Prominent Regions Research Articles

Use Efficiency, Reduction Potential, and Effects of Fertilizers on Carbon Emissions in China’s Major Citrus Regions

Enhancing the efficiency of fertilizer utilization and advancing fertilizer reduction efforts constitutes a pivotal initiative for augmenting the quality and productivity of the citrus industry; this constitutes an indispensable prerequisite for attaining green and sustainable development. Utilizing panel data from seven prominent mandarin-producing regions and seven prominent tangerine-producing regions in China spanning from 2002 to 2022, this study employed the stochastic frontier analysis (SFA) method to develop a translog production function model for precisely measuring the fertilizer use efficiency for mandarins and tangerines. Employing the calculated optimal fertilizer use rates, we further ascertained the fertilizer reduction potential for mandarin and tangerine; then, we estimated the associated carbon emission reduction potential within these key citrus regions. The research revealed the following findings: the overall level of citrus fertilizer use efficiency in China is comparatively low, with the mean values for mandarin and tangerine fertilizer use efficiency being merely 0.4403 and 0.3887, respectively, indicating substantial room for improvement by approximately 60%; substantial potential exists for decreasing fertilizer use in China’s citrus industry, with average reduction potentials of 66.27% for mandarins and 64.83% for tangerines, signifying a notable redundancy in fertilizer application within major citrus-producing areas. The magnitude of carbon emission reduction potential through the diminution of citrus fertilizer use is tremendous. When optimal fertilizer rates are applied, the average carbon emission reductions resulting from fertilizer reduction in mandarins and tangerines amount to 815.8681 kg/hm2 and 602.3551 kg/hm2, respectively. The average carbon reduction potential for mandarins and tangerines reach levels of 55.9673% and 61.1299%, respectively, both surpassing the threshold of 55%. Significant differences exist in the technical efficiency of fertilizer input, reduction potential, and carbon emission mitigation potential among major citrus-producing regions. Citrus orchards in Guangdong exhibit higher potential for fertilizer reduction but demonstrate a relatively low level of technical efficiency. In contrast, Hunan Province shows an opposite trend, necessitating the development of region-specific strategies. Therefore, to minimize citrus fertilizer use and augment the technical efficiency of citrus fertilizer, it is imperative to comprehensively integrate and promote the “three new” technologies aimed at reducing fertilizer use and enhancing its efficiency within the citrus industry; implement a regional coordinated development strategy for citrus fertilizer reduction; and intensify policy guidance, publicity, and training efforts related to citrus fertilizer reduction, efficiency enhancement, and carbon emissions reduction.

Spatial Distribution and Disease Incidence of Guava Wilt Village Survey Across Prayagraj and Kaushambi, Uttar Pradesh, India

A comprehensive roving survey was conducted in the Prayagraj and Kaushambi districts of Uttar Pradesh, India, both prominent guava-producing regions. The survey, which included 30 villages, revealed widespread guava wilt with incidence rates ranging from 19.04% to 37.93%, averaging 30.19% across the region. Three forms of wilt these are quick, slow, and partial were identified. Kaushambi district had a higher incidence rate (31.56%) compared to Prayagraj district (28.82%). Indarapur village in Kaushambi recorded the highest wilt incidence (37.93%). The survey also found that 66.67% of farmers lacked formal training in guava cultivation, contributing to poor disease management. Guava wilt was most prevalent in older orchards (10+ years) and primarily affected the Allahabad Safeda variety, which had the highest wilt incidence (33.64%). In contrast, the Pant Prabhat variety exhibited the lowest wilt incidence (22.95%). The causal pathogen, Fusarium oxysporum f. sp. psidii, was isolated from diseased plants. Three fungal isolates displayed variations in colony morphology, growth, and sporulation. Microconidia were non-septate and kidney-shaped, while macroconidia were thin- walled and typically 2-3 septate. Two isolates also produced chlamydospores. This study highlights the need for enhanced farmer training and the potential for cultivating wilt-tolerant varieties to mitigate the spread of the disease in the region.

Unveiling genetic anchors in saccharomyces cerevisiae: QTL mapping identifies IRA2 as a key player in ethanol tolerance and beyond.

