Effect Of Depth Research Articles

Effect of Recycled Concrete Aggregates on the Concrete Breakout Resistance of Headed Bars Embedded in Slender Structural Elements

Recycled concrete aggregates are potentially interesting for the precast concrete industry as they provide a new use for high-quality waste from its products’ life cycle. In precast concrete structures, it is common to use headed bars in several connection types between structural members. This paper presents the results of experimental tests to investigate the impact of replacing coarse natural aggregates with coarse recycled concrete aggregates in the concrete breakout strength of cast-in headed bars embedded in slender structural elements. Results of 12 tests on 16 mm headed bars embedded in 500 × 200 × 900 mm concrete members with an effective embedment depth of 110 mm are presented. The percentage of replacement of natural aggregates by recycled concrete aggregates was 0%, 30%, and 100%, and the flexural reinforcement ratio of the structural elements varied from 0.5% to 3.5%. The behavior and strength of the tested specimens are discussed, and comparisons with theoretical strength estimates are presented. The results showed that the concrete breakout strength of the headed bars was not affected by the use of recycled concrete aggregates and that the flexural reinforcement ratio significantly impacts the load-carrying capacity of the headed bars as they control the crack widths before failure.

Impact of anthropometric factors on chest compression depth during CPR provided by children aged 11–14 in a community-wide study

ObjectiveTo assess the depth of chest compressions (CC) provided by schoolchildren and their relation with providers’s anthropometric characteristics. MethodsWe organized 1-hour hands-on training sessions for 11-14y.o. in volunteering schools. After training, willing subjects performed 2 min of recorded continuous CCs by means of Laerdal Resusci Anne® with CPRMeter2®, with visual feedback. Compression pace was given by metronome; instructors supervised the correct body position. Collected data included age, sex, as well as measured body weight and height. ResultsWe analyzed records from N = 702 children (mean age: 12.76 ± 1.02 years, 379 (51.63%) boys) out of 761 participating in the study. Their mean median compression depth (MCD) was 46.70 ± 7.74 mm, which was below minimal effective CC depth advised by current guidelines (50 mm). This corresponded to low mean fraction of CCs ≥ 50 mm (CCF ≥ 50 mm, 42.86 ± 33.67%), and only 42.88% of children achieving at least 50% of compressions ≥ 50 mm. Boys had significantly higher mean MCD and CCF ≥ 50 mm than girls (MCD: 49.34 ± 7.05 mm vs 45.97 ± 8.07 mm, p < 0.0001; CCF ≥ 50 mm: 50.23 ± 32.90% vs 40.40 ± 34.97%, p < 0.0001). Age differentiated children who achieved at least 50% of compressions ≥50 mm from those who did not with AUC of 0.69 (for cut-off of 12.1 years: 85% sensitivity, 41% specificity), whereas weight offered an improved prediction (AUC 0.74; for cut-off 44.8 kg: 77.4% sensitivity, 61.1% specificity). ConclusionsSex, age and anthropometric factors are significant CC quality factors. Children with higher body weight are more likely to deliver CCF50%≥50 mm. Among the studied population, children ≥12 years old provided more effective chest compressions.

