bluuebunny/crossref_metadata_2025 huggingface dataset:
An Uncertainty Weighted Non-Cooperative Target Pose Estimation Algorithm, Based on Intersecting Vectors
Authors: Li Yunhui , Yan Yunhang , Xiu Xianchao , Miao Zhonghua
Published: November 2022

Aiming at the relative pose estimation of non-cooperative targets in space traffic management tasks, a two-step pose estimation method, based on spatially intersecting straight lines, is proposed, which mainly includes three aspects: (1) Use binocular vision to reconstruct the straight space line, and based on the direction vector of the straight line and the intersection of the straight line, solve the pose of the measured target in the measurement coordinate system, and obtain the initial value of the pose estimation. (2) Analyze the uncertainty of the spatial straight-line imaging, construct the uncertainty description matrix of the line, and filter the line features, accordingly. (3) Analyze the problems existing in the current linear distance measurement, construct the spatial linear back-projection error in the parametric coordinate space, and use the linear imaging uncertainty to weigh the projection error term to establish the optimization objective function of the pose estimation. Finally, the nonlinear optimization algorithm is used to iteratively solve the above optimization problem, to obtain high-precision pose estimation results. The experimental results show that the two-step pose estimation algorithm, proposed in this paper, can effectively achieve a high-precision and robust pose estimation for non-cooperative spatial targets. When the measurement distance is 10 m, the position accuracy can reach 10 mm, and the attitude measurement accuracy can reach 1°, which meets the pose estimation accuracy requirements of space traffic management.
Deep Learning-Based Method for Classification of Sugarcane Varieties
Authors: Kai Priscila Marques , de Oliveira Bruna Mendes , da Costa Ronaldo Martins
Published: November 2022

The classification of sugarcane varieties using products derived from remote sensing allows for the monitoring of plants with different profiles without necessarily having physical contact with the study objects. However, differentiating between varieties can be challenging due to the similarity of the spectral characteristics of each crop. Thus, this study aimed to classify four sugarcane varieties through deep neural networks, subsequently comparing the results with traditional machine learning techniques. In order to provide more data as input for the classification models, along with the multi-band values of the pixels and vegetation indices, other information can be obtained from the sensor bands through RGB combinations by reconciling different bands so as to yield the characteristics of crop varieties. The methodology created to discriminate sugarcane varieties consisted of a dense neural network, with the number of hidden layers determined by the greedy layer-wise method and multiples of four neurons in each layer; additionally, a 5-fold evaluation in the training data was composed of Sentinel-2 band data, vegetation indices, and RGB combinations. Comparing the results acquired from each model with the hyperparameters selected by Bayesian optimisation, except for the neural network with manually defined parameters, it was possible to observe a greater precision of 99.55% in the SVM model, followed by the neural network developed by the study, random forests, and kNN. However, the final neural network model prediction resulted in the 99.48% accuracy of a six-hidden-layers network, demonstrating the potential of using neural networks in classification. Among the characteristics that contributed the most to the classification, the chlorophyll-sensitive bands, especially B6, B7, B11, and some RGB combinations, had the most impact on the correct classification of samples by the neural network model. Thus, the regions encompassing the near-infrared and shortwave infrared regions proved to be suitable for the discrimination of sugarcane varieties.
Detecting Irrigation Events over Semi-Arid and Temperate Climatic Areas Using Sentinel-1 Data: Case of Several Summer Crops
Authors: Bazzi Hassan , Baghdadi Nicolas , Najem Sami , Jaafar Hadi , Le Page Michel , Zribi Mehrez , Faraslis Ioannis , Spiliotopoulos Marios
Published: November 2022

Irrigation monitoring is of great importance in agricultural water management to guarantee better water use efficiency, especially under changing climatic conditions and water scarcity. This study presents a detailed assessment of the potential of the Sentinel-1 (S1) Synthetic Aperture Radar (SAR) data to detect irrigation events at the plot scale. The potential of the S1 data to detect the irrigation events was carried out using the Irrigation Event Detection Model (IEDM) over semi-arid and temperate oceanic climates in five study sites in south Europe and the Middle East. The IEDM is a decision tree model initially developed to detect irrigation events using the change detection algorithm applied to the S1 time series data. For each study site and at each agricultural plot, all available S1 images during the period of irrigation were used to construct an S1 time series and apply the IEDM. Different types of major summer irrigated crops were analyzed in this study, including Maize, Soybean, Sorghum and Potato, mainly with the sprinkler irrigation technique. The irrigation detection accuracy was evaluated using S1 images and the IEDM against the climatic condition of the studied area, the vegetation development (by means of the normalized difference vegetation index, NDVI) and the revisit time of the S1 sensor. The main results showed generally good overall accuracy for irrigation detection using the S1 data, reaching 67% for all studied sites together. This accuracy varied according to the climatic conditions of the studied area, with the highest accuracy for semi-arid areas and lowest for temperate areas. The analysis of the irrigation detection as a function of the crop type showed that the accuracy of irrigation detection decreases as the vegetation becomes well developed. The main findings demonstrated that the density of the available S1 images in the S1 time series over a given area affects the irrigation detection accuracy, especially for temperate areas. In temperate areas the irrigation detection accuracy decreased from 70% when 15 to 20 S1 images were available per month to reach less than 56% when less than 10 S1 images per month were available over the study sites.
Sustainability of Small Farms in Serbia: A Comparative Analysis with the European Union
Authors: Jurjević Žana , Zekić Stanislav , Matkovski Bojan , Đokić Danilo
Published: November 2022