Ethanol stress in Saccharomyces cerevisiae is a well-studied phenomenon, but pinpointing specific genes or polymorphisms governing ethanol tolerance remains a subject of ongoing debate. Naturally found in sugar-rich environments, this yeast has evolved to withstand high ethanol concentrations, primarily produced during fermentation in the presence of suitable oxygen or sugar levels. Originally a defense mechanism against competing microorganisms, yeast-produced ethanol is now a cornerstone of brewing and bioethanol industries, where customized yeasts require high ethanol resistance for economic viability. However, yeast strains exhibit varying degrees of ethanol tolerance, ranging from 8 to 20%, making the genetic architecture of this trait complex and challenging to decipher. In this study, we introduce a novel QTL mapping pipeline to investigate the genetic markers underlying ethanol tolerance in an industrial bioethanol S. cerevisiae strain. By calculating missense mutation frequency in an allele located in a prominent QTL region within a population of 1011 S. cerevisiae strains, we uncovered rare occurrences in gene IRA2. Following molecular validation, we confirmed the significant contribution of this gene to ethanol tolerance, particularly in concentrations exceeding 12% of ethanol. IRA2 pivotal role in stress tolerance due to its participation in the Ras-cAMP pathway was further supported by its involvement in other tolerance responses, including thermotolerance, low pH tolerance, and resistance to acetic acid. Understanding the genetic basis of ethanol stress in S. cerevisiae holds promise for developing robust yeast strains tailored for industrial applications.

Global change drives potential niche contraction and range shift of globally threatened African vulture

Human-induced global change poses an increasingly severe threat to biodiversity, with species having limited population sizes being particularly vulnerable. Mapping and modeling the distribution ranges of such species, along with detecting potential range shifts and contractions at both local and regional scales, are essential for developing effective conservation plans. Ruppell's vulture Gyps rueppelli, an ecologically important bird species native to Africa, is experiencing a rapid decline in its range. The purpose of this study is to map and model potential regional spatio-temporal distribution of Ruppell's vulture in Africa, alongside detecting the possibility of the species' range shifts and contractions. A total of 804 rarefied localities were identified where the Ruppell's vulture was the dominant bird species. This study employed the Maximum Entropy (MaxEnt) algorithm to perform species distribution modeling for the Ruppell's vulture. The modeling considered current climate conditions (1970s-2000s) as a baseline, along with two future climate change scenarios (Shared Socioeconomic Pathways: SSPs 245 and 585) for two future time periods (2050s and 2070s). The model's performance was evaluated by optimizing settings and examining the Area under the Receiver Operating Characteristic Curve (AUC-ROC). Among the 13 bioclimatic and anthropogenic variables included in the model, four (isothermality, cropland expansion, anthropogenic biomes, and urban expansion (in order of importance)) emerged as the most influential drivers of Ruppell's vulture regional distribution. All considered Species distribution models (SDMs) achieved high predictive performance, with AUC-ROC values exceeding 0.9.The model predicted a total of approximately 19,453 ha of suitable habitat for Ruppell's vultures in Africa, with East Africa identified as the most prominent region under the current climate scenario. Isothermality (38.8%) was the primary factor influencing Ruppell's vulture distribution, followed by agricultural expansion (29.9%) and anthropogenic biomes (7.2%) in the face of global change. The results reveal considerable future habitat loss (up to 61%) for Ruppell's vultures in the study area, alongside an eastward range shift (longitudinal axis) by the 2050s under projected climate change scenarios. These imply that Ruppell's vultures face imminent population decline and range shift due to significant habitat loss and climate change. Hence, prioritizing the development and implementation of a coordinated conservation program that incorporates captive breeding and assisted migration is critical to save this vulture species in its native African range.

