Limited Input Research Articles

Sensitivity analysis of SWAT streamflow and water quality to the uncertainty in soil properties generated by the SoLIM model

Hydrological models are essential tools for understanding natural processes, yet they often encounter with uncertainties due to limited input data and simplified assumptions. Soil data is a crucial input parameter for hydrological models, but it faces cartographic challenges due to lack of standardized soil mapping methods. To explore the applicability of soil datasets derived from the Third Law of Geography in hydrological models, the Soil Landscape Inference Model (SoLIM) based on this principle is used to generate a soil dataset. This dataset is then utilized as input data for a semi-distributed hydrological model (Soil and Water Assessment Tool, SWAT) to assess the impact of soil dataset uncertainty on performance of the hydrological model. This study, utilized the Fengyu River Watershed in Yunnan Province, China as an example, showed that: 1) Although the integration of SoLIM-generated soil data into the SWAT model introduces sensitivity in sediment yield, the overall results remain reliable. This suggests that soil datasets based on the Third Law of Geography can serve as input for semi-distributed hydrological models. 2) Uncertainty introduced by SoLIM-generated soil data affects parameters such as evaporation, total porosity of the soil layer (Φd), and groundwater infiltration coefficient (Ksat). These variations influence the sensitivity of SWAT’s predictions regarding streamflow and water quality. 3) Factors influencing model outcomes include steeper slopes in certain areas, which enhance streamflow velocity and flow rates, resulting in more dynamic and non-linear responses. Additionally, soil water dynamics exhibit higher temporal and spatial variability in forests compared to pasture and rice paddy fields, where vegetation tends to be more uniform, maintaining stable soil water loss pathways and reducing uncertainty in streamflow predictions. The study provides a foundation for studies addressing the difficulties in acquiring high-precision soil datasets for such models.

Gender-inclusive urban planning: European frameworks as a potential methodology for Arabian Gulf urban parks

PurposeThis trigger article explores established European gender-mainstreaming urban planning frameworks of urban parks to analyze and identify ways that could be applied or adapted for Arabian Gulf cities. It aims to accelerate the mainstreaming process, increasing women’s participation and inclusion in urban spaces, paving the way for future research.Design/methodology/approachAnalytical frameworks are created by reviewing legal and policy evolution, city-issued guidelines and European cities’ case studies (Barcelona, Berlin, Paris and Vienna) engaged in gender mainstreaming in urban planning. These analytical tools are then applied to assess two Arabian Gulf parks (Al Azaiba Wadi Park, Muscat, Oman, and Dahl Al-Hamam Park, Doha, Qatar), suggesting a possible future research methodology.FindingsSuccess factors in European cities include integrating female perspectives, detailed user analysis, specific planning solutions and political will. Despite differing social and cultural contexts, commonalities exist for female park users in Europe and the Arabian Gulf. This trigger article proposes a methodology for assessing the potential effective application or adaptation of European established frameworks in the Gulf context to accelerate gender mainstreaming.Research limitations/implicationsThe paper proposes a methodology for future research, noting limitations such as limited input on design preparation processes in Gulf City park case studies, a lack of feedback from park users and only a brief overview of sociocultural differences. Future research should explore nuanced cultural contexts, including historical processes and gender dynamics, thorough literature review, expanded case study analysis and participatory approaches. The proposed methodology aims to demonstrate how European frameworks can guide gender-mainstreaming efforts in diverse contexts, facilitating collaborative solutions for inclusive urban planning.Practical implicationsDrawing from decades of European gender-mainstreaming activities, the paper distills urban planning principles and best practices for application or adaptation in the Arab Gulf.Social implicationsEnhancing the park experience for women through planning and design boosts gender equality in cities, benefiting their physical and social well-being.Originality/valueAn original methodology is suggested for adapting well-developed European gender-mainstreaming frameworks to Arabian Gulf parks. Authored by a male, this paper aims to advance gender issues in planning while exploring the role men can play in contributing to such.

