaker on a tree, 6.55 m above the height of the array, from different horizontal distances in a forest. The results showed that the localization error of azimuth and elevation angle was equal to or less than 5 degrees and 15 degrees, respectively, in most of cases when the horizontal distance from the array was equal to or less than 35 m. We then conducted a field observation of vocalizations to monitor birds in a forest. The results showed that the system can successfully detect how birds use the soundscape horizontally and vertically. This can contribute to bioacoustic and ecoacoustic research, including behavioral observations and study of biodiversity.
Atmospheric Density Inversion Based on Swarm-C Satellite Accelerometer
Authors: Yin Lirong , Wang Lei , Tian Jiawei , Yin Zhengtong , Liu Mingzhe , Zheng Wenfeng
Published: March 2023

We used the Swarm-C accelerometer data to invert the orbital atmospheric density in this study. First, the Swarm-C satellite mission data were obtained from the ESA’s public data platform, and preliminary data error correction was performed. This paper refers to the calibration method of GRACE-A satellite accelerometer data. It adds linear temperature correction on the original basis. Moreover, this study’s accelerometer data correction results were compared with the data correction results published by the ESA. In order to explore the influence of light radiation on the accelerometer, we established a geometric model of Swarm-C to simulate the physical shape of the satellite surface. The light radiation pressure model and the shadow area judgment model were established, the change in the light radiation acceleration during the transition process of the satellite from the umbra area to the penumbra area and then to the shadowless area was studied, and the state transition during the transition process was analyzed. Finally, the atmospheric drag coefficient was calculated based on the Sentman model. Atmospheric density inversion calculations were performed using the above data. We show the spatial distribution of atmospheric density at a fixed latitude, testing our results during geomagnetic storms. We compared the density results with existing research data, demonstrating the effectiveness of our approach.
The Application of Intelligent Data Models for Dementia Classification
Authors: AlShboul Rabah , Thabtah Fadi , Walter Scott Alexander James , Wang Yun
Published: March 2023

Background and Objective: Dementia is a broad term for a complex range of conditions that affect the brain, such as Alzheimer’s disease (AD). Dementia affects a lot of people in the elderly community, hence there is a huge demand to better understand this condition by using cost effective and quick methods, such as neuropsychological tests, since pathological assessments are invasive and demand expensive resources. One of the promising initiatives that deals with dementia and Mild Cognitive Impairment (MCI) is the Alzheimer’s Disease Neuroimaging Initiative (ADNI), which includes cognitive tests, such as Clinical Dementia Rating (CDR) scores. The aim of this research is to investigate non-invasive dementia indicators, such as cognitive features, that are typically diagnosed by clinical assessment within ADNI’s data to understand their effect on dementia. Methods: To achieve the aim, machine learning techniques have been utilized to classify patients into Cognitively Normal (CN), MCI, or having dementia, based on the sum of CDR scores (CDR-SB) besides demographic variables. Particularly, the performance of Support Vector Machine (SVM), K-nearest neighbors (KNN), Decision Trees (C4.5), Probabilistic Naïve Bayes (NB), and Rule Induction (RIPPER) is measured with respect to different evaluation measures, including specificity, sensitivity, and harmonic mean (F-measure), among others, on a large number of cases and controls from the ADNI dataset. Results: The results indicate competitive performance when classifying subjects from the baseline selected variables using machine learning technology. Though we observed fairly good results across all machine learning algorithms utilized, there was still variation in the performance ability, indicating that some algorithms, such as NB and C4.5, are better suited to the task of classifying dementia status based on our baseline data. Conclusions: Using cognitive tests, such as CDR-SB scores, with demographic attributes to pinpoint to dementia using machine learning can be seen a less invasive approach that could be good for clinical use to aid in the diagnosis of dementia. This study gives an indication that a comprehensive assessment tool, such as CDR, may be adequate in assessing and assigning a dementia class to patients, upon their visit, in order to speed further clinical procedures.
Modeling of Multi-Level Planning of Shifting Bottleneck Resources Integrated with Downstream Wards in a Hospital
Authors: Tayyab Aisha , Ullah Saif , Mahmood Toqeer , Ghadi Yazeed Yasin , Latif Bushra , Aljuaid Hanan
Published: March 2023

