bluuebunny/crossref_metadata_2025 huggingface dataset:
Dream Divination in a Context of Social Disruption: Julian’s Vision of the Two Trees
Authors: Neil Bronwen
Published: May 2024

The late Roman discourse on divinatory dreams and their interpretation reflects a context of cultural fragmentation. The political turbulence of the 350s to 360s was due partly to the ongoing external war with the (Persian) Sasanians, but also to the internal struggle between the heirs of Constantine for rule over the Roman empire, still undergoing a process of Christianization when Julian was acclaimed Augustus in 360. A third arena of fragmentation was religious beliefs and practices during the 350s and 360s. The contested transformation of Rome in the religious sphere was the context of Julian’s dynastic vision of the two trees, received in late 358 or 359.
An Advanced Terrain Vegetation Signal Detection Approach for Forest Structural Parameters Estimation Using ICESat-2 Data
Authors: Li Yifan , Shen Xin , Cao Lin
Published: May 2024

Accurate forest structural parameters (such as forest height and canopy cover) support forest carbon monitoring, sustainable forest management, and the implementation of silvicultural practices. The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), which is a spaceborne Light Detection and Ranging (LiDAR) satellite, offers significant potential for acquiring precise and extensive information on forest structural parameters. However, the ICESat-2 ATL08 product is significantly influenced by the geographical environment and forest characteristics, maintaining considerable potential for enhancing the accuracy of forest height estimation. Meanwhile, it does not focus on providing canopy cover data. To acquire accurate forest structural parameters, the Terrain Signal Neural Network (TSNN) framework was proposed, integrating Computer Vision (CV), Ordering Points to Identify the Clustering Structure (OPTICS), and deep learning. It encompassed an advanced approach for detecting terrain vegetation signals and constructing deep learning models for estimating forest structural parameters using ICESat-2 ATL03 raw data. First, the ATL03 footprints were visualized as Profile Raster Images of Footprints (PRIF), implementing image binarization through adaptive thresholding and median filtering denoising to detect the terrain. Second, the rough denoising buffers were created based on the terrain, combining with the OPTICS clustering and Gaussian denoising algorithms to recognize the terrain vegetation signal footprints. Finally, deep learning models (convolutional neural network (CNN), ResNet50, and EfficientNetB3) were constructed, training standardized PRIF to estimate forest structural parameters (including forest height and canopy cover). The results indicated that the TSNN achieved high accuracy in terrain detection (coefficient of determination (R2) = 0.97) and terrain vegetation signal recognition (F-score = 0.72). The EfficientNetB3 model achieved the highest accuracy in forest height estimation (R2 = 0.88, relative Root Mean Squared Error (rRMSE) = 13.5%), while the CNN model achieved the highest accuracy in canopy cover estimation (R2 = 0.80, rRMSE = 18.5%). Our results have significantly enhanced the accuracy of acquiring ICESat-2 forest structural parameters, while also proposing an original approach combining CV and deep learning for utilizing spaceborne LiDAR data.
Revisiting the Role of Sensors for Shaping Plant Research: Applications and Future Perspectives
Authors: Tyagi Anshika , Mir Zahoor Ahmad , Ali Sajad
Published: May 2024

Plant health monitoring is essential for understanding the impact of environmental stressors (biotic and abiotic) on crop production, and for tailoring plant developmental and adaptive responses accordingly. Plants are constantly exposed to different stressors like pathogens and soil pollutants (heavy metals and pesticides) which pose a serious threat to their survival and to human health. Plants have the ability to respond to environmental stressors by undergoing rapid transcriptional, translational, and metabolic reprogramming at different cellular compartments in order to balance growth and adaptive responses. However, plants’ exceptional responsiveness to environmental cues is highly complex, which is driven by diverse signaling molecules such as calcium Ca2+, reactive oxygen species (ROS), hormones, small peptides and metabolites. Additionally, other factors like pH also influence these responses. The regulation and occurrence of these plant signaling molecules are often undetectable, necessitating nondestructive, live research approaches to understand their molecular complexity and functional traits during growth and stress conditions. With the advent of sensors, in vivo and in vitro understanding of some of these processes associated with plant physiology, signaling, metabolism, and development has provided a novel platform not only for decoding the biochemical complexity of signaling pathways but also for targeted engineering to improve diverse plant traits. The application of sensors in detecting pathogens and soil pollutants like heavy metal and pesticides plays a key role in protecting plant and human health. In this review, we provide an update on sensors used in plant biology for the detection of diverse signaling molecules and their functional attributes. We also discuss different types of sensors (biosensors and nanosensors) used in agriculture for detecting pesticides, pathogens and pollutants.
Cross-Domain Knowledge Transfer for Sustainable Heterogeneous Industrial Internet-of-Things Networks
Authors: Gong Zhenzhen , Cui Qimei , Ni Wei
Published: May 2024

In this article, a novel cross-domain knowledge transfer method is implemented to optimize the tradeoff between energy consumption and information freshness for all pieces of equipment powered by heterogeneous energy sources within smart factory. Three distinct groups of use cases are considered, each utilizing a different energy source: grid power, green energy source, and mixed energy sources. Differing from mainstream algorithms that require consistency among groups, the proposed method enables knowledge transfer even across varying state and/or action spaces. With the advantage of multiple layers of knowledge extraction, a lightweight knowledge transfer is achieved without the need for neural networks. This facilitates broader applications in self-sustainable wireless networks. Simulation results reveal a notable improvement in the ’warm start’ policy for each equipment, manifesting as a 51.32% increase in initial reward compared to a random policy approach.
LJCD-Net: Cross-Domain Jamming Generalization Diagnostic Network Based on Deep Adversarial Transfer
Authors: Zhang Zhichao , Deng Zhongliang , Liu Jingrong , Ding Zhenke , Liu Bingxun
Published: May 2024