COMPARATIVE STUDY OF FLAT SLAB DESIGN USING VARIOUS INTERNATIONAL CODES

The structural function of beamless floor system is to collect the gravity load and other load such as wind load etc. and to transfer to the vertical structural elements i.e. column through the combined capacity in flexure and shear. In Flat slab, the slab is directly supported on the columns. Moments and shear values are usually the largest over the columns. However, when spans are relatively small and imposed load is low, the thickness of slab can be increased to reduce the stresses at slab-column joint which would result in providing greater effective depth for negative moments occurring near the column. With increasing span and live load intensity, the thickness requirement increases which does not give economical solution. So, to tackle this problem flaring of the column at top is done such that the plan geometry at the column head is similar to that of the column. The column capital is intended primarily to increase the capacity of the slab to resist punching shear. The column capital stiffens the slab, which help in controlling the floor deflection. Thus, the floor span can be increased to some extent. Beyond a certain range of span, further stiffening of the slab is required which is achieved by increasing the slab thickness of the slab panel around the column capital. This portion of the slab is known as drop panel. For flat slabs and flat plates supported directly by columns, shear may be the critical factor in design. In almost all tests of such structures, failures have been due to shear or perhaps shear and torsion. These conditions are particularly serious around the exterior columns. In the present study, attempt has been made to understand different methods of analysis of flat slab and parameters, which governs the design parameters of the flat slab. Attempts have been made to study the provisions of flat slab in well-known international standards such as ACI 318, EC-2 and IS 456:2000. Keywords: Flat slab, drop panel, Column head, IS 456:2000, ACI 318, EC-2

Enhancing self-healing of asphalt mixtures containing recycled concrete aggregates and reclaimed asphalt pavement using induction heating

Given the growing demand for sustainable road construction materials, this research aims to evaluate the synergistic effects of incorporating recycled concrete aggregate (RCA) and reclaimed asphalt pavement (RAP) in asphalt mixtures. Induction heating was also utilized to enhance the self-healing ability of asphalt mixtures, with the intention of prolonging their lifespan and mitigating environmental impacts. This research assessed the pavement performance of recycled asphalt mixtures (RAM) with different RCA and RAP dosages. The induction heating performance, cooling performance, and effective heating depth of RAM were tested. Lastly, the cumulative healing efficiency of the RAM was evaluated under multiple "fracture-healing" conditions. Findings indicated that the inclusion of RCA and RAP negatively influenced the water stability, high-temperature deformation resistance, and crack resistance at low temperatures of the RAM. However, when the RCA content was 20 % and the RAP content did not exceed 20 %, the road performance of the RAM met the requirements. The contents of the RCA and RAP had no discernible effect on the RAM's induction heating capability. Moreover, when the RCA content was 20 % and the RAP content did not exceed 10 %, the effective heating depth of the RAM could reach 50 mm. Using fracture energy recovery rate as the healing index, after three cycles of "fracture-healing", the RAM with 20 % RCA achieved the highest cumulative healing efficiency of 291 %. The cumulative healing efficiency of recycled asphalt mixtures with 20 % RCA and 10 % RAP was close to that of normal asphalt mixtures. This research contributes to reducing the consumption of non-renewable resources, minimizing environmental impact, and maintaining or even improving the performance of asphalt pavements by optimizing the use of RCA and RAP.

P-529 Optical Genomic Mapping (OGM) enable cryptic chromosomal rearrangements in a couple with recurrent miscarriages undergoing preimplantation genetic diagnosis

