Miracle-dz/UniFAR-FTUs huggingface dataset:
APP: A* Post-Processing Algorithm for Robots With Bidirectional Shortcut and Path Perturbation

Paths generated by A* and other graph-search-based planners are widely used in the robotic field. Due to the restricted node-expansion directions, the resulting paths are usually not the shortest. Besides, unnecessary heading changes, or zig-zag patterns, exist even when no obstacle is nearby, which is inconsistent with the human intuition that the path segments should be straight in wide-open space due to the absence of obstacles. This article puts forward a general and systematic post-processing algorithm for A* and other graph-search-based planners. The A* post-processing algorithm, called APP, is developed based on the costmap, which is widely used in commercial service robots. First, a bidirectional vertices reduction algorithm is proposed to tackle the asymmetry of the path and the environments. During the forward and backward vertices reduction, a thorough shortcut strategy is put forward to improve the path-shortening performance and avoid unnecessary heading changes. Second, an iterative path perturbation algorithm is adopted to locally reduce the number of unnecessary heading changes and improve the path smoothness. Comparative experiments are then carried out to validate the superiority of the proposed method. Quantitative performance indexes show that APP outperforms the existing methods in planning time, path length as well as the number of unnecessary heading changes. Finally, field navigation experiments are carried out to verify the practicability of APP.
Understanding Women’s Knowledge, Awareness, and Perceptions of STIs/STDs in Asia: A Scoping Review

Objective: This study aimed to conduct a scoping review to collect current literature on the knowledge, awareness, and perception (KAP) of sexually transmitted infections/diseases (STIs/STDs) among women in Asia. Methodology: The PRISMA-Scoping methodology was used in this study to extract papers from four databases published between 2018 and 2022. Sixty-eight articles were included after screening and elimination. Results: The studies on KAP of STIs/STDs among women were largely undertaken in Southeast Asia (Indonesia, Malaysia, and Vietnam) and South Asia (India, Pakistan, and Bangladesh). Regardless of the specific cohort of women studied, research indicates consistently low levels of knowledge and awareness across Asia. This trend seems to be more prevalent among female commercial sex workers, women with lower educational levels, and those in poorer socioeconomic positions. In South Asia, cultural, sociological, economic, and gender inequalities, particularly male domination, all have a significant impact on KAP levels. Conclusion: As education is a major factor that influences health behaviour, this review underscores the need to allocate more resources to educational initiatives, particularly targeting vulnerable groups such as sex workers, transgender women, pregnant women, and rural housewives. This strategic focus may contribute significantly to preventing STIs/STDs, particularly in less developed regions/countries.
Haskap Juicing Method Effects on Haskap Juice Quality

This research is the first study on the influence of juicing methods on the ‘Aurora’ haskap juice quality. Three common juice extraction methods, namely, using a steam juicer, a centrifugal juicer, and a bladder wine press, were applied for haskap juice extraction. Both physicochemical assays and tests of antioxidant activities were employed to evaluate the qualities of the haskap juice. Generally, the centrifugal juicer increased and the steam juicer reduced concentrations of measured juice components relative to the press. The juice from the centrifugal juicer had the highest cloudiness. Sugar concentrations were about 40% lower in steam juice compared to the centrifugal juicer. Pressed juice had a slightly lower soluble solid content than the juice made with the centrifugal juicer and concentrations of glucose and fructose were similar between these methods. The methods altered pH and malic acid content without affecting the concentrations of tartaric and malic acids. Similar effects of juicing methods were seen in secondary compounds associated with health benefits and antioxidant capacities. Anthocyanin concentrations in press and centrifugal juicer extracts were similar, about 1.6 times higher than steam juicer extracts. Total phenolics and antioxidant activities were from two to four times higher in the centrifugal juicer extraction compared to steam juicing. Ascorbic acid and total flavonoid content in the haskap juice was increased by centrifugal juicing and decreased by steam juicing compared to the press. Overall, the juice extracted with the centrifugal juicer had concentrations of juice components that were 1.5–2 times higher than the steam juicer. The pressed juice had similar to slightly lower concentrations of components compared to the centrifugal juicer.
Development and evaluation of a new catchment‐scale model of floodplain sedimentation