Planning and scheduling critical resources in hospitals is significant for better service and profit generation. The current research investigates an integrated planning and scheduling problem at different levels of operating rooms, intensive care units, and wards. The theory of constraints is applied to make plans and schedules for operating rooms based on the capacity constraints of the operating room itself and downstream wards. A mixed integer linear programming model is developed considering shifting bottleneck resources among the operating room, intensive care unit, and hospital wards to maximize the utilization of resources at all levels of planning. Different sizes of planning and scheduling problems of the hospital, including small, medium, and large sizes, are created with variable arrivals and surgery durations and solved using a CPLEX solver for validating the developed models. Later, the application of the proposed models in the real world to develop planning systems for hospitals is discussed, and future extensions are suggested.
Development and Field Validation of Wireless Sensors for Railway Bridge Modal Identification
Authors: Zanelli Federico , Debattisti Nicola , Mauri Marco , Argentino Antonio , Belloli Marco
Published: March 2023

Bridges are strategic infrastructures which are subject to degradation during their lifetime. Therefore, structural health monitoring is becoming an essential tool in this field to drive maintenance activities. Conventional vibration monitoring systems relying on wired sensors present several limitations for continuous monitoring projects on a huge number of structures. In this work, a smart wireless monitoring system is developed for bridge modal identification with the aim of providing an alternative tool to wired sensors in this field. The main peculiarities of the designed wireless accelerometers are the low cost, the ease of installation on the structure, and the long-term autonomy granted by the use of energy harvesting techniques. To assess their measurement performance, some prototypes were installed for a field test on a railway bridge and significant data were acquired. Through the processing of the collected data, bridge main natural frequencies were estimated, and their values were in good agreement with the reference ones obtained with a conventional system. The assessment of the developed solution paves the way to the instrumentation of many bridges with the aim of performing continuous monitoring activities using simple diagnostic indicators, such as the variation of frequencies in time.
The Role of a Reward in Shaping Multiple Football Agents’ Behavior: An Empirical Study
Authors: Kim So , Kim Ji , Lee Jee
Published: March 2023

In reinforcement learning (RL), a reward formed with a scalar value is seen as a sufficient means to guide an agent’s behavior. A reward drives an agent to seek out an optimal policy to solve a problem (or to achieve a goal) under uncertainty. In this paper, we aimed to probe the benefit of such a scalar reward in the shaping of coordination policy using artificial football scenarios. In a football setting, a team normally practices two types of strategies: one is a primary formation, that is, the default strategy of a team regardless of their opponents (e.g., 4-4-2, 4-3-3), and the other is an adaptive strategy, that is, a reactive tactic responding to the spontaneous changes of their opponents. We focused here on the primary formation as a team coordination policy that can be trained by a reward using multi-agent RL (MARL) algorithms. Once a team of multiple football agents has successfully learned a primary formation based on a reward-driven approach, we assumed that the team is able to exhibit the primary formation when facing various opponent teams they have never faced in due course to receive a reward. To precisely examine this behavior, we conducted a large number of simulations with twelve artificial football teams in an AI world cup environment. Here, we trained two MARL-based football teams with a team guided by a random walk formation. Afterwards, we performed the artificial football matches with the most competitive of the twelve teams that the MARL-based teams had never played against. Given the analyses of the performance of each football team with regard to their average score and competitiveness, the results showed that the proposed MARL teams outperformed the others with respect to competitiveness, although these teams were not the best with respect to the average score. This indicated that the coordination policy of the MARL-based football teams was moderately consistent against both known and unknown opponents due to the successful learning of a primary formation following the guidance of a scalar reward.
A Statistical Approach to Identify Appropriate Sampling Scheme Capable of Geographical Identification Analysis of the Protected Origin Pulse Crops in Greece
Authors: Tsirogiannis George , Zotos Anastasios , Mazarakioti Eleni C. , Kokkotos Efthimios , Kontogeorgos Achilleas , Patakas Angelos , Ladavos Athanasios
Published: March 2023