Throughout the EU, small farms have varying degrees of importance, which points to the need to analyse the role of small farms. This study, in addition to EU countries, includes Serbia, a candidate country for EU membership. This study aims to provide an overview of agriculture’s structural characteristics by creating an Index of Agricultural Structural Characteristics (ASC Index). The analysis was carried out at the regional level, which provides a more detailed overview of the structural characteristics of agriculture in the EU and Serbia. The results indicate several regional differences in the EU along north–south and west–east divisions. Serbian agriculture is predominantly made up of small farms and corresponds to the southern model of European agriculture. In terms of the west–east division, Serbian agriculture also corresponds to the structure of Central and Eastern Europe countries (CEEC), with which it shares a historical heritage of a centrally planned economy. Changes in Serbian agricultural policy must be directed toward regions with unfavourable structural characteristics. Special attention should be given to small farms in cases where there is potential to improve competitiveness and where there is a good basis for improving the social sustainability of rural areas.
Dietary Oregano Essential Oil Supplementation Influences Production Performance and Gut Microbiota in Late-Phase Laying Hens Fed Wheat-Based Diets
Authors: Gao Fei , Zhang Lianhua , Li Hui , Xia Fei , Bai Hongtong , Piao Xiangshu , Sun Zhiying , Cui Hongxia , Shi Lei
Published: November 2022

This study aimed to investigate the potential effects of OEO on production performance, egg quality, fatty acid composition in yolk, and cecum microbiota of hens in the late phase of production. A total of 350 58-week-old Jing Tint Six laying hens were randomly divided into five groups: (1) fed a basal diet (control); (2) fed a basal diet + 5 mg/kg flavomycin (AGP); (3) fed a basal diet + 100 mg/kg oregano essential oil + 20 mg/kg cinnamaldehyde (EO1); (4) fed a basal diet + 200 mg/kg oregano essential oil + 20 mg/kg cinnamaldehyde (EO2); (5) fed a basal diet + 300 mg/kg oregano essential oil + 20 mg/kg cinnamaldehyde (EO3). Compared to the control group, group EO2 exhibited higher (p < 0.05) egg production during weeks 5–8 and 1–8. EO2 had a lower feed conversion ratio than the control group during weeks 1–8. The content of monounsaturated fatty acid (MUFA) in EO2 was higher (p < 0.05) than that of the control and AGP groups. EO2 increased (p < 0.05) the abundance of Actinobacteriota and decreased the abundance of Desulfovibri in the cecum. The abundances of Anaerofilum, Fournierella, Fusobacterium, and Sutterella were positively correlated with egg production, feed conversion ratio, and average daily feed intake, while the abundances of Bacteroides, Desulfovibrio, Lactobacillus, Methanobrevibacter, and Rikenellaceae_RC9_gut_group were negatively correlated with egg production, feed conversion ratio, and average daily feed intake. Dietary supplementation with 200 mg/kg OEO and 20 mg/kg cinnamaldehyde could improve egg-production performance, decrease feed conversion ratio, and alter the fatty acid and microbial composition of eggs from late-phase laying hens.
Impact of Variability Factors on Hair Cortisol, Blood Count and Milk Production of Donkeys
Authors: Salari Federica , Mariti Chiara , Altomonte Iolanda , Gazzano Angelo , Martini Mina
Published: November 2022

The increased interest in donkeys because of their milk has led to changes in their farm management. Little is known about the effect of the farming systems on donkey health and welfare. Measuring hair cortisol concentrations is an emerging method to assess stress in animals. To the best of our knowledge, no cortisol assessment has been done on dairy donkeys; similarly, only a few studies have investigated donkey haematological values. The aim of this study was to evaluate the effects of the lactation phase, parity and season on blood parameters, milk yield and quality and hair cortisol in dairy donkeys. Individual samples of milk, blood and mane hair were taken from twenty jennies at 1, 6 and 10 months after parturition. Higher values of hair cortisol were found in the first sampling, suggesting temporary stress during the peri-parturition. The parity influenced the number of blood cells, which was lower in the pluriparous jennies. The season affected milk quality and mean corpuscular haemoglobin and mean corpuscular haemoglobin concentration. The latters might represent the adaptation to the environmental conditions. This study contributes to a better understanding of the biochemical processes occurring in lactating jennies, and to their physiological and wellbeing status.
Effect of β-Mannanase Addition during Whole Pigs Fattening on Production Yields and Intestinal Health
Authors: Sánchez-Uribe Pedro , Romera-Recio Eva , Cabrera-Gómez Carolina G. , Hernández-Rodríguez Elisa V. , Lamrani Álvaro , González-Guijarro Belén , de Pascual-Monreal Clara , Mendonça-Pascoal Livia , Martínez-Alarcón Laura , Ramis Guillermo
Published: November 2022