Abstract Study question Could Optical Genomic Mapping (OGM) detect chromosomal structural variations (SVs) below the detection limit of conventional cytogenetic diagnostics in couples with recurrent miscarriages? Summary answer The Next-generation cytogenetics of Optical Genomic Mapping confirmed a cryptic chromosomal translocation in a couple with recurrent miscarriages undergoing preimplantation genetic testing for aneuploidies (PGT-A). What is known already Chromosomal structural variants (SVs) significantly contribute to infertility, recurrent pregnancy losses and congenital defects... Approximately 2-4% of couples facing recurrent miscarriages carry chromosomal abnormalities. Currently, their detection is based on convencional cytogenetic methods (karyotyping, FISH or array-CGH), however cryptic chromosomal rearrangements are difficult to identify. Next-generation cytogenetics such as OGM is able to detect SVs with a resolution of &amp;lt; 500 kb, allowing a better understanding of the cause of patient’s infertility. In our study, we utilized OGM to confirm a cryptic translocation in a couple experiencing recurrent miscarriages, aiming to enhance our understanding of the underlying causes of their infertility. Study design, size, duration Our study focuses on a non-consanguineous couple diagnosed with recurrent pregnancy loss that attended to our private fertility clinic to undergo assisted reproductive treatment (ART) with their autologous semen, oocytes and PGT-A. Conventional cytogenetic technique was performed on both partners by G-banding karyotyping. PGT-A was performed by NGS (Veriseq, Illumina). Optical Genomic Mapping (OGM) served as a diagnostic tool, complemented by lymphocyte FISH analysis to confirm chromosomal structural variations in these infertile patients. Participants/materials, setting, methods Ultra High Molecular Weight genomic DNA (UHMW gDNA) was extracted from frozen human peripheral blood of both partners. UHMW gDNA was quantified using Qubit dsDNA assay BR kit and Qubit 4 Fluorometer (Thermo Fisher Scientific). We used Saphyr system (Bionano Genomics) for cytogenetic diagnosis. The consensus genomic map was constructed by de novo assembly in the Bionano Solve data analysis software. SVs were identified by aligning Optical Genomic Map with the reference genome GRCh38/hg38. Main results and the role of chance The couple had suffered four prior miscarriages and undergoing three PGT-A cycles. Both partners provided a normal karyotype. Among 13 biopsied embryos, four showed CNVs on chromosomes 2(p24.3è25.3, 12Mb) and 6(p24.1è25.3, 11Mb). Following this result we suspected that the couple might has a cryptic chromosomal structural abnormalities. To test our hypothesis, we performed OGM. We analysed UHMW gDNA samples from both partners. OGM generated an average of 823.67Gb. The average N50 molecule length (≥150kb) was 318kb. The average mapping rate was 90.75% and the average labeling rate was 15.30 labels per 100kb. The average effective coverage depth was 242X. OGM successfully identified a cryptic translocation in the male partner ogm[GRch38]t(2;6)(p24.3;24.1), while the female partner showed no abnormalities. Translocation breakpoints identified by OGM were Chr2:12.560.508 and Chr6:11.622.593. The translocated segments were near to the detection limit of conventional cytogenetic analysis and with similar pattern that could be the reason for missed detection by karyotyping. Importantly, we identified one gene disrupted by OGM rearrangement breakpoint: MIR3681HG. A previous study considered that CNV loci of 2p24.3, including MIR3681HG gene, could be associated with spermatogenesis. The lymphocyte FISH analysis finding corroborated the OGM result. Limitations, reasons for caution OGM is a very powerful method for detecting chromosomal structural variants in human genomes and is able to detect cryptic structural variants. However, this is a very new diagnostic application, so there is limited experience with OGM for correlating structural variations results to infertility diagnosis in couples with recurrent miscarriages. Wider implications of the findings A balanced cryptic translocation has been identified in a couple with recurrent miscarriages. Our case report demonstrates the high diagnostic capability of Optical Genomic Mapping for cryptic SVs. OGM technology allows a more accurate cytogenetic diagnosis by detecting cryptic structural variants with high resolution in infertile patients with recurrent miscarriages. Trial registration number not applicable

Prediction of flexural strength in FRP bar reinforced concrete beams through a machine learning approach