In this study, we aimed to develop a sampling method that could be used in geographical discrimination studies of Protected Geographical Indication (PGI) dry beans (Phaseolus vulgaris L.) by considering the geoclimatic variability within the cultivation zone of the analyzed product. The Regional Unit of Kastoria in Greece, a major area of protected designation origin of pulse crops, was selected for detailed investigation. Meteorological data were collected from five weather stations in different subregions of Kastoria (Argos Orestiko, Kalochori, Lakkomata, Lithia, and Polykarpi), over a period of six years (2015 to 2020), along with data of soil texture. The collected data were analyzed in order to determine statistically significant differences among the subregions with regard to the aforementioned parameters. A seasonality pattern was observed for all subregions concerning the microclimate, which splits the data into two clusters. Moreover, a significant variation of the soil textures was revealed, frequently affecting farmers’ decision regarding agronomic practices, leading to the unique stable-isotope ratios and multi-elemental composition. This study guides the dry bean sample collection and will enable the designation of the boundaries of protected origin regions and enable future sampling schemes for stable-isotope and multi-elemental analysis.
Noninvasive In Vivo Estimation of HbA1c Based on the Beer–Lambert Model from Photoplethysmogram Using Only Two Wavelengths
Authors: Turja Mrinmoy Sarker , Kwon Tae-Ho , Kim Hyoungkeun , Kim Ki-Doo
Published: March 2023

Glycated hemoglobin (HbA1c) is the most important factor in diabetes control. Since HbA1c reflects the average blood glucose level over the preceding three months, it is unaffected by a patient’s activity level or diet before a test. Noninvasive HbA1c measurement reduces both the pain and complications associated with fingertip piercing to collect blood. Photoplethysmography is helpful for measuring HbA1c without blood samples. Herein, only two wavelengths (615 and 525 nm) were used to estimate HbA1c noninvasively, where two different ratio calibrations were applied and their performances were compared to a work that used three wavelengths. For the fingertip type, the Pearson’s r values for HbA1c estimates were 0.896 and 0.905, considering the ratio calibrations for the blood vessel and whole finger models, respectively. Using another value (HbA1c) calibration in addition to the ratio calibrations, we could improve this performance such that the Pearson’s r values of the HbA1c levels were 0.929 and 0.930 for the blood vessel and whole finger models, respectively. In a previous study, using three wavelengths, the Pearson’s r values were 0.916 and 0.959 for the blood vessel and whole finger models, respectively. Here, the RCF of the SpO2 estimation was 0.986 when the SpO2 ratio calibration was applied, while in a previous study, the RCF values of the SpO2 estimation were 0.983 and 0.986 for the blood vessel and whole finger models, respectively. Thus, we have shown that HbA1c estimation using only two wavelengths has a comparable performance to previous studies.
Seismic Performance Assessment of Pile-Supported Wharfs: 2D Frame Analysis Method Considering Both Inertial and Kinematic Forces
Authors: Boyke Christino , Nagao Takashi
Published: March 2023

Frame analysis (FA) is known for its efficiency and low computer resource requirements. However, compared to finite element analysis (FEA), the applicability of FA for the seismic performance assessment of pile-supported wharves (PSWs) is limited, particularly in the ability to account for the kinematic force caused by ground displacement during an earthquake. This study aimed to develop a two-dimensional FA method for PSW seismic response analysis that considers a combination of inertial and kinematic forces. We performed FA and FEA and compared the results. First, we targeted the PSW model without considering the soil slope and discussed the spectral acceleration (SA) evaluation method for calculating inertial force. As a result, an equation for estimating a damping coefficient to evaluate the SA in accordance with the PSW width and natural period was proposed. Next, we targeted the PSW model by considering the soil slope and proposed a method to evaluate the kinematic force based on the amount of ground displacement and the soil spring characteristics. The results revealed that using the proposed method, FA, by considering kinematic and inertial forces, could reproduce the bending moments of the piles comparable to those calculated using FEA. Therefore, solely considering the inertial force for a PSW on a soil slope may cause the bending moment to be underestimated.
Robustness of Contrastive Learning on Multilingual Font Style Classification Using Various Contrastive Loss Functions
Authors: Memon Irfanullah , Muhammad Ammar ul Hassan , Choi Jaeyoung
Published: March 2023

