High-order Van Hove singularities and their connection to flat bands

The flattening of single-particle band structures plays an important role in the quest for novel quantum states of matter due to the crucial role of interactions. Recent advances in theory and experiment made it possible to construct and tune systems with nearly flat bands, ranging from graphene multilayers and moire' materials to kagome' metals and ruthenates. While theoretical models predict exactly flat bands under certain ideal conditions, evidence was provided that these systems host high-order Van Hove points, i.e., points of high local band flatness and power-law divergence in energy of the density of states. In this review, we examine recent developments in engineering and realising such weakly dispersive bands. We focus on high-order Van Hove singularities and explore their connection to exactly flat bands. We provide classification schemes and discuss interaction effects. We also review experimental evidence for high-order Van Hove singularities and point out future research directions.Comment: Review article, to appear in Annual Review of Condensed Matter Physic

Плазменное получение тепловой энергии из сульфатного лигнина

This article shows an overview and analysis of the literature on methods of using sludge lignin. This product obtained after treatment of pulp. As a result of calculating the optimum composition of water, organic materials with mechanical impurities from the adiabatic combustion temperature of about 1200 K were determined. Using the obtained results of experimental studies have been carried out in a plasma reactor of the catalytic reactor and has been optimized. The obtained results can be used to create industrial enterprises based on plasma catalytic reactors for waste sludge lignin for the purpose of obtaining heat

Plasticity Resembling Spike-Timing Dependent Synaptic Plasticity: The Evidence in Human Cortex

Spike-timing dependent plasticity (STDP) has been studied extensively in a variety of animal models during the past decade but whether it can be studied at the systems level of the human cortex has been a matter of debate. Only recently newly developed non-invasive brain stimulation techniques such as transcranial magnetic stimulation (TMS) have made it possible to induce and assess timing dependent plasticity in conscious human subjects. This review will present a critical synopsis of these experiments, which suggest that several of the principal characteristics and molecular mechanisms of TMS-induced plasticity correspond to those of STDP as studied at a cellular level. TMS combined with a second phasic stimulation modality can induce bidirectional long-lasting changes in the excitability of the stimulated cortex, whose polarity depends on the order of the associated stimulus-evoked events within a critical time window of tens of milliseconds. Pharmacological evidence suggests an NMDA receptor mediated form of synaptic plasticity. Studies in human motor cortex demonstrated that motor learning significantly modulates TMS-induced timing dependent plasticity, and, conversely, may be modulated bidirectionally by prior TMS-induced plasticity, providing circumstantial evidence that long-term potentiation-like mechanisms may be involved in motor learning. In summary, convergent evidence is being accumulated for the contention that it is now possible to induce STDP-like changes in the intact human central nervous system by means of TMS to study and interfere with synaptic plasticity in neural circuits in the context of behavior such as learning and memory

Support Vector Machine Classifiers Show High Generalizability in Automatic Fall Detection in Older Adults

Falls are a major cause of morbidity and mortality in neurological disorders. Technical means of detecting falls are of high interest as they enable rapid notification of caregivers and emergency services. Such approaches must reliably differentiate between normal daily activities and fall events. A promising technique might be based on the classification of movements based on accelerometer signals by machine-learning algorithms, but the generalizability of classifiers trained on laboratory data to real-world datasets is a common issue. Here, three machine-learning algorithms including Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF) were trained to detect fall events. We used a dataset containing intentional falls (SisFall) to train the classifier and validated the approach on a different dataset which included real-world accidental fall events of elderly people (FARSEEING). The results suggested that the linear SVM was the most suitable classifier in this cross-dataset validation approach and reliably distinguished a fall event from normal everyday activity at an accuracy of 93% and similarly high sensitivity and specificity. Thus, classifiers based on linear SVM might be useful for automatic fall detection in real-world applications

The IceCube Neutrino Observatory: Instrumentation and Online Systems

The IceCube Neutrino Observatory is a cubic-kilometer-scale high-energy neutrino detector built into the ice at the South Pole. Construction of IceCube, the largest neutrino detector built to date, was completed in 2011 and enabled the discovery of high-energy astrophysical neutrinos. We describe here the design, production, and calibration of the IceCube digital optical module (DOM), the cable systems, computing hardware, and our methodology for drilling and deployment. We also describe the online triggering and data filtering systems that select candidate neutrino and cosmic ray events for analysis. Due to a rigorous pre-deployment protocol, 98.4% of the DOMs in the deep ice are operating and collecting data. IceCube routinely achieves a detector uptime of 99% by emphasizing software stability and monitoring. Detector operations have been stable since construction was completed, and the detector is expected to operate at least until the end of the next decade.Comment: 83 pages, 50 figures; updated with minor changes from journal review and proofin

Evaluation of Three Machine Learning Algorithms for the Automatic Classification of EMG Patterns in Gait Disorders

