bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of the Response Signals of an Electronic Nose Sensor for Differentiation between Fusarium Species
Authors: Borowik Piotr , Dyshko Valentyna , Tarakowski Rafał , Tkaczyk Miłosz , Okorski Adam , Oszako Tomasz
Published: September 2023

Fusarium is a genus of fungi found throughout the world. It includes many pathogenic species that produce toxins of agricultural importance. These fungi are also found in buildings and the toxins they spread can be harmful to humans. Distinguishing Fusarium species can be important for selecting effective preventive measures against their spread. A low-cost electronic nose applying six commercially available TGS-series gas sensors from Figaro Inc. was used in our research. Different modes of operation of the electronic nose were applied and compared, namely, gas adsorption and desorption, as well as modulation of the sensor’s heating voltage. Classification models using the random forest technique were applied to differentiate between measured sample categories of four species: F. avenaceum, F. culmorum, F. greaminarum, and F. oxysporum. In our research, it was found that the mode of operation with modulation of the heating voltage had the advantage of collecting data from which features can be extracted, leading to the training of machine learning classification models with better performance compared to cases where the sensor’s response to the change in composition of the measured gas was exploited. The optimization of the data collection time was investigated and led to the conclusion that the response of the sensor at the beginning of the heating voltage modulation provides the most useful information. For sensor operation in the mode of gas desorption/absorption (i.e., modulation of the gas composition), the optimal time of data collection was found to be longer.
MoTI: A Multi-Stage Algorithm for Moving Object Identification in SLAM
Authors: Hu Changqing , Liu Manlu , Zhang Su , Xie Yu , Tan Liguo
Published: September 2023

Simultaneous localization and mapping (SLAM) algorithms are widely applied in fields such as autonomous driving and target tracking. However, the effect of moving objects on localization and mapping remains a challenge in natural dynamic scenarios. To overcome this challenge, this paper proposes an algorithm for dynamic point cloud detection that fuses laser and visual identification data, the multi-stage moving object identification algorithm (MoTI). The MoTI algorithm consists of two stages: rough processing and precise processing. In the rough processing stage, a statistical method is employed to preliminarily detect dynamic points based on the range image error of the point cloud. In the precise processing stage, the radius search strategy is used to statistically test the nearest neighbor points. Next, visual identification information and point cloud registration results are fused using a method of statistics and information weighting to construct a probability model for identifying whether a point cloud cluster originates from a moving object. The algorithm is integrated into the front-end of the LOAM system, which significantly improves the localization accuracy. The MoTI algorithm is evaluated on an actual indoor dynamic environment and several KITTI datasets, and the results demonstrate its ability to accurately detect dynamic targets in the background and improve the localization accuracy of the robot.
A Mobile Health Application Using Geolocation for Behavioral Activity Tracking
Authors: Emish Mohamed , Kelani Zeyad , Hassani Maryam , Young Sean D.
Published: September 2023

The increasing popularity of mHealth presents an opportunity for collecting rich datasets using mobile phone applications (apps). Our health-monitoring mobile application uses motion detection to track an individual’s physical activity and location. The data collected are used to improve health outcomes, such as reducing the risk of chronic diseases and promoting healthier lifestyles through analyzing physical activity patterns. Using smartphone motion detection sensors and GPS receivers, we implemented an energy-efficient tracking algorithm that captures user locations whenever they are in motion. To ensure security and efficiency in data collection and storage, encryption algorithms are used with serverless and scalable cloud storage design. The database schema is designed around Mobile Advertising ID (MAID) as a unique identifier for each device, allowing for accurate tracking and high data quality. Our application uses Google’s Activity Recognition Application Programming Interface (API) on Android OS or geofencing and motion sensors on iOS to track most smartphones available. In addition, our app leverages blockchain and traditional payments to streamline the compensations and has an intuitive user interface to encourage participation in research. The mobile tracking app was tested for 20 days on an iPhone 14 Pro Max, finding that it accurately captured location during movement and promptly resumed tracking after inactivity periods, while consuming a low percentage of battery life while running in the background.
Fast CU Partition Algorithm for Intra Frame Coding Based on Joint Texture Classification and CNN
Authors: Wang Ting , Wei Geng , Li Huayu , Bui ThiOanh , Zeng Qian , Wang Ruliang
Published: September 2023

High-efficiency video coding (HEVC/H.265) is one of the most widely used video coding standards. HEVC introduces a quad-tree coding unit (CU) partition structure to improve video compression efficiency. The determination of the optimal CU partition is achieved through the brute-force search rate-distortion optimization method, which may result in high encoding complexity and hardware implementation challenges. To address this problem, this paper proposes a method that combines convolutional neural networks (CNN) with joint texture recognition to reduce encoding complexity. First, a classification decision method based on the global and local texture features of the CU is proposed, efficiently dividing the CU into smooth and complex texture regions. Second, for the CUs in smooth texture regions, the partition is determined by terminating early. For the CUs in complex texture regions, a proposed CNN is used for predictive partitioning, thus avoiding the traditional recursive approach. Finally, combined with texture classification, the proposed CNN achieves a good balance between the coding complexity and the coding performance. The experimental results demonstrate that the proposed algorithm reduces computational complexity by 61.23%, while only increasing BD-BR by 1.86% and decreasing BD-PSNR by just 0.09 dB.
Adoption of Transformer Neural Network to Improve the Diagnostic Performance of Oximetry for Obstructive Sleep Apnea
Authors: Almarshad Malak Abdullah , Al-Ahmadi Saad , Islam Md Saiful , BaHammam Ahmed S. , Soudani Adel
Published: September 2023