The presence of β-mannans in feed can produce a futile and chronic immune stimulation in fattening pigs. In this trial, a 1-4-endo-D-β-mannanase was added to the feed (HC) during growth and fattening (0.03% of Hemicell HT) and physical performance and pathological data were recorded, and intestinal integrity and immune activation were studied by molecular biomarkers, compared to a control group (CON). The treatment diet was reduced in energy content by 40 Kcal/kg NE. From each group, 113 and 112 animals housed in 8 pens were individually identified and weighed three times: at 7th, 63rd and 116th days in feed. The FCR was calculated for groups of two pens and ADG individually. There was no difference in ADG (CON = 0.836, HC = 0.818) nor in FCR between groups (p = 0.486). During growth, there was a higher frequency of normal feces in HC and there were also no differences in the frequency of gastric lesions. A significant increase in Claudin, Occludin, IFN-γ and IL8 was observed in the CON in feces and a significant decrease in IL-6 in HC. In tissues, there were differences for IL-12p40, TNF-alpha in jejunum (increased CON) and TGF-β in ileum and jejunum, (decreased HC). The economic performance was EUR 4.7 better in the treated group. In conclusion, the addition of 1-4-endo-D-β--mannanase to the feed with a 1.6% reduction in net energy compared to the control, allowed the animals to perform as well as the animals on the higher energy diet, with lower prevalence of diarrhea.
The Effect of Gut Microbiota and Probiotics on Metabolism in Fish and Shrimp
Authors: Ringø Einar , Harikrishnan Ramasamy , Soltani Mehdi , Ghosh Koushik
Published: November 2022

The present paper presents an overview of the effects of gut microbiota and probiotics on lipid-, carbohydrate-, protein- and amino acid metabolism in fish and shrimp. In probiotic fish studies, the zebrafish (Danio rerio) model is the most frequently used, and probiotic administration reveals the effect on glucose homeostasis, anti-lipidemic effects and increasing short-chain fatty acids, and increased expressions of genes related to carbohydrate metabolism and innate immunity, along with down-regulation of oxidative stress-related genes. Further, improved length of the intestinal villi and expression of nutrient transporters in fish owing to probiotics exposure have been documented. The present review will present an appraisal of the effect of intestinal microbiota and probiotic administration on the metabolism of nutrients and metabolites related to stress and immunity in diverse fish- and shrimp species. Furthermore, to give the reader satisfactory information on the topics discussed, some information from endothermic animals is also presented.
Influence of Culture Media and Environmental Factors (Water Potential and Temperature) on Mycelial Growth of Phytopythium vexans (de Bary), the Causal Agent of Dieback Disease in Apple Trees
Authors: Jabiri Salma , El Hamss Hajar , Amraoui Mohammed Bendriss , Lahlali Rachid
Published: November 2022

This study aimed at evaluating the effects of culture media and environmental factors (temperature and water potential (Ψw)) on the growth of the pathogenic fungus Phytopythium vexans (de Bary) associated with root rot and dieback disease in apple trees. Tomato agar, potato dextrose agar (PDA), and soybean agar were the most favourable for rapid mycelial growth, with optimum growth recorded for PDA medium. To determine the environmental conditions that promoted the development of this phytopathogen, the effects of temperature (5–30 °C), water potential (Ψw) (−15.54; −0.67 MPa) (0.89–0.995 aw), and their interaction were evaluated on the in vitro radial growth rates of the five isolates of P. vexans and on their latency phase (time period prior to growth). The results of this study showed that temperature, water potential, and their interaction had significant effects (p < 0.001) on the radial growth rates and latency phases of all tested P. vexans isolates. All isolates were able to grow throughout the temperature range (5 to 30 °C), with the maximum radial growth rate being observed at the highest temperatures, 25–30 °C. Growth was seen to be faster at −0.67 MPa (0.995 aw) at 25 °C and 30 °C. No growth was observed at Ψw < −5.44 MPa (0.96 aw), regardless of the temperature. It was found that the length of the latency phase depended significantly on both environmental factors. The longest latency phases (5 days on average) were recorded at a temperature of 5 °C and Ψw of −0.67 MPa (0.995 aw) and −2.69 MPa (0.98 aw), while the shortest latency phases were observed at a temperature of 30 °C and a Ψw of −0.67 MPa (0.995 aw), with an average of 0.2 days. The findings from this study could help to understand the impact of these environmental factors on the occurrence of diseases caused by P. vexans and more likely to design a reliable preventive control strategy based on the avoidance of conditions that play in favour of the phytopathogen.
Influencing Factors of Particulate Matter Concentration in the Metro Carriage and the Corresponding Inhalation Intake Estimation: A Field Measurement in Chengdu
Authors: Huang Shenghao , Wang Han , Wu Dan , Ma Rongjiang , Sun Liangliang , Deng Mengsi
Published: November 2022