PurposeThe purpose of this study is to assess the incorporation of fiber reinforced polymer (FRP) bars in concrete as a reinforcement enhances the corrosion resistance in a concrete structure. However, FRP bars are not practically used due to a lack of standard codes. Various codes, including ACI-440-17 and CSA S806-12, have been established to provide guidelines for the incorporation of FRP bars in concrete as reinforcement. The application of these codes may result in over-reinforcement. Therefore, this research presents the use of a machine learning approach to predict the accurate flexural strength of the FRP beams with the use of 408 experimental results.Design/methodology/approachIn this research, the input parameters are the width of the beam, effective depth of the beam, concrete compressive strength, FRP bar elastic modulus and FRP bar tensile strength. Three machine learning algorithms, namely, gene expression programming, multi-expression programming and artificial neural networks, are developed. The accuracy of the developed models was judged by R2, root means squared and mean absolute error. Finally, the study conducts prismatic analysis by considering different parameters. including depth and percentage of bottom reinforcement.FindingsThe artificial neural networks model result is the most accurate prediction (99%), with the lowest root mean squared error (2.66) and lowest mean absolute error (1.38). In addition, the result of SHapley Additive exPlanation analysis depicts that the effective depth and percentage of bottom reinforcement are the most influential parameters of FRP bars reinforced concrete beam. Therefore, the findings recommend that special attention should be given to the effective depth and percentage of bottom reinforcement.Originality/valuePrevious studies revealed that the flexural strength of concrete beams reinforced with FRP bars is significantly influenced by factors such as beam width, effective depth, concrete compressive strength, FRP bars’ elastic modulus and FRP bar tensile strength. Therefore, a substantial database comprising 408 experimental results considered for these parameters was compiled, and a simple and reliable model was proposed. The model developed in this research was compared with traditional codes, and it can be noted that the model developed in this study is much more accurate than the traditional codes.

Cosmological constraints from the eBOSS Lyman-α forest using the PRIYA simulations

We present new cosmological parameter constraints from the eBOSS Lyman-α forest survey. We use a new theoretical model and likelihood based on the PRIYA simulation suite. PRIYA is the first suite to resolve the Lyman-α forest in a (120 Mpc/h)3 volume, using a multi-fidelity emulation technique. We use PRIYA to predict Lyman-α forest observables with ≲ 1% interpolation error over an 11 dimensional (9 simulated, 2 in post-processing) parameter space. We identify an internal tension within the flux power spectrum data. Once the discrepant data is removed, we find the primeval scalar spectral index measured at a pivot scale of k 0 = 0.78 Mpc-1 to be nP = 1.009+0.027 -0.018 at 68% confidence. This measurement from the Lyman-α forest flux power spectrum alone is in reasonable agreement with Planck, and in tension with earlier eBOSS analyses. The amplitude of matter fluctuations is σ 8 = 0.733+0.026 -0.029 at 68% confidence, in agreement with Dark Energy Survey weak lensing measurements and other small-scale structure probes and in tension with CMB measurements from Planck and ACT. The effective optical depth to Lyman-α photons from our pipeline is in good agreement with earlier high resolution measurements. We find a linear power at z = 3 and k = 0.009 s/km of Δ2 L = 0.302+0.024 -0.027 with a slope n eff = -2.264+0.026 -0.018. Our flux power spectrum only chains prefer a low level of heating during helium reionization. When we add IGM temperature data we find nP = 0.983 ± 0.020 and σ 8 = 0.703+0.023 -0.027. Our chains prefer an early and long helium reionization event, as suggested by measurements from the helium Lyman-α forest. In the near future we will use our pipeline to infer cosmological parameters from the DESI Lyman-α data.

Shear Reinforcement of Concrete Beams Using Iraqi Bamboo

Steel rebars require significant energy to produce and are prone to corrosion when exposed to ambient air. Therefore, Iraqi bamboo replaces steel stirrups and steel rebars in this research. Five reinforced concrete beams are tested using four-point load testing. The first beam was cast without stirrups to achieve shear failure and is labeled a control specimen. Three other beams had Iraqi bamboo instead of steel at the critical sections. The last beam had steel stirrups spaced at a distance half of the effective depth of the beam. It is found that the Iraqi bamboo can increase the load-carrying capacity of the beam by up to 15% if compared with the beam with no stirrups. Also, the ductility of the beam has increased by 17%. However, the maximum load-carrying capacity of the steel stirrup beam was larger than that of the bamboo stirrups by at least 21%. The main reason is the spacing of the stirrups, which is the main factor influencing the beams' behavior prone to shear failure.

