Trends and challenges in neuroengineering: toward "Intelligent" neuroprostheses through brain-"brain inspired systems" communication

Future technologies aiming at restoring and enhancing organs function will intimately rely on near-physiological and energy-efficient communication between living and artificial biomimetic systems. Interfacing brain-inspired devices with the real brain is at the forefront of such emerging field, with the term "neurobiohybrids" indicating all those systems where such interaction is established. We argue that achieving a "high-level" communication and functional synergy between natural and artificial neuronal networks in vivo, will allow the development of a heterogeneous world of neurobiohybrids, which will include "living robots" but will also embrace “intelligent” neuroprostheses for augmentation of brain function. The societal and economical impact of intelligent neuroprostheses is likely to be potentially strong, as they will offer novel therapeutic perspectives for a number of diseases, and going beyond classical pharmaceutical schemes. However, they will unavoidably raise fundamental ethical questions on the intermingling between man and machine and more specifically, on how deeply it should be allowed that brain processing is affected by implanted "intelligent" artificial systems. Following this perspective, we provide the reader with insights on ongoing developments and trends in the field of neurobiohybrids. We address the topic also from a "community building" perspective, showing through a quantitative bibliographic analysis, how scientists working on the engineering of brain-inspired devices and brain-machine interfaces are increasing their interactions. We foresee that such trend preludes to a formidable technological and scientific revolution in brain-machine communication and to the opening of new avenues for restoring or even augmenting brain function for therapeutic purposes

Effect of Particle Size Distribution and Packing Characteristics on Railroad Ballast Shear Strength: A Numerical Study Using the Discrete Element Method

Railroad infrastructure plays a significant role in sustaining the economy of a country, and facilitates fast, safe and reliable transportation of passengers as well as commodities. Significant capital investments are required for the construction and maintenance of a railroad network that is structurally and functionally adequate. The ballast layer is one of the main structural components of a conventional rail track system, and comprises coarse-grained unbound particles, often as large as in size. The ballast as a load-bearing layer resists train-induced stresses through particle-particle interaction. Accordingly, particle-size distribution and packing characteristics are important factors that govern the mechanical behavior of the ballast layer under loading. A well-performing ballast layer should ideally possess optimum drainage characteristics to ensure rapid removal of surface water and adequate shear strength to restrain the track against excessive movement under loading. In-depth understanding of different factors affecting ballast behavior can help reduce recurrent costs associated with ballast maintenance. Conducting common shear strength tests on coarse-grained geomaterials such as railroad ballast, and performing parametric studies to quantify the effects of different material, specimen, and test parameters on shear strength properties is often not feasible in standard geotechnical engineering laboratories due to the significantly large specimen and test setup requirements. In such situations, the Discrete Element Method (DEM) that facilitates micromechanical analysis of particulate matter becomes a logical alternative. The primary objective of this research effort is to study the effects of particle-size distribution and packing characteristics on the shear strength behavior of railroad ballast. This was accomplished by simulating commonly used laboratory shear strength tests such as Direct Shear Test and Triaxial Monotonic Shear Strength Test using DEM. A commercially available three-dimensional DEM package (Particle Flow Code - PFC3D®) was used for this purpose. Published laboratory-test data were used to calibrate the numerical model. A series of parametric analyses were subsequently carried out to quantify the individual effects of different variables being studied on ballast shear strength behavior. In an effort to increase ballast shear strength through better packing within the granular matrix, a new gradation parameter, termed as the “Coarse-to-Fine (C/F) Ratio” was proposed. Changing the ‘coarse’ and ‘fine’ fractions within a particular gradation specification, the resulting effect on ballast shear strength was studied. In addition to studying the particle-to-particle interaction within the ballast matrix, this study also focused on studying the phenomenon of geogrid-ballast interaction under different packing conditions. A recently developed parameter known as the “Geogrid Gain Factor” was used to quantify the benefits of geogrid reinforcement of ballast. The ultimate objective was to further the understanding of ballast behavior under loading, which will ultimately lead to the design and construction of better-performing railroad tracks

Processing and analysis of multichannel extracellular neuronal signals: state-of-the-art and challenges