Urbanization promotes the development of the subway system, and the particulate matter (PM) concentrations inside have received increasing attention. This study first measured the dynamic PM2.5 and PM10 concentrations in a metro carriage in Chengdu and explored the dominant influencing factors. The personal inhalation intakes of different routes were evaluated. The results showed that the in-carriage PM2.5 and PM10 concentrations ranged from 11 to 74 μg/m3 (mean: 36.7 μg/m3) and 13 to 89 μg/m3 (mean: 40.1 μg/m3), respectively. When the train passed from the overground to underground, the in-carriage PM2.5 and PM10 concentrations increased by 30.4% and 32.9%, respectively. No specific linear relationship between passenger number and in-carriage PM concentrations was found. In-carriage PM concentrations decreased after the carriage doors were opened on the platforms. PM2.5 inhalation intakes ranged from 1.08 to 9.52, with a mean of 4.24 µg. For the passengers with the same age and sex, the average inhaled PM2.5 intake in the metro carriage on the route with more underground platforms was higher. This study not only revealed the PM characteristics in the Chengdu metro system for the first time, but also provided guidelines for reducing the in-carriage PM concentrations to build a healthier travel environment.
Thermal and Acoustic Characterization of Innovative and Unconventional Panels Made of Reused Materials
Authors: Neri Manuela
Published: November 2022

Europe calls for a transition to the circular economy model based on recycling, reuse, the proper design of products, and repair. Recycling requires energy and chemical products for waste processing; on the contrary, reusing reduces the impact of transportation and expands the life of materials that cannot be recycled. This article highlights the characteristics of selected end-of-life materials; it aims to raise awareness among manufacturers to consider products’ conscious design to facilitate their reuse in different sectors. Panels 7 cm thick, realized by assembling cardboard packaging, egg boxes, bulk polyester, and felt, have been experimentally tested to understand whether they can be installed indoors to improve thermal and acoustic comfort. The panels’ equivalent thermal conductivity λeq measured through the guarded hot plate method is 0.071 W/mK. Acoustic tests have been performed in a sound transmission room and a reverberation room. The weighted sound reduction index Rw is 19 dB, the weighted sound absorption coefficient αw is 0.30, and the noise reduction coefficient NRC is 0.64. The measured properties have been compared to those of commercial materials, and the results show that the panels have interesting properties from the thermal and acoustic points of view. They could be employed in the building sector and in disadvantaged contexts where low-income people cannot afford commercial insulating materials. Although other factors, such as fire resistance, need to be evaluated, these results show that the proposed approach is feasible.
Comparison of Atmospheric Turbulence Characteristics over Sea Surface and Land Surface before, during, and after Typhoons
Authors: Bi Xueyan , Lu Chao , Liu Chunxia , Huang Jian , Yang Honglong , Zhao Zhongkuo , Song Qingtao
Published: November 2022

The goal of the paper is to reveal discrepancies of turbulent variables over different surfaces (sea, island, land) based on the measurements taken on three towers during (including before and after) seven typhoon episodes from 2008 to 2018. The atmospheric stability, turbulent spectrum, friction velocity, turbulent kinetic energy, dissipative heating, and gust factor are examined. The similar turbulent characteristics over sea and on the island reinforce the previous conclusion that the turbulent measurements on the island mainly represent the sea surface. The turbulent characteristics over sea and on land are very different due to the different underlying surface roughness. The unstable (stable) condition dominates on the sea (land) surface. Turbulent spectra both over sea and on land follow the canonical Kolmogorov’s power law with the −5/3 slope. The cospectra on land are more peaked than those over sea. All of the friction velocity, turbulent kinetic energy, and dissipative heating increase with increasing 10 m wind speed, and those on land are much larger than those over sea. The distributions of gust factors widen and shift to higher on land than those over sea. The distributions of gust factors at heights of 10 m and 40 m are biased to higher values than those at heights of 160 m and 320 m on land.
Possibilities for a Quick Onsite Safety-State Assessment of Stand-Alone Lithium-Ion Batteries
Authors: Koch Daniel , Schweiger Hans-Georg
Published: November 2022

Electric vehicles’ high-voltage lithium-ion batteries are complex systems and can be sources of several hazards for interacting people. Sophisticated battery management systems (BMS) therefore constantly monitor their characteristics and varying states, to keep the battery within desired operational conditions and to mitigate safety risks as well as excessive degradation. However, there can be several situations where the battery is not in normal operation (e.g., a stand-alone battery) and a fully functional BMS monitoring function is not available. When necessary to interact with the system, its safety state must be deduced to ensure the safety of interactors. This can be a challenging task depending on a situation’s characteristics (time pressure, technical knowledge of involved people). Thus, this article discusses how the safety state of electric vehicle batteries can be evaluated quickly even by untrained people. To develop a solution, different scenarios, which require a battery’s state assessment, and the options for collecting relevant information are motivated and discussed, respectively. Finally, a mobile interface that can evaluate and display the safety state by using BMS-internal data is described and demonstrated.
A Study of Projection-Based Attentive Spatial–Temporal Map for Remote Photoplethysmography Measurement
Authors: Kim Dae-Yeol , Cho Soo-Young , Lee Kwangkee , Sohn Chae-Bong
Published: November 2022

