bluuebunny/crossref_metadata_2025 huggingface dataset:
Spatial Distribution, Source Analysis, and Health Risk Assessment of Heavy Metals in the Farmland of Tangwang Village, Huainan City, China
Authors: Liu Ying , Shen Wenjing , Fan Kaixuan , Pei Weihao , Liu Shaomin
Published: February 2024

The impacts of heavy metal pollution in arable soil on agricultural production, environmental health, and the wellbeing of urban and rural residents cannot be overlooked. It has become a significant bottleneck in achieving comprehensive rural revitalization. To accurately grasp the characteristics of heavy metal pollution in suburban cultivated soil, Tangwang Village (a suburb of Huainan City) was subjected to scrutiny. The contents of heavy metals (Hg, Cu, Hg, As, Pb, Cr, Cd, and Zn) in the topsoil of cultivated land in this area were detected, and their spatial distribution characteristics were analyzed using inverse distance spatial interpolation. (1) After conducting a comprehensive analysis and thorough examination of the PMF model sources, it was determined that Cu, Cd, and Zn exhibit a direct correlation with agricultural practices, collectively contributing to a cumulative percentage of 21.10%. Meanwhile, Cr is derived from a combination of sources, including both natural parent materials and human activities, accounting for a total proportion of 24.45%. Notably, lead emissions from automobile exhausts constitute a significant source, while arsenic is primarily associated with dispersed factories and their respective operations, contributing to respective proportions of 36.38% and 18.07%. It is evident that agricultural practices, transportation, and industrial activities are the main reasons for heavy metal pollution in arable soil. (2) The evaluation of geological accumulation indicators reveals that the level of soil arsenic accumulation pollution is mild to moderate (1.199). On the other hand, the cumulative pollution level of Cd, Hg, Cr, and Cu was relatively low (0.462→0.186), whereas the levels of Pb and Zn were below the threshold. (3) The assessment of the ecological risk index revealed that the predominant elements posing potential ecological risks in the investigated region were Hg, As, and Cd, with average Ei values of E(Hg) = 86.81, E(As) = 80.67, and E(Cd) = 67.83, respectively. (4) The human health risk assessment revealed significant differences in the single non-carcinogenic risk values of heavy metals generated by different exposure pathways, with oral ingestion > dermal contact > oral nasal inhalation. Children were more susceptible to the toxic effects of heavy metals compared to adults. Both As and Cr caused an increased risk of cancer in both children and adults, which is a matter of great concern. The results of this study contribute to a more accurate description of the sources of heavy metals in farmland soil. This study indicates that the application of PMF for soil source analysis yields clear results that can be further applied. This research also has potential policy significance as it can help to improve the sustainability of ecosystems by coordinating both environmental and human activities.
Geographic Information System-Based Analysis of Reclaimable Idle Cropland for Agrivoltaics in Kansai, Japan: Enhancing Energy and Food Security
Authors: Nakata Hideki , Ogata Seiichi
Published: February 2024

Decarbonization and idle cropland reclaiming pose critical policy challenges. Agrivoltaic systems (AVSs), which merge agriculture and photovoltaics, offer a promising solution by reducing land use conflicts between agriculture and energy production. This study develops a GIS-based methodology to identify reclaimable idle croplands and assess the AVS’s contribution to agriculture and photovoltaic energy in Japan’s Kansai region. Using official geographic data and excluding high-risk areas, this study employs GIS for quantitative analysis. Our method detected more reclaimable idle cropland than previous studies. Focusing on food security while limiting AVS installations to reclaimable idle croplands could still generate up to 4564.08 GWh of electricity (0.8% of regional consumption) and 930.82 tons of soybeans (6.2% of regional yield). Under a more stringent scenario that excludes areas less suitable for solar power, 5 of 227 municipalities could achieve 15% electricity self-sufficiency through AVS. This research uncovers the existence of reclaimable idle croplands that could not be detected by existing methods and a decentralized power source available alongside food security maintenance. These insights will inform decarbonization and agricultural policy and guide the development of supportive and regulatory AVS frameworks.
Myricetin Inhibits Photodegradation of Profenofos in Water: Pathways and Mechanisms
Authors: Zhang Nan , Yang Yawei , Wang Xin , Shi Taozhong , Lv Pei , Li Qing X. , Hua Rimao
Published: February 2024

Profenofos is a detectable insecticide in the environment with strong toxicity to non-targeted organisms. Photodegradation is a main transformation of profenofos in the environment. Myricetin is a flavonoid that strongly scavenges free radicals. The effect of myricetin on the photodegradation of profenofos was studied. The half-lives (T1/2) of profenofos were 1.7–7.0 and 90 h under artificial light and sunlight. The photolysis rate of profenofos decreased by 1.87–4.72 and 7.62 times with the addition of 20 ratios of myricetin. Free radicals reacting with profenofos were •OH and 1O2, and the key free radical was •OH. Myricetin strongly scavenged •OH and 1O2 which rapidly reacted with profenofos. O-(2-Chlorophenyl)-O-ethyl-S-propyl phosphorothioate (M3) and O-(2-chlorophenyl)-O-ethyl phosphorothioate (M4) were major and new photoproducts of profenofos. According to the Ecological Structure Activity Relationships, photodegradation of profenofos was a detoxification process, but myricetin inhibited the photodegradation of profenofos and its photoproducts. These results highlight the implication of myricetin on the fate and potential risk of profenofos in the environment.
The Antioxidant Salidroside Ameliorates the Quality of Postovulatory Aged Oocyte and Embryo Development in Mice
Authors: Liu Kexiong , Zhang Luyao , Xu Xiaoling , Xiao Linli , Wen Junhui , Zhang Hanbing , Zhao Shuxin , Qiao Dongliang , Bai Jiahua , Liu Yan
Published: February 2024