Fine‐sediment storage within floodplain systems typically represents a significant component of the catchment sediment budget and a primary control on sediment‐associated nutrient and contaminant fluxes at the basin scale. However, quantitative modeling of floodplain sedimentation within whole catchments represents a significant challenge, not least because hydraulic controls on sediment transport and deposition processes operate at fine spatial scales that cannot be resolved by basin‐scale models. This paper outlines a new approach to addressing this problem in order to develop a computationally efficient model of floodplain sedimentation, which retains a strong physical basis. The approach involves two stages. First, a simple theoretical model of overbank sedimentation is presented, which quantifies the relationship between floodplain geometry, overbank discharge, and total sedimentation rate. Second, the precise form that such a relationship should take is established by analysis of the output from high‐resolution flow and suspended sediment transport models applied to 22 floodplain reaches along three U.K. rivers. This analysis supports a simple power law model between discharge in excess of bankfull and sedimentation rate per unit valley floor length per unit sediment concentration. The power law model is then incorporated within a sediment budget framework and implemented using a Monte Carlo approach to allow an assessment of uncertainty in the model parameterization. The relative likelihood of competing model structures is determined using estimates of mean annual floodplain sediment storage derived by analysis of the 137Cs content of floodplain sediment cores. Comparison of 137Cs‐derived sedimentation rates with uncertainty‐bounded model predictions highlights the potential for assessing the significance of overbank sedimentation within catchment sediment budgets using the simple power law model.
Identification of Soil Types and Salinity Using MODIS Terra Data and Machine Learning Techniques in Multiple Regions of Pakistan

Soil, a significant natural resource, plays a crucial role in supporting various ecosystems and serves as the foundation of Pakistan’s economy due to its primary use in agriculture. Hence, timely monitoring of soil type and salinity is essential. However, traditional methods for identifying soil types and detecting salinity are time-consuming, requiring expert intervention and extensive laboratory experiments. The objective of this study is to propose a model that leverages MODIS Terra data to identify soil types and detect soil salinity. To achieve this, 195 soil samples were collected from Lahore, Kot Addu, and Kohat, dating from October 2022 to November 2022. Simultaneously, spectral data of the same regions were obtained to spatially map soil types and salinity of bare land. The spectral reflectance of band values, salinity indices, and vegetation indices were utilized to classify the soil types and predict soil salinity. To perform the classification and regression tasks, the study employed three popular techniques in the research community: Random Forest (RF), Ada Boost (AB), and Gradient Boosting (GB), along with Decision Tree (DT), K-Nearest Neighbor (KNN), and Extra Tree (ET). A 70–30 test train validation split was used for the implementation of these techniques. The efficacy of the multi-class classification models for soil types was evaluated using accuracy, precision, recall, and f1-score. On the other hand, the regression models’ performances were evaluated and compared using R-squared (R2), Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The results demonstrated that Random Forest outperformed other methods for both predicting soil types (accuracy = 65.38, precision = 0.60, recall = 0.57, and f1-score = 0.57) and predicting salinity (R2 = 0.90, MAE = 0.56, MSE = 0.98, RMSE = 0.97). Finally, the study designed a web portal to enable real-time prediction of soil types and salinity using these models. This web portal can be utilized by farmers and decision-makers to make informed decisions regarding soil, crop cultivation, and agricultural planning.
Enhancing Pediatric Adnexal Torsion Diagnosis: Prediction Method Utilizing Machine Learning Techniques