Structural topology optimization based on deep learning

In the mechanical design of structures, traditional topology optimization methods involve numerous finite element iterative analyses, leading to a significant expenditure of computational resources. Therefore, the improved multi-scale gradient generative adversarial networks topology optimization technique is proposed. The topology optimization condition parameters are compressed into a low-dimensional latent space feature representation using the encoder, allowing the model to better extract features from these parameters. To speed up model training, the generator and discriminator networks use lightweight residual convolutional blocks. The hybrid attention mechanism extracts prominent region features from the topology optimization structure map. The model training process is guided by a multi-dimensional fusion loss function to enhance the quality of generated model samples. Finally, transferring the parameters of the low-resolution topology optimization model to the high-resolution model enables complete training on a limited amount of high-resolution topology optimization datasets. The experimental data on the low- and high-resolution topology optimization datasets demonstrate that, when compared to alternative methods, this method produces better-quality topology optimization structure maps. Additionally, it can generate high-resolution topology optimization structure maps in minimal time, enabling real-time topology optimization.

Accurate pneumoconiosis staging via deep texture encoding and discriminative representation learning.

Accurate pneumoconiosis staging is key to early intervention and treatment planning for pneumoconiosis patients. The staging process relies on assessing the profusion level of small opacities, which are dispersed throughout the entire lung field and manifest as fine textures. While conventional convolutional neural networks (CNNs) have achieved significant success in tasks such as image classification and object recognition, they are less effective for classifying fine-grained medical images due to the need for global, orderless feature representation. This limitation often results in inaccurate staging outcomes for pneumoconiosis. In this study, we propose a deep texture encoding scheme with a suppression strategy designed to capture the global, orderless characteristics of pneumoconiosis lesions while suppressing prominent regions such as the ribs and clavicles within the lung field. To further enhance staging accuracy, we incorporate an ordinal label distribution to capture the ordinal information among profusion levels of opacities. Additionally, we employ supervised contrastive learning to develop a more discriminative feature space for downstream classification tasks. Finally, in accordance with standard practices, we evaluate the profusion levels of opacities in each subregion of the lung, rather than relying on the entire chest X-ray image. Experimental results on the pneumoconiosis dataset demonstrate the superior performance of the proposed method confirming its effectiveness for accurate pneumoconiosis staging.

Applying 3D scanning to evaluate facial symmetry in Asian populations

BackgroundFacial symmetry enhances attractiveness, with various therapies available to improve it. However, trends in facial asymmetry remain unclear. This study evaluates differences between specific facial regions using 3D scanning to guide clinical treatment. MethodThe 3dMD face™ system (3dMD Ltd, USA) scanned 88 subjects' faces. Geomagic Wrap 2021 analyzed the left and right sides. Differences in eyebrow peaks, eyebrow tails, eye ends, and mouth corners were evaluated. ResultsMore subjects showed a drooping right side of the face in eyebrow peaks, eyebrow tails, eye ends, mouth corners, tubercula mentale, and mandibular margin positions. A higher proportion had deeper left nasolabial folds and marionette lines, while more had deeper right tear ducts and lateral chin depressions. More subjects exhibited a prominent right forehead and parotid masseter region, with a prominent left temporal and cheek region. Conclusion3D scanning effectively assesses facial asymmetry in clinical practice. Research indicates significant differences in asymmetry between facial regions. Evaluating these differences pre-treatment can guide the selection of therapeutic methods, improving facial asymmetry treatment. Level of Evidence3 (Diagnostic)

Advancing in RGB-D Salient Object Detection: A Survey

The human visual system can rapidly focus on prominent objects in complex scenes, significantly enhancing information processing efficiency. Salient object detection (SOD) mimics this biological ability, aiming to identify and segment the most prominent regions or objects in images or videos. This reduces the amount of data needed to process while enhancing the accuracy and efficiency of information extraction. In recent years, SOD has made significant progress in many areas such as deep learning, multi-modal fusion, and attention mechanisms. Additionally, it has expanded in real-time detection, weakly supervised learning, and cross-domain applications. Depth images can provide three-dimensional structural information of a scene, aiding in a more accurate understanding of object shapes and distances. In SOD tasks, depth images enhance detection accuracy and robustness by providing additional geometric information. This additional information is particularly crucial in complex scenes and occlusion situations. This survey reviews the substantial advancements in the field of RGB-Depth SOD, with a focus on the critical roles played by attention mechanisms and cross-modal fusion methods. It summarizes the existing literature, provides a brief overview of mainstream datasets and evaluation metrics, and quantitatively compares the discussed models.