The photoplethysmography (PPG) signal contains various information that is related to CVD (cardiovascular disease). The remote PPG (rPPG) is a method that can measure a PPG signal using a face image taken with a camera, without a PPG device. Deep learning-based rPPG methods can be classified into three main categories. First, there is a 3D CNN approach that uses a facial image video as input, which focuses on the spatio-temporal changes in the facial video. The second approach is a method that uses a spatio-temporal map (STMap), and the video image is pre-processed using the point where it is easier to analyze changes in blood flow in time order. The last approach uses a preprocessing model with a dichromatic reflection model. This study proposed the concept of an axis projection network (APNET) that complements the drawbacks, in which the 3D CNN method requires significant memory; the STMap method requires a preprocessing method; and the dyschromatic reflection model (DRM) method does not learn long-term temporal characteristics. We also showed that the proposed APNET effectively reduced the network memory size, and that the low-frequency signal was observed in the inferred PPG signal, suggesting that it can provide meaningful results to the study when developing the rPPG algorithm.
Comparative Analysis of Deep Learning Models Used in Impact Analysis of Coronavirus Chest X-ray Imaging
Authors: Nallakaruppan Musiri Kailasanathan , Ramalingam Subhashini , Somayaji Siva Rama Krishnan , Prathiba Sahaya Beni
Published: November 2022

The impact analysis of deep learning models for COVID-19-infected X-ray images is an extremely challenging task. Every model has unique capabilities that can provide suitable solutions for some given problem. The prescribed work analyzes various deep learning models that are used for capturing the chest X-ray images. Their performance-defining factors, such as accuracy, f1-score, training and the validation loss, are tested with the support of the training dataset. These deep learning models are multi-layered architectures. These parameters fluctuate based on the behavior of these layers, learning rate, training efficiency, or over-fitting of models. This may in turn introduce sudden changes in the values of training accuracy, testing accuracy, loss or validation loss, f1-score, etc. Some models produce linear responses with respect to the training and testing data, such as Xception, but most of the models provide a variation of these parameters either in the accuracy or the loss functions. The prescribed work performs detailed experimental analysis of deep learning image neural network models and compares them with the above said parameters with detailed analysis of these parameters with their responses regarding accuracy and loss functions. This work also analyses the suitability of these model based on the various parameters, such as the accuracy and loss functions to various applications. This prescribed work also lists out various challenges on the implementation and experimentation of these models. Solutions are provided for enhancing the performance of these deep learning models. The deep learning models that are used in the prescribed work are Resnet, VGG16, Resnet with VGG, Inception V3, Xception with transfer learning, and CNN. The model is trained with more than 1500 images of the chest-X-ray data and tested with around 132 samples of the X-ray image dataset. The prescribed work analyzes the accuracy, f1-score, recall, and precision of these models and analyzes these parameters. It also measures parameters such as training accuracy, testing accuracy, loss, and validation loss. Each epoch of every model is recorded to measure the changes in these parameters during the experimental analysis. The prescribed work provides insight for future research through various challenges and research findings with future directions.
A Machine Learning-Empowered Workflow to Discriminate Bacillus subtilis Motility Phenotypes
Authors: Mayer Benjamin , Holtrup Sven , Graumann Peter L.
Published: November 2022

Bacteria that are capable of organizing themselves as biofilms are an important public health issue. Knowledge discovery focusing on the ability to swarm and conquer the surroundings to form persistent colonies is therefore very important for microbiological research communities that focus on a clinical perspective. Here, we demonstrate how a machine learning workflow can be used to create useful models that are capable of discriminating distinct associated growth behaviors along distinct phenotypes. Based on basic gray-scale images, we provide a processing pipeline for binary image generation, making the workflow accessible for imaging data from a wide range of devices and conditions. The workflow includes a locally estimated regression model that easily applies to growth-related data and a shape analysis using identified principal components. Finally, we apply a density-based clustering application with noise (DBSCAN) to extract and analyze characteristic, general features explained by colony shapes and areas to discriminate distinct Bacillus subtilis phenotypes. Our results suggest that the differences regarding their ability to swarm and subsequently conquer the medium that surrounds them result in characteristic features. The differences along the time scales of the distinct latency for the colony formation give insights into the ability to invade the surroundings and therefore could serve as a useful monitoring tool.
The Integration of NOSACQ-50 with Importance-Performance Analysis Technique to Evaluate and Analyze Safety Climate Dimensions in the Construction Sector in Saudi Arabia
Authors: Alamoudi Mohammed
Published: November 2022

In Saudi Arabia, one of the fastest-growing sectors is the construction industry. With the increasing numbers of accidents, it is worth reviewing the preventive solutions to the threats that have been identified. Therefore, the aim of this study was to apply the NOSACQ-50 survey to measure the safety climate (SC), and to dissect the strengths and weaknesses of each dimension in the survey using the importance-performance analysis (IPA) method. Based on the results, several strategies for improving the SC were recommended. A total of 296 construction workers were involved. According to the proposed method, the major strengths were management and worker commitment to safety, prioritizing safety during work, workers’ trust safety systems, and management competence in safety. The weaknesses were unstrict judgment in safety issues, the absence of workers’ suggestions in establishing safety rules, and near-miss incidents that went unreported. To mitigate these gaps, management should focus on strict judgement in safety-related issues, reporting all incident types, periodic meetings or reports with the workers, improved safety training, and checkup rounds. The outcomes of this study will assist organizations by providing a new validated SC assessment methodology that will help in determining aspects of strength and weakness that affect their SC, which will improve safety behavior and reduce accidents.
An Empirical Study of the Human Error-Related Factors Leading to Site Accidents in the Iranian Urban Construction Industry
Authors: Chan Daniel W. M. , Baghbaderani Alireza Babaie , Sarvari Hadi
Published: November 2022