Global Navigation Satellite Systems (GNSS) offer comprehensive position, navigation, and timing (PNT) estimates worldwide. Given the growing demand for reliable location awareness in both indoor and outdoor contexts, the advent of fifth-generation mobile communication technology (5G) has enabled expansive coverage and precise positioning services. However, the power received by the signal of interest (SOI) at terminals is notably low. This can lead to significant jamming, whether intentional or unintentional, which can adversely affect positioning receivers. The diagnosis of jamming types, such as classification, assists receivers in spectrum sensing and choosing effective mitigation strategies. Traditional jamming diagnosis methodologies predominantly depend on the expertise of classification experts, often demonstrating a lack of adaptability for diverse tasks. Recently, researchers have begun utilizing convolutional neural networks to re-conceptualize a jamming diagnosis as an image classification issue, thereby augmenting recognition performance. However, in real-world scenarios, the assumptions of independent and homogeneous distributions are frequently violated. This discrepancy between the source and target distributions frequently leads to subpar model performance on the test set or an inability to procure usable evaluation samples during training. In this paper, we introduce LJCD-Net, a deep adversarial migration-based cross-domain jamming generalization diagnostic network. LJCD-Net capitalizes on a fully labeled source domain and multiple unlabeled auxiliary domains to generate shared feature representations with generalization capabilities. Initially, our paper proposes an uncertainty-guided auxiliary domain labeling weighting strategy, which estimates the multi-domain sample uncertainty to re-weight the classification loss and specify the gradient optimization direction. Subsequently, from a probabilistic distribution standpoint, the spatial constraint imposed on the cross-domain global jamming time-frequency feature distribution facilitates the optimization of collaborative objectives. These objectives include minimizing both the source domain classification loss and auxiliary domain classification loss, as well as optimizing the inter-domain marginal probability and conditional probability distribution. Experimental results demonstrate that LJCD-Net enhances the recognition accuracy and confidence compared to five other diagnostic methods.
3D Object Detection under Urban Road Traffic Scenarios Based on Dual-Layer Voxel Features Fusion Augmentation
Authors: Jiang Haobin , Ren Junhao , Li Aoxue
Published: May 2024

To enhance the accuracy of detecting objects in front of intelligent vehicles in urban road scenarios, this paper proposes a dual-layer voxel feature fusion augmentation network (DL-VFFA). It aims to address the issue of objects misrecognition caused by local occlusion or limited field of view for targets. The network employs a point cloud voxelization architecture, utilizing the Mahalanobis distance to associate similar point clouds within neighborhood voxel units. It integrates local and global information through weight sharing to extract boundary point information within each voxel unit. The relative position encoding of voxel features is computed using an improved attention Gaussian deviation matrix in point cloud space to focus on the relative positions of different voxel sequences within channels. During the fusion of point cloud and image features, learnable weight parameters are designed to decouple fine-grained regions, enabling two-layer feature fusion from voxel to voxel and from point cloud to image. Extensive experiments on the KITTI dataset demonstrate the significant performance of DL-VFFA. Compared to the baseline network Second, DL-VFFA performs better in medium- and high-difficulty scenarios. Furthermore, compared to the voxel fusion module in MVX-Net, the voxel feature fusion results in this paper are more accurate, effectively capturing fine-grained object features post-voxelization. Through ablative experiments, we conducted in-depth analyses of the three voxel fusion modules in DL-VFFA to enhance the performance of the baseline detector and achieved superior results.
Detection of Road Crack Images Based on Multistage Feature Fusion and a Texture Awareness Method
Authors: Guo Maozu , Tian Wenbo , Li Yang , Sui Dong
Published: May 2024

Structural health monitoring for roads is an important task that supports inspection of transportation infrastructure. This paper explores deep learning techniques for crack detection in road images and proposes an automatic pixel-level semantic road crack image segmentation method based on a Swin transformer. This method employs Swin-T as the backbone network to extract feature information from crack images at various levels and utilizes the texture unit to extract the texture and edge characteristic information of cracks. The refinement attention module (RAM) and panoramic feature module (PFM) then merge these diverse features, ultimately refining the segmentation results. This method is called FetNet. We collect four public real-world datasets and conduct extensive experiments, comparing FetNet with various deep-learning methods. FetNet achieves the highest precision of 90.4%, a recall of 85.3%, an F1 score of 87.9%, and a mean intersection over union of 78.6% on the Crack500 dataset. The experimental results show that the FetNet approach surpasses other advanced models in terms of crack segmentation accuracy and exhibits excellent generalizability for use in complex scenes.
Vision-Based Estimation of Force Balance of Near-Suspended Melt Pool for Drooping and Collapsing Prediction
Authors: Luo Longxi , Qian Enze , Lu Tao , Pan Jingren , Liu Minghao , Liu Changmeng , Guo Yueling , Bi Luzheng
Published: May 2024