Scoring polysomnography for obstructive sleep apnea diagnosis is a laborious, long, and costly process. Machine learning approaches, such as deep neural networks, can reduce scoring time and costs. However, most methods require prior filtering and preprocessing of the raw signal. Our work presents a novel method for diagnosing obstructive sleep apnea using a transformer neural network with learnable positional encoding, which outperforms existing state-of-the-art solutions. This approach has the potential to improve the diagnostic performance of oximetry for obstructive sleep apnea and reduce the time and costs associated with traditional polysomnography. Contrary to existing approaches, our approach performs annotations at one-second granularity. Allowing physicians to interpret the model’s outcome. In addition, we tested different positional encoding designs as the first layer of the model, and the best results were achieved using a learnable positional encoding based on an autoencoder with structural novelty. In addition, we tried different temporal resolutions with various granularity levels from 1 to 360 s. All experiments were carried out on an independent test set from the public OSASUD dataset and showed that our approach outperforms current state-of-the-art solutions with a satisfactory AUC of 0.89, accuracy of 0.80, and F1-score of 0.79.
Comparative Analysis of the Clustering Quality in Self-Organizing Maps for Human Posture Classification
Authors: Ekemeyong Awong Lisiane Esther , Zielinska Teresa
Published: September 2023

The objective of this article is to develop a methodology for selecting the appropriate number of clusters to group and identify human postures using neural networks with unsupervised self-organizing maps. Although unsupervised clustering algorithms have proven effective in recognizing human postures, many works are limited to testing which data are correctly or incorrectly recognized. They often neglect the task of selecting the appropriate number of groups (where the number of clusters corresponds to the number of output neurons, i.e., the number of postures) using clustering quality assessments. The use of quality scores to determine the number of clusters frees the expert to make subjective decisions about the number of postures, enabling the use of unsupervised learning. Due to high dimensionality and data variability, expert decisions (referred to as data labeling) can be difficult and time-consuming. In our case, there is no manual labeling step. We introduce a new clustering quality score: the discriminant score (DS). We describe the process of selecting the most suitable number of postures using human activity records captured by RGB-D cameras. Comparative studies on the usefulness of popular clustering quality scores—such as the silhouette coefficient, Dunn index, Calinski–Harabasz index, Davies–Bouldin index, and DS—for posture classification tasks are presented, along with graphical illustrations of the results produced by DS. The findings show that DS offers good quality in posture recognition, effectively following postural transitions and similarities.
Intelligent ADL Recognition via IoT-Based Multimodal Deep Learning Framework
Authors: Javeed Madiha , Mudawi Naif Al , Alazeb Abdulwahab , Almakdi Sultan , Alotaibi Saud S. , Chelloug Samia Allaoua , Jalal Ahmad
Published: September 2023

Smart home monitoring systems via internet of things (IoT) are required for taking care of elders at home. They provide the flexibility of monitoring elders remotely for their families and caregivers. Activities of daily living are an efficient way to effectively monitor elderly people at home and patients at caregiving facilities. The monitoring of such actions depends largely on IoT-based devices, either wireless or installed at different places. This paper proposes an effective and robust layered architecture using multisensory devices to recognize the activities of daily living from anywhere. Multimodality refers to the sensory devices of multiple types working together to achieve the objective of remote monitoring. Therefore, the proposed multimodal-based approach includes IoT devices, such as wearable inertial sensors and videos recorded during daily routines, fused together. The data from these multi-sensors have to be processed through a pre-processing layer through different stages, such as data filtration, segmentation, landmark detection, and 2D stick model. In next layer called the features processing, we have extracted, fused, and optimized different features from multimodal sensors. The final layer, called classification, has been utilized to recognize the activities of daily living via a deep learning technique known as convolutional neural network. It is observed from the proposed IoT-based multimodal layered system’s results that an acceptable mean accuracy rate of 84.14% has been achieved.
A Gated-Recurrent-Unit-Based Interacting Multiple Model Method for Small Bird Tracking on Lidar System
Authors: Han Bing , Wang Hongchang , Su Zhigang , Hao Jingtang , Zhao Xinyi , Ge Peng
Published: September 2023