Human errors are one of the major causes of accidents in the construction industry. Human errors can be caused by various factors across diverse types of projects. Hence, this research study seeks to determine the major factors influencing human errors associated with the urban construction industry (UCI). To achieve this, three rounds of Delphi survey were conducted with 17 experts engaged in construction site safety management. The Delphi panel members were determined using a targeted snowball sampling method. According to the results of the Delphi survey, 35 significant factors leading to the incidence of human errors in the UCI were identified and collated. Then, an empirical questionnaire was developed based on a five-point Likert measurement scale and distributed among construction experts to evaluate the impact level of each identified human error in the UCI. The questionnaire included 35 effective factors pertaining to human errors classified into five main groups of environmental factors, information systems/technological factors, individual factors (permanently related), individual factors (temporarily related), and organizational factors. Findings indicate that all evaluated factors are at a higher-than-average level and can be considered as the significant factors leading to the occurrence of site accidents attributed to human errors in the UCI. In addition, the top five most significant factors include improper work and safety culture, low level of technology deployed for equipment and safety protection, violation of safety regulations, rushing to do work, and lack of a proper education system in the organization. The results of this study can be useful for producing better-informed decisions by various major industrial practitioners and site safety managers.
Resilience in Lower Grade Glioma Patients
Authors: Fröhlich Ellen , Sassenrath Claudia , Nadji-Ohl Minou , Unteroberdörster Meike , Rückriegel Stefan , von der Brelie Christian , Roder Constantin , Forster Marie-Therese , Schommer Stephan , Löhr Mario , Pala Andrej , Goebel Simone , Mielke Dorothee , Gerlach Rüdiger , Renovanz Mirjam , Wirtz Christian Rainer , Onken Julia , Czabanka Marcus , Tatagiba Marcos Soares , Rohde Veit , Ernestus Ralf-Ingo , Vajkoczy Peter , Gansland Oliver , Coburger Jan
Published: November 2022

Current data show that resilience is an important factor in cancer patients’ well-being. We aim to explore the resilience of patients with lower grade glioma (LGG) and the potentially influencing factors. We performed a cross-sectional assessment of adult patients with LGG who were enrolled in the LoG-Glio registry. By phone interview, we administered the following measures: Resilience Scale (RS-13), distress thermometer, Montreal Cognitive Assessment Test for visually impaired patients (MoCA-Blind), internalized stigmatization by brain tumor (ISBI), Eastern Cooperative Oncological Group performance status (ECOG), patients’ perspective questionnaire (PPQ) and typical clinical parameters. We calculated correlations and multivariate regression models. Of 74 patients who were assessed, 38% of those showed a low level of resilience. Our results revealed significant correlations of resilience with distress (p < 0.001, −0.49), MOCA (p = 0.003, 0.342), ECOG (p < 0.001, −0.602), stigmatization (p < 0.001, −0.558), pain (p < 0.001, −0.524), and occupation (p = 0.007, 0.329). In multivariate analyses, resilience was negatively associated with elevated ECOG (p = 0.020, β = −0.383) and stigmatization levels (p = 0.008, β = −0.350). Occupation showed a tendency towards a significant association with resilience (p = 0.088, β = −0.254). Overall, low resilience affected more than one third of our cohort. Low functional status is a specific risk factor for low resilience. The relevant influence of stigmatization on resilience is a novel finding for patients suffering from a glioma and should be routinely identified and targeted in clinical routine.
Mitoferrin, Cellular and Mitochondrial Iron Homeostasis
Authors: Ali Md Yousuf , Oliva Claudia R. , Flor Susanne , Griguer Corinne E.
Published: November 2022

Iron is essential for many cellular processes, but cellular iron homeostasis must be maintained to ensure the balance of cellular signaling processes and prevent disease. Iron transport in and out of the cell and cellular organelles is crucial in this regard. The transport of iron into the mitochondria is particularly important, as heme and the majority of iron-sulfur clusters are synthesized in this organelle. Iron is also required for the production of mitochondrial complexes that contain these iron-sulfur clusters and heme. As the principal iron importers in the mitochondria of human cells, the mitoferrins have emerged as critical regulators of cytosolic and mitochondrial iron homeostasis. Here, we review the discovery and structure of the mitoferrins, as well as the significance of these proteins in maintaining cytosolic and mitochondrial iron homeostasis for the prevention of cancer and many other diseases.
Deep Molecular and In Silico Protein Analysis of p53 Alteration in Myelodysplastic Neoplasia and Acute Myeloid Leukemia
Authors: Madarász Kristóf , Mótyán János András , Bedekovics Judit , Miltényi Zsófia , Ujfalusi Anikó , Méhes Gábor , Mokánszki Attila
Published: November 2022