Wire-arc additive manufacturing (WAAM) is favored by the industry for its high material utilization rate and low cost. However, wire-arc additive manufacturing of lattice structures faces problems with forming accuracy such as broken rod and surface morphology defects, which cannot meet the industrial demand. This article innovatively combines the melt pool stress theory with visual perception algorithms to visually study the force balance of the near-suspended melt pool to predict the state of the melt pool. First, the method for melt pool segmentation was studied. The results show that the optimized U-net achieved high accuracy in melt pool segmentation tasks, with accuracies of 98.18%, MIOU 96.64%, and Recall 98.34%. In addition, a method for estimating melt pool force balance and predicting normal, sagging, and collapsing states of the melt pool is proposed. By combining experimental testing with computer vision technology, an analysis of the force balance of the melt pool during the inclined rod forming process was conducted, showing a prediction rate as high as 90% for the testing set. By using this method, monitoring and predicting the state of the melt pool is achieved, preemptively avoiding issues of broken rods during the printing process. This approach can effectively assist in adjusting process parameters and improving welding quality. The application of this method will further promote the development of intelligent unmanned WAAM and provide some references for the development of artificial intelligence monitoring systems in the manufacturing field.
Uttarakhand State Earthquake Early Warning System: A Case Study of the Himalayan Environment
Authors: Kumar Pankaj , Kamal  , Sharma Mukat Lal , Jakka Ravi Sankar , Pratibha
Published: May 2024

The increased seismic activity observed in the Himalayas, coupled with the expanding urbanization of the surrounding areas in northern India, poses significant risks to both human lives and property. Developing an earthquake early warning system in the region could help in alleviating these risks, especially benefiting cities and towns in mountainous and foothill regions close to potential earthquake epicenters. To address this concern, the government and the science and engineering community collaborated to establish the Uttarakhand State Earthquake Early Warning System (UEEWS). The government of Uttarakhand successfully launched this full-fledged operational system to the public on 4 August 2021. The UEEWS includes an array of 170 accelerometers installed in the seismogenic areas of the Uttarakhand. Ground motion data from these sensors are transmitted to the central server through the dedicated private telecommunication network 24 hours a day, seven days a week. This system is designed to issue warnings for moderate to high-magnitude earthquakes via a mobile app freely available for smartphone users and by blowing sirens units installed in the buildings earmarked by the government. The UEEWS has successfully issued alerts for light earthquakes that have occurred in the instrumented region and warnings for moderate earthquakes that have triggered in the vicinity of the instrumented area. This paper provides an overview of the design of the UEEWS, details of instrumentation, adaptation of attributes and their relation to earthquake parameters, operational flow of the system, and information about dissemination of warnings to the public.
SensorNet: An Adaptive Attention Convolutional Neural Network for Sensor Feature Learning
Authors: Ge Jiaqi , Xu Gaochao , Lu Jianchao , Xu Xu , Li Long , Meng Xiangyu
Published: May 2024

This work develops a generalizable neural network, SENSORNET, for sensor feature learning across various applications. The primary challenge addressed is the poor portability of pretrained neural networks to new applications with limited sensor data. To solve this challenge, we design SensorNet, which integrates the flexibility of self-attention with multi-scale feature locality of convolution. Moreover, we invent patch-wise self-attention with stacked multi-heads to enrich the sensor feature representation. SensorNet is generalizable to pervasive applications with any number of sensor inputs, and is much smaller than the state-of-the-art self-attention and convolution hybrid baseline (0.83 M vs. 3.87 M parameters) with similar performance. The experimental results show that SensorNet is able to achieve state-of-the-art performance compared with the top five models on a competition activity recognition dataset (SHL’18). Moreover, pretrained SensorNet in a large inertial measurement unit (IMU) dataset can be fine-tuned to achieve the best accuracy on a much smaller IMU dataset (up to 5% improvement in WISDM) and to achieve the state-of-the-art performance on an EEG dataset (SLEEP-EDF-20), showing the strong generalizability of our approach.
Exploring Sustainable Urban Transportation: Insights from Shared Mobility Services and Their Environmental Impact
Authors: Garus Ada , Mourtzouchou Andromachi , Suarez Jaime , Fontaras Georgios , Ciuffo Biagio
Published: May 2024

The transportation landscape is witnessing profound changes due to technological advancements, necessitating proactive policy responses to harness innovation and avert urban mobility disruption. The sharing economy has already transformed ridesharing, bicycle-sharing, and electric scooters, with shared autonomous vehicles (SAVs) poised to reshape car ownership. This study pursues two objectives: firstly, to establish a market segmentation for shared ride services and secondly, to evaluate the environmental impact of ridesharing in different contexts. To mitigate potential biases linked to stated preference data, we analysed the navette service, utilized by a research institute in Europe, closely resembling future SAVs. The market segmentation relied on hierarchical cluster analysis using employee survey responses, while the environmental analysis was grounded in the 2019 navette service data. Our analysis revealed four unique employee clusters: Cluster 1, emphasizing active transportation and environmental awareness; Cluster 2, showing openness towards SAVs given reliable alternatives are available; Cluster 3, the largest segment, highlighting a demand for policy support and superior service quality; and Cluster 4, which places a premium on time, suggesting a potential need for strategies to make the service more efficient and, consequently, discourage private car use. These findings highlight a general willingness to adopt shared transport modes, signalling a promising transition to shared vehicle ownership with significant environmental benefits achievable through service design and policy measures.
Green Finance, Green Technology Innovation and the Upgrading of China’s Industrial Structure: A Study from the Perspective of Heterogeneous Environmental Regulation
Authors: Zhao Ke , Wu Chao , Liu Jinquan , Liu Yongfu
Published: May 2024

