Variable Slope Research Articles

Influence of atlas-choice on age and time effects in large-scale brain networks in the context of healthy aging

Abstract Introduction: There is accumulating cross-sectional evidence of decreased within-network resting-state functional connectivity (RSFC) and increased between-network RSFC when comparing older to younger samples, but results from longitudinal studies with healthy aging samples are sparse and less consistent. Some of the variability might occur due to differences in network definition and the fact that most atlases were trained on young adult samples. Applying these atlases to older cohorts implies the generalizability of network definitions to older individuals. However, because age is linked to a less segregated network architecture, this assumption might not be valid. To account for this, the Atlas55+ (A55) was recently published. The A55 was trained on a sample of people over the age of 55, making the network solutions suitable for studies on the aging process. Here, we want to compare the A55 to the popular Yeo-Krienen atlas to investigate whether and to what extent differences in network definition influence longitudinal changes of RSFC. For this purpose, the following networks were investigated: the occipital network (ON, “visual network”), the pericentral network (PN, “somatomotor network”), the medial frontoparietal network (M-FPN, “default network”), the lateral frontoparietal network (L-FPN, “control network”), and the midcingulo-insular network (M-CIN, “salience network”). Methods: Analyses were performed using longitudinal data from cognitively healthy older adults (N = 228, mean age at baseline = 70.8 years) with five measurement points over 7 years. To define the five networks, we used different variants of the two atlases. The spatial overlap of the networks was quantified using the dice similarity coefficient (DSC). RSFC trajectories within networks were estimated with latent growth curve models. Models of varying complexity were calculated, ranging from a linear model without interindividual variability in intercept and slope to a quadratic model with variability in intercept and slope. In addition, regressions were calculated in the models to explain the potential variance in the latent factors by baseline age, sex, and education. Finally, the regional homogeneity and the silhouette coefficient were computed, and the spin test and Wilcoxon-Mann-Whitney test were used to evaluate how well the atlases fit the data. Results: Median DSC across all comparisons was 0.67 (range: 0.20–0.93). The spatial overlap was higher for primary processing networks in comparison to higher-order networks and for intra-atlas comparisons versus inter-atlas comparisons. Three networks (ON, PN, M-FPN) showed convergent shapes of trajectories (linear vs. quadratic), whereas the other two networks (L-FPN, M-CIN) showed differences in change over time depending on the atlas used. The 95% confidence intervals of the estimated time and age effects overlapped in most cases, so that differences were mainly evident regarding the p-value. The evaluation of the fit of the atlases to the data indicates that the Yeo-Krienen atlas is more suitable for our dataset, although it was not trained on a sample of older individuals. Conclusions: The atlas choice affects the estimated average RSFC in some networks, which highlights the importance of this methodological decision for future studies and calls for careful interpretation of already published results. Ultimately, there is no standard about how to operationalize networks. However, future studies may use and compare multiple atlases to assess the impact of network definition on outcomes. Ideally, the fit of the atlases to the data should be assessed, and heuristics such as “similar age range” or “frequently used” should be avoided when selecting atlases. Further, the validity of the networks should be evaluated by computing their associations with behavioral measures.

Plasma drug screening using paper spray mass spectrometry with integrated solid phase extraction.

Drug overdoses have risen dramatically in recent years. We developed a simple nontargeted method using a disposable paper spray cartridge with an integrated solid phase extraction column. This method was used to screen for ~160 fentanyl analogs, synthetic cannabinoids, other synthetic drugs, and traditional drugs of abuse in over 300 authentic overdose samples collected at emergency departments in Indianapolis. A solid phase extraction step was implemented on the paper spray cartridge to enable subnanograms per milliliter synthetic drugs screening in plasma. Analysis was performed on a quadrupole orbitrap mass spectrometer using the sequential window acquisition of all theoretical fragment ion spectra approach in which tandem mass spectrometry was performed using 7 m/z isolation windows in the quadrupole. Calibration curves with isotopically labeled internal standards were constructed for 35 of the most frequently encountered synthetic and traditional illicit drugs by US toxicology labs. Additional qualitative-only drugs in a suspect screening list were also included. Limits of detection in plasma for synthetic cannabinoids ranged from 0.1 to 0.5 and 0.1 to 0.3ng/mL for fentanyl and its analogs and between 1 and 5ng/mL for most other drugs. Relative matrix effects were evaluated by determining the variation of the calibration slope in 10 different lots of biofluid and found to be between 3% and 20%. The method was validated on authentic overdose samples collected from two emergency departments in Indianapolis, Indiana, from suspected or known overdoses. Commonly detected synthetic drugs included fentanyl related substances, designer benzodiazepines such as flubromazolam, and the synthetic cannabinoid 5F-PB-22.