Font is a crucial design aspect, however, classifying fonts is challenging compared with that of other natural objects, as fonts differ from images. This paper presents the application of contrastive learning in font style classification. We conducted various experiments to demonstrate the robustness of contrastive image representation learning. First, we built a multilingual synthetic dataset for Chinese, English, and Korean fonts. Next, we trained the model using various contrastive loss functions, i.e., normalized temperature scaled cross-entropy loss, triplet loss, and supervised contrastive loss. We made some explicit changes to the approach of applying contrastive learning in the domain of font style classification by not applying any image augmentation. We compared the results with those of a fully supervised approach and achieved comparable results using contrastive learning with fewer annotated images and a smaller number of training epochs. In addition, we also evaluated the effect of applying different contrastive loss functions on training.
Exploiting the Potential of Overlapping Cropping for Real-World Pedestrian and Vehicle Detection with Gigapixel-Level Images
Authors: Wang Chunlei , Feng Wenquan , Liu Binghao , Ling Xinyang , Yang Yifan
Published: March 2023

Pedestrian and vehicle detection is widely used in intelligent assisted driving, pedestrian counting, drone aerial photography, and other applications. Recently, with the development of gigacameras, gigapixel-level images have emerged. The large field of view and high resolution provide global and local information, which enables object detection in real-world scenarios. Although existing pedestrian and vehicle detection algorithms have achieved remarkable success for standard images, their methods are not suitable for ultra-high-resolution images. In order to improve the performance of existing pedestrian and vehicle detectors in real-world scenarios, we used a sliding window to crop the original images to solve this problem. When fusing the sub-images, we proposed a midline method to reduce the cropped objects that NMS could not eliminate. At the same time, we used varifocal loss to solve the imbalance between positive and negative samples caused by the high resolution. We also found that pedestrians and vehicles were separable in size and comprised more than one target type. As a result, we improved the detector performance with single-class object detection for pedestrians and vehicles, respectively. At the same time, we provided many useful strategies to improve the detector. The experimental results demonstrated that our method could improve the performance of real-world pedestrian and vehicle detection.
Characterization of Laser-Induced Photothermal Vibration for Young’s Modulus Imaging toward Computer-Aided Detection
Authors: Mikami Katsuhiro , Ishinoda Akihiro , Nemoto Mitsutaka
Published: March 2023

The stiffness of tumor cells has a significant influence on invasion and metastasis strategies. In this study, we developed a novel detection method, called laser resonance frequency analysis (L-RFA), for soft tissue tumors in physical oncology. In addition, we evaluated the characteristics of the laser-induced photo-thermal elastic wave (LIPTEW) obtained by L-RFA using agarose gels with different stiffnesses to simulate soft tissues. The LIPTEW diagnosis based on the audible wave range indicated a great potential too, which allows for the measurement of the stiffness of single cells while maintaining organ geometry. In particular, we observed vibrations with high spatial resolution of less than one-tenth of the laser irradiation spot size. From the obtained results, our proposed machine learning method achieved high accuracy and precision, with coefficient of determination R2 = 0.950. The characterization of the LIPTEW on the L-RFA to predict single cell stiffness could be a milestone for future studies on physical oncology, soft-tissue tumor stiffness diagnoses, and medical imaging technologies.
Calculating the Limits of Detection in Laser-Induced Breakdown Spectroscopy: Not as Easy as It Might Seem
Authors: Poggialini Francesco , Legnaioli Stefano , Campanella Beatrice , Cocciaro Bruno , Lorenzetti Giulia , Raneri Simona , Palleschi Vincenzo
Published: March 2023