An explainable weakly supervised model for multi-disease detection and localization from thoracic X-rays

Thoracic diseases are a major source of mortality, often requiring diagnosis from plain chest X-rays. However, differentiating between complex conditions based on subtle radiographic patterns poses challenges even for experts. Recently, deep learning methods have shown promise in automating thoracic disease detection from chest radiographs. Many existing approaches focus on the diseased organs in the radiographs by utilizing spatial regions that contribute significantly to the model’s prediction. Expert radiologists, on the other hand, first identify the prominent region before determining whether those regions are abnormal or not. Therefore, incorporating localization information through deep learning models could result in significant improvements in automatic disease classification. Motivated by this, we have proposed a generalized weakly supervised Confidence-Aware Probabilistic Class Activation Map (CAPCAM) classification model that localizes anomalies for thoracic disease. The CAPCAM used CX-Ultranet as the backbone with the combination of Confidence Aware Network (CAN) and Anomaly Detection Network (ADN) without having any localization labeling. This learning from the backbone helps the model to utilize all components of the feature extracted and, therefore eliminating the need to train them individually reducing the time taken. We have experimentally shown that the proposed CAPCAM method sets a new state-of-the-art benchmark by achieving accuracy in terms of Intersection of bounding box (IoBB) in the range of 85 % - 94 %, and Dice scores in the range of 88 %-90 % for all thirteen diseases on two publicly available large-scale CXR datasets–NIH, Stanford and CheXpert. Testing across different noise levels and different levels of blurred level assessed real-world viability. We have also added a layer of explainability to show how the image is processed. This study demonstrates deep learning’s potential to augment radiologists’ decision-making by providing fast, accurate automated aids for thoracic disease diagnosis. The proposed CAPCAM model could be readily translatable to improve clinical workflows.

Finite element method-based investigation of heat exchanger hydrodynamics: Effects of triangular vortex generator shape and size on flow characteristics

Heat exchangers (HEs) play a critical role in numerous industrial and engineering applications by facilitating efficient thermal energy transfer. In the pursuit of enhancing the performance of such systems, this study focuses on the hydrodynamic effects of two distinctive vortex generators (VGs) within a turbulent airflow channel, operating under steady-state conditions. Arranged in a staggered manner, the first vortex generator (VG) adopts a rectangular structure positioned in the upper section, while the second VG, triangular in shape, is situated on the opposing wall at varying heights, ranging from 40 to 80 mm in 10 mm increments. A further examination of the triangular VG includes two cases, one featuring an inclined front-face and the other showcasing an inclined rear-face. The turbulent airflow within the channel is accurately represented using the Newtonian fluid model and the standard k-epsilon turbulence model, while the governing equations are solved through the finite element method. A non-uniform mesh, consisting of triangular and square elements with a specific focus on refining the mesh near walls, is designed to capture boundary layer effects and effectively resolve intricacies in near-wall flow dynamics. The investigation unveils dynamic responses within the channel, characterized by notable flow distortions and prominent regions of recirculation, demonstrating the effectiveness of both rectangular and triangular VGs. Importantly, the analysis shows that tilting the triangular VG’s back-face notably improves the hydrodynamic structure of the HE channel, leading to enhanced recirculation cells and substantially increased performance. In particular, increasing the height of triangular VGs significantly enhances flow velocity within the channel. For instance, the axial velocity increased by 33.8% when the VG height was raised from 40 to 80 mm in the first triangular case, while an increase of about 37.9% was observed in the second triangular case at the lowest inlet velocity of 7.8 m/s. In addition, triangular VGs with an inclined back-surface achieved higher axial velocities compared to those with an inclined front-surface, with a 13.5% increase at the smallest height and a 17.0% increase at the maximum height. Furthermore, increasing the inlet velocity to 9.8 m/s resulted in a 17.1% higher axial velocity in the second model, reaching 55.4 m/s compared to 47.3 m/s in the first model. These findings underscore the importance of optimizing the triangular VG shape, height, and inlet conditions to maximize the hydrodynamic performance of HE systems, leading to potential energy savings and improved efficiency.