Lidar presents a promising solution for bird surveillance in airport environments. However, the low observation refresh rate of Lidar poses challenges for tracking bird targets. To address this problem, we propose a gated recurrent unit (GRU)-based interacting multiple model (IMM) approach for tracking bird targets at low sampling frequencies. The proposed method constructs various GRU-based motion models to extract different motion patterns and to give different predictions of target trajectory in place of traditional target moving models and uses an interacting multiple model mechanism to dynamically select the most suitable GRU-based motion model for trajectory prediction and tracking. In order to fuse the GRU-based motion model and IMM, the approximation state transfer matrix method is proposed to transform the prediction of GRU-based network into an explicit state transfer model, which enables the calculation of the models’ probability. The simulation carried out on an open bird trajectory dataset proves that our method outperforms classical tracking methods at low refresh rates with at least 26% improvement in tracking error. The results show that the proposed method is effective for tracking small bird targets based on Lidar systems, as well as for other low-refresh-rate tracking systems.
Statement Recognition of Access Control Policies in IoT Networks
Authors: Ma Li , Yang Zexian , Bu Zhaoxiong , Lao Qidi , Yang Wenyin
Published: September 2023

Access Control Policies (ACPs) are essential for ensuring secure and authorized access to resources in IoT networks. Recognizing these policies involves identifying relevant statements within project documents expressed in natural language. While current research focuses on improving recognition accuracy through algorithm enhancements, the challenge of limited labeled data from individual clients is often overlooked, which impedes the training of highly accurate models. To address this issue and harness the potential of IoT networks, this paper presents FL-Bert-BiLSTM, a novel model that combines federated learning and pre-trained word embedding techniques for access control policy recognition. By leveraging the capabilities of IoT networks, the proposed model enables real-time and distributed training on IoT devices, effectively mitigating the scarcity of labeled data and enhancing accessibility for IoT applications. Additionally, the model incorporates pre-trained word embeddings to leverage the semantic information embedded in textual data, resulting in improved accuracy for access control policy recognition. Experimental results substantiate that the proposed model not only enhances accuracy and generalization capability but also preserves data privacy, making it well-suited for secure and efficient access control in IoT networks.
Research on a Method of Locating Civil Aviation Radio Interference Sources Based on Time Difference of Arrival and Frequency Difference of Arrival for Four Unmanned Aerial Vehicles
Authors: Zhou Chao , Zhu Xingyu , Xiong Renhe , Hu Kun , Ouyang Feng , Huang Chi , Huang Tao
Published: September 2023

Monitoring and analyzing radio interference sources play a crucial role in ensuring the safe operation of civil aviation navigation, communication, airport management, and air traffic control. Traditional ground monitoring methods are slow and inadequate for tracking aerial and mobile interference sources effectively. Although flight methods such as helicopters and airships can effectively monitor aerial interference, the flight approval process is time-consuming and expensive. This paper investigates a novel approach to locating civil aviation radio interference sources using four unmanned aerial vehicles (UAVs) to address this issue. It establishes a model for aerial positioning of radio interference sources with the four UAVs and proposes a method for time synchronization and data communication among them. The paper conducts simulations of the four-UAV time–frequency difference positioning method, analyzing the geometric accuracy dilution with different deployment configurations of the UAVs, positioning biases, and root mean square errors (RMSEs) under varying interference source movement speeds. The simulation results provide crucial data to support subsequent experiments.
A Compensation Method for Nonlinearity Errors in Optical Interferometry
Authors: Li Yanlu , Dieussaert Emiel
Published: September 2023

Optical coherent detection is widely used for highly sensitive sensing applications, but nonlinearity issues pose challenges in accurately interpreting the system outputs. Most existing compensation methods require access to raw measurement data, making them not useful when only demodulated data are available. In this study, we propose a compensation method designed for direct application to demodulated data, effectively addressing the 1st and 2nd-order nonlinearities in both homodyne and heterodyne systems. The approach involves segmenting the distorted signal, fitting and removing baselines in each section, and averaging the resulting distortions to obtain precise distortion shapes. These shapes are then used to retrieve compensation parameters. Simulation shows that the proposed method can effectively reduce the deviation caused by the nonlinearities without using the raw data. Experimental results from a silicon-photonics-based homodyne laser Doppler vibrometry prove that this method has a similar performance as the conventional Heydemann correction method.
Characterizing Bodyweight-Supported Treadmill Walking on Land and Underwater Using Foot-Worn Inertial Measurement Units and Machine Learning for Gait Event Detection
Authors: Song Seongmi , Fernandes Nathaniel J. , Nordin Andrew D.
Published: September 2023

Gait rehabilitation commonly relies on bodyweight unloading mechanisms, such as overhead mechanical support and underwater buoyancy. Lightweight and wireless inertial measurement unit (IMU) sensors provide a cost-effective tool for quantifying body segment motions without the need for video recordings or ground reaction force measures. Identifying the instant when the foot contacts and leaves the ground from IMU data can be challenging, often requiring scrupulous parameter selection and researcher supervision. We aimed to assess the use of machine learning methods for gait event detection based on features from foot segment rotational velocity using foot-worn IMU sensors during bodyweight-supported treadmill walking on land and underwater. Twelve healthy subjects completed on-land treadmill walking with overhead mechanical bodyweight support, and three subjects completed underwater treadmill walking. We placed IMU sensors on the foot and recorded motion capture and ground reaction force data on land and recorded IMU sensor data from wireless foot pressure insoles underwater. To detect gait events based on IMU data features, we used random forest machine learning classification. We achieved high gait event detection accuracy (95–96%) during on-land bodyweight-supported treadmill walking across a range of gait speeds and bodyweight support levels. Due to biomechanical changes during underwater treadmill walking compared to on land, accurate underwater gait event detection required specific underwater training data. Using single-axis IMU data and machine learning classification, we were able to effectively identify gait events during bodyweight-supported treadmill walking on land and underwater. Robust and automated gait event detection methods can enable advances in gait rehabilitation.
Accurate Visual Simultaneous Localization and Mapping (SLAM) against Around View Monitor (AVM) Distortion Error Using Weighted Generalized Iterative Closest Point (GICP)
Authors: Lee Yangwoo , Kim Minsoo , Ahn Joonwoo , Park Jaeheung
Published: September 2023