Postovulatory aging is known to impair the oocyte quality and embryo development due to oxidative stress in many different animal models, which reduces the success rate or pregnancy rate in human assisted reproductive technology (ART) and livestock timed artificial insemination (TAI), respectively. Salidroside (SAL), a phenylpropanoid glycoside, has been shown to exert antioxidant and antitumor effects. This study aimed to investigate whether SAL supplementation could delay the postovulatory oocyte aging process by alleviating oxidative stress. Here, we show that SAL supplementation decreases the malformation rate and recovers mitochondrial dysfunction including mitochondrial distribution, mitochondrial membrane potential (ΔΨ) and ATP content in aged oocytes. In addition, SAL treatment alleviates postovulatory aging-caused oxidative stress such as higher reactive oxygen species (ROS) level, lower glutathione (GSH) content and a reduced expression of antioxidant-related genes. Moreover, the cytoplasmic calcium ([Ca2+]c) and mitochondrial calcium ([Ca2+]mt) of SAL-treated oocytes return to normal levels. Notably, SAL suppresses the aging-induced DNA damage, early apoptosis and improves spindle assembly in aged oocytes, ultimately elevating the embryo developmental rates and embryo quality. Finally, the RNA-seq and confirmatory experience showed that SAL promotes protective autophagy in aged oocytes by activating the MAPK pathway. Taken together, our research suggests that supplementing SAL is an effective and feasible method for preventing postovulatory aging and preserving the oocyte quality, which potentially contributes to improving the successful rate of ART or TAI.
Development of Deployable Reflector Antenna for the SAR-Satellite: Part 1. Design and Analysis of the Main Reflector Using Honeycomb Sandwich Composite Structure
Authors: Kim Hyun-Guk , Kim Dong-Geon , Do Ryoon-Ho , Koo Kyung-Rae , Yu Young-Joon
Published: February 2024

The deployable reflector antenna based on the synthetic aperture radar is a satellite component that consists of a unit structure in the form of a folded reflector. During the launch process, this satellite antenna is in the stowed condition to improve storage efficiency. It is then deployed to perform the space mission in the on-orbit condition. Due to these structural characteristics of the deployable reflector antenna, the reflector is possible to be loaded in the limited volume of the launch vehicle with the reduced size. Additionally, because the deployable reflector antenna is made by the lightweight material of the carbon fiber reinforced polymer and honeycomb core, it can reduce the launching cost and improve the revisit interval. In this paper, the conceptual design of the main reflector of the deployable reflector antenna was conducted. The main reflector was designed as the honeycomb sandwich composite structure. To design the main reflector, the stacking sequence of the composite material and honeycomb core was investigated to maximize the structural stiffness and minimize the antenna’s mass. Subsequently, finite element analyses including modal, quasi-static, structural–thermal coupling, and transient response were performed to numerically evaluate the structural performance of the lightweight composite reflector antenna.
Enhancing Dehumidification in the Cable Room of a Ring Main Unit through CFD-EMAG Coupling Simulation and Experimental Verification
Authors: Yan Yaoyu , Xing Futang , Gao Haonan , Mei Dan
Published: February 2024

The cable room, located at the base of the ring main unit, is prone to water vapor due to its proximity to damp cable holes and its relatively enclosed structure. This may penetrate internally and ultimately affect operational safety. Therefore, a dehumidifier was introduced to utilize dry air for internal circulation. To enhance the dehumidification in the cable room, the cable room device was designed for experimental research. Meanwhile, computational fluid dynamics (CFD)-electromagnetic (EMAG) coupling simulation is used to calculate the power loss of heat sources and their influence on multiple physical fields in numerical simulations. The feasibility of this study was confirmed by comparing the relative humidity, temperature, and velocity values between the experimental and numerical approaches. Furthermore, the layout of the ventilation pipes was modified to a vertical distribution, with upward supply and downward suction, to improve the airflow. The results indicate that the maximum relative errors in temperature, relative humidity, and velocity are only 3.61%, 7.14%, and 7.14%, respectively, which fall within an acceptable range. On this basis, additional simulation analysis was conducted on the humidity, dew point temperature, and airflow within the cable room, using an optimized model with a more comprehensive internal structure and cables. After implementing an optimized ventilation pipe layout, the relative humidity at the corresponding measuring points can decrease by up to 10.6%. The dew point temperature has decreased by 2.61 °C and the airflow has become more stable.
A Review of Techniques and Bio-Heat Transfer Models Supporting Infrared Thermal Imaging for Diagnosis of Malignancy
Authors: D’Alessandro Giampaolo , Tavakolian Pantea , Sfarra Stefano
Published: February 2024