Helium reionization from empirical quasar luminosity functions before and after JWST

ABSTRACT Recently, models of the quasar luminosity function (QLF) rooted on large observational compilations have been produced that, unlike their predecessors, feature a smooth evolution with time. This bypasses the need to assume an ionizing emissivity evolution when simulating helium reionization with observations-based QLF, thus yielding more robust constraints. We combine one such QLF with a cosmological hydrodynamical simulation and 3D multifrequency radiative transfer. The simulated reionization history is consistently delayed in comparison to most other models in the literature. The predicted intergalactic medium temperature is larger than the observed one at $z \lesssim 3$. Through forward modelling of the He ii Lyman $\alpha$ forest, we show that our model produces an extended helium reionization and successfully matches the bulk of the observed effective optical depth distribution, although it overionizes the Universe at $z\lesssim 2.8$ as the effect of small-scale Lyman Limit Systems not being resolved. We thoroughly characterize transmission regions and dark gaps in He ii Lyman $\alpha$ forest sightlines. We quantify their sensitivity to the helium reionization, opening a new avenue for further observational studies of this epoch. Finally, we explore the implications for helium reionization of the large number of active galactic nuclei revealed at $z\gtrsim 5$ by JWST. We find that such modifications do not affect any observable at $z\le 4$, except in our most extreme model, indicating that the observed abundance of high-z AGNs does not bear consequences for helium reionization.

Smart insect-computer hybrid robots empowered with enhanced obstacle avoidance capabilities using onboard monocular camera

Insect-computer hybrid robots are receiving increasing attention as a potential alternative to small artificial robots due to their superior locomotion capabilities and low manufacturing costs. Controlling insect-computer hybrid robots to travel through terrain littered with complex obstacles of various shapes and sizes is still challenging. While insects can inherently deal with certain obstacles by using their antennae to detect and avoid obstacles, this ability is limited and can be interfered with by control signals when performing navigation tasks, ultimately leading to the robot being trapped in a specific place and having difficulty escaping. Hybrid robots need to add additional sensors to provide accurate perception and early warning of the external environment to avoid obstacles before getting trapped, ensuring smooth navigation tasks in rough terrain. However, due to insects’ tiny size and limited load capacity, hybrid robots are very limited in the sensors they can carry. A monocular camera is suitable for insect-computer hybrid robots because of its small size, low power consumption, and robust information acquisition capabilities. This paper proposes a navigation algorithm with an integrated obstacle avoidance module using a monocular camera for the insect-computer hybrid robot. The monocular cameras equipped with a monocular depth estimation algorithm based on deep learning can produce depth maps of environmental obstacles. The navigation algorithm generates control commands that can drive the hybrid robot away from obstacles according to the distribution of obstacle distances in the depth map. To ensure the performance of the monocular depth estimation model when applied to insect-computer hybrid robotics scenarios, we collected the first dataset from the viewpoint of a small robot for model training. In addition, we propose a simple but effective depth map processing method to obtain obstacle avoidance commands based on the weighted sum method. The success rate of the navigation experiment is significantly improved from 6.7% to 73.3%. Experimental results show that our navigation algorithm can detect obstacles in advance and guide the hybrid robots to avoid them before they get trapped.

MFE‐MVSNet: Multi‐scale feature enhancement multi‐view stereo with bi‐directional connections

AbstractRecent advancements in deep learning have significantly improved performance in the multi‐view stereo (MVS) domain, yet achieving a balance between reconstruction efficiency and quality remains challenging for learning‐based MVS methods. To address this, we introduce MFE‐MVSNet, designed for more effective and precise depth estimation. Our model incorporates a pyramid feature extraction network, featuring efficient multi‐scale attention and multi‐scale feature enhancement modules. These components capture pixel‐level pairwise relationships and semantic features with long‐range contextual information, enhancing feature representation. Additionally, we propose a lightweight 3D UNet regularization network based on depthwise separable convolutions to reduce computational costs. This network employs bi‐directional skip connections, establishing a fluid relationship between encoders and decoders and enabling cyclic reuse of building blocks without adding learnable parameters. By integrating these methods, MFE‐MVSNet effectively balances reconstruction quality and efficiency. Extensive qualitative and quantitative experiments on the DTU dataset validate our model's competitiveness, demonstrating approximately 33% and 12% relative improvements in overall score compared to MVSNet and CasMVSNet, respectively. Compared to other MVS networks, our approach more effectively balances reconstruction quality with efficiency.