Deep Learning–Based Production Forecasting and Data Assimilation in Unconventional Reservoir

Summary Developing unconventional reservoirs such as shale oil is vital for fulfilling the need for energy consumption in the world. Oil production from shale reservoirs is still the most complicated and uncertain phenomenon because of its complex fracture networking, low matrix porosity, and permeability. Production forecasting is crucial for decision-making and tactical exploitation of subsurface resources during production. Traditional methods, such as the Arps decline model and reservoir simulation methods, face significant challenges in forecasting hydrocarbon production due to the highly nonlinear and heterogeneous nature of rocks and fluids. These methods are prone to substantial deviations in forecasting results and show limited applicability to unconventional reservoirs. Therefore, it is essential to improve the production forecasting capability with the help of a data-driven methodology. The data set for modeling is collected from two prominent shale oil-producing regions, the Eagle Ford and the Bakken. The Bakken data set is used to train and test the models, and the Eagle Ford data set is used to validate the model. The random search method was used to optimize the model parameters, and the window sliding technique was used to find a suitable window size to predict future values efficiently. The combination of different deep learning (DL) methods has designed a total of six hybrid models: gated recurrent unit (GRU), long short-term memory (LSTM), and temporal convolutional network (TCN). These models can capture the spatial and temporal patterns in the oil production data. The results concluded that the TCN-GRU model performed best statistically and computationally compared with other individual and hybrid models. The robust model can accelerate decision-making and reduce the overall forecasting cost.

Kongunadu: A Tapestry of History, Culture and Legacy

Kongunadu, a prominent region in Tamil Nadu, boasts a rich historical, cultural, and environmental tapestry. This synopsis delves into its multifaceted aspects, including geographical features like Kurinji, Mullai, and Marut, which contribute to its fertile land and abundant water sources. Known as the ‘country of Kongu,’ it is celebrated for producing precious beads and gems, symbolizing its wealth and natural bounty. The term 'Kongu,' originating from Sangam literature, embodies various meanings—from honey and wine to forest, fragrance, and gold—reflecting the region’s diverse resources and cultural significance. Additionally, Kongunadu serves as the eastern frontier (Kongu > Kongu), encapsulating its geographical orientation. Culturally, Kongunadu is depicted as the ‘shelter’ of the Yavanas in ancient texts, highlighting its role as a cultural melting pot and trading hub. The Northeast Monsoon, locally referred to as ‘Konga Rain,’ plays a pivotal role in the region’s agriculture and livelihoods, further shaping its socio-economic fabric.

The Cygnus Allscale Survey of Chemistry and Dynamical Environments: CASCADE. III. The large scale distribution of DCO+, DNC, and DCN in the DR21 filament

Deuterated molecules and their molecular D/H-ratios ($R_D$(D)) are important diagnostic tools with which to study the physical conditions of star-forming regions. The degree of deuteration, $R_D$(D), can be significantly enhanced over the elemental D/H-ratio depending on physical parameters such as temperature, density, and the ionization fraction. Within the Cygnus Allscale Survey of Chemistry and Dynamical Environments (CASCADE), we aim to explore the large-scale distribution of deuterated molecules in the nearby ($d 1.5$ kpc) Cygnus-X region, a giant molecular cloud complex that hosts multiple sites of high-mass star formation. We focus on the analysis of large-scale structures of deuterated molecules in the filamentary region hosting the prominent region DR21 and DR21(OH), a molecular hot core that is in an earlier evolutionary state. The DR21 filament has been imaged using the IRAM 30-m telescope in a variety of deuterated molecules and transitions. Here we discuss the HCO+ HNC, and HCN molecules and their deuterated isotopologs DCO+ DNC, and DCN, and their observed line emissions at 3.6, 2, and 1.3 mm. The spatial distributions of integrated line emissions from DCO+ DNC, and DCN reveal morphological differences. Notably DCO+ displays the most extended emission, characterized by several prominent peaks. Likewise, DNC exhibits multiple peaks, although its emission appears less extended compared to DCO+ . In contrast to the extended emission of DCO+ and DNC, DCN appears the least extended, with distinct peaks. Focusing only on the regions where all three molecules are observed, the mean deuteration ratios for each species, $R_D$, are 0.01 for both DNC and DCN, and $=0.005$ for DCO+ respectively. Anticorrelations are found with deuterated molecules and dust temperature or $N$( H2 The strongest anticorrelation is found with $R_D$( DCO+ ) and $N$( H2 ), with a Pearson correlation coefficient of $ -0.74$. We analyzed the SiO emission as a tracer for shocks and the $N$(HCO)/$N$( H^13CO+ ) as a tracer for increased photodissociation by ultraviolet radiation. It is suggested that the anticorrelation of $R_D$( DCO+ ) and $N$( H2 ) is a result of a combination of an increased photodissociation degree and shocks. A strong positive correlation between the ratio of integrated intensities of DCN and DNC with their 13C-isotopologs is found in high-column-density regions. The positive relationship between the ratios implies that the D-isotopolog of the isomers could potentially serve as a tracer for the kinetic gas temperature.