Detecting Shoreline Changes on the Beaches of Hainan Island (China) for the Period 2013–2023 Using Multi-Source Data

This study presents an in-depth analysis of the dynamic beach landscapes of Hainan Island, which is located at the southernmost tip of China. Home to over a hundred natural and predominantly sandy beaches, Hainan Island confronts significant challenges posed by frequent marine natural disasters and human activities. Addressing the urgent need for long-term studies of beach dynamics, this research involved the use of CoastSat to extract and analyze shoreline data from 20 representative beaches and calculate the slopes of 119 sandy beaches around the island for the period from 2013 to 2023. The objective was to delineate the patterns of beach evolution that contribute to the prevention of sediment loss, the mitigation of coastal hazards, and the promotion of sustainable coastal zone management. By employing multi-source remote sensing imagery and the CoastSat tool, this investigation validated slope measurements across selected beaches, demonstrating consistency between the calculated and actual distances despite minor anomalies. The effective use of the finite element solution (FES) in the 2014 global tidal model for tidal corrections further aligned the coastlines with the mean shoreline, underscoring CoastSat’s utility in enabling precise coastal studies. The analysis revealed significant seasonal variations in shoreline positions, with approximately half of the monitored sites showing a seaward progression in summer and a retreat in winter, which were linked to variations in wave height. The southern beaches exhibited distinct seasonal variations, which contrasted with the general trend due to differing wave impacts. The western and southern shores showed erosion, while the northern and eastern shores displayed accretion. The calculated slopes across the island indicated that the southern beaches had steeper slopes, while the northern areas exhibited more pronounced slope variations due to wave and tidal impacts. These findings highlight the critical role of integrated coastal management and erosion control strategies in safeguarding Hainan Island’s beaches. By understanding the mechanisms driving seasonal and regional shoreline changes, effective measures can be developed to mitigate the impacts of erosion and enhance the resilience of coastal ecosystems amidst changing environmental conditions. This research provides a foundational basis for future efforts aimed at the sustainable development and utilization of coastal resources on Hainan Island.

Iot device for monitoring the tilt of containment in basket gabions

In the present work, an IoT (Internet of Things) electronic device was developed, used, and evaluated to monitor and measure the inclination of a containment structure executed in box gabions. This device collected information from an accelerometer, sending it over the internet to a cloud server, where this data was stored, allowing access to the history of the slope variation. The readings collected by the equipment made it possible to correlate its variations to the executive phases of the containment and to conduct statistical analyses to verify whether the variations captured by the sensor consisted of a possible lack of containment.

Jovian Magnetosheath Turbulence Driven by Whistler

Jupiter’s magnetosheath is a natural yet complex laboratory for analyzing compressible plasma turbulence. Recent observations by the Juno mission provide a promising opportunity for the first time to reckon the energy cascade rate in the magnetohydrodynamic scales in the vicinity of Jupiter’s space. In the present work, a two-dimensional model is constructed for a whistler wave that is nonlinearly coupled with a wave magnetic field via ion density perturbation. The dynamics of whistler wave propagating in the direction of the magnetic field are derived within the limit of the two-fluid modeling approach. The magnetic field localization along with magnetic field spectra and spectral slope variations are estimated to realize the turbulence generation and energy cascade from large to small scales in the Jovian magnetosheath region. The simulated magnetic field spectrum in the wave number (in the unit of ion inertial length ρ i ) consists of turbulence in the inertial range with a spectral slope of −1.4 and a spectral knee at k ρ i = 1. Subsequently, the spectral slope increases to −2.6 and the spectrum becomes steeper. The simulated magnetic field spectrum in the wave number is further translated into the frequency domain using the whistler wave dispersion relation and by considering the Taylor frozen-in condition. The analytically estimated magnetic field spectrum slopes, i.e., −1.8 and −4.2 at low and high frequencies are further compared with recent Juno mission observations. The comparison further affirms the existence of Kolmogorov scaling, a spectral knee, and steepening in the spectrum at high frequencies. Furthermore, it is found that the two-fluid model can reasonably simulate the turbulence effects in Jovian magnetosheath in terms of magnetic field spectral distribution in wave number and frequency domains.