In recent years multichannel neuronal signal acquisition systems have allowed scientists to focus on research questions which were otherwise impossible. They act as a powerful means to study brain (dys)functions in in-vivo and in in-vitro animal models. Typically, each session of electrophysiological experiments with multichannel data acquisition systems generate large amount of raw data. For example, a 128 channel signal acquisition system with 16 bits A/D conversion and 20 kHz sampling rate will generate approximately 17 GB data per hour (uncompressed). This poses an important and challenging problem of inferring conclusions from the large amounts of acquired data. Thus, automated signal processing and analysis tools are becoming a key component in neuroscience research, facilitating extraction of relevant information from neuronal recordings in a reasonable time. The purpose of this review is to introduce the reader to the current state-of-the-art of open-source packages for (semi)automated processing and analysis of multichannel extracellular neuronal signals (i.e., neuronal spikes, local field potentials, electroencephalogram, etc.), and the existing Neuroinformatics infrastructure for tool and data sharing. The review is concluded by pinpointing some major challenges that are being faced, which include the development of novel benchmarking techniques, cloud-based distributed processing and analysis tools, as well as defining novel means to share and standardize data

The influence of religiosity on safety behavior of workers: A Proposed Framework

There has been a growing body of studies on religion and human safety behaviour in recent years. However, psychologists seem to be more inclined to pairing religiosity and non-occupational risky behaviour (such as smoking, substance abuse, drinking and driving) in their studies, while safety scientists have hardly explored the influence of religiosity on occupational safety behaviour such as taking shortcuts or breaking the rules. To close this gap, this paper suggests that empirical studies should be conducted to explore possible associations between religiosity and safety behaviour at the workplace. To facilitate such studies, a conceptual framework is proposed based on the Theory of Planned Behaviour (TPB). This paper explains the rationale of choosing TPB. While TPB postulates that both the behavioural intention and perceived behavioural control explain the behaviour, it is interesting to examine the effect of religiosity on occupational behaviour. Examining religiosity as a new construct in occupational safety behaviour studies can help trigger the interest of other religious scholars, psychologists and safety scientists to use religiosity as a construct more rigorously in their future studies on safety to address the gap. Such studies can also help formulate or enhance safety interventions, since these human-related incidents and accidents seem endemic in high-risk industries

Coherently controlled entanglement generation in a binary Bose-Einstein condensate

Considering a two-component Bose-Einstein condensate in a double-well potential, a method to generate a Bell state consisting of two spatially separated condensates is suggested. For repulsive interactions, the required tunnelling control is achieved numerically by varying the amplitude of a sinusoidal potential difference between the wells. Both numerical and analytical calculations reveal the emergence of a highly entangled mesoscopic state.Comment: 6 pages, 6 figures, epl2.cl

An automated method for characterization of evoked single-trial local field potentials recorded from rat barrel cortex under mechanical whisker stimulation

Rodents explore their surroundings through whisking by localizing objects and detecting textures very precisely. During such tactile exploration, whisker deflection is first mechanically transduced by receptors and then information encoded throughout the somatosensory pathway ending in the somatosensory ‘barrel’ cortex. In the barrel cortex, tactile information from a single whisker is segregated and processed in a cortical column corresponding to the deflected whisker. Local Field Potentials (LFPs) generated by whisker deflection in the barrel cortex present typical signatures in terms of shape and amplitude that are related to the activation of the local neuronal populations. Therefore, rigorous analysis of such responses may reveal important features about the function of underlying neuronal microcircuits. In this context, software methods for characterizing single-trial LFPs are needed that are also suitable for online extraction of LFP features and for brain–machine interfacing applications. In this work, we present an automated and efficient method to analyze evoked LFP responses in the rat barrel cortex through automatic removal of stimulation artifacts, detection of single events and characterization of their relevant parameters. Evoked single-trial LFPs recorded under two different anesthetics are examined to demonstrate the feasibility, accuracy and applicability of the method

Aplikasi Sistem Pendukung Keputusan Diagnosa Penyakit Paru-paru Dengan Metode Forward Chaining

In Indonesian lung disease have a high death. Tubercolosis (TB) world report (2006) by World Health Organization (WHO), Indonesia still the third biggest after India and China with around 539.000 cases and around 101.000 peoples die for a year. Derived from those fact it need more attention from mass society. My research can be used to decision support system which used to lung disease diagnosed, and know what kind of disease from their symptom. Decision support system use production rule method for representating knowledge about the kind of lung disease and their symptom. This inference engine use tree method and forward chaining. Result derived from this research shown that tree method and forward chaining can be used in finding lung disease from their symptom