The present review aims to analyze the application of infrared thermal imaging, aided by bio-heat models, as a tool for the diagnosis of skin and breast cancers. The state of the art of the related technical procedures, bio-heat transfer modeling, and thermogram post-processing methods is comprehensively reviewed. Once the thermal signatures of different malignant diseases are described, the updated thermographic techniques (steady-state and dynamic) used for cancer diagnosis are discussed in detail, along with the recommended best practices to ensure the most significant thermal contrast observable between the cancerous and healthy tissues. Regarding the dynamic techniques, particular emphasis is placed on innovative methods, such as lock-in thermography, thermal wave imaging, and rotational breast thermography. Forward and inverse modeling techniques for the bio-heat transfer in skin and breast tissues, supporting the thermographic examination and providing accurate data for training artificial intelligence (AI) algorithms, are reported with a special focus on real breast geometry-based 3D models. In terms of inverse techniques, different data processing algorithms to retrieve thermophysical parameters and growth features of tumor lesions are mentioned. Post-processing of infrared images is also described, citing both conventional processing procedures and applications of AI algorithms for tumor detection.
New Approaches of Stochastic Models to Examine the Vibration Features in Roller Bearings
Authors: Bhatti Saima , Shaikh Asif Ali , Mansoor Asif , Hussain Murtaza
Published: February 2024

Machinery components undergo wear and tear over time due to regular usage, necessitating the establishment of a robust prognosis framework to enhance machinery health and avert catastrophic failures. This study focuses on the collection and analysis of vibration data obtained from roller bearings experiencing various fault conditions. By employing a combination of techniques sourced from existing literature, distinct configurations within vibration datasets were examined to pinpoint the primary defects in roller bearings. The significant features identified through this analysis were utilized to formulate optimized stochastic model equations. These models, developed separately for inner and outer race fault features in comparison to healthy bearing features under random conditions, offer valuable insights into machinery prognosis. The application of these models aids in effective maintenance management, optimization of machinery performance, and the minimization of catastrophic failures and downtime, thereby contributing to overall machinery reliability.
A Local Pre-Rerouting Algorithm to Combat Sun Outage for Inter-Satellite Links in Low Earth Orbit Satellite Networks
Authors: Jin Jifeng , Shang Lin , Yang Zijian , Wang Haiwang , Li Guotong
Published: February 2024

Satellite networks show the development trend in global coverage, flexible access, and reliable transmission. They are the key to building a wide coverage, massive connection, three-dimensional, all-round, all-weather, space-, air- and ground-integrated information network. Due to its high frequency and large bandwidth, the laser inter-satellite link (ISL) is easily interfered with by sunlight. When sun outage occurs, the laser inter-satellite link will be interrupted for a long time. Most of the existing satellite link failure-routing algorithms are distributed and have poor load balancing capabilities. The centralized algorithm calculates paths independently for different services, and the effect is not ideal. In this paper, a local pre-rerouting algorithm (LPRA) is proposed. The center node collects the link information from the limited area before sun outage and the service information in the link that is about to be interrupted, and reroutes these services before sun outage occurs. After the occurrence of sun outage, services that originally needed to go through the failed link are forwarded according to the path calculated in advance, thereby avoiding link congestion and reducing the packet loss rate. Simulation results show that the proposed algorithm performs better than existing algorithms in terms of packet loss ratio, total delay and load balancing capabilities.
PCCAU-Net: A Novel Road Extraction Method Based on Coord Convolution and a DCA Module
Authors: Xue Xiaoqin , Ren Chao , Yin Anchao , Zhou Ying , Liu Yuanyuan , Ding Cong , Lu Jiakai
Published: February 2024

In the domain of remote sensing research, the extraction of roads from high-resolution imagery remains a formidable challenge. In this paper, we introduce an advanced architecture called PCCAU-Net, which integrates Pyramid Pathway Input, CoordConv convolution, and Dual-Inut Cross Attention (DCA) modules for optimized performance. Initially, the Pyramid Pathway Input equips the model to identify features at multiple scales, markedly enhancing its ability to discriminate between roads and other background elements. Secondly, by adopting CoordConv convolutional layers, the model achieves heightened accuracy in road recognition and extraction against complex backdrops. Moreover, the DCA module serves dual purposes: it is employed at the encoder stage to efficiently consolidate feature maps across scales, thereby fortifying the model’s road detection capabilities while mitigating false positives. In the skip connection stages, the DCA module further refines the continuity and accuracy of the features. Extensive empirical evaluation substantiates that PCCAU-Net significantly outperforms existing state-of-the-art techniques on multiple benchmarks, including precision, recall, and Intersection-over-Union(IoU). Consequently, PCCAU-Net not only represents a considerable advancement in road extraction research, but also demonstrates vast potential for broader applications, such as urban planning and traffic analytics.
Prediction of Leaf Break Resistance of Green and Dry Alfalfa Leaves by Machine Learning Methods
Authors: Ercan Uğur , Kabas Onder , Moiceanu Georgiana
Published: February 2024