Contribution of environmental DNA toward fungal Red Listing

In navigating the biodiversity crisis, a major uncertainty is the conservation status of inconspicuous, yet megadiverse and functionally crucial, soil organisms. Massive datasets on soil biota are accumulating through molecular sampling approaches, but to date these datasets have provided only limited input into conservation planning and management. We investigated how environmental DNA (eDNA) data of soil macrofungi contribute to regional Red List assessments, which are currently based on fruiting bodies (hereafter, fruit‐bodies). In our test region of Estonia (northern Europe), which contained ~15,000 fruit‐body records for 1583 assessed species, an average soil sample increased the range estimates of Threatened and Near Threatened fungal species by 0.18%. Five hundred soil samples almost doubled their known localities and added 19% previously unrecorded species. However, even after accumulating >1000 soil samples, about half of the Threatened and Near Threatened species known by fruit‐bodies remained undetected through eDNA techniques. Effective conservation assessment of soil fungi thus requires both fruit‐body and eDNA data; therefore, special efforts are needed to make these data available to conservationists.

Deciphering the Role of Total Water Storage Anomalies in Mediating Regional Flooding

AbstractRegional floods result from various flood generation mechanisms. Traditional analyses mainly link flooding to extreme rainfall, with limited input from soil moisture. Total water storage (TWS) is a holistic measure of basin wetness, including additional storage components from surface water, snow, and groundwater. Utilizing a new 5‐day Gravity Recovery and Climate Experiment and its Follow On (GRACE(‐FO)) data set, we investigated the linkage between short‐term TWS anomaly (TWSA) and regional flooding. The 5‐day TWSA solutions revealed flood signals missed by monthly TWSA solutions. Global basins exhibit distinct storage‐discharge co‐evolution patterns, offering new insights into flood mechanisms and propensity. Our bivariate event analyses show the annual maximum river discharges co‐occur more often with the TWSA maxima than with precipitation in many basins. Further analyses revealed TWSA's time‐lagged effect on river discharge, particularly in basins susceptible to floods triggered by saturation‐excess runoff. The 5‐day TWSA provides a new source of information for enhancing global flood preparedness.

A Hierarchical Multi-Task Learning Framework for Semantic Annotation in Tabular Data.

To optimize the utilization and analysis of tables, it is essential to recognize and understand their semantics comprehensively. This requirement is especially critical given that many tables lack explicit annotations, necessitating the identification of column types and inter-column relationships. Such identification can significantly augment data quality, streamline data integration, and support data analysis and mining. Current table annotation models often address each subtask independently, which may result in the neglect of constraints and contextual information, causing relational ambiguities and inference errors. To address this issue, we propose a unified multi-task learning framework capable of concurrently handling multiple tasks within a single model, including column named entity recognition, column type identification, and inter-column relationship detection. By integrating these tasks, the framework exploits their interrelations, facilitating the exchange of shallow features and the sharing of representations. Their cooperation enables each task to leverage insights from the others, thereby improving the performance of individual subtasks and enhancing the model's overall generalization capabilities. Notably, our model is designed to employ only the internal information of tabular data, avoiding reliance on external context or knowledge graphs. This design ensures robust performance even with limited input information. Extensive experiments demonstrate the superior performance of our model across various tasks, validating the effectiveness of unified multi-task learning framework in the recognition and comprehension of table semantics.

Boundary-Aware Gradient Operator Network for Medical Image Segmentation.