This study systematically examines pediatric adnexal torsion, proposing a diagnostic approach using machine learning techniques to distinguish it from acute appendicitis. Our retrospective analysis involved 41 female pediatric patients divided into two groups: 21 with adnexal torsion (group 1) and 20 with acute appendicitis (group 2). In group 1, the average age was 10 ± 2.6 years, while in group 2, it was 9.8 ± 21.9 years. Our analysis found no statistically significant age distinctions between these two groups. Despite acute lower abdominal pain being a common factor, group 1 displayed shorter pain duration (28.9 h vs. 46.8 h, p < 0.05), less vomiting (28% vs. 50%, p < 0.05), lower fever incidence (4.7% vs. 50%, p < 0.05), reduced leukocytosis (57% vs. 75%, p < 0.05), and CRP elevation (30% vs. 80%, p < 0.05) compared to group 2. Machine learning techniques, specifically support vector classifiers, were employed using clinical presentation, pain duration, white blood cell counts, and ultrasound findings as features. The classifier consistently demonstrated an average predictive accuracy of 87% to 97% in distinguishing adnexal torsion from appendicitis, as confirmed across various SVM models employing different kernels. Our findings emphasize the capacity of support vector machines (SVMs) and machine learning as a whole to augment diagnostic accuracy when distinguishing between adnexal torsion and acute appendicitis. Nevertheless, it is imperative to validate these results through more extensive investigations and explore alternative machine learning models for a comprehensive understanding of their diagnostic capabilities.
Behavior of Zr–1Nb alloy in coarse- and ultrafine-grain states under laser-induced shock wave loading

The work is devoted to the study of the Zr-1Nb alloy in coarse-grained and ultrafine-grained states under laser-induced shock-wave loading. This material is of interest due to the application for the manufacture of shells for fuel elements of nuclear reactors. The properties of this alloy in the ultrafine-grained state is attracted for the reliability improvement of fuel rods in wide range of load intensity. Shock wave loading was carried out using a Beamtech SGR-Extra-10 high-energy nanosecond laser. The free surface velocity profiles were registered by the VISAR system. Mechanical characteristics are obtained using velocity profiles. It is shown that the spall strength and dynamic elastic limit for the coarse-grained state are higher than for the ultrafine-grained state. In general, the Zr-1Nb alloy in the ultrafine-grained state is more susceptible to spall fracture, including laser shock peening. Numerical simulation of the process under study has been carried out using statistically based nonlinear model of solid with defects and finite element method to describe the deformation behavior and fracture of the material under shock-wave loading. Simulation results are qualitatively consistent with experiments in the prediction of the conditions of spall failure.
Assessment of the impacts of the 1997–98 El Niño on the Asian‐Australia Monsoon

Singular Value Decomposition (SVD) analyses of rainfall and sea surface temperature (SST) are carried out globally over the entire tropics and regionally over the Asia‐Australian (AA)‐monsoon domain. Contributions to monsoon rainfall predictability by basin‐scale SST forcing and regional SST coupling are evaluated from cumulative anomaly correlation with dominant regional SVD modes. The observed 1997–98 AA‐monsoon anomalies are found to be very complex with approximately 34% of the anomalies of the Asian (boreal) summer monsoon and 74% of the Australia (austral) monsoon attributable to basin‐scale SST influence associated with El Niño. For the boreal and austral monsoon respectively, regional coupled processes contribute an additional 19% and 10%, leaving about 47% and 16% due to other factors including high frequency transients. Results suggest that in order to improve seasonal‐to‐interannual predictability of the AA‐monsoon, we need to investigate and exploit not only monsoon‐El Niño relationship, but also intrinsic monsoon regional coupled processes.
Benzophenanthridine Alkaloid Chelerythrine Elicits Necroptosis of Gastric Cancer Cells via Selective Conjugation at the Redox Hyperreactive C-Terminal Sec498 Residue of Cytosolic Selenoprotein Thioredoxin Reductase