In the era of green economic development, green finance serves as a crucial catalyst for green technological innovation, and both may significantly drive the upgrading of industrial structures. This study combines green finance, green technological innovation, and industrial structure into a research framework, analyzing data from 29 Chinese provinces (2003–2020) to empirically assess their impacts on China’s industrial structure using a two-way fixed-effects model. The results show the following: first, green finance and green technological innovation can significantly promote the upgrading of China’s industrial structure directly and synergistically, a finding corroborated by various robustness tests. Secondly, heterogeneity analysis reveals that there is a “path-dependency effect” in the development of green finance and technology innovation: in areas with higher population density, more developed technological markets, and lower fiscal pressure, the synergistic promotion of the upgrading of industrial structure is stronger. Thirdly, further research indicates that green finance and technology innovation impact the upgrading of industrial structure variably under command-and-control, market-incentive, and voluntary environmental-regulation tools. The most effective policy is the voluntary regulation tool, which involves higher levels of public participation. This study offers valuable insights for fostering green technology innovation, refining environmental policies, and enhancing the optimization and upgrading of industrial structure.
Real-Time Control of Sintering Moisture Based on Temporal Fusion Transformers
Authors: Chen Xinping , Cheng Jinyang , Zhou Ziyun , Lu Xinyu , Ye Binghui , Jiang Yushan
Published: May 2024

The quality of sintered ore, which serves as the primary raw material for blast furnace ironmaking, is directly influenced by the moisture in the sintering mixture. In order to improve the precision of water addition in the sintering process, this paper proposes an intelligent model for predicting water-filling volume based on Temporal Fusion Transformer (TFT), whose symmetry enables it to effectively capture long-term dependencies in time series data. Utilizing historical sintering data to develop a prediction model for the amount of mixing and water addition, the results indicate that the TFT model can achieve the R squared of 0.9881, and the root mean square error (RMSE) of 3.5951. When compared to the transformer, long short-term memory (LSTM), and particle swarm optimization–long short-term memory (PSO-LSTM), it is evident that the TFT model outperforms the other models, improving the RMSE by 8.5403, 6.9852, and 0.453, respectively. As an application, the TFT model provides an effective interval reference for moisture control in normal sintering processes, which ensures that the error is within 1 t.
Evolutionary Profile of Mayaro Virus in the Americas: An Update into Genome Variability
Authors: Marinho Mikaela dos Santos , Ferreira Giulia Magalhães , Grosche Victória Riquena , Nicolau-Junior Nilson , Campos Túlio de Lima , Santos Igor Andrade , Jardim Ana Carolina Gomes
Published: May 2024

The Mayaro virus (MAYV) is an arbovirus with emerging potential, though with a limited understanding of its epidemiology and evolution due to the lack of studies and surveillance. Here, we investigated 71 MAYV genome sequences from the Americas available at GenBank and characterized the phylogenetic relationship among virus strains. A phylogenetic analysis showed that sequences were grouped according to the genotypes L, D, and N. Genotype D sequences were closely related to sequences collected in adjacent years and from their respective countries, suggesting that isolates may have originated from circulating lineages. The coalescent analysis demonstrated similar results, indicating the continuous circulation of the virus between countries as well. An unidentified sequence from the USA was grouped with genotype D, suggesting the insertion of this genotype in the country. Furthermore, the recombination analysis detected homologous and three heterologous hybrids which presented an insertion into the nsP3 protein. Amino acid substitutions among sequences indicated selective pressure sites, suggesting viral adaptability. This also impacted the binding affinity between the E1–E2 protein complex and the Mxra8 receptor, associated with MAYV entry into human cells. These results provide information for a better understanding of genotypes circulating in the Americas.
A Prospective Study Investigating Immune Checkpoint Molecule and CD39 Expression on Peripheral Blood Cells for the Prognostication of COVID-19 Severity and Mortality
Authors: Gambichler Thilo , Rüth Jonas , Goesmann Silke , Höxtermann Stefan , Skrygan Marina , Susok Laura , Becker Jürgen C. , Overheu Oliver , Schmidt Wolfgang , Reinacher-Schick Anke
Published: May 2024

In patients with COVID-19, broad panels of immune checkpoint molecules (ICPMs) and the purinergic signaling have not been studied in parallel. We aimed to perform in-depth immunophenotyping of major cell subsets present in human peripheral blood of COVID-19 patients and controls using PD1, TIM3, LAG3, TIGIT, and CD200R, as well as CD39, as markers for the purinergic signaling pathway. We studied 76 COVID-19 patients and 12 healthy controls using peripheral blood mononuclear cells on flow cytometry. Univariable and multivariable statistics were performed. All ICPMs studied were significantly overexpressed on different cell subsets of COVID-19 patients when compared with healthy controls. Elevated lactate dehydrogenase; C-reactive protein; age; and high expression of CD45+, CD39+CD45+, TIM3+CD39+CD4+CD45+, and TIM3+CD39+CD8+CD3+CD4+ cells were significantly associated with severe COVID-19. On multivariable analysis, however, only high expression of CD39+CD45+ (OR 51.4, 95% CI 1.5 to 1763) and TIM3+CD39+CD4+CD3+CD45+ (OR 22.6, 95% CI 1.8 to 277) cells was an independent predictor for severe COVID-19. In conclusion, numerous ICPMs are overexpressed in COVID-19 patients when compared with healthy controls, suggesting a pathophysiological role of these molecules in SARS-CoV-2 infection. However, only TIM3 in co-expression with CD39 remained as a significant independent prognostic ICPM on multivariable analysis. The flow cytometric evaluation of TIM3+CD39+CD4+CD3+CD45+, as well as CD39+CD45+, is a powerful tool for the prognostication of COVID-19 patients on hospital admission.
Experimental Infection Models and Their Usefulness for White Spot Syndrome Virus (WSSV) Research in Shrimp
Authors: Cox Natasja , De Swaef Evelien , Corteel Mathias , Van Den Broeck Wim , Bossier Peter , Nauwynck Hans J. , Dantas-Lima João J.
Published: May 2024