Towards healthy cities: Modeling restorative potential of urban environments by coupling LiDAR-derived 3D metrics with panorama-based online survey

With the growing concern for the psychological well-being of urban dwellers, there has been a recognized need to explore the role of green environments in facilitating mental restorative benefits from the demands of city life. However, a significant gap exists in quantitatively modeling the restorative potential of urban environments, especially in terms of comprehensive 3D characteristics beyond mere greenery. This research gap has hindered our understanding of how to plan and design urban spaces that promote mental health. To bridge this gap, we conducted a study using Singapore as a case study to model the restorative potential for landscape planning and design by integrating LiDAR-derived 3D metrics, outcomes of an online survey, and a machine learning-based regularized regression model. We first collected data from 100 scenes, including both residential neighborhoods and urban parks, by extracting point cloud information from LiDAR scans for calculating a diverse set of 41 3D spatial metrics. Alongside these objective measurements, we conducted an online survey (N = 1500) using the Perceived Restorativeness Scale (PRS) where respondents evaluated the restorative potential of these scenes presented as interactive panoramic images. We subsequently utilized a regularized regression technique based on machine learning by conducting K-fold cross-validation (K = 10) with 1251 tests to identify the feature metrics significantly contributing to restorative potential. Our findings highlighted the importance of the green volumetric ratio as the most significant positive factor for restorative potential. Additionally, the results on the metrics of grey horizontal diversity and grey cluster number indicated a positive impact of unregulated and unaggregated grey components on restorative potential. The results on green horizontal diversity suggested that a uniform and concentrated spatial arrangement of greenery is a positive predictor, while other factors like front-background ratio, colorfulness, and slope variation also positively influenced restorative potential. In essence, our study not only provides novel insights but also introduces innovative methods for quantitatively assessing and characterizing the quality of urban environments that enhance psychological well-being. These findings inform the practical planning and design strategies aimed at fostering healthy and sustainable urban spaces.

Abstract 935: Co-clinical endometrial cancer (EC) patient-derived organoids (PDO) to assess functional effects of replication stress (RS)-targeting agents