Targeting thioredoxin reductase (TXNRD) with low-weight molecules is emerging as a high-efficacy anti-cancer strategy in chemotherapy. Sanguinarine has been reported to inhibit the activity of TXNRD1, indicating that benzophenanthridine alkaloid is a fascinating chemical entity in the field of TXNRD1 inhibitors. In this study, the inhibition of three benzophenanthridine alkaloids, including chelerythrine, sanguinarine, and nitidine, on recombinant TXNRD1 was investigated, and their anti-cancer mechanisms were revealed using three gastric cancer cell lines. Chelerythrine and sanguinarine are more potent inhibitors of TXNRD1 than nitidine, and the inhibitory effects take place in a dose- and time-dependent manner. Site-directed mutagenesis of TXNRD1 and in vitro inhibition analysis proved that chelerythrine or sanguinarine is primarily bound to the Sec498 residue of the enzyme, but the neighboring Cys497 and remaining N-terminal redox-active cysteines could also be modified after the conjugation of Sec498. With high similarity to sanguinarine, chelerythrine exhibited cytotoxic effects on multiple gastric cancer cell lines and suppressed the proliferation of tumor spheroids derived from NCI-N87 cells. Chelerythrine elevated cellular levels of reactive oxygen species (ROS) and induced endoplasmic reticulum (ER) stress. Moreover, the ROS induced by chelerythrine could be completely suppressed by the addition of N-acetyl-L-cysteine (NAC), and the same is true for sanguinarine. Notably, Nec-1, an RIPK1 inhibitor, rescued the chelerythrine-induced rapid cell death, indicating that chelerythrine triggers necroptosis in gastric cancer cells. Taken together, this study demonstrates that chelerythrine is a novel inhibitor of TXNRD1 by targeting Sec498 and possessing high anti-tumor properties on multiple gastric cancer cell lines by eliciting necroptosis.
Impact of tundra ecosystems on the surface energy budget and climate of Alaska

The sensitivity of regional terrestrial climate to the characteristics of tundra ecosystems has been investigated by a series of sensitivity experiments concentrating on the summer of 1995. Validation of the NCAR Land Surface Model and the Arctic Regional Climate System Model for this season indicate their adequacy for this study. Comparisons of the simulated climate in response to a wet meadow tundra or a dry heath tundra results in an expected cooling and moistening of both the local area and the adjoining sea ice and forested regions. The impact of atmospheric cloud-radiation feedbacks is to reduce the cooling as the summer progresses, although moistening continues, associated with increased precipitation in some areas. The spatial variability of the response is dependent upon prevailing synoptic conditions, which act to enhance moisture advection in certain areas. This study indicates that vegetation variation within the Arctic has substantial climatic effects that extend beyond the Arctic. In addition, the perturbations in the summer season could have profound implications of Arctic wintertime climate and issues of snow-albedo feedback and spring melt.
HSGA: A Hybrid LSTM-CNN Self-Guided Attention to Predict the Future Diagnosis From Discharge Narratives

The prognosis of a patient’s re-admission and the forecast of future diagnoses is a critical task in the process of inferring clinical outcomes. The discharge summaries recorded in the Electronic Health Records (EHR) are stinking rich, but they are also heterogeneous, sparse, noisy, and biased, and hinder the learning algorithms that aim to extract actionable insights from them. The existing approaches use the current admission’s International Classification of Diseases (ICD) codes as the input features, but they do not fully describe the progression of the patient. Other systems apply the attention mechanisms directly to these notes without the guidance of domain knowledge, resulting in distorted predictions. In this work, we propose a hybrid LSTM-CNN self-guided attention model that aims to predict the ICD diagnosis that is likely to cause the next readmission within 90 days since the current discharge using the discharge narratives. Since the notes contain unnecessary tokens, the model leverages the recent advances in deep learning to predict the patient’s future diagnosis by reducing the number of tokens from the notes to be considered for prediction. We use a 1D CNN (1-Dimensional Convolutional Neural Network) to capture all features from the note and concurrently an LSTM (Long Short-Term Memory) is used to extract the features of clinically meaningful Concept Unique Identifiers (CUI) that are fetched from the note itself to build a knowledge base. The textual knowledge base guides the learning module about which n-grams from the note to focus on for prediction. We consider 3 prediction scenarios: diagnosis category prediction, the probability of the occurrence of one of the top 20 disease conditions, and ICD9 codes prediction. For the diagnosis category prediction, our proposed model achieves a macro-average ROC of 0.82 and a micro-average ROC of 0.79, an AUROC of 0.87 for the top 20 most appearing diseases prediction, and a macro-average Recall of 0.8 and a micro-average Recall of 0.84 for ICD9 codes prediction respectively. The predictive accuracy of the model is assessed through the prediction of heart failure onset and for all these prediction scenarios, the results show that the hybrid approach outperforms the existing baselines.
Zero-Pole Optimization of a Novel High-Quality-Factor Planar Helical Resonator