The objectives of this paper will be to discuss the issues related to the determination of the limits of detection (LOD) in laser-induced breakdown spectroscopy (LIBS) analytical applications. The derivation of the commonly used ‘3-sigma over slope’ rule and its evolution towards the new official definition recently adopted by the International Union of Pure and Applied Chemistry (IUPAC) will be illustrated. Methods for extending the calculation of the LOD to LIBS multivariate analysis will also be discussed, using as an example the detection of Cu traces in cast iron samples by LIBS.
The Harris Extended Bilal Distribution with Applications in Hydrology and Quality Control
Authors: Maya Radhakumari , Irshad Muhammed Rasheed , Ahammed Muhammed , Chesneau Christophe
Published: March 2023

In this research work, a new three-parameter lifetime distribution is introduced and studied. It is called the Harris extended Bilal distribution due to its construction from a mixture of the famous Bilal and Harris distributions, resulting from a branching process. The basic properties, such as the moment generating function, moments, quantile function, and Rényi entropy, are discussed. We show that the hazard rate function has ideal features for modeling increasing, upside-down bathtub, and roller-coaster data sets. In a second part, the Harris extended Bilal model is investigated from a statistical viewpoint. The maximum likelihood estimation is used to estimate the parameters, and a simulation study is carried out. The flexibility of the proposed model in a hydrological data analysis scenario is demonstrated using two practical data sets and compared with important competing models. After that, we establish an acceptance sampling plan that takes advantage of all of the features of the Harris extended Bilal model. The operating characteristic values, the minimum sample size that corresponds to the maximum possible defects, and the minimum ratios of lifetime associated with the producer’s risk are discussed.
Between Spring Banquet and Cannibal Banquet: Cannibalistic Imagery in Meret Oppenheim’s Works
Authors: Stronciwilk Agata Anna
Published: March 2023

This article discusses the cannibalistic imagery in Meret Oppenheim’s works. The crucial aspect is the comparison of two versions of a seemingly similar event in which anthropophagic motifs were significantly present—Spring Banquet and Cannibal Banquet. Often mistaken or wrongly attributed, these events were essentially non-identical and evoked contrasting meanings. The comparison of Spring Banquet and Cannibal Banquet proves that they represent contradictory aspects of anthropophagic imagery. The primary research question is whether Oppenheim’s use of cannibalistic motifs differs from the manner in which they are utilized by male surrealists. Therefore, cannibalistic imagery in the works of Oppenheim is described in the context of avant-garde preoccupation with gendered anthropophagy in which a woman is imagined as an object of consumption. This article concludes with an argument that Oppenheim attempted to subvert the prevailing meanings of cannibalism in surrealism. This article also discusses Oppenheim’s other artworks with anthropophagic connotations, including Ma gouvernante—My Nurse—mein Kindermädchen (1936) and the less-known Bon appétit, Marcel! (The White Queen, 1966).
City-Level CH4 Emissions from Anthropogenic Sources and Its Environmental Behaviors in China’s Cold Cities
Authors: Song Weiwei , Yao Wanying , Zhao Yixuan , Wang Mengying , Chen Ruihan , Zhu Zhiyu , Gao Zhi , Li Chunhui , Liang Miao , Yu Dajiang
Published: March 2023

Distinguished features of cities influence the characteristics of CH4 emissions. A city-level emission inventory represents the characteristics of CH4 on a smaller scale, according to the special factors in each city. A city-level emission inventory was established to reveal the characteristics and source profile of CH4 emissions in the coldest province, which is a typical provincial cold region in northeast China. The dominant sources were identified for targeted cities. Rice cultivation, coal mining, oil and gas exploitation, and livestock are the dominant emission sectors. Emissions from other sectors, including wastewater disposal, biomass burning, landfill, etc. were also estimated. The provincial CH4 emissions increased gradually from 2003 to 2012, up to 2993.26 Gg with an annual increase rate of 2.85%; the emissions were 2740.63 in 2020. The emissions of CH4 in Harbin, Daqing, Jiamusi, and Hegang cities were higher than in the other nine cities, which were 337.23 Gg, 330.01 Gg, 328.55 Gg, and 307.42 Gg in 2020, respectively. Agriculture, including the rice cultivation, livestock, and biomass burning sectors contributed to 51.24–62.12% of total emissions, and the contributions increased gradually. Coal mining, oil and gas exploration, and fossil fuel combustion are energy-related sources, which contributed up to 37.91% of the total emissions, and the proportion kept decreasing to 23.87% in 2020. Furthermore, meteorological factors are especially relevant to the region, by which the differences of ambient temperature are over 60 °C (±30 °C). In the summer, CH4 emissions from the rice cultivation, biomass burning, livestock, and landfill sectors are obviously distinct from the heating period (winter), while few differences in CH4 emissions are found from wastewater disposal and the fossil fuel production sectors.
Diurnal Variation in Concentration of Culturable Bacterial and Fungal Bioaerosols in Winter to Spring Season
Authors: Heo Ki Joon , Jeong Sang Bin , Lim Cheol Eun , Lee Gun Woong , Lee Byung Uk
Published: March 2023

