Explore the vast range of titles available.

Problematic soils pose challenges to light infrastructure, such as pavements, due to their swelling and collapse characteristics. Traditional soil stabilizers like cement and lime, while successful, have limits in cost, environmental impact, and energy use. This study explores agricultural and industrial byproducts as alternative stabilizers. It aims to determine the California Bearing Ratio (CBR) of stabilized soils using modern Machine and Deep Learning (MDL) techniques. MDL models used are Multivariate Adaptive Regression Splines (MARS), Artificial Neural Networks (ANN), M5P Model Trees (M5P-MT), Extreme Gradient Boosting (XGBoost), Locally Weighted Polynomials (LWP), and Long Short-Term Memory (LSTM) networks. Two modeling approaches were created: approach I with 12 input variables and approach II with 7. Key input variables were Atterberg limits, Ordinary Portland Cement (OPC), Optimum Moisture Content (OMC), Maximum Dry Density (MDD), dust, and ashes. Feature importance was assessed using Sklearn permutation importance and SHapley Additive Explanation (SHAP). Six statistical measures were used to assess model effectiveness: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), RMSE-to-Standard Deviation Ratio (RSD), Variance Accounted For (VAF), 95% Uncertainty (U95), and Correlation Coefficient (R). All models had high prediction accuracy ( R  > 0.95), with method II being more straightforward. The LSTM model achieved the highest performance ( R  = 0.98; RMSE = 3.12), with XGBoost producing comparable results. The most influential variables, according to SHAP analysis, were OPC, additive type, and plasticity index. This study demonstrates the potential of Agricultural and Industrial Wastes (AIWs) in soil stabilization and the usefulness of MDL models, notably LSTM, in accurately predicting CBR levels.

Background and purposeAlice in Wonderland syndrome (AIWS) is a rare neurological disorder, characterized by an erroneous perception of the body schema or surrounding space. It may be caused by a variety of neurological disorders, but to date, there is no agreement on which brain areas are affected. The aim of this study was to identify brain areas involved in AIWS.MethodsWe conducted a literature search for AIWS cases following brain lesions. Patients were classified according to their symptoms as type A (somesthetic), type B (visual), or type C (somesthetic and visual). Using a lesion mapping approach, lesions were mapped onto a standard brain template and sites of overlap were identified.ResultsOf 30 lesions, maximum spatial overlap was present in six cases. Local maxima were identified in the right occipital lobe, specifically in the extrastriate visual cortices and white matter tracts, including the ventral occipital fasciculus, optic tract, and inferior fronto-occipital fasciculus. Overlap was primarily due to type B patients (the most prevalent type, n = 22), who shared an occipital site of brain damage. Type A (n = 5) and C patients (n = 3) were rarer, with lesions disparately located in the right hemisphere (thalamus, insula, frontal lobe, hippocampal/parahippocampal cortex).ConclusionsLesion-associated AIWS in type B patients could be related to brain damage in visual pathways located preferentially, but not exclusively, in the right hemisphere. Conversely, the lesion location disparity in cases with somesthetic symptoms suggests underlying structural/functional disconnections requiring further evaluation.

Abstract Background and purposeAlice in Wonderland syndrome (AIWS) is a neurological disorder characterized by erroneous perception of the body schema or surrounding space. Migraine is the primary cause of AIWS in adults. The pathophysiology of AIWS is largely unknown, especially regarding functional abnormalities. In this study, we compared resting-state functional connectivity (FC) of migraine patients experiencing AIWS, migraine patients with typical aura (MA) and healthy controls (HCs).MethodsTwelve AIWS, 12 MA, and 24 HCs were enrolled and underwent 3 T MRI scanning. Independent component analysis was used to identify RSNs thought to be relevant for AIWS: visual, salience, basal ganglia, default mode, and executive control networks. Dual regression technique was used to detect between-group differences in RSNs. Finally, AIWS-specific FC alterations were correlated with clinical measures.ResultsWith respect to HCs, AIWS and MA patients both showed significantly lower (p < 0.05, FDR corrected) FC in lateral and medial visual networks and higher FC in salience and default mode networks. AIWS patients alone showed higher FC in basal ganglia and executive control networks than HCs. When directly compared, AIWS patients showed lower FC in visual networks and higher FC in all other investigated RSNs than MA patients. Lastly, AIWS-specific FC alterations in the executive control network positively correlated with migraine frequency.ConclusionsAIWS and MA patients showed similar FC alterations in several RSNs, although to a different extent, suggesting common pathophysiological underpinnings. However, AIWS patients showed additional FC alterations, likely due to the complexity of AIWS symptoms involving high-order associative cortical areas.