Alfalfa holds an extremely significant place in animal nutrition when it comes to providing essential nutrients. The leaves of alfalfa specifically boast the highest nutritional value, containing a remarkable 70% of crude protein and an impressive 90% of essential vitamins. Due to this incredible nutritional profile, it becomes exceedingly important to ensure that the harvesting and threshing processes are executed with utmost care to minimize any potential loss of these invaluable nutrients present in the leaves. To minimize losses, it is essential to accurately determine the resistance of the leaves in both their green and dried forms. This study aimed to estimate the breaking resistance of green and dried alfalfa plants using machine learning methods. During the modeling phase, five different popular machine learning methods, Extra Trees (ET), Random Forest (RF), Gradient Boost (GB), Extreme Gradient Boosting (XGB), and CatBoost (CB), were used. The correlation coefficient (R2), root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) metrics were used to evaluate the models. The obtained metric results and the graphs obtained from the prediction values of the models revealed that the machine learning methods made successful predictions. The best R2 (0.9853), RMSE (0.0171), MAE (0.0099) and MAPE (0.0969) values for the dry alfalfa plant were obtained from the model established with the ET method, while the best RMSE (0.0616) and R2 (0.96) values for the green alfalfa plant were obtained from the model established with the RF method and the best MAE (0.0340) value was obtained from the model established with the ET method. Additionally, the best MAPE (0.1447) value was obtained from the model established with the GB method.
Model for Supporting Construction Workforce Planning Based on the Theory of Fuzzy Sets
Authors: Plebankiewicz Edyta , Karcińska Patrycja
Published: February 2024

The paper presents a model that aims to support the construction contractor in the process of construction workforce planning by verifying initial assumptions about the planned number of man-hours, determined using a widely available and widely used method. The construction of the mathematical labour planning model of was based on the fuzzy sets theory. As a result of the operation of the model, four detailed coefficients are obtained, which, after applying the weights of normalized groups of factors, allow to determine the overall result of the model. The model, by verifying the planned number of man-hours, directly influences the employment planning process at a construction site and also supports the scheduling of the overall progress of the works, making it assumedly closer to reality in terms of the involvement of employees as well as the execution time of the construction works.
Enhancing Dynagraph Card Classification in Pumping Systems Using Transfer Learning and the Swin Transformer Model
Authors: Dong Guoqing , Li Weirong , Dong Zhenzhen , Wang Cai , Qian Shihao , Zhang Tianyang , Ma Xueling , Zou Lu , Lin Keze , Liu Zhaoxia
Published: February 2024

The dynagraph card plays a crucial role in evaluating oilfield pumping systems’ performance. Nevertheless, classifying dynagraph cards can be quite difficult because certain operating conditions may exhibit similar patterns. Conventional classification approaches mainly involve labor-intensive manual analysis of these cards, leading to subjectivity, prolonged processing times, and vulnerability to human prejudices. In response to this challenge, our study introduces a novel approach that leverages transfer learning and the Swin Transformer model for classifying dynagraph cards across various operating conditions in rod pumping systems. Initially, the Swin Transformer model undergoes pre-training using the ImageNet-22k dataset. Subsequently, we fine-tune the model’s weights using actual dynagraph card datasets, facilitating direct classification analysis with dynagraph cards as input variables. The adoption of transfer learning significantly reduces the training time while enhancing the accuracy of condition diagnosis. To assess the effectiveness of our proposed method, we conducted a comparative evaluation against conventional models like ResNet50, DenseNet121, LeNet, and ViT. The findings demonstrate that our approach outperforms other methods, achieving an accuracy of 96%, thereby improving classification accuracy by 3–4%. Therefore, our approach, based on transfer learning and the Swin Transformer model, provides a better solution for practical problems involving similar dynagraph cards. It meets the requirements of oil field operations, enhancing economic benefits and work efficiency.
MSIE-Net: Associative Entity-Based Multi-Stage Network for Structured Information Extraction from Reports
Authors: Li Qiuyue , Sheng Hao , Sheng Mingxue , Wan Honglin
Published: February 2024

Efficient document recognition and sharing remain challenges in the healthcare, insurance, and finance sectors. One solution to this problem has been the use of deep learning techniques to automatically extract structured information from paper documents. Specifically, the structured extraction of a medical examination report (MER) can enhance medical efficiency, data analysis, and scientific research. While current methods focus on reconstructing table bodies, they often overlook table headers, leading to incomplete information extraction. This paper proposes MSIE-Net (multi-stage-structured information extraction network), a novel structured information extraction method, leveraging refined attention transformers and associated entity detection to enhance comprehensive MER information retrieval. MSIE-Net includes three stages. First, the RVI-LayoutXLM (refined visual-feature independent LayoutXLM) targets key information extraction. In this stage, the refined attention accentuates the interaction between different modalities by adjusting the attention score at the current position using previous position information. This design enables the RVI-LayoutXLM to learn more specific contextual information to improve extraction performance. Next, the associated entity detection module, RIFD-Net (relevant intra-layer fine-tuned detection network), identifies each test item’s location within the MER table body. Significantly, the backbone of RIFD-Net incorporates the intra-layer feature adjustment module (IFAM) to extract global features while homing in on local areas, proving especially sensitive for inspection tasks with dense and long bins. Finally, structured post-processing based on coordinate aggregation links the outputs from the prior stages. For the evaluation, we constructed the Chinese medical examination report dataset (CMERD), based on real medical scenarios. MSIE-Net demonstrated competitive performance in tasks involving key information extraction and associated entity detection. Experimental results validate MSIE-Net’s capability to successfully detect key entities in MER and table images with various complex layouts, perform entity relation extraction, and generate structured labels, laying the groundwork for intelligent medical documentation.
An Integrated Decision Support System for Low-Disturbance Surface Mining
Authors: Tao Yabin , Zhang Ruixin
Published: February 2024