White spot syndrome virus (WSSV) is marked as one of the most economically devastating pathogens in shrimp aquaculture worldwide. Infection of cultured shrimp can lead to mass mortality (up to 100%). Although progress has been made, our understanding of WSSV’s infection process and the virus–host–environment interaction is far from complete. This in turn hinders the development of effective mitigation strategies against WSSV. Infection models occupy a crucial first step in the research flow that tries to elucidate the infectious disease process to develop new antiviral treatments. Moreover, since the establishment of continuous shrimp cell lines is a work in progress, the development and use of standardized in vivo infection models that reflect the host–pathogen interaction in shrimp is a necessity. This review critically examines key aspects of in vivo WSSV infection model development that are often overlooked, such as standardization, (post)larval quality, inoculum type and choice of inoculation procedure, housing conditions, and shrimp welfare considerations. Furthermore, the usefulness of experimental infection models for different lines of WSSV research will be discussed with the aim to aid researchers when choosing a suitable model for their research needs.
Capsid Integrity Detection of Enteric Viruses in Reclaimed Waters
Authors: Puchades-Colera Pablo , Díaz-Reolid Azahara , Girón-Guzmán Inés , Cuevas-Ferrando Enric , Pérez-Cataluña Alba , Sánchez Gloria
Published: May 2024

Climate change, unpredictable weather patterns, and droughts are depleting water resources in some parts of the globe, where recycling and reusing wastewater is a strategy for different purposes. To counteract this, the EU regulation for water reuse sets minimum requirements for the use of reclaimed water for agricultural irrigation, including a reduction in human enteric viruses. In the present study, the occurrence of several human enteric viruses, including the human norovirus genogroup I (HuNoV GI), HuNoV GII, and rotavirus (RV), along with viral fecal contamination indicator crAssphage was monitored by using (RT)-qPCR methods on influent wastewater and reclaimed water samples. Moreover, the level of somatic coliphages was also determined as a culturable viral indicator. To assess the potential viral infectivity, an optimization of a capsid integrity PMAxx-RT-qPCR method was performed on sewage samples. Somatic coliphages were present in 60% of the reclaimed water samples, indicating inefficient virus inactivation. Following PMAxx-RT-qPCR optimization, 66% of the samples tested positive for at least one of the analyzed enteric viruses, with concentrations ranging from 2.79 to 7.30 Log10 genome copies (gc)/L. Overall, most of the analyzed reclaimed water samples did not comply with current EU legislation and contained potential infectious viral particles.
Oilfield Brine as a Source of Water and Valuable Raw Materials—Proof of Concept on a Laboratory Scale
Authors: Rotko Grzegorz , Knapik Ewa , Piotrowski Marcin , Marszałek Marta
Published: May 2024

Oilfield brine is the largest byproduct stream generated during the extraction of crude oil and natural gas and may be considered a resource for the production of potable water and valuable raw materials. The high salinity of such waters limits the application of typical membrane-based techniques. In most oilfields, waste cold energy from the process of the low-temperature separation of natural gas is available and may be used as a source of cold for the freezing desalination (FD) of brine. As a result of the FD process, two streams are obtained: partially desalinated water and concentrated brine. The partially desalinated water may be suitable for non-potable applications or as a feed for membrane desalination. The concentrated brine from the FD could be used as a feed for the recovery of selected chemicals. This paper focuses on verifying the above-described concept of the freezing desalination of oilfield brine on a laboratory scale. The brine from a Polish oilfield located in the Carpathian Foredeep was used as a feed. Four freezing–thawing stages were applied to obtain low-salinity water, which subsequently was treated by reverse osmosis. The obtained permeate meets the criteria recommended for irrigation and livestock watering. The concentrated brine enriched with iodine (48 mg/L) and lithium (14 mg/L) was subjected to recovery tests. Ion exchange resin Diaion NSA100 allowed us to recover 58% of iodine. Lithium recovery using Mn- and Ti-based sorbents varies from 52 to 93%.
Occurrence, Fate, and Mass Balance Analysis of Organophosphate Flame Retardants in a Municipal Wastewater Treatment Plant in Hunan Province, China
Authors: Liu Yang , Song Yang , Li Haipu , Yang Zhaoguang
Published: May 2024