Background: Mutation of the TP53 gene is one of the major drivers of myelodysplastic neoplasias (MDS) and acute myeloid leukemia with myelodysplasia-related changes (AML-MR). TP53 mutations present in these hematopoietic malignancies form a distinct molecular genetic cluster with a worse prognosis than without the alteration. However, besides well-characterized hot-spot variants, a significant proportion of TP53 alterations are of uncertain clinical significance. Methods: To enlighten so far unknown aspects, bone-marrow samples from altogether 77 patients are analyzed retrospectively with the diagnosis of AML-MR (26 cases), MDS-IB (12 cases), and MDS-LB (39 cases) according to WHO 2022 guidelines. Next-generation sequencing results are correlated with histological, cytogenetic, and survival data. Results: Twenty out of the 30 TP53 mutation types detected by NGS are not categorized in current public databases; thus, their clinical significance remained mysterious. Because of the interpretation difficulties and the absence of clinical correlations, pathogenicity is established based on in silico approaches. The 12 pathogenicity classification systems, as well as protein stability, protein–DNA, protein–protein interaction, and post-translational modification analyses are applied. We found statistically significant differences between AML/MDS groups considering p53 pathogenicity, protein structural changes, and overall survival. The largest number of abnormalities with the most severe consequences are found in AML-MR cases. Conclusions: These molecular and in silico protein data further support that MDS with increased-blast (MDS-IB) is an intermediate group between AML-MR and MDS with low-blast (MDS-LB) patients, which frequently progresses to AML and is therefore considered a pre-leukemic condition.
Tortuous Cardiac Intercalated Discs Modulate Ephaptic Coupling
Authors: Ivanovic Ena , Kucera Jan P.
Published: November 2022

Cardiac ephaptic coupling, a mechanism mediated by negative electric potentials occurring in the narrow intercellular clefts of intercalated discs, can influence action potential propagation by modulating the sodium current. Intercalated discs are highly tortuous due to the mingling of plicate and interplicate regions. To investigate the effect of their convoluted structure on ephaptic coupling, we refined our previous model of an intercalated disc and tested predefined folded geometries, which we parametrized by orientation, amplitude and number of folds. Ephaptic interactions (assessed by the minimal cleft potential and amplitude of the sodium currents) were reinforced by concentric folds. With increasing amplitude and number of concentric folds, the cleft potential became more negative during the sodium current transient. This is explained by the larger resistance between the cleft and the bulk extracellular space. In contrast, radial folds attenuated ephaptic interactions and led to a less negative cleft potential due to a decreased net cleft resistance. In conclusion, despite limitations inherent to the simplified geometries and sodium channel distributions investigated as well as simplifications regarding ion concentration changes, these results indicate that the folding pattern of intercalated discs modulates ephaptic coupling.
Timing of Systemic Steroids and Neurodevelopmental Outcomes in Infants < 29 Weeks Gestation
Authors: Kandraju Hemasree , Jasani Bonny , Shah Prakesh S. , Church Paige T. , Luu Thuy Mai , Ye Xiang Y. , Stavel Miroslav , Mukerji Amit , Shah Vibhuti ,   the CNN Investigators,   the CNFUN Investigators
Published: November 2022

Objective: To determine the association between postnatal age (PNA) at first administration of systemic postnatal steroids (sPNS) for bronchopulmonary dysplasia (BPD) and mortality or significant neurodevelopmental impairment (sNDI) at 18–24 months corrected age (CA) in infants < 29 weeks’ gestation. Methods: Data from the Canadian Neonatal Network and Canadian Neonatal Follow-up Network databases were used to conduct this retrospective cohort study. Infants exposed to sPNS for BPD after the 1st week of age were included and categorized into 8 groups based on the postnatal week of the exposure. The primary outcome was a composite of mortality or sNDI. A multivariable logistic regression model adjusting for potential confounders was used to determine the association between the sPNS and ND outcomes. Results: Of the 10,448 eligible infants, follow-up data were available for 6200 (59.3%) infants. The proportion of infants at first sPNS administration was: 8%, 17.5%, 23.1%, 18.7%, 12.6%, 8.3%, 5.8%, and 6% in the 2nd, 3rd, 4th, 5th, 6th, 7th, 8–9th, and ≥10th week of PNA respectively. No significant association between the timing of sPNS administration and the composite outcome of mortality or sNDI was observed. The odds of sNDI and Bayley-III motor composite < 70 increased by 1.5% (95% CI 0.4, 2.9%) and 2.6% (95% CI 0.9, 4.4%), respectively, with each one-week delay in the age of initiation of sPNS. Conclusions: No significant association was observed between the composite outcome of mortality or sNDI and PNA of sPNS. Among survivors, each week’s delay in initiation of sPNS may increase the odds of sNDI and motor delay.
Experimental Evaluation of Foam Diversion for EOR in Heterogeneous Carbonate Rocks
Authors: Taha Motaz , Patil Pramod , Nguyen Quoc
Published: November 2022