Low-disturbance mining in surface mining (LDM) can transform traditional surface mine production systems into a more sustainable model by reducing the disturbance of surface mining, minimizing pollutant emissions, and reducing ecological impacts. The purpose of this paper is to explore the LDM evaluation method by applying multi-criteria decision-making to provide technical support for LDM implementation. Therefore, an evaluation method based on the combination of the fuzzy analytical hierarchy process (F-AHP) and grey clustering was proposed. Analyzed in terms of the current status of the evaluation indicators (reality) and the significance of the development of the LDM (desirability). Determined the weights and low-disturbance (LD) levels of the evaluation indicators. Combined with the fuzzy technique for order preference by similarity to an ideal solution (F-TOPSIS), the low-disturbance open pit mining paths are ranked, and finally, the decision support system for low-disturbance mining in surface mining is constructed. This study not only enriches the existing literature on related technologies but also lays the foundation for further research on LDM and provides exploratory insights for deeper improvement of LD level in surface mining.
Adversarial Attacks with Defense Mechanisms on Convolutional Neural Networks and Recurrent Neural Networks for Malware Classification
Authors: Alzaidy Sharoug , Binsalleeh Hamad
Published: February 2024

In the field of behavioral detection, deep learning has been extensively utilized. For example, deep learning models have been utilized to detect and classify malware. Deep learning, however, has vulnerabilities that can be exploited with crafted inputs, resulting in malicious files being misclassified. Cyber-Physical Systems (CPS) may be compromised by malicious files, which can have catastrophic consequences. This paper presents a method for classifying Windows portable executables (PEs) using Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). To generate malware executable adversarial examples of PE, we conduct two white-box attacks, Jacobian-based Saliency Map Attack (JSMA) and Carlini and Wagner attack (C&W). An adversarial payload was injected into the DOS header, and a section was added to the file to preserve the PE functionality. The attacks successfully evaded the CNN model with a 91% evasion rate, whereas the RNN model evaded attacks at an 84.6% rate. Two defense mechanisms based on distillation and training techniques are examined in this study for overcoming adversarial example challenges. Distillation and training against JSMA resulted in the highest reductions in the evasion rates of 48.1% and 41.49%, respectively. Distillation and training against C&W resulted in the highest decrease in evasion rates, at 48.1% and 49.9%, respectively.
Cricket Protein as an Innovative Emulsifier for Avocado Oil: Formulation and Characterization of Sustainable Emulsions
Authors: Trujillo-Cayado Luis A. , García-Domínguez Irene , Rodríguez-Luna Azahara , Hurtado-Fernández Elena , Santos Jenifer
Published: February 2024

The use of cricket protein in emulsions is in line with the growing interest in sustainable food sources, as crickets require minimal resources and produce lower greenhouse gas emissions than traditional livestock. Research in this area suggests that incorporating cricket protein into emulsions not only improves their nutritional value but also contributes to the development of environmentally friendly and functional food products. This study proposes the use of cricket protein for the stabilization of emulsions formulated with avocado oil as a dispersed phase. This oil is rich in monounsaturated fats, particularly oleic acid, and a variety of bioactive compounds. In the first part of this study, we assessed the influence of the emulsifier concentration and found that 2 wt.% is the optimum because a depletion flocculation effect was produced. Subsequently, processing was optimized using ultrasonication so that the higher energy input produced emulsions with a droplet diameter of less than 700 nm. Finally, rhamsan gum was added to the formulation, producing emulgels with improved pseudoplastic behavior and physical stability. This study demonstrates that cricket protein in combination with rhamsan gum is capable of forming stable, low-droplet-size emulgels with potential applications in encapsulation systems.
Widest Path in Networks with Gains/Losses
Authors: Tayyebi Javad , Rîtan Mihai-Lucian , Deaconu Adrian Marius
Published: February 2024

In this paper, the generalized widest path problem (or generalized maximum capacity problem) is studied. This problem is denoted by the GWPP. The classical widest path problem is to find a path from a source (s) to a sink (t) with the highest capacity among all possible s-t paths. The GWPP takes into account the presence of loss/gain factors on arcs as well. The GWPP aims to find an s-t path considering the loss/gain factors while satisfying the capacity constraints. For solving the GWPP, three strongly polynomial time algorithms are presented. Two algorithms only work in the case of losses. The first one is less efficient than the second one on a CPU, but it proves to be more efficient on large networks if it parallelized on GPUs. The third algorithm is able to deal with the more general case of losses/gains on arcs. An example is considered to illustrate how each algorithm works. Experiments on large networks are conducted to compare the efficiency of the algorithms proposed.
The Average Sentinel of the Heat Equation with an Unknown Reaction
Authors: Selatnia Houria , Ayadi Abdelhamid , Rezzoug Imad
Published: February 2024