Abstract Background: EC is the most commonly diagnosed gynecologic cancer in the US and has both rising incidence and mortality. Uterine serous cancer (USC) and uterine carcinosarcoma (UCS) are high grade ECs characterized by TP53 mutation and molecular alterations suggesting high replication stress (RS) and cell cycle dysregulation. These molecular alterations could engender sensitivity to RS-targeting strategies such as WEE1 inhibition. In a single arm phase 2 of 35 patients, the WEE1 inhibitor adavosertib demonstrated clinical activity in recurrent USC, with an overall response rate of 29.4%. To support the development of predictive biomarkers of sensitivity to RS targeting agents, we report on the establishment of co-clinical EC PDOs from pre- and on-treatment biopsies from patients with USC or UCS enrolled to a translational trial of adavosertib. Methods: Patients with USC or UCS consented for trial participation underwent pre- and on-treatment biopsies while receiving adavosertib monotherapy. Biopsies were dissociated and passaged as 3D organoid cultures in Matrigel in an optimized media. Established PDOs were seeded in 3D microfluidic devices, treated with adavosertib in a dose-dependent manner and assessed for viability after 6 days of treatment by dual labeling live/dead imaging. IC50 values were calculated using log(inhibitor) vs response - variable slope (four parameters) model in GraphPad Prism. To assess the effect of adavosertib on replication fork speed, DNA fiber assays were conducted with PDOs treated with adavosertib for 72 hours. Finally, FFPE blocks were created from PDOs for assessment of DNA damage repair biomarkers by IHC. Results: 19 biopsies were obtained from 11 participating patients. 6 biopsies did not yield sufficient tumor cells for PDO generation. From the remaining 13 samples, 5 PDOs were successfully established (3 USC and 2 UCS), with a higher success rate when 3 biopsy cores were received (3 of 4; 75%) compared to when 2 biopsy cores were received (2 of 9; 22 %). Dual labeling live/dead imaging of PDOs treated with adavosertib resulted in IC50 values ranging from 113nM to 856nM. DNA fiber assays revealed consistent decrease in fork speed with adavosertib exposure, consistent with the hypothesized effect of adavosertib in increasing RS in these cells. A dose dependent effect on replication fork speed was observed, with greater decreases in fork speed with increasing concentrations of adavosertib. Due to the limited number of samples and the number of PDOs generated from on-treatment as opposed to pre-treatment biopsies, clinical correlation between PDO adavosertib sensitivity and clinical activity was not formally assessed. IHC analysis of potential DNA damage biomarkers is ongoing. Conclusions: We describe the successful establishment of co-clinical EC PDOs for functional evaluation of sensitivity to RS targeting agents. Citation Format: Elena Ivanova, Aisha L. Saldanha, Shrabasti Roychoudhury, Bose Kochupurakkal, Ari P. Zlota, Clare E. Padrick, Ha V. Vo, Eli K. Greenberg, Hannah Sawyer, Carina Feeney, Courtney H. Qi, Swati Narayan, Jennifer D. Curtis, Nabihah Tayob, Marisa R. Nucci, Panagiotis Konstantinopoulos, Dipanjan Chowdhury, Geoffrey Shapiro, Cloud P. Paweletz, Ursula A. Matulonis, Joyce F. Liu. Co-clinical endometrial cancer (EC) patient-derived organoids (PDO) to assess functional effects of replication stress (RS)-targeting agents [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 935.

Experimental and numerical study of the effect of particle size distribution on hydrocyclone classification

Classification by particle size using hydrocyclones is a fundamental step in various industries, yet the influence of material properties remains an area of limited understanding. This paper presents a comprehensive experimental and numerical investigation to illuminate the complex relationship between the feed particle size distribution (PSD) and the classification process. The feed PSD is characterised by two crucial parameters: the median size (d50f) and the distribution coefficient (σJ), derived from Johnson’s SB function. The results reveal non-monotonic variations in cut size and curve slope during physical tests where the two parameters are not separated. With one parameter held constant in numerical tests, an increase in d50f generally leads to a decrease in pressure drop but an increase in water split, while σJ exhibits an opposite trend. The interplay between the two parameters and their effects on cut size and cut sharpness is intricate, primarily determined by the characteristics of the flow field and the relative sizes of the cut size and d50f in the feed. These findings underscore the critical role of feed material properties in operating hydrocyclones for fine particle classification.

Design of Runge-Kutta optimization for fractional input nonlinear autoregressive exogenous system identification with key-term separation

Population-based metaheuristic algorithms have gained significant attention in research community due to its effectiveness in solving complex optimization problems in diverse fields. In this study, knacks of population-based Runge-Kutta optimizer (RUN) are exploited for the identification of fractional input non-linear exogenous auto-regressive (FINARX) system with key term separation. The fractional order calculus operator of the Grünwald-Letnikov derivative is exploited to develop FINARX from a conventional non-linear auto-regressive exogenous system. The identification scheme for FINARX model is implemented through a mean-square-error-based fitness function. RUN utilizes the slope variations calculated by the well-known Runge-Kutta method for an effective search mechanism in the exploration and exploitation phases. Moreover, an enhanced solution quality mechanism is employed for speedy convergence and keeping the movement toward the best solution by escaping the local optima. The robustness of the algorithm is analyzed by multiple variations of non-linearity as well as different noise scenarios. The performance of the RUN to identify the FINARX system is validated in terms of convergence rate, fitness value, robustness, and accuracy in weight estimation. The effectiveness of the RUN is further assessed through exhaustive simulations with their statistics as well as comparison with the standard recent counterparts, including the Whale optimization algorithm, Reptile Search algorithm, and Aquila optimizer on different performance indices for the FINARX system.