The occurrence, distribution, removal, and mass loadings of common organophosphate flame retardants (OPFRs) in an advanced municipal wastewater treatment plant (WWTP) were comprehensively investigated. The OPFRs were mainly partitioning in the dissolved phase, and the total concentrations ranged from 1364 to 1701 ng/L in influent, 678~1064 in effluent, and 177~470 ng/g dw in residual sludge. Tributoxyethyl phosphate and tris(2-chloroethyl) phosphate were the abundant compounds in both the dissolved phase and adsorbed phase. The removal frequencies and mechanisms of the OPFRs were highly associated with the properties of compounds. According to the mass balance analysis, 14.9%, 13.0%, and 11.2% of the total OPFR loads were removed in the traditional treatment, tertiary treatment, and to the sludge, respectively. The mass loadings and environmental emissions of the OPFRs were 0.67~291 μg/d/person and 0.57~107 μg/d/person, respectively. The effluent discharged from the WWTP posed ecological risks to the receiving river, which requires being paid more attention.
A Novel GIS-SWMM-ABM Approach for Flood Risk Assessment in Data-Scarce Urban Drainage Systems
Authors: Ahmad Shakeel , Jia Haifeng , Ashraf Anam , Yin Dingkun , Chen Zhengxia , Ahmed Rasheed , Israr Muhammad
Published: May 2024

Urbanization and climate change pose a critical challenge to stormwater management, particularly in rapidly developing cities. These cities experience increasingly impervious surfaces and more intense rainfall events. This study investigates the effectiveness of the existing drainage system in Lahore, Pakistan, a megacity challenged by rapid urbanization and the impacts of climate change. To address the lack of predefined storm patterns and limited historical rainfall records, we employed a well-established yet adaptable methodology. This methodology utilizes the log-Pearson type III (LPT-III) distribution and alternating block method (ABM) to create design hyetographs for various return periods. This study applied the stormwater management model (SWMM) to a representative community of 2.71 km2 to assess its drainage system capacity. Additionally, geographic information systems (GISs) were used for spatial analysis of flood risk mapping to identify flood-prone zones. The results indicate that the current drainage system, designed for a 2-year return period, is inadequate. For example, a 2-year storm produced a total flood volume of 0.07 million gallons, inundating approximately 60% of the study area. This study identified flood risk zones and highlighted the limitations of the system in handling future, more intense rainfall events. This study emphasizes the urgent need for infrastructure improvements to handle increased runoff volumes such as the integration of low-impact development practices. These nature-based solutions enhance infiltration, reduce runoff, and improve water quality, offering a sustainable approach to mitigating flood risks. Importantly, this study demonstrates that integrating LPT-III and ABM provides a robust and adaptable methodology for flood risk assessment. This approach is particularly effective in developing countries where data scarcity and diverse rainfall patterns may hinder traditional storm modeling techniques. Our findings reveal that the current drainage system is overwhelmed, with a 2-year storm exceeding its capacity resulting in extensive flooding, affecting over half of the area. The application of LPT-III and ABM improved the flood risk assessment by enabling the creation of more realistic design hyetographs for data-scarce regions, leading to more accurate identification of flood-prone areas.
Irmãos de Arma, Irmãos de Vida
Authors: Xavier Moreira Nádia , Selestino Sabrina , Machado Caminha Viviane
Published: May 2024

As relações sociais e os laços de pertencimento construídos pelos militares, a luz da categoria família, tem-se constituído objeto de reflexão na contemporaneidade. Tendo por base pesquisa de caráter sócio etnográfico e como referência a expressão “família militar”, o presente artigo analisa a forja de laços sedimentados pelas circunstâncias adversas vivenciadas por esses sujeitos advindas das movimentações pelo território nacional e pelo afastamento do convívio com a família de origem.
ПРАВОВЕ РЕГУЛЮВАННЯ ОСОБЛИВОСТЕЙ ЗМІНИ ІСТОТНИХ УМОВ ПРАЦІ В ПЕРІОД ОГОЛОШЕНОГО ВОЄННОГО СТАНУ ЯК ГАРАНТІЯ ЗАБЕЗПЕЧЕННЯ ПРАВ ПРАЦІВНИКІВ ТА РОБОТОДАВЦІВ
Authors: Шабанов Р. І.
Published: May 2024

Розширення співпраці з Європейським Союзом і посилення інтегративних процесів супроводжуються необхідністю виконання певних вимог щодо приведення чинного законодавства у відповідність до міжнародних стандартів. Євроінтеграційні про-цеси вимагають гармонізації трудового законодавства в тому числі. Гармонізація законо-давства є одним із ключових завдань, розв’язання якого сприятиме поглибленню інтеграцій-них процесів у рамках створення ЄС. Гармонізація трудового законодавства як один із векторів потужних євроінтеграційних процесів зумовлена орієнтацією на міжнародні стан-дарти та необхідністю підвищення гарантування дотримання та захисту прав як працівників, так і роботодавців. Об’єднання науковців, практиків та нормотворців для реалізації зазначених процесів забезпечить прийняття науково обгрунтованих, виважених, агрументованих та своєчасних рішень для досягнення стратегічних цілей і завдань національного, національно-еконо-мічного, громадсько-політичного та культурного розвитку, що стало невід’ємною реалією сьогодення. Удосконалення трудового законодавства повинно створити умови для формування цілісної системи правового регулювання соціально-трудових відносин. Сфера соціально-трудових правовідносин обтяжується обставинами воєнного часу, що зумовлює необхідність відповідного реагування з боку законодавця. З аналізу норм чинного законодавства, яким врегульовані особливості організації трудових відносин в умовах воєнного стану, простежується звуження прав працівників. Проте такі кроки мають тимчасовий характер, є доцільними та виправданими. Адже визначення оптимальних підходів до правового регулювання трудових відносин в умовах воєнного стану є важливою гарантією законності та забезпечення дотримання прав як працівників, так й роботодавців.
Türkiye’deki Kesimhane Atıklarından ve Çiftlik Hayvanları Gübrelerinden Elde Edilebilecek Biyogaz Potansiyelinin Tespiti Üzerine Bir Araştırma
Authors: Sözer Salih
Published: May 2024