Accurately estimating the pose of a vehicle is important for autonomous parking. The study of around view monitor (AVM)-based visual Simultaneous Localization and Mapping (SLAM) has gained attention due to its affordability, commercial availability, and suitability for parking scenarios characterized by rapid rotations and back-and-forth movements of the vehicle. In real-world environments, however, the performance of AVM-based visual SLAM is degraded by AVM distortion errors resulting from an inaccurate camera calibration. Therefore, this paper presents an AVM-based visual SLAM for autonomous parking which is robust against AVM distortion errors. A deep learning network is employed to assign weights to parking line features based on the extent of the AVM distortion error. To obtain training data while minimizing human effort, three-dimensional (3D) Light Detection and Ranging (LiDAR) data and official parking lot guidelines are utilized. The output of the trained network model is incorporated into weighted Generalized Iterative Closest Point (GICP) for vehicle localization under distortion error conditions. The experimental results demonstrate that the proposed method reduces localization errors by an average of 39% compared with previous AVM-based visual SLAM approaches.
Graph Sampling-Based Multi-Stream Enhancement Network for Visible-Infrared Person Re-Identification
Authors: Jiang Jinhua , Xiao Junjie , Wang Renlin , Li Tiansong , Zhang Wenfeng , Ran Ruisheng , Xiang Sen
Published: September 2023

With the increasing demand for person re-identification (Re-ID) tasks, the need for all-day retrieval has become an inevitable trend. Nevertheless, single-modal Re-ID is no longer sufficient to meet this requirement, making Multi-Modal Data crucial in Re-ID. Consequently, a Visible-Infrared Person Re-Identification (VI Re-ID) task is proposed, which aims to match pairs of person images from the visible and infrared modalities. The significant modality discrepancy between the modalities poses a major challenge. Existing VI Re-ID methods focus on cross-modal feature learning and modal transformation to alleviate the discrepancy but overlook the impact of person contour information. Contours exhibit modality invariance, which is vital for learning effective identity representations and cross-modal matching. In addition, due to the low intra-modal diversity in the visible modality, it is difficult to distinguish the boundaries between some hard samples. To address these issues, we propose the Graph Sampling-based Multi-stream Enhancement Network (GSMEN). Firstly, the Contour Expansion Module (CEM) incorporates the contour information of a person into the original samples, further reducing the modality discrepancy and leading to improved matching stability between image pairs of different modalities. Additionally, to better distinguish cross-modal hard sample pairs during the training process, an innovative Cross-modality Graph Sampler (CGS) is designed for sample selection before training. The CGS calculates the feature distance between samples from different modalities and groups similar samples into the same batch during the training process, effectively exploring the boundary relationships between hard classes in the cross-modal setting. Some experiments conducted on the SYSU-MM01 and RegDB datasets demonstrate the superiority of our proposed method. Specifically, in the VIS→IR task, the experimental results on the RegDB dataset achieve 93.69% for Rank-1 and 92.56% for mAP.
Adaptive Fuzzy Event-Triggered Cooperative Control for Multi-Robot Systems: A Predefined-Time Strategy
Authors: Tian Xuehong , Huang Xin , Liu Haitao , Mai Qingqun
Published: September 2023

A predefined-time adaptive fuzzy cooperative controller with event triggering is proposed for multi-robot systems that takes into account external disturbances, input saturation, and model uncertainties in this paper. First, based on the asymmetric tan-type barrier Lyapunov function, a predefined-time controller is proposed to acquire a quick response and more precise convergence time under the directed communication topology. Second, predefined-time fuzzy logic systems are developed to approximate external disturbances and model uncertainties. Third, a dynamic relative threshold event-triggered mechanism is improved to save the communication resources of the robots. Subsequently, the proof procedure for the predefined-time stability is given using the Lyapunov stability theorem. Finally, some simulation examples, including a comparative experi-ment on multi-robot systems, are provided to test the effectiveness of the above algorithm.
The Influence of Visibility on the Opportunity to Perform Flight Operations with Various Categories of the Instrument Landing System
Authors: Kwasiborska Anna , Grabowski Mateusz , Sedláčková Alena Novák , Novák Andrej
Published: September 2023