Diurnal variations in the concentrations of culturable fungal and bacterial bioaerosols were measured during winter and spring. Significant variations in concentrations of bacterial bioaerosols were observed during the day in this measurement campaign. The bacterial bioaerosol concentration exhibited two peaks during the morning and evening periods in the winter season. Diurnal variation in bacterial bioaerosols was greater in spring than that in winter. However, fungal bioaerosol concentrations were not affected by diurnal and seasonal changes. Environmental properties such as temperature, relative humidity, and ultraviolet irradiation intensity were measured, and their relationship with bioaerosol concentrations was analyzed. The surrounding temperature was suspected as a significant factor. This diurnal variation in culturable bioaerosols can explain various public health phenomena. Variations in the concentrations of non-biological aerosol particles were also analyzed.
Characteristics of Heat Waves in Mainland China since 1961 Based on Absolute and Relative Methods
Authors: Ji Honghua , Feng Aiqing , Zhao Yufei , Liao Jie , Zhang Zhisen , Gu Changgui , Feng Aixia
Published: March 2023

Based on gridded temperature data from the China Meteorological Administration (CMA), two types of methods, i.e., absolute methods and relative methods, respectively, were used to identify heat waves in Mainland China. Four statistical indicators, including the occurrence frequency, duration days, earliest occurrence date, and latest extinction date, were constructed to analyze the spatial-temporal characteristics of heat waves, especially on the annual and decadal change trends. Firstly, we found that both the frequency and the duration of heat waves decreased in the period from 1960 to 1989 but increased in the 1990s and increased significantly from the early 2000s to the 2010s. Spatially, the frequency and the duration obtained by each type of method are significantly different among different regions when considering different facts, such as different regions that have different degrees of tolerance to heat waves. Secondly, the decadal distribution characteristics of the earliest occurrence date and the latest extinction date of heat waves well capture the hot summer, the stronger sensitivity of winter to warming than other seasons, and the gradually increasing intensity of heat waves. It provides a multidimensional reference for the cause analysis and prediction of extreme heat waves in China.
On Using Piecewise Fractional Differential Operator to Study a Dynamical System
Authors: Khan Shahid , Khan Zareen A. , Alrabaiah Hussam , Zeb Salman
Published: March 2023

This research work is devoted to undertaking a dynamical system representing SARS-CoV-19 disease under the concept of piecewise fractional-order derivative using the Caputo concept since long-memory and short-memory terms are not well explained by ordinary fractional differential equations. It has been found that for such disruption, piecewise operators of fractional derivatives have been found useful in many cases. Therefore, we study a compartmental model of susceptible and infected individuals under the concept of piecewise derivative. We establish the existence theory of the considered model by using some Banach and Schauder fixed-point theorems. Keeping the importance of stability, a pertinent result related to the said area is also developed. The said concept of stability is based on the concept given by Ulam and Hyers. Further, to derive the numerical results, we use the Euler method to develop a numerical scheme for the considered model. Using real available data, we have presented various graphical presentations of two compartments against different fractional orders and various values of isolation parameters. The crossover behaviors in the dynamics can be clearly observed, which is explained by the piecewise operators, not the usual fractional-order derivative.
A Novel Phytogenic Formulation, EUBIO-BPSG, as a Promising One Health Approach to Replace Antibiotics and Promote Reproduction Performance in Laying Hens
Authors: Tran Nguyen Minh Hieu , Kuo Tien-Fen , Lin Wen-Yu , Peng Tzu-Chia , Yang Greta , Lin Chih-Yu , Chang Ting-Hsiang , Yang Yu-Liang , Ho Cheng-Hsun , Ou Bor-Rung , Yang Chu-Wen , Liang Yu-Chuan , Yang Wen-Chin
Published: March 2023