In this paper, we analyze the identification of the amount of pollutant discharged problem by each source in a heat system when the dynamics of the state are governed by a parameterized unknown operator. In this way, we introduce the notion of average sentinel. The decomposition method is used to solve the equation of this problem, the gradient method is used to calculate the averaged control, and the combination of the two methods is used to estimate the pollution terms. Numerical example is given to confirm this result.
Analytical Solution of Generalized Bratu-Type Fractional Differential Equations Using the Homotopy Perturbation Transform Method
Authors: Alhamzi Ghaliah , Gouri Aafrin , Alkahtani Badr Saad T. , Dubey Ravi Shanker
Published: February 2024

In this study, we present the generalized form of the higher-order nonlinear fractional Bratu-type equation. In this generalization, we deal with a generalized fractional derivative, which is quite useful from an application point of view. Furthermore, some special cases of the generalized fractional Bratu equation are recognized and examined. To solve these nonlinear differential equations of fractional order, we employ the homotopy perturbation transform method. This work presents a useful computational method for solving these equations and advances our understanding of them. We also plot some numerical outcomes to show the efficiency of the obtained results.
Ectopic Expression of a Wheat R2R3-Type MYB Gene in Transgenic Tobacco Enhances Osmotic Stress Tolerance via Maintaining ROS Balance and Improving Root System Architecture
Authors: Azab Omar , Ben Romdhane Walid , El-Hendawy Salah , Ghazy Abdelhalim , Zakri Adel M. , Abd-ElGawad Ahmed M. , Al-Doss Abdullah
Published: February 2024

Water scarcity is a critical cause of plant yield loss and decreased quality. Manipulation of root system architecture to minimize the impact of water scarcity stresses may greatly contribute towards an improved distribution of roots in the soil and enhanced water and nutrient uptake abilities. In this study, we explored the potential of TaMYB20 gene, a wheat gene belonging to the R2R3-MYB transcription factor family, to improve root system architecture in transgenic tobacco plants. The full-length TaMYB20 gene was isolated from Triticum aestivum.cv. Sakha94 and used to produce genetically engineered tobacco plants. The transgenic plants exhibited enhanced tolerance to extended osmotic stress and were able to maintain their root system architecture traits, including total root length (TRL), lateral root number (LRN), root surface area (RSa), and root volume (RV), while the wild-type plants failed to maintain the same traits. The transgenic lines presented greater relative water content in their roots associated with decreased ion leakage. The oxidative stress resulted in the loss of mitochondrial membrane integrity in the wild-type (WT) plants due to the overproduction of reactive oxygen species (ROS) in the root cells, while the transgenic lines were able to scavenge the excess ROS under stressful conditions through the activation of the redox system. Finally, we found that the steady-state levels of three PIN gene transcripts were greater in the TaMYB20-transgenic lines compared to the wild-type tobacco. Taken together, these findings confirm that TaMYB20 is a potentially useful gene candidate for engineering drought tolerance in cultivated plants.
Diet Diversity of the Fluviatile Masu Salmon, Oncorhynchus masou (Brevoort 1856) Revealed via Gastrointestinal Environmental DNA Metabarcoding and Morphological Identification of Contents
Authors: Li Lijuan , Yin Xuwang , Wan Qianruo , Rusitanmu Dilina , Han Jie
Published: February 2024

Masu salmon, Oncorhynchus masou (Brevoort 1856), a commercially important fish species endemic to the North Pacific Ocean, attained national second-level protected animal status in China in 2021. Despite this recognition, knowledge about the trophic ecology of this fish remains limited. This study investigated the diet diversity of fluviatile Masu salmon in the Mijiang River, China, utilizing the gastrointestinal tract environmental DNA (GITeDNA) metabarcoding and morphological identification. The results revealed a diverse prey composition, ranging from terrestrial and aquatic invertebrates to small fishes. The fluviatile Masu salmon in general consumed noteworthily more aquatic prey than terrestrial prey. There were much more prey taxa and a higher diet diversity detected by GITeDNA metabarcoding than by morphological identification. GITeDNA metabarcoding showed that larger and older Masu salmon consumed significantly more terrestrial insects than aquatic prey species did, with 7366 verses 5012 sequences in the group of ≥20 cm, 9098 verses 4743 sequences in the group of ≥100 g and 11,540 verses 729 sequences in the group of age 3+. GITeDNA metabarcoding also showed size- and age-related diet diversity, indicating that the dietary niche breadth and trophic diversity of larger and older Masu salmon increased with food resources expanding to more terrestrial prey. Terrestrial invertebrates of riparian habitats play a vital role in the diet of fluviatile Masu salmon, especially larger individuals, highlighting their importance in connecting aquatic and terrestrial food webs. Conservation plans should prioritize the protection and restoration of riparian habitats. This study advocates the combined use of GITeDNA metabarcoding and morphological observation for a comprehensive understanding of fish diet diversity.
Neutrophil Activity and Extracellular Matrix Degradation: Drivers of Lung Tissue Destruction in Fatal COVID-19 Cases and Implications for Long COVID
Authors: Narasaraju Teluguakula , Neeli Indira , Criswell Sheila L. , Krishnappa Amita , Meng Wenzhao , Silva Vasuki , Bila Galyna , Vovk Volodymyr , Serhiy Zolotukhin , Bowlin Gary L. , Meyer Nuala , Luning Prak Eline T. , Radic Marko , Bilyy Rostyslav
Published: February 2024