Immiscible gas injection applied to heterogeneous carbonate reservoirs can be inefficient due to poor conformance control. Foam mobility control is proposed in this work as a solution for gas conformance issues in such reservoirs. A unique experimental program was developed to evaluate alkyl polyglucoside (APG) stabilized foam for foaming ability, emulsion-forming tendency and resistance to oil. Dynamic methane foam behavior is systematically studied through single and dual injection core flooding experiments, simulating foam diversion during immiscible methane flooding in a layered reservoir with a significant layer permeability contrast. Results show a stable foam-oil system with no viscous emulsions at very high formation brine salinity (144,000 ppm total dissolved solids). Single-core floods for the high permeability layer (Unit-A) showed that foam viscosity of 27 cP could be achieved at 11% oil saturation (So). Under similar oil-wet condition, the low permeability zone (Unit-B) could generate foam of 21 cP at 18.9% So, indicating an increase in injected fluid mobility reduction with permeability. Dual-core injection experiments, which is designed to evaluate accurately fluid diversion capacity of such foams, reveals remarkable dynamic foam behaviors. While the water-wet condition indicates the scalability of foam behaviors (i.e., the ability of foam to control fluid mobility against the variation of rock permeability) between the single and composite core systems, the oil-wet condition confirms good foam resistance to residual oil that resulted in an increase in Unit B production from 46 to 82%, and 74 to 85% for Unit-A. Moreover, dual-core floods representing premature waterfloods (i.e., higher oil saturation) shows even more dramatic incremental oil recovery (44 to 81% in Unit-A and 17.5 to 71% in Unit-B), evidencing the ability of foam to self-viscosify with permeability variation at varying oil saturations.
Unusual Ileocecal Ulcers after Liver Transplantation for Hepatitis B Cirrhosis and Hepatocellular Carcinoma
Authors: He Kang , Xi Zhifeng , Xia Qiang
Published: November 2022

We presented a case demonstrating ileocecal ulcers after liver transplantation for hepatitis B cirrhosis and hepatocellular carcinoma. The patient presented 4 years post-transplant with paroxysmal right lower abdominal pain. Due to a mild increase in the leukocyte and neutrophil count, infectious diseases were initially suspected. However, probiotic treatment did not help improve the symptom. An enhanced CT scan revealed a thickening in the ileocecal region, and endoscopy later showed multiple giant and deep ulcers in the ileocecal region with polypoid hyperplasia. Histopathology of an ulcer biopsy displayed benign ulcers, and chronic inflammation with non-caseous granulomas, without signs of a fungus or parasite infection. Epithelial exfoliation with atypical hyperplasia was observed, and a tacrolimus-induced ileocecal ulcer was considered by a pathologist. Clinical manifestation, lab findings, radiology, and pathology characteristics of ulcers were not consistent with the pathogenesis of ischemia, tuberculosis, CMV, EBV, tumor, or inflammatory bowel diseases. Abdominal pain was gradually relieved and subsided with the discontinuation of tacrolimus and corticosteroid administration.
A Systems Biology Approach on the Regulatory Footprint of Human Endogenous Retroviruses (HERVs)
Authors: Markopoulos Georgios S.
Published: November 2022

Human endogenous retroviruses (HERVs) are a family of endogenous retroviruses that comprise the ~8.93% of the human genome sequence, with a high proportion being human specific. The recent expansion of repeated HERV sequences has offered a framework for genetic and epigenetic innovation. In the current report, a systematic approach is implemented to catalogue regulatory elements within HERVs, as a roadmap to potential functions of HERV sequences in gene networks. ENCODE Project has offered a wealth of epigenetic data based on omics technologies. I analyzed the presence of HERV sequences on consensus cis-regulatory elements (cCREs) from ENCODE data. On the one side, HERVs are in 1 out of 9 cCREs (>100.000 cCREs in total), dispersed within the genome and present in cis-regulatory regions of ~81% of human genes, as calculated following gene enrichment analysis. On the other side, promoter-associated HERV cCREs are present adjacent to (in a 200 bp window) the transcription start sites of 256 human genes. Regulatory network production, followed by centrality analysis led to the discovery of 90 core genes containing HERV-associated promoters. Pathway analysis on the core network genes and their immediate neighbors revealed a regulatory footprint that, among others, is associated with inflammation, chemokine signaling and response to viral infection. Collectively, these results support the concept that the expansion of regulatory sequences derived from HERVs is critical for epigenetic innovation that may have wired together genes into novel transcriptional networks with critical roles in cellular physiology and pathology.
Optimized Path Planning Strategy to Enhance Security under Swarm of Unmanned Aerial Vehicles
Authors: Manikandan Kayalvizhi , Sriramulu Ramamoorthy
Published: November 2022

Unmanned Aerial Vehicles (UAVs) are widely deployed in military surveillance operations, especially the quadcopter UAVs which are easy to operate and considerably quieter. However, UAVs encounter problems in secure path planning during navigation and are prone to cyber security attacks. Further, due to the UAV battery capacity, the operating time for surveillance is limited. In this paper, we propose a novel Resilient UAV Path Optimization Algorithm (RUPOA) which provides an optimal path under security attacks such as denial-of-service (DoS) and Man-in-the-Middle (MITM) attacks. The performance efficiency of the proposed path planning algorithm is compared with the existing path planning algorithms based on execution time. To achieve secure path planning in UAVs and to mitigate security attacks, a blockchain-aided security solution is proposed. To prevent security attacks, smart contracts are generated where the devices are registered with gasLimit. The blockchain consensus mechanism allows for secure and tamper-free transmission of data between the Ground Control Station (GCS) and a swarm of UAVs. The performance efficiency of the blockchain model is evaluated based on network latency which is the total execution time across the blockchain network.