Organik materyallerin oksijensiz ortamda çürütülmesi sonucu elde edilen biyogaz, yenilenebilir enerji kaynaklarından biridir. Günümüzde hem çevre kirliliğini önlemede hem de doğaya zarar vermeden temiz enerji üretiminde tercih edilen biyogaz teknolojisi, popülerliğini artıran ve hızla yaygınlaşan bir yöntemdir. Türkiye’nin nüfusu her geçen gün artmakta ve buna bağlı olarak tarımsal ürün üretimi ve enerji tüketimi de artmaktadır. Enerji konusunda kendi kendine yetemeyen ülkemizin mevcut kaynaklarını en iyi şekilde değerlendirmesi bir gerekliliktir. Türkiye’de artan nüfusun et ihtiyacının karşılanabilmesi için mevcut çiftlik hayvanı sayısı da buna bağlı olarak yükselmektedir. Buna paralel olarak çiftliklerde oluşan taze gübre miktarı, kesimhanelerde oluşan kan ve işkembe içerikleri de artmaktadır. Bu çalışmada 2022 yılı verileri kullanılarak kesilen çiftlik hayvanlarının kan ve işkembe içeriklerinden elde edilebilecek biyogaz miktarları ile mevcut çiftlik hayvanlarının taze gübre miktarından elde edilebilecek biyogaz miktarları hesaplanmıştır. Çalışma sonucuna göre kesimhane atıkları ve çiftlik hayvanlarının gübrelerinden elde edilebilecek biyogaz miktarı yıllık olarak 24,495 milyar m3 ve bu miktardaki biyogazdan elde edilebilecek elektrik enerjisi miktarı 4,41×1010 kWh’dir.
Effect of Stationary Circuit and Moving Circuit Trainings on Selected Physical Fitness Components among University Men Students
Authors: Karthikeyan P
Published: May 2024

The purpose of the study was to find out the effects of stationary circuit training [SCT] and Moving circuit training [MCT] on selected physical fitness components namely muscular endurance [ME] and cardio respiratory endurance [CRE] among university men students. To achieve this purpose of the study, forty-five men students studying men students studying Department of Physical Education, Annamalai University, Chidambaram, Tamil Nadu, India were selected as subjects at random. Their age ranged between 18 to 24 years. The selected subjects were divided into three equal groups of fifteen each namely stationary circuit training group [SCTG], Moving circuit training group [MCTG] and control group [CG]. The experimental group I underwent stationary circuit training [SCT] and group II underwent Moving circuit training [MCT] for three days per week for twelve weeks whereas the control group maintained their daily routine activities and no special training was given to them. The following physical fitness components, namely muscular endurance [ME] and cardio respiratory endurance [CRE] were selected as criterion variables. Thesubjects of the three groups were tested on selected physical fitness components namely muscular endurance and cardio respiratory endurance using standardized tests namely bend knee sit-ups and Cooper’s 12 min Run / Walk test at prior and immediately after the training period. The collecteddata were analyzed statistically through analysis of covariance (ANCOVA) to find out the significant difference, if any among the groups. Whenever the obtained “F” ratio was found to be significant,the Scheffe’s test was applied as post hoc test to find out the paired mean difference, if any. The .05 level of confidence was fixed to test the level of significance which was considered appropriate. The results of the study showed that there was a significant difference exist among SCTG, MCTG and CG on selected physical fitness components namely ME and CRE. And also, SCTG and MCTG showed significant improvement on ME and CRE when compared to control group.
MENOPOZ BİLGİSİ ÖLÇEĞİ GELİŞTİRME: GEÇERLİLİK VE GÜVENİRLİK ÇALIŞMASI
Authors: Mete Burak , İnaltekin Ayşe , Sucu Mete , Demirhindi Hakan , Tanır Ferdi , Atun Ütük Fatma
Published: May 2024

Bu çalışmada 18 yaş ve üstü kadınlarda menopoz bilgisini ölçen Menopoz Bilgisi Ölçeği geliştirilmesi amaçlanmaktadır. Çalışmada Çukurova Üniversitesi Halk Sağlığı Anabilim Dalı eğitim ve araştırma uygulama bölgesinde kolayda örnekleme yöntemi ile 397 kadına ulaşılmıştır. Ölçek geçerlilik ve güvenirlik analizleri kapsamında kapsam geçerliliği, açımlayıcı faktör analizi, iç tutarlılık güvenirlik analizleri yapılmıştır. Ölçek yapısını belirlemek için temel bileşenler analizi ve varimax rotasyon tekniği kullanılmıştır. Güvenirlik analizinde iç tutarlılık (Cronbach alfa katsayısı) yöntemi, madde toplam korelasyon analizi ve test tekrar test yöntemi kullanılmıştır. Veri setinin faktör analizi için uygunluk analizinde KMO katsayısı 0,931, Bartlett’s Testi p
PENDAMPINGAN PRODUKSI PODCAST YOUTUBE UNTUK MENDEKATKAN TEKNOLOGI PADA SISWA MADRASAH NAUNGAN PESANTREN
Authors: Malayati Robi'ah Machtumah , Masruroh Sayidah Afyatul
Published: May 2024