Meteorological conditions significantly affect air traffic safety and can also affect a pre-planned flight plan. Difficult meteorological conditions are particularly hazardous during take-off and landing procedures. Still, they can also cause disruptions to air traffic by causing, for example, delays to air traffic or diversion of aircraft to other airports. From the airlines’ point of view, such situations are not beneficial if flights are diverted to other airports due to reduced visibility at the airport caused by fog and haze. For flight operations, a popular navigation system with a precision approach is the ILS, which has several categories enabling an approach even in adverse meteorological conditions. However, not every airport has a high-category ILS, and setting up such navigation equipment is lengthy and costly. The main objective of this article is to analyze the impact of meteorological conditions, particularly visibility, on the possibility of performing flight procedures with different ILS categories. The study was designed to quantify the limitations associated with meteorological conditions with specific ILS equipment at a given airport. The research questions for this study include the following: What were the meteorological conditions in terms of visibility? What impact did the visibility parameter have on the performance of landing operations at the airport under study? Can an indication of the probability of stopping landing operations be important in recommendations for scheduling airline flights to avoid delays? Three airports were selected for the analysis: Warsaw Chopin Airport, Warsaw Modlin Mazowiecki Airport, and Krakow John Paul II Airport. The analysis was based on approximately 52,000 METAR dispatches in 2019 and 2022. The research indicated during which periods landing procedures were most frequently halted and calculated such a change with a different category of ILS. For the Kraków Airport, the probability of stopping landing procedures in any month was calculated, along with recommendations for flight schedule planning for this airport. The research results can be used to better plan airline flight schedules, avoiding hours with a high probability of reduced visibility, which may result in rerouting flights to another airport. Long-term low clouds and reduced visibility affect the safety of operations but also cause delays.
An ISAR Image Component Recognition Method Based on Semantic Segmentation and Mask Matching
Authors: Zhu Xinli , Zhang Yasheng , Lu Wang , Fang Yuqiang , He Jun
Published: September 2023

The inverse synthetic aperture radar (ISAR) image is a kind of target feature data acquired by radar for moving targets, which can reflect the shape, structure, and motion information of the target, and has attracted a great deal of attention from the radar automatic target recognition (RATR) community. The identification of ISAR image components in radar satellite identification missions has not been carried out in related research, and the relevant segmentation methods of optical images applied to the research of semantic segmentation of ISAR images do not achieve ideal segmentation results. To address this problem, this paper proposes an ISAR image part recognition method based on semantic segmentation and mask matching. Furthermore, a reliable automatic ISAR image component labeling method is designed, and the satellite target component labeling ISAR image samples are obtained accurately and efficiently, and the satellite target component labeling ISAR image data set is obtained. On this basis, an ISAR image component recognition method based on semantic segmentation and mask matching is proposed in this paper. U-Net and Siamese Network are designed to complete the ISAR image binary semantic segmentation and binary mask matching, respectively. The component label of the ISAR image is predicted by the mask matching results. Experiments based on satellite component labeling ISAR image datasets confirm that the proposed method is feasible and effective, and it has greater comparative advantages compared to other classical semantic segmentation networks.
Research on the Three-Machines Perception System and Information Fusion Technology for Intelligent Work Faces
Authors: Feng Haotian , Fang Xinqiu , Chen Ningning , Song Yang , Liang Minfu , Wu Gang , Zhang Xinyuan
Published: September 2023

The foundation of intelligent collaborative control of a shearer, scraper conveyor, and hydraulic support (three-machines) is to achieve the precise perception of the status of the three-machines and the full integration of information between the equipment. In order to solve the problems of information isolation and non-flow, independence between equipment, and weak cooperation of three-machines due to an insufficient fusion of perception data, a fusion method of the equipment’s state perception system on the intelligent working surface was proposed. Firstly, an intelligent perception system for the state of the three-machines in the working face was established based on fiber optic sensing technology and inertial navigation technology. Then, the datum coordinate system is created on the working surface to uniformly describe the status of the three-machines and the spatial position relationship between the three-machines is established using a scraper conveyor as a bridge so that the three-machines become a mutually restricted and collaborative equipment system. Finally, an indoor test was carried out to verify the relational model of the spatial position of the three-machines. The results indicate that the intelligent working face three-machines perception system based on fiber optic sensing technology and inertial navigation technology can achieve the fusion of monitoring data and unified expression of equipment status. The research results provide an important reference for building an intelligent perception, intelligent decision-making, and automatic execution system for coal mines.
“The Truthfulness Lies in the Process, Not the Outcome” Using Artistic Practices to Further Truth-Telling and Memorialization in the Philippines
Authors: Destrooper Tine
Published: September 2023