The effects of stellar feedback on molecular clumps in the Lagoon Nebula (M8)

Context. The Lagoon Nebula (M8) is host to multiple regions with recent and ongoing massive star formation, due to which it appears as one of the brightest H II regions in the sky. M8-Main and M8 East, two prominent regions of massive star formation, have been studied in detail over the past few years, while large parts of the nebula and its surroundings have received little attention. These largely unexplored regions comprise a large sample of molecular clumps that are affected by the presence of massive O- and B-type stars. Thus, exploring the dynamics and chemical composition of these clumps will improve our understanding of the feedback from massive stars on star-forming regions in their vicinity. Aims. We established an inventory of species observed towards 37 known molecular clumps in M8 and investigated their physical structure. We compared our findings for these clumps with the galaxy-wide sample of massive dense clumps observed as part of the APEX Telescope Large Area Survey of the Galaxy (ATLASGAL). Furthermore, we investigated the region for signs of star formation and stellar feedback. Methods. To obtain an overview of the kinematics and chemical abundances across the sample of molecular clumps in the M8 region, we conducted an unbiased line survey for each clump. We used the Atacama Pathfinder EXperiment (APEX) 12m submillimetre telescope and the 30 m telescope of the Institut de Radioastronomie Millimétrique (IRAM) to conduct pointed on-off observations of 37 clumps in M8. These observations cover bandwidths of 53 GHz and 40 GHz in frequency ranges from 210 GHz to 280 GHz and from 70 GHz to 117 GHz, respectively. Temperatures were derived from rotational transitions of acetonitrile, methyl acetylene, and para-formaldehyde. Additional archival data from the Spitzer, Herschel, MSX, APEX, WISE, JCMT, and AKARI telescopes were used to investigate the morphology of the region and to derive the physical parameters of the dust emission by fitting spectral energy distributions to the observed flux densities. Results. Across the observed M8 region, we identify 346 transitions from 70 different molecular species, including isotopologues. While many species and fainter transitions are detected exclusively towards M8 East, we also observe a large chemical variety in many other molecular clumps. We detect tracers of photo-dissociation regions (PDRs) across all the clumps, and 38% of these clumps show signs of star formation. In our sample of clumps with extinctions between 1 and 60 mag, we find that PDR tracers are most abundant in clumps with relatively low H2 column densities. When comparing M8 clumps to ATLASGAL sources at similar distances, we find them to be slightly less massive (median 10 M⊙) and have compatible luminosities (median 200 L⊙) and radii (median 0.16 pc). In contrast, dust temperatures of the clumps in M8 are found to be increased by approximately 5 K (25%), indicating substantial external heating of the clumps by radiation of the present O- and B-type stars. Conclusions. This work finds clear and widespread effects of stellar feedback on the molecular clumps in the Lagoon Nebula. While the radiation from the O- and B-type stars possibly causes fragmentation of the remnant gas and heats the molecular clumps externally, it also gives rise to extended PDRs on the clump surfaces. Despite this fragmentation, the dense cores within 38% of the observed clumps in M8 are forming a new generation of stars.