An enhanced ant colony optimization algorithm for global path planning of deep-sea mining vehicles

Due to the complex variations in slope within deep-sea mining areas, effective path planning for mining vehicle operations is crucial for minimizing energy consumption. However, traditional ant colony algorithms (ACO) neglect the effect of a terrain slope in mining areas. Additionally, these algorithms exhibit limitations such as slow convergence and susceptibility to local optima. To address these issues, this study proposes an enhanced ant colony algorithm, called DYACO, for mining vehicle path optimization. This algorithm dynamically adjusts heuristic information, pheromone volatilization factor, pheromone update strategy, and state transition probability during the iterative process to enhance traditional ACO. Simulation experiments were conducted to comprehensively assess the proposed model, revealing that DYACO not only generates optimal solutions but also demonstrates significant advantages in terms of convergence speed and turning times. Furthermore, DYACO converts the time required for mining vehicles to traverse different slope regions into distances, then incorporating slope effects to path planning for deep-sea mining vehicles. In comparison to ACO, DYACO achieves a 15.3% reduction in the length of an optimal path and a 70.0% decrease in the number of turn times.

The Effect of a New Generation of Ankle Foot Orthoses on Sloped Walking in Children with Hemiplegia Using the Gait Real Time Analysis Interactive Lab (GRAIL).

Cerebral palsy poses challenges in walking, necessitating ankle foot orthoses (AFOs) for stability. Gait analysis, particularly on slopes, is crucial for effective AFO assessment. The study aimed to compare the performance of commercially available AFOs with a new sports-specific AFO in children with hemiplegic cerebral palsy and to assess the effects of varying slopes on gait. Eighteen participants, aged 6-11, with hemiplegia, underwent gait analysis using GRAIL technology. Two AFO types were tested on slopes (uphill +10 deg, downhill -5 deg, level-ground). Kinematic, kinetic, and spatiotemporal parameters were analyzed. The new AFO contributed to significant changes in ankle dorsi-plantar-flexion, foot progression, and trunk and hip rotation during downhill walking. Additionally, the new AFO had varied effects on spatiotemporal gait parameters, with an increased stride length during downhill walking. Slope variations significantly influenced the kinematics and kinetics. This study provides valuable insights into AFO effectiveness and the impact of slopes on gait in hemiplegic cerebral palsy. The findings underscore the need for personalized interventions, considering environmental factors, and enhancing clinical and research approaches for improving mobility in cerebral palsy.

Exploring nearshore bed dynamics of a mixed beach using the depth of closure conceptual model

Mixed sediment beaches are globally commonplace, yet little is understood of the extent and behaviour of their nearshore zones, potentially underestimating total cross-shore change. This paper is the first study to investigate the lateral and vertical extent of the active zone of the gravel-rich mixed beach in Pevensey Bay, a study site on the South East UK coastline. Morphodynamic change in the nearshore zone was studied at a range of timescales (days, months, years) suggesting that the width of the active nearshore zone correlated with the magnitude of the peak morphological change, whilst the depth of closure was influenced by bed slope, grain size and local variation in wave conditions. A conceptual model detailing the physical parameters responsible for local variations in the depth of closure was used to help understand differences between the observed and predicted depths of closure. Finally, ongoing chronic loss of sediment from below Mean Sea Level (MSL) was examined, which was shown to be independent of the depth of closure, but closely linked to the wider geomorphic setting of the bay.