The Philippines have never known a unified state-sanctioned narrative about the violence that happened during the Marcos dictatorship. In order to resist ongoing disinformation campaigns that seek to erase evidence and memories of past violence, various institutional and civil society actors are currently initiating interventions in the domain of truth and memorialization. Notably, artists, curators, and creative professionals are engaging in various kinds of so-called ‘narrative documentation’ and ‘narrative change-making’. Several of these initiatives mobilize spatial dynamics and co-created processes to facilitate more complex forms of truth-telling and memorialization, which foreground complexity and ambiguity, and which prompt more engaged forms of truth-listening. This article zooms in on a specific project that mobilizes traditional artisan and artistic techniques and forms to revisit women’s experiences of historical and ongoing violence by crafting layered and ambiguous narratives about harm. In doing so, the Weaving Women’s Words on Wounds of War project seeks to further memorialization, truth-telling, and truth-listening about gendered violence. Through an analysis embedded in scholarship on memory, truth, and artistic practice, I argue that it is the generation of ambiguous and complex narratives that invites an active and relational type of engagement and listening. This holds potential for resisting the erasure of complex forms of violence, both in the context of the Philippines, as well as in other contexts where truth or memorialization initiatives may be incapable of capturing the gravity of lived experiences of violence or of facilitating genuine listening.
Muscle Activation and Ground Reaction Force between Single-Leg Drop Landing and Jump Landing among Young Females during Weight-Acceptance Phase
Authors: Pilanthananond Metaneeya , Tharawadeepimuk Kittichai , Saengsirisuwan Vitoon , Limroongreungrat Weerawat
Published: September 2023

Single-leg drop landing (SLDL) and jump landing (SLJL) are frequently used as assessment tools for identifying potential high-risk movement patterns; thus, understanding differences in neuromuscular responses between these types of landings is essential. This study aimed to compare lower extremity neuromuscular responses between the SLDL and SLJL. Thirteen female participants performed an SLDL and SLJL from a 30-cm box height. Vertical ground reaction force (vGRF), time to peak vGRF, and surface electromyography (sEMG) data were collected. Continuous neuromuscular responses, peak vGRF, and time to peak vGRF were compared between the tasks. Statistical parametric mapping (SPM) analysis demonstrated that the SLJL had a significantly higher sEMG activity in the rectus femoris (RF), vastus lateralis (VL) and vastus medialis (VM) within the first 10% of the landing phase compared with SLDL. At 20–30% of the landing phase, sEMGs in the RF and VL during the SLDL were significantly higher compared with SLJL (p < 0.05). A higher peak vGRF and shorter time to peak vGRF was observed during SLJL (p < 0.05). In conclusion, our findings highlight that SLJL exhibited greater RF, VL, and VM activities than SLDL at initial impact (10% landing), coinciding with a higher peak vGRF and shorter time to attain peak vGRF. Our findings support the role of the quadriceps as the primary energy dissipator during the SLJL.
Datasets Construction and Development of QSAR Models for Predicting Micronucleus In Vitro and In Vivo Assay Outcomes
Authors: Khondkaryan Lusine , Tevosyan Ani , Navasardyan Hayk , Khachatrian Hrant , Tadevosyan Gohar , Apresyan Lilit , Chilingaryan Gayane , Navoyan Zaven , Stopper Helga , Babayan Nelly
Published: September 2023

In silico (quantitative) structure–activity relationship modeling is an approach that provides a fast and cost-effective alternative to assess the genotoxic potential of chemicals. However, one of the limiting factors for model development is the availability of consolidated experimental datasets. In the present study, we collected experimental data on micronuclei in vitro and in vivo, utilizing databases and conducting a PubMed search, aided by text mining using the BioBERT large language model. Chemotype enrichment analysis on the updated datasets was performed to identify enriched substructures. Additionally, chemotypes common for both endpoints were found. Five machine learning models in combination with molecular descriptors, twelve fingerprints and two data balancing techniques were applied to construct individual models. The best-performing individual models were selected for the ensemble construction. The curated final dataset consists of 981 chemicals for micronuclei in vitro and 1309 for mouse micronuclei in vivo, respectively. Out of 18 chemotypes enriched in micronuclei in vitro, only 7 were found to be relevant for in vivo prediction. The ensemble model exhibited high accuracy and sensitivity when applied to an external test set of in vitro data. A good balanced predictive performance was also achieved for the micronucleus in vivo endpoint.
Per- and Polyfluoroalkyl Substances (PFAS) in Community Water Systems (CWS) and the Risk of Thyroid Cancer: An Ecological Study
Authors: Alsen Mathilda , Leung Angela M. , van Gerwen Maaike
Published: September 2023

Thyroid cancer incidence has been steadily increasing over the past decade in the United States (US). A discussion exists regarding the potential contribution of exposure to endocrine-disrupting chemicals, encompassing certain per- and poly-fluoroalkyl substances (PFASs). This ecological study evaluated the potential correlation between PFAS levels in drinking water and thyroid cancer incidence in the US. Data on age-adjusted thyroid cancer incidence rate (per 100,000 persons) by county were obtained from the Centers for Disease Control and Prevention (CDC) for US counties with available data in 2015–2019. Data on PFAS concentrations in the drinking water of selected community water systems (CWSs) were obtained from the CDC National Environmental Public Health Tracking Network in 2013–2015. The correlation between PFASs in CWSs and thyroid cancer incidence was calculated using Spearman correlation. A statistically significant correlation was found between perfluorooctanoic acid (PFOA) (r = 0.031; p = 0.043), perfluorononanoic acid (PFNA) (r = 0.058; p ≤ 0.001), and thyroid cancer incidence. The results suggest a potential link between certain PFAS exposures and thyroid cancer risk. However, due to the nature of an ecological study, no conclusions can be drawn at the individual level or causality. More research is needed, particularly on an individual level to allow for more detailed exposure assessment.
The Statistics of Primordial Black Holes in a Radiation-Dominated Universe: Recent and New Results
Authors: Germani Cristiano , Sheth Ravi K.
Published: September 2023