Medical image segmentation is a crucial task in computer-aided diagnosis. Although convolutional neural networks (CNNs) have made significant progress in the field of medical image segmentation, the convolution kernels of CNNs are optimized from random initialization without explicitly encoding gradient information, leading to a lack of specificity for certain features, such as blurred boundary features. Furthermore, the frequently applied down-sampling operation also loses the fine structural features in shallow layers. Therefore, we propose a boundary-aware gradient operator network (BG-Net) for medical image segmentation, in which the gradient convolution (GConv) and the boundary-aware mechanism (BAM) modules are developed to simulate image boundary features and the remote dependencies between channels. The GConv module transforms the gradient operator into a convolutional operation that can extract gradient features; it attempts to extract more features such as images boundaries and textures, thereby fully utilizing limited input to capture more features representing boundaries. In addition, the BAM can increase the amount of global contextual information while suppressing invalid information by focusing on feature dependencies and the weight ratios between channels. Thus, the boundary perception ability of BG-Net is improved. Finally, we use a multi-modal fusion mechanism to effectively fuse lightweight gradient convolution and U-shaped branch features into a multilevel feature, enabling global dependencies and low-level spatial details to be effectively captured in a shallower manner. We conduct extensive experiments on eight datasets that broadly cover medical images to evaluate the effectiveness of the proposed BG-Net. The experimental results demonstrate that BG-Net outperforms the state-of-the-art methods, particularly those focused on boundary segmentation.

Blue carbon assessment in Avicennia marina sediments and vegetation along the Red Sea Coast of Egypt: Improving methods and insights

Mangrove forests play a crucial role in the coastal environment. A plethora of research is focusing on mangroves’ role in climate mitigation through sequestering and storing CO2. This study aims to assess the organic carbon (OC) stocks and carbon sequestration rate (CSR) of the largest stands of mangroves in the Red Sea Coast of Egypt, to evaluate their significance in climate mitigation strategies. Intensive sampling was conducted in mangrove stands of Safaga, Qulaan, and Hamata to investigate OC and sediment properties along depth intervals to 1 m deep. Additionally, OC stored in the above- and below-ground biomass was estimated using Landsat 9 OLI/TIRS satellite images. TOC content (∼0.50%) and stocks in the sediments (54.49 ± 31.71–86.00 ± 29.00 MgC/ha) were very low compared to the global average for mangrove stands, and comparable to the mangrove forest in the Middle East region. This can be attributed to the desert and semi-desert conditions, limited riverine input, and low precipitation rates. Moreover, the above-ground biomass, estimated from Normalized Difference Vegetation Index, exhibited extremely low OC storage. Therefore, the sediment acted as the main pool of organic carbon in these mangrove ecosystems. The research findings revealed that mangrove stands in Egypt exhibit low CSR, ranging from 11.94 to 18.02 g C m−2 year−1. Additionally, the study determined that the stable nature of mangrove stands in Egypt makes their annual CO2 emissions negligible. Despite local studies suggesting otherwise, it is highly unlikely that mangroves in Egypt contribute to the climate mitigation related to CO2 sequestration. Therefore, it is important to implement restoration plans to ensure the preservation of other ecological services provided by mangroves along the Red Sea Coast.

TVNeRF: Improving few-view neural volume rendering with total variation maximization

Neural Radiance Fields (NeRF) has profoundly impacted few-shot novel view synthesis and 3D reconstruction with its innovative rendering techniques and high-quality output. However, synthesizing novel views from sparse inputs remains challenging for NeRF. With limited view inputs, NeRF tends to overfit the available views, leading to reconstruction discrepancies and artifacts. To address this issue, we propose TVNeRF, which introduces two complementary regularization methods. By maximizing the total variation of unseen rays, we control the density distribution of ray sampled points to prevent excessive sampling in non-critical areas, thereby reducing artifacts and floaters. Concurrently, minimizing the accumulated opacity of unseen rays allows for the effective selection of important rays and sampled points without additional computational overhead, while also mitigating potential over-regularization effects. These combined strategies not only prevent overfitting but also achieve effects similar to the Least Absolute Shrinkage and Selection Operator (LASSO) regularization. Moreover, TVNeRF achieves these improvements without the need for pre-trained models or prior knowledge. By utilizing these straightforward yet effective regularization techniques, TVNeRF can deliver results comparable to state-of-the-art models while maintaining computational efficiency. This approach highlights that substantial improvements in NeRF performance can be realized through targeted regularization methods, effectively addressing the challenges posed by sparse view synthesis.