Living with landslides: Land use on unstable hillslopes in a rural tropical mountainous environment in DR Congo

Landslides are processes that naturally occur on numerous hillslopes across the world. In inhabited regions, landslides are commonly seen as a threat and a land degradation process. Yet, in densely-populated rural mountainous regions in the tropics, local communities have often no choice but to live on steep terrains naturally impacted by landslides. Besides, landslides may also be a source of opportunities for these communities. However, little is known on the rationale underlying land use in landslides. The aim of this study was to assess the extent, modes of valorization and degree of satisfaction of famers exploiting landslides in a populated rural mountainous environment of DR Congo (territory of Kalehe). We interviewed 82 farmers living on 57 representative landslides, these mass movements having been selected according to their characteristics (size, type) and position along the hillslopes and taking into account accessibility or safety constraints. We show that almost all landslides are being exploited by farmers and that they adapt their land use to the type of landslide. Indeed, significant contrasts are observed between landslides and the surrounding hillslopes for subsistence crops, forests, eucalyptus plantations and pasture. Farmers also adapt land use according to local variations in slope or wetness within a given landslide. Nearly half of the farmers reported that their land was more valuable inside than outside landslides. Better soil fertility, higher soil moisture, lower sand or stone content, lower slopes are some of the main factors that increase the land value, offering more favorable conditions for cropping than on land outside landslides. Despite the perceived risk of landsliding, famers settlement on unstable slopes appears justified by the immediacy of the benefits that outweigh the potential dangers. Better understanding the reasons for the settlement of populations on unstable slopes may help devise better risk reduction strategies.

Multimodel ensemble estimation of Landsat-like global terrestrial latent heat flux using a generalized deep CNN-LSTM integration algorithm

Accurate estimates of high-spatial-resolution global terrestrial latent heat flux (LE) from Landsat data are crucial for many basic and applied research. Yet current Landsat-derived LE products were developed using single algorithm with large uncertainties and discrepancies. Here we proposed a convolutional neural network-long short-term memory (CNN-LSTM)-based integrated LE (CNN-LSTM-ILE) framework that integrates five Landsat-derived physical LE algorithms, topography-related variables (elevation, slope and aspect) and eddy covariance (EC) observations to estimate 30-m global terrestrial LE. CNN-LSTM-ILE not only conserves good performance of LE estimation from pure deep learning (DL) algorithm, but partially inherits physical mechanism of the individual physical algorithms for improving the generalization of the integration algorithms for extreme cases. CNN-LSTM is an algorithm that combines two deep learning structures (CNN and LSTM) to effectively utilize the spatial and temporal information contained in the forcing inputs. The data were collected from 190 globally distributed EC observations from 2000 to 2015 and were provided by FLUXNET. The cross-validation results indicated that the CNN-LSTM integration algorithm improved the LE estimates by reducing the root mean square error (RMSE) of 5–8 W/m2 and increasing Kling-Gupta efficiency (KGE) of 0.05–0.16 when compared with the individual LE algorithms and the results of three other machine learning integration algorithms (multiple linear regression, MLR; random forest, RF; and deep neural networks, DNN). The CNN-LSTM integration algorithm had highest KGE (0.81) and R2 (0.66) compared to ground-measured and was applied to generate the Landsat-like regional and global terrestrial LE. An innovation of our strategy is that the CNN-LSTM-ILE model integrates pixel proximity effects and daily LE variations to enhance the accuracy of 16-day LE estimations. This approach can produce a more reliable Landsat-like global terrestrial LE product to improve the representativeness of heterogeneous regions for monitoring hydrological variables.

Effects of root traits on soil detachment capacity driven by farmland abandonment

Vegetation restoration significantly affected root traits during farmland abandonment, thereby considerably altering the soil detachment process. However, few studies have investigated the effects of root traits on soil detachment capacity under different ages of farmland abandonment. To examine the effects of root trait changes caused by vegetation restoration on soil detachment capacity by overland flow (Dc) and soil resistance to erosion under different ages of farmland abandonment, flow scouring tests under five shear stress levels (2.80–12.95 Pa) were conducted on 125 undisturbed soil samples collected using an indoor variable slope flume. The results showed that, compared to slope farmland, the root traits of abandoned farmland (5, 12, 24, and 36 years) increased considerably, and the Dc and erodibility decreased by 51.3–93.9 % and 61.8–94.1 %, respectively. The critical shear stress of abandoned farmland gradually increased with the age of farmland abandonment, but remained less than that of slope farmland. Among hydraulic parameters, stream power was the optimal index for predicting Dc, while among root morphological traits, root surface area density (RSAD) was the best indicator for predicting Dc. The median soil particle size (D50) and cohesion are important factors that affect soil detachment. According to the Water Erosion Prediction Project (WEPP) model, Dc can be predicted using the RSAD and soil cohesion (R2 = 0.95, NSE = 0.95). In addition, a new model was established to estimate the soil detachment capacity based on D50 (R2 = 0.97, NSE = 0.97). In the present study, the performance of the model was improved compared with that of previous studies.