MFRWA: A Multi-Frequency Rayleigh Wave Approximation Method for Concrete Carbonation Depth Evaluation

Carbonation depth is essential to determine the durability and predict the remaining service life of concrete structures. This study proposes a multi-frequency Rayleigh wave approximation method (MFRWA) to evaluate carbonation depth by exploiting the frequency-dependent penetration depths of ultrasonic Rayleigh waves. A series of numerical simulations are conducted to investigate the effective penetration depth of Rayleigh waves and the feasibility of the proposed MFRWA method on carbonation depth evaluation. Subsequently, the accelerated carbonation experiment is conducted to evaluate the carbonation depth using low-frequency and high-frequency Rayleigh waves, and the measured results from the Rayleigh wave method are compared with the ones from the phenolphthalein indicator and thermalgravimetric analysis (TGA) method. The results show that carbonation depth measured by Rayleigh wave method meets well with the one from TGA technique, demonstrating that the proposed method could provide a non-destructive and precise carbonation depth estimation. The proposed MFRWA method contributes a novel scheme for concrete carbonation evaluation and holds substantial potential in both laboratory and field applications.

Interannual radial growth response of Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) to severe droughts: an analysis along a gradient of soil properties and rooting characteristics

Key messageWe analyzed stem growth responses of Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) to severe drought in 2003/04 and 2018. The results showed high drought tolerance in sandy, loamy, and most silty soils, with limitations on clayey soils. This study indicates the susceptibility of Douglas-firs with shallow root systems to extreme drought and the importance of deep rooting for high drought resilience.ContextAlthough Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) is considered a more drought-tolerant substitute for Norway spruce (Picea abies (L.) Karst.) in Europe, there is considerable uncertainty about the drought tolerance of Douglas-fir under climate change, specifically concerning soil properties.AimsThis study aimed to assess the influence of soil texture, plant-available water capacity, and rooting characteristics on the interannual stem growth response of Douglas-fir when exposed to severe drought.MethodsAlong a soil texture gradient from sand to clay, we selected seven closely spaced sites at elevations of approximately 500 m a.s.l. in southern Germany. Mixed-effects models were used to analyze the effects of soil physical and rooting characteristics on growth response indices (resistance, recovery, resilience) related to the severe to extreme droughts in 2003/04 and 2018.ResultsDouglas-fir showed high drought tolerance in sandy, loamy, and most silty soils. However, the results suggest a higher drought stress risk on clayey soils, as well as at specific silty sites with shallow root systems. A higher effective rooting depth increased the resilience of Douglas-fir during the extreme drought in 2018.ConclusionDouglas-fir demonstrated its drought tolerance in most soil textures. In addition, this study supports the need for combined above- and below-ground investigations on factors influencing drought tolerance and the importance of rooting for drought resilience.

Genome-wide comparative analysis reveals selection signatures for reproduction traits in prolific Suffolk sheep.

The identification of genome-wide selection signatures can reveal the potential genetic mechanisms involved in the generation of new breeds through natural or artificial selection. In this study, we screened the genome-wide selection signatures of prolific Suffolk sheep, a new strain of multiparous mutton sheep, to identify candidate genes for reproduction traits and unravel the germplasm characteristics and population genetic evolution of this new strain of Suffolk sheep. Whole-genome resequencing was performed at an effective sequencing depth of 20× for genomic diversity and population structure analysis. Additionally, selection signatures were investigated in prolific Suffolk sheep, Suffolk sheep, and Hu sheep using fixation index (F ST) and heterozygosity H) analysis. A total of 5,236.338Gb of high-quality genomic data and 28,767,952 SNPs were obtained for prolific Suffolk sheep. Moreover, 99 selection signals spanning candidate genes were identified. Twenty-three genes were significantly associated with KEGG pathway and Gene Ontology terms related to reproduction, growth, immunity, and metabolism. Through selective signal analysis, genes such as ARHGEF4, CATIP, and CCDC115 were found to be significantly correlated with reproductive traits in prolific Suffolk sheep and were highly associated with the mTOR signaling pathway, the melanogenic pathway, and the Hippo signaling pathways, among others. These results contribute to the understanding of the evolution of artificial selection in prolific Suffolk sheep and provide candidate reproduction-related genes that may be beneficial for the establishment of new sheep breeds.