Sedimentary environment shift and organic matter enrichment mechanism in response to volcanic ash influence: A case study of the Permian Lucaogou Formation, Santanghu Basin, NW China

The second member of the Lucaogou Formation (P2l2) in the Tiaohu and Malang Sags of the Santanghu Basin (study area) underwent periodic volcanic activity and frequent lithological changes. This study comprehensively analyzed the organic geochemistry, mineral composition, organic matter (OM) types, volcanic cycle, and palaeoenvironment of shale in the study area. Techniques such as total organic carbon (TOC), rock pyrolysis (Rock-Eval), organic petrology, scanning electron microscopy (SEM) with energy-dispersive spectrum (EDS) analysis, X-ray diffraction (XRD), trace elements, and saturated hydrocarbon gas chromatography and mass spectrometry (GC–MS) were employed. The findings suggest that limited terrigenous input during the sedimentary period of the P2l2 led to the deposition of a distinctive mixture of volcanic ash (felsic) and carbonate (dolomite and calcite), with a low average clay mineral content of 6%. The P2l2 shale emerged as a high-quality source rock, predominantly composed of type I and II kerogens, with moderate OM maturity. The deposition environment was characterized by hot and arid conditions, high salinity, and intensive reducibility, which was favorable for algae development and conducive to OM preservation. Notably, two lamalginite types, labeled as lamalginite “A” and lamalginite “B,” were identified. Lamalginite “B”-rich shales were deposited in a hotter and drier climate compared to lamalginite “A”-rich shales. Lamalginite “B”- rich shale inexhibited high levels of C28 regular sterane and β-carotenes, distinguishing it from lamalginite “A”-rich shale. A comprehensive analysis involving organic petrology, SEM, sedimentary environment, and biomarker characteristics suggests that lamalginite “B” may be a salt-tolerant green alga, while lamalginite “A” may be a cyanobacterium. Finally, an OM enrichment model for the P2l2 shale was established.

Exploring the impact of primer length on efficient gene detection via high-throughput sequencing

Reverse transcription (RT) is a crucial step in most RNA analysis methods. Optimizing protocols for this initial stage is critical for effective target detection, particularly when working with limited input RNA. Several factors, such as the input material quality and reaction conditions, influence RT efficiency. However, the effect of RT primer length on gene detection efficiency remains largely unknown. Thus, we investigate its impact by generating RNA-seq libraries with random RT primers of 6, 12, 18, or 24 nucleotides. To our surprise, the 18mer primer shows superior efficiency in overall transcript detection compared to the commonly used 6mer primer, especially in detecting longer RNA transcripts in complex human tissue samples. This study highlights the critical role of primer length in RT efficiency, which has significant potential to benefit various transcriptomic assays, from basic research to clinical diagnostics, given the central role of RT in RNA-related analyses.

Psychologists' experience of working with people who hear voices and their views on the idea of voice simulation training: A qualitative study.

Research indicates psychologists typically lack subjective understanding of voice hearing. Voice hearing simulation training has potential to improve understanding, empathy and confidence among clinicians, but psychologists have had limited input into its development. This study investigated psychologists' and a psychotherapist's clinical experience of working with people who hear voices and their views and recommendations for voice hearing simulation training. This was a qualitative study. Clinical psychologists and one psychotherapist (N = 17) participated in semi-structured interviews. Thematic analysis was used to analyse transcribed data. The Clinical Experience theme comprised of subthemes Barriers to engagement, Diversity of voice hearing experiences, Lack of subjective understanding, Curiosity about voice hearing, Empathy for voice hearers, Using personal experiences to relate to voices, Clinical experience increases knowledge, Importance of supervision and colleague's support, Benefits of experiential training, Limited training in non-psychosis settings and Clinical experience increases confidence. The Views on Voice Hearing Simulation Training theme comprised of subthemes Improves subjective understanding, Concern about distress, Discomfort as a strength of voice hearing simulations, Artificiality of simulation, Increases empathy and Over-estimation of understanding. The Recommendations theme comprised of subthemes Discuss artificiality, Co-production, Promote voice diversity, Support staff and Maximise inclusivity. Findings indicate that clinical experience improves psychologists' confidence and knowledge, yet participants reported a lack of subjective understanding of voice hearing. Co-produced simulation training between individuals who hear voices and clinicians was anticipated to improve subjective understanding, empathy and therapeutic relationships, which could support a range of staff and improve quality of care delivered.