Pulmonary fibrosis, severe alveolitis, and the inability to restore alveolar epithelial architecture are primary causes of respiratory failure in fatal COVID-19 cases. However, the factors contributing to abnormal fibrosis in critically ill COVID-19 patients remain unclear. This study analyzed the histopathology of lung specimens from eight COVID-19 and six non-COVID-19 postmortems. We assessed the distribution and changes in extracellular matrix (ECM) proteins, including elastin and collagen, in lung alveoli through morphometric analyses. Our findings reveal the significant degradation of elastin fibers along the thin alveolar walls of the lung parenchyma, a process that precedes the onset of interstitial collagen deposition and widespread intra-alveolar fibrosis. Lungs with collapsed alveoli and organized fibrotic regions showed extensive fragmentation of elastin fibers, accompanied by alveolar epithelial cell death. Immunoblotting of lung autopsy tissue extracts confirmed elastin degradation. Importantly, we found that the loss of elastin was strongly correlated with the induction of neutrophil elastase (NE), a potent protease that degrades ECM. This study affirms the critical role of neutrophils and neutrophil enzymes in the pathogenesis of COVID-19. Consistently, we observed increased staining for peptidyl arginine deiminase, a marker for neutrophil extracellular trap release, and myeloperoxidase, an enzyme-generating reactive oxygen radical, indicating active neutrophil involvement in lung pathology. These findings place neutrophils and elastin degradation at the center of impaired alveolar function and argue that elastolysis and alveolitis trigger abnormal ECM repair and fibrosis in fatal COVID-19 cases. Importantly, this study has implications for severe COVID-19 complications, including long COVID and other chronic inflammatory and fibrotic disorders.
PAK1-Dependent Regulation of Microtubule Organization and Spindle Migration Is Essential for the Metaphase I–Metaphase II Transition in Porcine Oocytes
Authors: Peng Lei , He Yijing , Wang Weihan , Dai Jianjun , Li Qiao , Ju Shiqiang
Published: February 2024

P21-activated kinase 1 (PAK1) is a critical downstream target that mediates the effect of small Rho GTPase on the regulation of cytoskeletal kinetics, cell proliferation, and cell migration. PAK1 has been identified as a crucial regulator of spindle assembly during the first meiotic division; however, its roles during the metaphase I (MI) to metaphase II (MII) transition in oocytes remain unclear. In the present study, the potential function of PAK1 in regulating microtubule organization and spindle positioning during the MI–MII transition was addressed in porcine oocytes. The results showed that activated PAK1 was co-localized with α-tubulin, and its expression was increased from the MI to MII stage (p < 0.001). However, inhibiting PAK1 activity with an inhibitor targeting PAK1 activation-3 (IPA-3) at the MI stage decreased the first polar body (PB1) extrusion rate (p < 0.05), with most oocytes arrested at the anaphase-telophase (ATI) stage. IPA-3-treated oocytes displayed a decrease in actin distribution in the plasma membrane (p < 0.001) and an increase in the rate of defects in MII spindle reassembly with abnormal spindle positioning (p < 0.001). Nevertheless, these adverse effects of IPA-3 on oocytes were reversed when the disulfide bond between PAK1 and IPA-3 was reduced by dithiothreitol (DTT). Co-immunoprecipitation revealed that PAK1 could recruit activated Aurora A and transform acidic coiled-coil 3 (TACC3) to regulate spindle assembly and interact with LIM kinase 1 (LIMK1) to facilitate actin filament-mediated spindle migration. Together, PAK1 is essential for microtubule organization and spindle migration during the MI–MII transition in porcine oocytes, which is associated with the activity of p-Aurora A, p-TACC3 and p-LIMK1.
Power-to-Noise Optimization in the Design of Neural Recording Amplifier Based on Current Scaling, Source Degeneration Resistor, and Current Reuse
Authors: Wang Zhen , Wang Xiao , Shu Guijun , Yin Meng , Huang Shoushuang , Yin Ming
Published: February 2024