Oxygen and Air Density Retrieval Method for Single-Band Stellar Occultation Measurement

The stellar occultation technique is capable of atmospheric trace gas detection using the molecule absorption characteristics of the stellar spectra. In this paper, the non-iterative and iterative retrieval methods for oxygen and air density detection by stellar occultation are investigated. For the single-band average transmission data in the oxygen 761 nm A-band, an onion-peeling algorithm is used to calculate the effective optical depth of each atmospheric layer, and then the optical depth is used to retrieve the oxygen number density. The iteration method introduces atmospheric hydrostatic equilibrium and the ideal gas equation of state, and it achieves a more accurate retrieval of the air density under the condition of a priori temperature deviation. Finally, this paper analyzes the double solution problem in the iteration process and the ideas to improve the problem. This paper provides a theoretical basis for the development of a new type of atmospheric density detection method.

The fifth data release of the Kilo Degree Survey: Multi-epoch optical/NIR imaging covering wide and legacy-calibration fields

We present the final data release of the Kilo-Degree Survey (KiDS-DR5), a public European Southern Observatory (ESO) wide-field imaging survey optimised for weak gravitational lensing studies. We combined matched-depth multi-wavelength observations from the VLT Survey Telescope and the VISTA Kilo-degree INfrared Galaxy (VIKING) survey to create a nine-band optical-to-near-infrared survey spanning 1347 deg2. The median r-band 5σ limiting magnitude is 24.8 with median seeing 0.7″. The main survey footprint includes 4 deg2 of overlap with existing deep spectroscopic surveys. We complemented these data in DR5 with a targeted campaign to secure an additional 23 deg2 of KiDS- and VIKING-like imaging over a range of additional deep spectroscopic survey fields. From these fields, we extracted a catalogue of 126 085 sources with both spectroscopic and photometric redshift information, which enables the robust calibration of photometric redshifts across the full survey footprint. In comparison to previous releases, DR5 represents a 34% areal extension and includes an i-band re-observation of the full footprint, thereby increasing the effective i-band depth by 0.4 magnitudes and enabling multi-epoch science. Our processed nine-band imaging, single- and multi-band catalogues with masks, and homogenised photometry and photometric redshifts can be accessed through the ESO Archive Science Portal.

Enhancing ToF-SIMS OLED Data Analysis with Neural Networks and Mathematical Spectral Mixing.

This study presents a method employing artificial neural networks (ANN) for automated interpretation and depth profiling of organic multilayers using a limited set of time-of-flight secondary ion mass spectrometry (ToF-SIMS) spectra. To overcome the challenges of acquiring massive data sets for OLEDs, training data was generated by combining existing ToF-SIMS data sets with mathematically generated spectra. The classification model achieved an impressive 99.9% accuracy in identifying the mixed layers of the OLED dyes. The study demonstrates the synergy of ToF-SIMS and ANN analysis for effective classification and depth profiling of the OLED layers, providing valuable insights for the development and optimization of organic electronic devices.