Class A prediction of debris flow impact forces on dual rigid and flexible barriers: MPM modelling

Modelling flow-like landslides interacting with barriers poses significant challenges, particularly to predict the impact dynamics. For the design, it is essential to assess the forces exerted by the flow, the depth and velocity of the flow during the impact, the stress and strain distribution in the barrier, and the amount of overflowing material. The direct measurement of these quantities in well-controlled flow-like landslides against monitored barriers serves as a valuable means of understanding the flow-barrier interactions, and it is instrumental in calibrating and validating numerical models. Flume tests were conducted in the Hong Kong University of Science and Technology using the 28-m-long flume for investigating the efficacy of single and dual rigid and flexible barriers. These tests were utilised as part of a “Class A prediction” exercise in May 2022, with the international research community involved. In this context a numerical model based on Material Point Method (MPM) was used to forecast the flow dynamics and the flow-barrier interactions. These predictions were made in advance, without knowledge of the outcomes to be predicted, and were based on limited input quantities such as geometry, barrier setup and material properties. The capabilities and limitations of the employed MPM numerical model are discussed.

Smart Barcelona: the gap between inspiring rhetoric and lackluster implementation in transformative approaches

ABSTRACT Barcelona is championed as the exemplary radical smart city, and yet scholarly understandings are typically limited to its theoretical approach and policy rhetoric. This paper, however, interrogates the extent to which Smart Barcelona has been implemented. It deploys a novel method of thematically analyzing 51 public talks by Barcelona’s first Chief Technology Officer, cross-examined with policy documents – to unpack the smart agenda. This agenda is then contrasted with insights into the ‘actual smart city’ implementation via interviews with 10 key informants. Findings reveal a lackluster progress including limited citizen input, ongoing reliance on ‘Big Tech’, and missing integration capacity.

Assessment of stabilizing feedback control of walking: A tutorial

Walking without falling requires stabilization of the trajectory of the body center of mass relative to the base of support. Model studies suggest that this requires active, feedback control, i.e., the nervous system must process sensory information on the state of the body to generate descending motor commands to the muscles to stabilize walking, especially in the mediolateral direction. Stabilization of bipedal gait is challenging and can be impaired in older and diseased individuals. In this tutorial, we illustrate how gait analysis can be used to assess the stabilizing feedback control of gait. We present methods ranging from those that require limited input data (e.g. position data of markers placed on the feet and pelvis only) to those that require full-body kinematics and electromyography. Analyses range from simple kinematics analyses to inverse dynamics. These methods assess stabilizing feedback control of human walking at three levels: 1) the level of center of mass movement and horizontal ground reaction forces, 2) the level of center of mass movement and foot placement and 3) the level of center of mass movement and the joint moments or muscle activity. We show how these can be calculated and provide a GitHub repository (https://github.com/VU-HMS/Tutorial-stabilizing-walking) which contains open access Matlab and Python code to calculate these. Finally, we discuss what information on feedback control can be learned from each of these.