Generation of genetically modified Friedreich’s ataxia induced pluripotent stem cell lines and isogenic control lines carrying an inducible neurogenin-2 expression cassette

Friedreich’s ataxia (FRDA) is a rare neurodegenerative disease caused by an expansion of a GAA repeat sequence within the Frataxin (FXN) gene. Prominent regions of neurodegeneration include sensory neurons within the dorsal root ganglia. Here we present a set of genetically modified FRDA induced pluripotent stem cell (iPSC) lines that carry an inducible neurogenin-2 (NGN2) expression cassette. Exogenous expression of NGN2 in iPSC derived neural crest progenitors efficiently generates functionally mature sensory neurons. These cell lines will provide a streamlined source of FRDA iPSC sensory neurons for studying both disease mechanism and screening potential therapeutics.

Insights on the dip of fault zones in Southern California from modeling of seismicity with anisotropic point processes

Accurate models of fault zone geometry are important for scientific and hazard applications. While seismicity can provide high-resolution point measurements of fault geometry, extrapolating these measurements to volumes may involve making strong assumptions. This is particularly problematic in distributed fault zones, which are commonly observed in immature faulting regions. In this study, we focus on characterizing the dip of fault zones in Southern California with the goal of improving fault models. We introduce a novel technique from spatial point process theory to quantify the orientation of persistent surficial features in seismicity, even when embedded in wide shear zones. The technique makes relatively mild assumptions about fault geometry and is formulated with the goal of determining the dip of a fault zone at depth. The method is applied to 11 prominent seismicity regions in Southern California. Overall, the results compare favorably with the geometry models provided by the SCEC Community Fault Model and other focused regional studies. More specifically, we find evidence that the Southern San Andreas and San Jacinto fault zones are both northeast dipping at seismogenic depths at the length scales of 1.0–4.0 km. In addition, we find more limited evidence for some depth dependent variations in dip that suggest a listric geometry. The developed technique can provide an independent source of information from seismicity to augment existing fault geometry models.

A national assessment of the economic and wellbeing impacts of recreational surfing in Australia

Surfing is a cultural ecosystem service, providing recreational benefits to over 50 million people worldwide and fostering economic activity through retail and tourism industries. Despite its growing popularity and threats to natural environments, surfing’s economic and social impacts remain sparsely documented. This study provides a nationwide assessment of surfing’s impact on Australia’s economy and participants’ wellbeing, using an online survey of Australian surfers (n=569). Domestic surf-related expenditure was estimated at A$3719 per surfer per year, including retail (A$1858) and travel (A$1861). Heterogeneity analyses reveal differences in expenditure patterns across regions, age groups and modalities of engagement in the sport. Aggregating across a population of 727,328 Australian adult surfers, the direct input into the market economy is estimated at A$2.71 billion per year. Standardized wellbeing measures indicate that over 80 % of participants experience positive effects from surfing, particularly in community connectedness, physical, and mental health. However, major concerns exist regarding the sustainability of surfing environments due to erosion and overcrowding pressures. Following the SEEA EA’s ‘logic chain’ framework, this study provides a set of baseline results that may be inputted into accounting processes considering recreational ecosystems services. Further, the systematic approach used in this study could be replicated by comparative analyses across time and other prominent surf regions. As coastal areas worldwide urgently respond to climate threats and the need to support the ‘blue economy’, a better understanding the socio-economic values derived from ocean-based recreation can help inform coastal polices aimed at fostering more resilient environments.