We review the nonlinear statistics of Primordial Black Holes that form from the collapse of over-densities in a radiation-dominated Universe. We focus on the scenario in which large over-densities are generated by rare and Gaussian curvature perturbations during inflation. As new results, we show that the mass spectrum follows a power law determined by the critical exponent of the self-similar collapse up to a power spectrum dependent cutoff, and that the abundance related to very narrow power spectra is exponentially suppressed. Related to this, we discuss and explicitly show that both the Press–Schechter approximation and the statistics of mean profiles lead to wrong conclusions for the abundance and mass spectrum. Finally, we clarify that the transfer function in the statistics of initial conditions for Primordial Black Holes formation (the abundance) does not play a significant role.
Evaluation of Beeswax Supplementation on Productive Performance of Growing Assaf Lambs
Authors: Gaafar Hamed Mohamed , Dell’Anno Matteo , Rossi Luciana , Mohsen Mohamed Kamel , Abdel-Raouf El-Sayed Mohamed , El-Nahrawy Mostafa Mohamed , Amer Abdeen Wajeeh
Published: September 2023

The aim of this work was to assess the effects of beeswax supplementation on growth rate, feed intake, nutrient digestion, rumen fermentation, blood parameters, and economic sustainability in Assaf lambs. Eighteen growing Assaf (5 months old) lambs were separated into three experimental groups (n = 6 lambs/group). The lambs were fed a basal diet without supplementation (G1) or supplemented with 2 and 4 g beeswax/head/day in G2 and G3 groups, respectively. Zootechnical performance was evaluated over a 90 day period. Feed digestibility was assessed in faeces through the acid insoluble-ash method, and rumen liquor was collected to measure ammonia (NH3-N) and total volatile fatty acid (TVFA) levels. Blood samples were obtained for the titration serum metabolites by colorimetric tests. The findings showed that G3 had an improved performance compared to the other groups (p < 0.01). The lambs in G3 revealed the highest nutrient digestibility and feed use, followed by G2, and G1. G3 recorded the highest economic efficiency followed by G2 and G1 (p < 0.01). The TVFA, acetate, and propionate concentrations were higher and the pH values, NH3-N, and butyrate concentrations were lower in G3 compared to G2 and particularly to G1 (p < 0.01). The concentrations of total protein, globulin, and glucose were significantly higher with 4 g beeswax (p < 0.05). However, albumin, cholesterol, total lipids, urea, creatinine, glutamic oxaloacetic transaminase (GOT), and glutamate pyruvate transaminase (GPT) concentrations as well as the albumin to globulin ratio decreased significantly with both levels of beeswax (p < 0.05). The addition of beeswax at the level of 4 g/head/day for growing Assaf lambs significantly improved the growth performance, digestibility, rumen fermentation, and blood serum parameters in addition to the economic efficiency.
Method Validation and Establishment of Reference Intervals for an Insulin-like Growth Factor-1 Chemiluminescent Immunoassay in Cats
Authors: Güssow Arne , Thalmeier Sabine , Gostelow Ruth , Langenstein Judith , Foerster Gesine , Bauer Natali , Hazuchova Katarina
Published: September 2023

Previously, radioimmunoassay (RIA) has been the only assay to measure insulin-like growth factor-1 (IGF-1) to diagnose hypersomatotropism (HS). Due to radiation concerns, availability, and the cost of IGF-1 RIA, validation of assays for automated analysers such as a chemiluminescent immunoassay (CLIA) is needed. The aim of this study was to validate a CLIA for measurement of feline IGF-1 (IMMULITE 2000® XPi, Siemens Medical Solutions Diagnostics, Malvern, PA, USA) compared to IGF1 RIA, establish reference interval (RI), and determine a cut-off value for diagnosis of HS in diabetic cats. Validation of assay performance included precision, linearity, and recovery studies. Right-sided RI was determined using surplus serum of 50 healthy adult cats. Surplus serum samples of diabetic cats with known IGF-1 concentration with (n = 32/68) and without HS (n = 36/68) were used for method comparison with RIA. The cut-off for diagnosis of HS was established using receiver operating characteristic (ROC) analysis. The intra-assay coefficient of variation (CV) was ≤4.7%, and the inter-assay CV was ≤5.6% for samples with low, medium, and high IGF-1 concentration. Linearity was excellent (R2 > 0.99). The correlation between CLIA and RIA was very high (rs = 0.97), with a mean negative bias for CLIA of 24.5%. The upper limit of RI was 670 ng/mL. ROC analysis showed an area under the curve of 0.94, with best cut-off for diagnosis of HS at 746 ng/mL (sensitivity, 84.4%; specificity, 97.2%). The performance of CLIA was good, and the RI and cut-off for HS diagnosis established in this study allow for CLIA to be used in routine work-up of diabetic cats.
Congenital Partial Colonic Agenesis in Dogs and Cats: Clinical, Biological, Diagnostic Imaging, Endoscopic and Histopathologic Characterization, a Retrospective Study
Authors: Remmel Paul , Gros Lucile , Mortier Jérémy , Freiche Valérie
Published: September 2023