A novel micro-solenoid resonator has been designed, simulated, and measured. The solenoid core consisted of a DuroidTM circuit board with a relative permittivity of 2.2. The resonator design incorporated four embedded copper vias with a radius of 125 µm and three surface conductors to form a rectangular coil. A pitch size of 250 µm was used for a 3.02 mm thick substrate. To enhance the resonator’s performance at higher frequencies, a capacitance was introduced in series through the via. This additional capacitor effectively couples the inductance, resistance, and stray capacitance. The optimization of the quality factor was investigated through pole transfer analysis, resulting in an increased resonance frequency of 12.25 GHz and an elevated Q-factor of 306. Moreover, besides its very high Q-factor, this resonator offers a simplified design and easy integration. An analytical lumped circuit model was employed to investigate the design, and the measured S-parameters closely matched the analytical model and electromagnetic simulation results. The tuned resonator exhibited a superior quality factor compared to other micro-resonators.
Uncertainty Analysis in Prestack Stratigraphic Inversion – A Second Insight

Stratigraphic inversion of prestack seismic data allows the determination of subsurface elastic parameters (density, P and S-impedances). Based on a Bayesian approach, the problem is formulated as a non-linear least-squares local optimization problem. The objective function to be minimized is composed of two terms, the first one measures the mismatch between the synthetic seismic data (computed via a forward operator) and the observed seismic data, the second one models geological a priori information on the subsurface model. It is crucial to estimate the a posteriori uncertainties because the solution model of the inversion is only one solution among the range of admissible models that fit the data and the a priori information . The goal of this paper is to propose an optimized deterministic method to estimate a posteriori uncertainties in stratigraphic inversion. The proposed method is based on the hypothesis that the covariance matrices describing the uncertainties on the data and on the model are laterally uncorrelated (no cross correlation among parameters of different traces). Moreover, the covariance matrix on the data is also supposed laterally stationary. Application on 2D synthetic PP data illustrates the performances of the method. Extensions and limitations of the method are discussed.
Benthic biology of two near-shore arctic locations, and potential impacts of sea level change, coastal erosion, and climate change

Near-shore benthic communities can undergo shifts in abundance and biodiversity in response to climate change especially changes in surface temperature, productivity, and geomorphology. One of the most dramatic effects is habitat modification: coastal erosion lead to increased deposition of sediment. Factors driving coastal erosion include isostatic sea-level rise and a variety of climatic change impacts, including reduced sea ice cover, increased summer rainfall, increased thawing of permafrost, and eustatic sea-level rise. -- Benthic communities were studied in two near-shore Arctic locations (Sachs Harbour and Gjoa Haven) associated with different degrees of coastal erosion. Sachs Harbour has a submergent shoreline with locally rapid coastal erosion. By contrast Gjoa Haven has an emergent shoreline with very little to no coastal erosion. Grab and drop-video were used to conduct benthic surveys of the two locations and detailed habitat maps were produced. Species richness was significantly greater in Gjoa Haven than in Sachs Harbour. Species composition differed greatly among locations and varied significantly among substrate types for grab and depth classes for video. Shallow (<10 m) mobile sand sheets with low biodiversity were the dominant habitat sampled in Sachs Harbour. Gravelly-sand or mud substrates (10-20 m) with high cover of macroalgae had the greatest biodiversity in Gjoa Haven. Macroalgae beds were found throughout the Gjoa Haven study area providing abundant food and shelter to benthic fauna. This high diversity is due to the heterogeneity of the substrate. Lastly, Gjoa Haven's sediment starved near-shore environment makes for a stable environment compared to Sachs Harbour near-shore environment, which receives a continuous supply of sediment as a result of coastal erosion and runoff. -- This study establishes a detailed baseline for two near-shore Arctic locations. Given the rapidity with which the Arctic ecosystems are changing this study will be valuable in designing future studies of biodiversity, and will enable detection of future climate driven change in near-shore arctic environments.