Sourdough fermentation for the valorization of sorghum flour: Microbiota characterization and metabolome profiling

Due to a large adaptability to different cultivation conditions and limited input compared to other cereals, sorghum is considered an emerging crop. Its antioxidant properties, high fiber content and low glycemic index also make it a valuable addition to a healthy diet, nevertheless, the presence of antinutritional factors and the lack of gluten, hamper its use as food ingredient. This study investigated the impact of sourdough fermentation on sorghum nutritional quality. Lactic acid bacteria dominating sorghum flour and sourdough were identified by culture-dependent analysis revealing Lactiplantibacillus plantarum as the dominant species found in the mature sourdough, whereas Weissella cibaria and Weissella paramesenteroides were the species isolated the most after the first refreshment. Among yeasts, Saccharomyces cerevisiae was the most prevalent. Lactic acid bacteria pro-technological and functional performances as starter were evaluated in sorghum type-II sourdoughs through an integrated characterization combining chromatographic and NMR spectroscopic techniques. The metabolic profile of the strains mainly grouped together W. cibaria strains and W. paramesenteroides AI7 which distinguished for the intense proteolysis but also for the presence of compounds particularly interesting from a physiological perspective (allantoin, glutathione, γ-aminobutyric acid and 2-hydroxy-3-methylbutyric acid), whose concentration increased during fermentation in a species or strain specific matter.

Sensorimotor Simulation's Influence on Stress: EEG and Autonomic Responses in Digital Interviews.

This study explored the role of sensorimotor simulation in modulating the stress response in individuals exposed to stressful digital simulated interviews. Participants were assigned to two different versions of a Digital Social Stress Test: a simulated version with a dynamic-realistic examining committee (Dyn-DSST) and a version with a static examining committee (Stat-DSST). During interview preparation, behavioral indices reflecting stress regulation and resistance, response times, and electroencephalographic (EEG) and autonomic indices were collected. Higher regulation scores were found for the Stat-DSST group compared to the Dyn-DSST group, probably induced by the presence of limited external sensory input in time and space, perceived as less stressful. The EEG results revealed a distinct contribution of the low- and high-frequency bands for both groups. Dyn-DSST required greater cognitive regulation effort due to the presence of a continuous flow of information, which can enhance sensory and motor activation in the brain. The SCR increased in the Dyn-DSST group compared to the Stat-DSST group, reflecting greater emotional involvement in the Dyn-DSST group and reduced sensory stimulation in the static version. In conclusion, the results suggest that sensorimotor simulation impacts the stress response differently in dynamic interviews compared to static ones, with distinct profiles based on behavioral, EEG, and autonomic measures.

People working with nature: a theoretical perspective on the co-production of Nature’s Contributions to People

ABSTRACT The co-production of Nature’s Contributions to People (NCP) is a set of processes in which anthropogenic inputs (i.e. material or non-material actions and the assets supporting these actions) and natural inputs (i.e. ecological structures and processes) interact to produce NCP. An interdisciplinary understanding of NCP co-production can support decision-making on ecosystem management or NCP use, given natural constraints, limited human inputs, possible adverse effects and trade-offs arising from co-production. In this paper, we show that mechanisms of co-production at the ecosystem level and the NCP flow level are fundamentally different. At the level of ecosystems, people manage natural structures and processes to influence the production of potential NCP (e.g. via planting, restoring, fertilizing). At this level, anthropogenic inputs can partially substitute for natural inputs, but natural inputs are necessary whereas anthropogenic inputs are not. At the level of flows, co-production actions convert potential NCP into realized NCP and quality of life (e.g. via harvesting, transporting, transforming, consuming, and appreciating NCP). At this level, anthropogenic inputs are complementary to natural inputs, although some substitutability can occur at the margin. Analysing the substitutability and complementarity between natural and anthropogenic capitals, as well as the adverse effects or mutual enhancement between them, is crucial for informed decision-making on landscape and NCP management. This understanding enables the identification of strategies that can ensure NCP supply and increase human well-being in a sustainable manner.