Deciphering the effect of variation in slope on flow characteristics in a vertical slot fishway

The effects of a vertical slot fishway slope (with slope values from 1.5% to 6%) on a flow field are numerically investigated, using a re-normalization group k – ε model. The distribution of the velocity, turbulence kinetic energy (TKE), average energy dissipation rate per unit volume (E), and vorticity for different slopes are systemically explored. The results indicate that, with an increase in slope, the appearance of downward flow in conjunction with an increase in vertical velocity results in three-dimensional flow characteristics. The recirculation region in Hs, at a 6.0% slope, was 20.9% less than that at a 1.5% slope. Meanwhile, the flow velocity in the vertical slot region grew with increasing slope, which would limit the passage of fish with burst speed lower than the velocity in the vertical slot region. The TKE and E may locally exceed the threshold at larger slopes. Furthermore, vorticity distribution shows little variability with increasing slope, but may interfere with the equilibrium of the fish in the vertical slot region. In addition, the change in water level has little effect on the flow field, which is changed by the increase in slope. These findings can aid vertical slot fishway designs especially in terms of the efficiency of fish passage.

Achievement of dynamic tablet defect detection mechanism using biaxial slope symmetry algorithm

An inspection in tablet appearance integrity before bottling is regarded as a routine task in a pharmaceutical factory. Although some methods such as automated optical instrument, video or artificial intelligence (AI) are currently available in industry, it usually pays for a complex computational process as well as high cost. Based on the symmetry of tablet appearance in reality, this study develops a biaxial scanning slope symmetry algorithm to realize a dynamic real-time tablet defect detection with a simple arithmetic operation. First, the tablet is discretely scanned using image sensor in two axes, i.e. X and Y directions, simultaneously. Second, the analogy output signals generated from the sensor during the scanning process is discretely digitized and stored in an array. Third, the coordinate of center point in the series data array is identified from every line scanning. Fourth, every section slope between two nearby center points from the first to last lines is formulated and calculated sequentially. Finally, the square mean error (SME) is used to evaluate the shape defect situation according to all accumulated errors from every slope variation. The experimental results verify that the proposed algorithm can achieve both fast and accurate detection performance.

Local projections in unstable environments

This paper develops a local projection estimator for estimating impulse responses in the presence of time variation. Importantly, we allow local instabilities in both slope coefficients and variances. Monte Carlo simulations illustrate that the method performs well in practice. Using our proposed estimator, we shed new light on the effects of fiscal policy shocks and the size of government spending multipliers. Our analysis uncovers the existence of instabilities that were unaccounted for in previous studies, and links time variation in the multipliers to the size of government debt.

Rainfall-Induced Probability of Failure in Spatially Variable Soil Slopes and a Case Study of the Konkan Railway Slope Failure

Rainfall-Induced Probability of Failure in Spatially Variable Soil Slopes and a Case Study of the Konkan Railway Slope Failure

Digital solutions in the determination of bearing capacity for straight and stepped mat foundations on sloping ground

On sloped sites where a mat foundation is to be used, because of the limited bearing capacity of the ground, it is necessary to cut into the sloped surface to provide a level base for the mat foundation. To reduce the amount of excavation and backfill a stepped mat may be used down the slope. Traditionally, determining the bearing capacity values for spread foundations on sloping ground has been a complex and time-consuming process. Several factors must be considered, including various variables (soil type, soil profile, slope angle and geometrical configuration of the infrastructure). The digital solution used in this study involves the use of advanced software that takes into account various input parameters and performs complex calculations to determine the bearing capacity of the foundation on horizontal and sloped ground. For this research, the analyses were done for three different slope angles (β = 5°, 10°, 15°) and a comparison is made between the bearing capacity of the ground for straight and stepped mat foundations using both analytical methods and finite element method.