Structure-aware feature stylization for domain generalization

Generalizing to out-of-distribution (OOD) data is a challenging task for existing deep learning approaches. This problem largely comes from the common but often incorrect assumption of statistical learning algorithms that the source and target data come from the same i.i.d. distribution. To tackle the limited variability of domains available during training, as well as domain shifts at test time, numerous approaches for domain generalization have focused on generating samples from new domains. Recent studies on this topic suggest that feature statistics from instances of different domains can be mixed to simulate synthesized images from a novel domain. While this simple idea achieves state-of-art results on various domain generalization benchmarks, it ignores structural information which is key to transferring knowledge across different domains. In this paper, we leverage the ability of humans to recognize objects using solely their structural information (prominent region contours) to design a Structural-Aware Feature Stylization method for domain generalization. Our method improves feature stylization based on mixing instance statistics by enforcing structural consistency across the different style-augmented samples. This is achieved via a multi-task learning model which classifies original and augmented images while also reconstructing their edges in a secondary task. The edge reconstruction task helps the network preserve image structure during feature stylization, while also acting as a regularizer for the classification task. Through quantitative comparisons, we verify the effectiveness of our method upon existing state-of-the-art methods on PACS, VLCS, OfficeHome, DomainNet and Digits-DG. The implementation is available at this repository.

Enhancement of the NORAD-Atomic-Data Database in Plasma

We report recent enhancements to the online atomic database at the Ohio State University, NORAD-Atomic-Data, that provide various parameters for radiative and collisional atomic processes dominant in astrophysical plasma. NORAD stands for Nahar Osu RADiative. The database belongs to the data sources, especially for the latest works, of the international collaborations of the Opacity Project and the Iron Project. The contents of the database are calculated values for energies, oscillator strengths, radiative decay rates, lifetimes, cross-sections for photoionization, electron-ion recombination cross-sections, and recombination rate coefficients. We have recently expanded NORAD-Atomic-Data with several enhancements over those reported earlier. They are as follows: (i) We continue to add energy levels, transition parameters, cross-sections, and recombination rates for atoms and ions with their publications. (ii) Recently added radiative atomic data contain a significant amount of transition data for photo-absorption spectral features corresponding to the X-ray resonance fluorescence effect, showing prominent wavelength regions of bio-signature elements, such as phosphorus ions, and emission bumps of heavy elements, such as of lanthanides, which may be created in a kilonova event. We are including (iii) collisional data for electron-impact-excitation, (iv) experimental data for energies and oscillator strengths for line formation, (v) experimental cross-sections for photoionization that can be applied for benchmarking and other applications, and (vi) the introduction of a web-based interactive feature to calculate spectral line ratios at various plasma temperature and density diagnostics, starting with our recently published data for P II. We presented a summary description of theoretical backgrounds for the computed data in the earlier paper. With the introduction of experimental results in the new version of NORAD, we present a summary description of measurement of high-resolution photoionization cross-sections at an Advanced Light Source of LBNL synchrotron set-up and briefly discuss other set-ups. These additions should make NORAD-Atomic-Data more versatile for various applications. For brevity, we provide information on the extensions and avoid repetition of data description of the original paper.

Timeless noncoding DNA contains cell-type preferential enhancers important for proper Drosophila circadian regulation

To address the contribution of transcriptional regulation to Drosophila clock gene expression and to behavior, we generated a series of CRISPR-mediated deletions within two regions of the circadian gene timeless (tim), an intronic E-box region and an upstream E-box region that are both recognized by the key transcription factor Clock (Clk) and its heterodimeric partner Cycle. The upstream deletions but not an intronic deletion dramatically impact tim expression in fly heads; the biggest upstream deletion reduces peak RNA levels and tim RNA cycling amplitude to about 15% of normal, and there are similar effects on tim protein (TIM). The cycling amplitude of other clock genes is also strongly reduced, in these cases due to increases in trough levels. These data underscore the important contribution of the upstream E-box enhancer region to tim expression and of TIM to clock gene transcriptional repression in fly heads. Surprisingly, tim expression in clock neurons is only modestly affected by the biggest upstream deletion and is similarly affected by a deletion of the intronic E-box region. This distinction between clock neurons and glia is paralleled by a dramatically enhanced accessibility of the intronic enhancer region within clock neurons. This distinctive feature of tim chromatin was revealed by ATAC-seq (assay for transposase-accessible chromatin with sequencing) assays of purified neurons and glia as well as of fly heads. The enhanced cell type-specific accessibility of the intronic enhancer region explains the resilience of clock neuron tim expression and circadian behavior to deletion of the otherwise more prominent upstream tim E-box region.