The Community Service Program (PkM) aims to introduce both the students of Madrasah Aliyah Nurul Qur'an Bendungrejo and the students to technology through the approach of producing content for YouTube podcasts. The PkM also aims to encourage the younger generation to be content producers in the digital era, not just consumers. This PkM is significant because, within the madrasah environment, previous activities have been focused on religious aspects, including limited extracurriculars such as the art of al banjari, khitobah, and qiroah. Hence, this PkM represents an innovative step. Using the Participatory Action Research (PAR) approach, the PkM involves students in identifying problems, seeking solutions, learning, and producing content for YouTube podcasts. The results have shown an improvement in their ability to conceptualize, interact, communicate, and produce content for YouTube podcasts. The PkM successfully enhanced the students' creativity and broadened their insights into opportunities in the digital domain. Sustained efforts are needed to equip the younger generation with relevant competencies to face technological advancements and social changes. This PkM has made a positive contribution by complementing madrasah activities and promoting the increased utilization of technology among both the santri and students.
Preliminary in silico study of a novel paratransgenic weapon against malaria
Authors: Sun Joshua , Candra Daniel Christopher , Pramanda Ihsan Tria , Bani Mario Donald , Wu Nicholas , Husada William , Alexander Jonathan , Saputra Silvan , Aberly Rachel Sofian , Fidelia Michelle
Published: May 2024

Malaria is an infectious disease caused by Plasmodium spp., a protist whose infection is spread by Anopheles mosquito as a vector. A potential method to counter the infection is through paratransgenesis, a promising genetic control approach. This study proposed a new approach by using a transgene carried in a modified Ti plasmid hosted in Asaia bacterium to carry out two purposes: sensing the presence of Plasmodium protein biomarkers and transferring the toxin transgene to the parasite upon detection, thereby killing it without harming the Asaia carrier. The biosensor mechanism was created by repurposing a TonB dependent iron-uptake transport pathway to transcribe the vir genes of Agrobacterium tumefaciens that facilitate the gene transfer. The aim of this in silico project was to serve as a preliminary study on the likelihood of success of the aforementioned biosensor mechanism. This study utilized Alphafold and RCSB (Research Collaboratory for Structural Bioinformatic Protein Data Bank). As for the research tools: PyMOL, ClusPro website, and PRODIGY (PROtein binDIng enerGY prediction) server was used for data preparation, protein-protein docking and binding affinity analysis respectively. The results were assessed with t-test to analyze the significance of the binding affinity, in comparison to other studies that employed similar methods. The result showed Plasmodium protein GGCS (Gamma-glutamylcysteine synthetase) having the highest binding affinity with FecA (Fe(3+) dicitrate transport protein A). The preliminary data suggested that introducing the toxin transgene may be possible through vir gene transcription from the TonB pathway.
Die Beziehung zur (erzählten) Welt herstellen. Literar- und kulturästhetisch am Gemeinsamen Gegenstand arbeiten
Authors: Rühlow Daniel , Granzow Stefanie
Published: May 2024

Die Merkmale inklusiver Bildung nach Textor sind mithilfe Georg Feusers entwicklungslogischer Didaktik einlösbar. Sein Ansatz betont hierbei die teils marginalisierte Beziehungsseite inklusiver Lernprozesse, auf die der Artikel daher fokussiert. Insbesondere ästhetische Lerngegenstände und -prozesse bieten Raum, um inklusives Lernen am Gemeinsamen Gegenstand im Sinne Feusers zu realisieren. Ausgehend von Feusers Konzeptbestandteilen wird anhand literar- und kulturästhetischer Fächerperspektiven am Beispiel von Kreationsmythen die Beziehung zur (literarischen) Welt als Gemeinsamer Gegenstand exemplifiziert und das unterrichtliche Vorgehen in einem auf ästhetische Lernprozesse bezogenen Modell vorgestellt. Die Momente des Kooperativen, Kommunikativen und Gemeinsamen bilden hierbei Ankerpunkte der argumentativ verfolgten fachlichen und fachdidaktischen Diskurslinien. Der Artikel bietet somit eine Legitimationsgrundlage für die didaktisch, methodisch sowie forschungsbezogen zu schärfende Perspektivierung und Stärkung der Beziehungsebene auf sowohl (inter-)subjektiver als auch lerngegenständlicher Seite. (DIPF/Orig.)
Back-Health Knowledge and Misconceptions related to the daily life activities of secondary school students
Authors: Monfort-Pañego Manuel , Biviá Antonio Hans Bosch , Miñana-Signes Viente , de Baranda Pilar Sainz , Noll Matias
Published: May 2024

This study analyzed secondary school students’ declarative knowledge and misconceptions re-lated to back care in daily activities. An exploratory cross-sectional study was conducted with 80 girls and 89 boys aged 14–18 years (M = 15.68, SD = 2.12). The Health Questionnaire on Back Care Knowledge in Activities of Daily Living was used to evaluate knowledge using the true answer model (TAM) and the misconception model (MM). Using the test-retest method, both models’ reliability was confirmed to be good (TAM = .75; MM = .77), while only a minimal measurement error was identified (TAM = -.01; MM = -.07). The average scores were 6.23 for the TAM and 2.29 for the MM. A repeated-measures ANOVA was used to examine the effects of gender and age on both models. The results showed no significant differences between both mod-els. The analysis indicated that students had the most accurate knowledge of the location and function of the spine, whereas misconceptions regarding anatomical understanding and body posture usage were common. An analysis of the results under Reassumption Theory emphasizes the significance of comprehending conceps such as load transmission and spinal stability to maintain back health, thus highlighting the need for improved education in these areas to address misconceptions and enhance overall back-care knowledge.