Image reconstruction for electrostatic tomography with deep convolutional neural network

Electrostatic tomography (EST) has the characteristics of being radiation-free and high real-time imaging performance. However, due to its passive measurement method, EST only provides limited input data equal to the number of electrode plates, which results in more serious ill-posed inverse problem. As a result, the imaging accuracy of traditional algorithms is low. To address this problem, an improved deep learning model is proposed for image reconstruction of EST, which is based on a convolutional neural network (CNN). The proposed network is built upon the LeNet-5 deep network model, which incorporates two convolution layers to extract data features and two maximum pooling layers to perform dimension reduction. Furthermore, the LeakyReLU activation function is employed to solve the gradient vanishing problem and accelerate the training process by introducing the non-zero gradients. By constructing an EST model, 8 representative distributions were simulated and 15,930 samples were generated. After 250 iterations, more accurate reconstructed images were generated. To verify the noise immunity of the network model, Gaussian white noises of 20 dB, 30 dB, and 50 dB were added to the test set, and the proposed model could still accurately reconstruct the positions and shapes of the samples. The network is further compared with the traditional algorithms, which reconstructs better results. In addition, the reconstruction results by the proposed model are still relatively accurate for several simulated random samples of different number, shapes and positions. Finally, a 16-electrode sensor array experimental system is designed. The experimental results show that the improved CNN algorithm has a better imaging effect than the traditional algorithms and the CNN algorithm, which is consistent with the simulation results, proving that the model has good generalization ability.

The limited effect of deforestation on stabilized subsoil organic carbon in a subtropical catchment

Abstract. Predicting the quantity of soil organic carbon (SOC) requires understanding how different factors control the amount of SOC. Land use has a major influence on the function of the soil as a carbon sink, as shown by substantial organic carbon (OC) losses from the soil upon deforestation. However, predicting the degree to which land use change affects the OC content in soils and the depth down to which this occurs requires context-specific information related to, for example, climate, geochemistry, and land use history. In this study, 266 samples from forests and agricultural fields were collected from 94 soil profiles down to 300 cm depth in a subtropical region (Arvorezinha, southern Brazil) to study the impact of land use on the amount of stabilized OC along the soil profile. We found that the stabilized OC content was not affected by land use below a depth of 90 cm. Along the soil profile, the amount of stabilized OC was predominantly controlled by land use and depth in addition to the silt and clay content and aluminium ion concentrations. Below 100 cm, none of the soil profiles reached a concentration of stabilized OC above 50 % of the stabilized OC saturation point (i.e. the maximum OC concentration that can physically be stabilized in these soils). Based on these results, we argue that it is unlikely that deeper soil layers can serve as an OC sink over a timescale relevant to global climate change due to the limited OC input in these deeper layers. Furthermore, we found that the soil weathering degree was not a relevant control on the amount of stabilized OC in our profiles because of the high weathering degree of the studied soils. It is therefore suggested that, while the soil weathering degree might be an effective controlling factor of OC stabilization over a large spatial scale, it is not an informative measure for this process at regional and local scales (with similar climate, bedrock, and weathering history) in highly weathered soils.

Multivariate formulation to predict the frictional strength of fiber reinforced soils using gene expression programming

Modeling and prediction approaches have attracted much interest in recent years to evaluate the efficiency of fiber reinforcement in improving the strength characteristics of loose soils. Several analytical and artificial intelligence (AI) based models have been presented to estimate friction angle of fiber reinforced soils (FRS). However, due to limited input characteristics, these analytical models lack accuracy, whereas AI-based models are developed on advanced programming methods and need substantial programming expertise, resulting in a lack of agreement in mechanism and application. Herein, we provided a multivariate formulation that uses gene expression programming (GEP) to predict the frictional strength of FRS using fifteen influencing factors, which, to the best of our knowledge, has never been reported before. In this regard, two mathematical models are presented, one of which incorporates both qualitative and quantitative input factors, while the other just considers quantitative variables. Laboratory experimental data on FRS from various sources was assessed and properly analysed within the proposed framework while taking into account various affecting factors. The results illustrate the efficiency of GEP in estimating FRS frictional strength and emphasis the need of considering several parameters for prediction. Sensitivity and parametric studies were also performed to identify the relevance of each influencing factor and to determine whether the prediction models meet physical processes or are purely empirical relations. Finally, a user-friendly computer program was developed based on the GEP derived mathematical formulations to anticipate the FRS friction angle, which offers practitioners great field applicability.