This article presents the design of a low-power, low-noise neural signal amplifier for neural recording. The structure reduces the current consumption of the amplifier through current scaling technology and lowers the input-referred noise of the amplifier by combining a source degeneration resistor and current reuse technologies. The amplifier was fabricated using a 0.18 μm CMOS MS RF G process. The results show the front-end amplifier exhibits a measured mid-band gain of 40 dB/46 dB and a bandwidth ranging from 0.54 Hz to 6.1 kHz; the amplifier’s input-referred noise was measured to be 3.1 μVrms, consuming a current of 3.8 μA at a supply voltage of 1.8 V, with a Noise Efficiency Factor (NEF) of 2.97. The single amplifier’s active silicon area is 0.082 mm2.
The Impact of Career Plateaus on Job Performance: The Roles of Organizational Justice and Positive Psychological Capital
Authors: Chang Po-Chien , Geng Xinqi , Cai Qihai
Published: February 2024

Previous studies suggest that career plateaus have detrimental effects on employees’ satisfaction and performance. Psychological distress generated by career plateaus hinders organizations from achieving the United Nations’ Sustainable Development Goals (UNSDGs) of ‘health and well-being at work’ (SDG-3) and ‘decent work’ (SDG-8). How to mitigate the negative impact of career plateaus becomes the key to enhancing sustainable well-being at work. However, the influencing mechanisms of career plateaus have not been fully discussed, especially regarding employees’ psychological processes. Drawing on the equity theory and the conservation of resource theory, this study examines the influence mechanism of career plateaus on employee job performance via organizational justice, with positive psychological capital moderating the process. Mplus and the Process macro for SPSS are adopted to conduct confirmatory factor analysis and regression analyses. Building on 368 supervisor–employee paired questionnaires with an average of eight employees per supervisor, empirical results indicate that employees who encounter career plateaus reduce their perceived organizational justice to discourage them from performing well in their jobs. Positive psychological capital, however, mitigates the negative effects of career plateaus on perceived organizational justice and the indirect effects of career plateaus on job performance through organizational justice. Theoretically, this study advances our understanding of the influence mechanism of career plateaus on employees’ job performance. Practical implications are also drawn for organizations to alleviate the negative impact of career plateaus to promote sustainable well-being at work.
Pitch Improvement in Attentional Blink: A Study across Audiovisual Asymmetries
Authors: Yang Haoping , Cai Biye , Tan Wenjie , Luo Li , Zhang Zonghao
Published: February 2024

Attentional blink (AB) is a phenomenon in which the perception of a second target is impaired when it appears within 200–500 ms after the first target. Sound affects an AB and is accompanied by the appearance of an asymmetry during audiovisual integration, but it is not known whether this is related to the tonal representation of sound. The aim of the present study was to investigate the effect of audiovisual asymmetry on attentional blink and whether the presentation of pitch improves the ability to detect a target during an AB that is accompanied by audiovisual asymmetry. The results showed that as the lag increased, the subject’s target recognition improved and the pitch produced further improvements. These improvements exhibited a significant asymmetry across the audiovisual channel. Our findings could contribute to better utilizations of audiovisual integration resources to improve attentional transients and auditory recognition decline, which could be useful in areas such as driving and education.
The Consequences of Anthropomorphic and Teleological Beliefs in a Global Pandemic
Authors: Roberts Andrew J. , Handley Simon , Polito Vince
Published: February 2024

To describe something in terms of its purpose or function is to describe its teleology. Previous studies have found that teleological beliefs are positively related to anthropomorphism, and that anthropomorphism decreases the perceived unpredictability of non-human agents. In the current study, we explore these relationships using the highly salient example of beliefs about the coronavirus pandemic. Results showed that both anthropomorphism and teleology were negatively associated with perceived uncertainty and threat, and positively associated with self-reported behavioural change in response to the pandemic. These findings suggest that highly anthropomorphic and teleological individuals may view coronavirus as agentive and goal-directed. While anthropomorphic and teleological beliefs may facilitate behavioural change in response to the pandemic, we also found that the associated reduction in uncertainty and threat may be detrimental to behavioural change. We discuss the implications of these findings for messaging about global events more broadly.
Paediatric Thoracic Imaging in Cystic Fibrosis in the Era of Cystic Fibrosis Transmembrane Conductance Regulator Modulation
Authors: O’Regan Patrick W. , Stevens Niamh E. , Logan Niamh , Ryan David J. , Maher Michael M.
Published: February 2024

Cystic fibrosis (CF) is one of the most common progressive life-shortening genetic conditions worldwide. Ground-breaking translational research has generated therapies that target the primary cystic fibrosis transmembrane conductance regulator (CFTR) defect, known as CFTR modulators. A crucial aspect of paediatric CF disease is the development and progression of irreversible respiratory disease in the absence of clinical symptoms. Accurate thoracic diagnostics have an important role to play in this regard. Chest radiographs are non-specific and insensitive in the context of subtle changes in early CF disease, with computed tomography (CT) providing increased sensitivity. Recent advancements in imaging hardware and software have allowed thoracic CTs to be acquired in paediatric patients at radiation doses approaching that of a chest radiograph. CFTR modulators slow the progression of CF, reduce the frequency of exacerbations and extend life expectancy. In conjunction with advances in CT imaging techniques, low-dose thorax CT will establish a central position in the routine care of children with CF. International guidelines regarding the choice of modality and timing of thoracic imaging in children with CF are lagging behind these rapid technological advances. The continued progress of personalised medicine in the form of CFTR modulators will promote the emergence of personalised radiological diagnostics.