Gut microbiota play a key role in health maintenance and disease pathogenesis in animals. Dietary phytochemicals are crucial factors shaping gut bacteria. Here, we investigated the function and mechanism of a phytogenic formulation, EUBIO-BPSG (BP), in laying hens. We found that BP dose-dependently improved health and egg production in 54-week-old hens. Furthermore, BP was correlated with increased fecal Lactobacillus, decreased Escherichia coli and Salmonella enterica, and reduced antibiotic resistance (AR) and antibiotic resistance genes (ARG) in chicken stools. The 16S rDNA data showed that BP increased seven genera of probiotics and reduced 13 genera of pathogens in chicken feces. In vitro co-culture experiments showed that BP at 4 µg/mL and above promoted growth of L. reuteri while large 100- and 200-fold higher doses suppressed growth of E. coli and S. enterica, respectively. Mechanistic studies indicated that L. reuteri and its supernatants antagonized growth of E. coli and S. enterica but not vice-versa. Five short-chain fatty acids and derivatives (SCFA) produced from L. reuteri directly killed both pathogens via membrane destruction. Furthermore, BP inhibited conjugation and recombination of ARG via interference with conjugation machinery and integrase activity in E. coli. Collectively, this work suggests that BP promotes host health and reproductive performance in laying hens through regulation of gut microbiota through increasing probiotics and decreasing pathogens and spreading ARG.
‘Distraction Vaginogenesis’: Preliminary Results Using a Novel Method for Vaginal Canal Expansion in Rats
Authors: Meyer Hannah , Trosclair Lexus , Clayton Sean D. , O’Quin Collyn , Connelly Zachary , Rieger Ross , Dao Nhi , Alhaque Ahmed , Minagar Andrew , White Luke A. , Solitro Giovanni , Shah-Bruce Mila , Welch Valerie L. , Villalba Stephanie , Alexander Jonathan Steven , Sorrells Donald
Published: March 2023

Vaginal atresia is seen in genetic disorders such as Mayer–Rokitansky–Küster–Hauser (MRKH) syndrome, which can cause significant sexual dysfunction. Current treatments include surgical reconstruction or mechanical dilation of the vaginal canal. Mechanical dilation requires patients to be highly motivated and compliant while surgical reconstruction has high rates of complications. This study evaluated a novel vaginal expansion sleeve (VES) method as an alternative treatment for vaginal atresia. The proprietary cylindrical VES is a spring-like device consisting of polyethylene terephthalate helicoid trusses capped at each end with a fixed diameter resin cap for fixation within tissues. Following the development of the VES and mechanical characterization of the force–length relationships within the device, we deployed the VES in Sprague Dawley rat vaginas anchored with nonabsorbable sutures. We measured the VES length–tension relationships and post-implant vaginal canal expansion ex vivo. Vaginal histology was examined before and after implantation of the VES devices. Testing of 30 mm sleeves without caps resulted in an expansion force of 11.7 ± 3.4 N and 2.0 ± 0.1 N at 50% and 40%, respectively. The implanted 20 mm VES resulted in 5.36 mm ± 1.18 expansion of the vaginal canal, a 32.5 ± 23.6% increase (p = 0.004, Student t test). Histological evaluation of the VES implanted tissue showed a significant thinning of the vaginal wall when the VES was implanted. The novel VES device resulted in a significant expansion of the vaginal canal ex vivo. The VES device represents a unique alternative to traditional mechanical dilation therapy in the treatment of vaginal atresia and represents a useful platform for the mechanical distension of hollow compartments, which avoids reconstructive surgeries and progressive dilator approaches.