Gait disorders are common in neurodegenerative diseases and distinguishing between seemingly similar kinematic patterns associated with different pathological entities is a challenge even for the experienced clinician. Ultimately, muscle activity underlies the generation of kinematic patterns. Therefore, one possible way to address this problem may be to differentiate gait disorders by analyzing intrinsic features of muscle activations patterns. Here, we examined whether it is possible to differentiate electromyography (EMG) gait patterns of healthy subjects and patients with different gait disorders using machine learning techniques. Nineteen healthy volunteers (9 male, 10 female, age 28.2 ± 6.2 years) and 18 patients with gait disorders (10 male, 8 female, age 66.2 ± 14.7 years) resulting from different neurological diseases walked down a hallway 10 times at a convenient pace while their muscle activity was recorded via surface EMG electrodes attached to 5 muscles of each leg (10 channels in total). Gait disorders were classified as predominantly hypokinetic (n = 12) or ataxic (n = 6) gait by two experienced raters based on video recordings. Three different classification methods (Convolutional Neural Network—CNN, Support Vector Machine—SVM, K-Nearest Neighbors—KNN) were used to automatically classify EMG patterns according to the underlying gait disorder and differentiate patients and healthy participants. Using a leave-one-out approach for training and evaluating the classifiers, the automatic classification of normal and abnormal EMG patterns during gait (2 classes: “healthy” and “patient”) was possible with a high degree of accuracy using CNN (accuracy 91.9%), but not SVM (accuracy 67.6%) or KNN (accuracy 48.7%). For classification of hypokinetic vs. ataxic vs. normal gait (3 classes) best results were again obtained for CNN (accuracy 83.8%) while SVM and KNN performed worse (accuracy SVM 51.4%, KNN 32.4%). These results suggest that machine learning methods are useful for distinguishing individuals with gait disorders from healthy controls and may help classification with respect to the underlying disorder even when classifiers are trained on comparably small cohorts. In our study, CNN achieved higher accuracy than SVM and KNN and may constitute a promising method for further investigation

Lying obliquely—a clinical sign of cognitive impairment: cross sectional observational study

Objective To determine if failure to spontaneously orient the body along the longitudinal axis of a hospital bed when asked to lie down is associated with cognitive impairment in older patients

Motor Sequence Learning Deficits in Idiopathic Parkinson’s Disease Are Associated With Increased Substantia Nigra Activity

Previous studies have shown that persons with Parkinson’s disease (pwPD) share specific deficits in learning new sequential movements, but the neural substrates of this impairment remain unclear. In addition, the degree to which striatal dopaminergic denervation in PD affects the cortico-striato-thalamo-cerebellar motor learning network remains unknown. We aimed to answer these questions using fMRI in 16 pwPD and 16 healthy age-matched control subjects while they performed an implicit motor sequence learning task. While learning was absent in both pwPD and controls assessed with reaction time differences between sequential and random trials, larger error-rates during the latter suggest that at least some of the complex sequence was encoded. Moreover, we found that while healthy controls could improve general task performance indexed by decreased reaction times across both sequence and random blocks, pwPD could not, suggesting disease-specific deficits in learning of stimulus-response associations. Using fMRI, we found that this effect in pwPD was correlated with decreased activity in the hippocampus over time. Importantly, activity in the substantia nigra (SN) and adjacent bilateral midbrain was specifically increased during sequence learning in pwPD compared to healthy controls, and significantly correlated with sequence-specific learning deficits. As increased SN activity was also associated (on trend) with higher doses of dopaminergic medication as well as disease duration, the results suggest that learning deficits in PD are associated with disease progression, indexing an increased drive to recruit dopaminergic neurons in the SN, however, unsuccessfully. Finally, there were no differences between pwPD and controls in task modulation of the cortico-striato-thalamo-cerebellar network. However, a restricted nigral-striatal model showed that negative modulation of SN to putamen connection was larger in pwPD compared to controls during random trials, while no differences between the groups were found during sequence learning. We speculate that learning-specific SN recruitment leads to a relative increase in SN- > putamen connectivity, which returns to a pathological reduced state when no learning takes plac

Tips and tricks in tremor treatment

Tremor, whether arising from neurological diseases, other conditions, or medication side effects, significantly impacts patients' lives. Treatment complexities necessitate clear algorithms and strategies. Levodopa remains pivotal for Parkinson's tremor, though response variability exists. Some dopamine agonists offer notable tremor reduction targeting D2 receptors. Propranolol effectively manages essential tremor and essential tremor plus (ET/ET +), sometimes with primidone for added benefits, albeit dose-dependent side effects. As reserve medications anticholinergics and clozapine are used for treatment of parkinsonian tremor, 1-Octanol and certain anticonvulsant drugs for tremor of other orign, especially ET. Therapies such as invasive deep brain stimulation and lesional focused ultrasound serve for resistant cases. A medication review is crucial for all forms of tremor, but it is particularly important if medication may have triggered the tremor. Sensor-based detection and non-drug interventions like wristbands and physical therapy broaden diagnostic and therapeutic horizons, promising future tremor care enhancements. Understanding treatment nuances is a key for tailored tremor management respecting patient needs and tolerability. Successful strategies integrate pharmacological, non-invasive, and technological modalities, aiming for optimal symptom control and improved quality of life