Purpose To review the vestibular, aural, and perceptual symptoms of vestibular migraine (VM) that may present alongside vertigo. Recent Findings Increased research attention to the wide spectrum of symptoms presenting in VM patients has improved understanding of this disorder, with recent identification of five different VM phenotypes. Research into the clinical overlap between VM and other chronic vestibular syndromes such as persistent postural-perceptual dizziness and mal-de-debarquement syndrome reveals a range of vestibular symptoms and hints at pathophysiological connections between migraine and vestibular dysfunction. Studies of migraine treatment for hearing loss suggest patients presenting with aural symptoms may have an underlying diagnosis of migraine and deserve a trial of migraine preventives. Research into the neurologic basis of the perceptual disorder Alice in Wonderland syndrome has revealed brain areas that are likely involved and may help explain its prevalence in VM patients. Summary VM is a sensory processing disorder that presents with more than just vertigo. Understanding the range of potential symptoms improves diagnosis and treatment for migraine patients whose diagnosis may be missed when only the symptoms identified in the diagnostic criteria are considered.

The warming of the Arctic Intermediate Water （AIW） is studied based on the analyses of hydro- graphic observations in the Canada Basin of the Arctic Ocean during 1985-2006. It is shown that how the anomalously warm AIW spreads in the Canada Basin during the observation time through the analysis of the AIW temperature spatial distribution in different periods. The results indicate that by 2006, the entire Canada Basin has almost been covered by the warming AIW. In order to study interannual variability of the AIW in the Canada Basin, the Canada Basin is divided into five regions according to the bottom topography. From the interannual variation of AIW temperature in each region, it is shown that a cooling period follows after the warming event in upstream regions. At the Chukchi Abyssal Plain and Chukchi Plateau, upstream of the Arctic Circumpolar Boundary Current （ACBC） in the Canada Basin, the AIW temperature reached maximum and then started to fall respectively in 2000 and 2002. However, the AIW in the Canada Abyssal Plain and Beaufort Sea continues to warm monotonically until the year 2006. Furthermore, it is revealed that there is convergence of the AIW depth in the five different regions of the Canada Basin when the AIW warming occurs during observation time. The difference of AIW depth between the five regions of the Canada Basin is getting smaller and smaller, all approaching 410 m in recent years. The results show that depth convergence is related to the variation of AIW potential density in the Canada Basin.

Purpose - The purpose of this article is to provide a critical review of Bergman's study on the deep web. In addition, this study brings a new concept into the discussion, the academic invisible web (AIW). The paper defines the academic invisible web as consisting of all databases and collections relevant to academia but not searchable by the general-purpose internet search engines. Indexing this part of the invisible web is central to scientific search engines. This paper provides an overview of approaches followed thus far.Design methodology approach - Provides a discussion of measures and calculations, estimation based on informetric laws. Also gives a literature review on approaches for uncovering information from the invisible web.Findings - Bergman's size estimate of the invisible web is highly questionable. This paper demonstrates some major errors in the conceptual design of the Bergman paper. A new (raw) size estimate is given.Research limitations implications - The precision of this estimate is limited due to a small sample size and lack of reliable data.Practical implications - This study can show that no single library alone will be able to index the academic invisible web. The study suggests a collaboration to accomplish this task.Originality value - Provides library managers and those interested in developing academic search engines with data on the size and attributes of the academic invisible web.