Congenital diseases of the large intestine of dogs and cats have scarcely been reported and mostly include fistula, atresia or colonic duplication. Cases of partial colonic agenesis have rarely been described. The purpose of this study was to report a cohort of dogs and cats diagnosed with partial colonic agenesis. The colon was measured during colonoscopy or contrast-radiography and compared to the average length described in the literature. Six dogs and 17 cats were retrospectively included. Depending on the case, partial colonic agenesis could represent an incidental finding or the likeliest cause of clinical signs. Diarrhea was reported in most cases, and no specific clinical or biological abnormality was observed. Median age of presentation was variable and long asymptomatic periods were common. Abdominal ultrasound was useful and identified a short colon in 14/17 cats but only in one dog. Endoscopy was useful to confirm the diagnosis and to identify associated lesions and complications. Among others, colonic stenosis was reported in 8/9 cases that had lifelong clinical signs and the shortest colon length. This anatomical abnormality could promote chronic inflammation that might generate fibrosis and ultimately stenosis.
Improving Cryopreservation Efficiency and Pregnancy Rate through Superovulation with Follicle-Stimulating Hormone in Korean Hanwoo Cows via Ovum Pick Up
Authors: Kim Daehyun , Yi Junkoo
Published: September 2023

The aim of this study was to devise an efficient technique for generating embryos from high-quality bovine females. Oocytes were collected from 20 control and 15 Hanwoo (Bos taurus coreanae) females treated with the FSH. A combination of decreasing FSH doses (36, 36, 24, and 24 mg, 12 h apart), progesterone, estrogen, and prostaglandins were administered to synchronize and mildly stimulate the animals. The FSH-treated group (1125 oocytes) and control group (1022 oocytes) exhibited a higher proportion of grade A and B oocytes (88.2%) than the other grades (p < 0.05), with most at the germinal vesicle 2 stage (64.0%). Moreover, the FSH-treated group achieved a notably higher blastocyst rate (44.7%) compared to the control group (31.1%) (p < 0.05). After undergoing vitrification and in vitro culture (IVC) warming, embryos in the FSH group exhibited higher re-expansion rates (grade 1: 86.9%; grades 2 and 3: 57.9%) compared to those in the control (p < 0.05). This highlights the positive impact of FSH treatment on in vitro embryo production (IVEP) and the OPU rate.
Metodología para mejoramiento comercial de la oferta de bares en Ciudades Creativas Gastronómicas: caso Portoviejo, Ecuador
Authors: Torres Chávez Osmel , Álvarez Ojeda Vladimir , Espinoza Manfugás Julia María , Rodríguez Véliz María Lourdes
Published: September 2023

Las ciudades creativas gastronómicas, constituyen una oportunidad para el desarrollo del turismo sostenible y responsable. El presente estudio tiene como objetivo proponer un procedimiento para el proceso de comercialización de la oferta de servicios de alimentos y bebidas en bares localizados en la capital de Portoviejo. Los métodos y procedimientos de recopilación de datos, análisis e interpretación de los resultados, que aportaron al desarrollo de la investigación fueron de tipo descriptivo y documental. Entre los principales resultados se distingue la creación de platillos típicos como las botanas y de bebidas representado por los cocteles con ingredientes derivados del patrimonio gastronómico inmaterial manabita. Para finalizar, se enfatiza, el desconocimiento, falta de dominio en las operaciones para la atención al cliente; así como, carencia de sensibilidad para la preservación y promoción de los valores que distinguen la cultura culinaria ancestral y tradicional como atributo distintivo y competitivo para este tipo de establecimiento.
Diagnóstico de la cadena de valor de mantequilla de maní en Tosagua, Ecuador
Authors: Bello Parra Roberto Orlando , Valarezo Beltrón Carlos Oswaldo , Valarezo Molina María José
Published: September 2023

La presente investigación tuvo como objetivo analizar la cadena de valor en relación con la producción de mantequilla de maní, ampliamente consumida y popular, en los cantones de Tosagua, Bolívar y Junín, en la provincia de Manabí. El estudio se lleva a cabo a través de un enfoque descriptivo, que incluye la recopilación de datos a través de encuestas estructuradas y direccionadas a los diversos actores del entorno productivo, como productores, y otras microempresas relacionadas, con el fin de obtener una comprensión holística de las diferentes características que presenta la producción y comercialización de la mantequilla a base de este producto. Se identifican varios desafíos y oportunidades en el entorno productivo, como la falta de asociatividad, uso nulo de ventas en medios digitales, tiempos elevados de despacho, nivel limitado de tecnología, y se definen estrategias que contribuyan a la mejora de la producción de este derivado del maní.