Impacts of Farming Activities on Nitrogen Degradability under a Temperate Continental Monsoon Climate

Nutrient fertilizer application to agricultural land has led to greenhouse gas emissions and has altered soil nitrogen (N) deposition. In soil, N can be degraded in four ways: entering surface water through water flow, absorption by plants and microorganisms, decomposition into gas, and deposition as minerals. This study proposes the concept of N degradability and aims to clarify how farming activities affect N degradability in soil. Over 260 soil profiles were excavated, and the effective soil depth, coordinates, soil types, and vegetation were recorded at each measurement point. The following characteristics were determined in the soil samples: pH, organic matter, total N, total phosphorus, total potassium, total soluble N, available phosphorus, and available potassium. The sample characteristics were subjected to Pearson correlation analysis, principal component analysis, and one-way analysis of variance. The 260 samples included four soil types: dark brown soil, black soil, albic black soil, and meadow soil. Black soil exhibited more stable N levels compared with the other three soil types, showing a tendency towards N accumulation. Ground vegetation was categorized into seven types: forest, rice, maize, red adzuki bean, grassland, soybean, and others. Forests contributed the most to N deposition. Conversely, planting maize led to a tendency for N loss compared with forests. This study can provide a reference for the sustainable development of agriculture and the balance of ecological protection.

Deep Learning-Based Automatic River Flow Estimation Using RADARSAT Imagery

Estimating river flow is a key parameter for effective water resource management, flood risk prevention, and hydroelectric facilities planning. Yet, traditional gauging methods are not reliable under very high flows or extreme events. Hydrometric network stations are often sparse, and their spatial distribution is not optimal. Therefore, many river sections cannot be monitored using traditional flow measurements and observations. In the last few decades, satellite sensors have been considered as complementary observation sources to traditional water level and flow measurements. This kind of approach has provided a way to maintain and expand the hydrometric observation network. Remote sensing data can be used to estimate flow from rating curves that relate instantaneous flow (Q) to channel cross-section geometry (effective width or depth of the water surface). Yet, remote sensing has limitations, notably its dependence on rating curves. Due to their empirical nature, rating curves are limited to specific river sections (reaches) and cannot be applied to other watercourses. Recently, deep-learning techniques have been successfully applied to hydrology. The primary goal of this study is to develop a deep-learning approach for estimating river flow in the Boreal Shield ecozone of Eastern Canada using RADARSAT-1 and -2 imagery and convolutional neural networks (CNN). Data from 39 hydrographic sites in this region were used in modeling. A new CNN architecture was developed to provide a straightforward estimation of the instantaneous river flow rate. Our results yielded a coefficient of determination (R2) and a Nash–Sutcliffe value of 0.91 and a root mean square error of 33 m3/s. Notably, the model performs exceptionally well for rivers wider than 40 m, reflecting its capability to adapt to varied hydrological contexts. These results underscore the potential of integrating advanced satellite imagery with deep learning to enhance hydrological monitoring across vast and remote areas.

Shear contribution of beams of different sizes strengthened in shear with anchored CFRP strips

Carbon-fibre-reinforced polymer (CFRP) materials can be used as structural reinforcement in engineering projects, but its effectiveness is jeopardised by debonding of the CFRP. Anchoring of the CFRP is thus crucial for efficient shear strengthening. The effect of anchoring on the shear resistance of nine CFRP U-wrapped concrete beams with varying shear span-to-depth (SSD) ratios (1.0, 1.5 and 2.0) and depths of 300–900 mm was investigated in this work. Nine unanchored beams and nine plain (without CFRP wrapping) concrete beams were also included in the comparison, considering the influence of the beam effective depth and the SSD ratio, with reference to prior research. An evaluation of the shear contribution of the CFRP wrapping was conducted, particularly for cases involving large dimensions and diverse SSD ratios. The anchoring effect was found to be most pronounced in small-scale beams with a large SSD ratio, demonstrating an improvement of over 60% compared with the CFRP shear-strengthened beam without anchoring. In contrast to beams without CFRP wrapping, the shear performance was enhanced by approximately 90%. Additionally, formulas for calculating the shear capacity from different national specifications were analysed and compared. A new calculation method for CFRP shear resistance was established and its accuracy was verified.