The dynamics and determinants of Kuwait's long-run economic growth

This paper develops a quarterly macro-econometric model for the Kuwaiti economy estimated over the period 1979Q2-2013Q1, allowing us to investigate the long-run role of oil income in the development of Kuwait as well as the direct effects of oil revenue, foreign output, and equity price shocks on real output. More specifically, we examine to what extent Kuwaiti real output in the long run is shaped by oil revenue through their impact on capital accumulation, and technological transfers through foreign output. Using the same modelling strategy we also explore the role of oil income in terms of long-run private and public sector output growth (separately). The estimates suggest that real domestic output in the long run is influenced by oil revenues and foreign output (a proxy for technological progress), and technological growth in Kuwait is on a par with the rest of the world. Furthermore, while we show that both oil revenues and foreign output drive growth in the public sector, it seems that technological progress is the main (and only) driver for private sector real growth. Finally, our results show that oil revenue and global equity market shocks have a large and significant long-run impact on Kuwait's real output and public sector GDP. In comparison, the effects of the foreign output shock is muted

Cognitive based neural prosthetics

Intense activity in neural prosthetic research has recently demonstrated the possibility of robotic interfaces that respond directly to the nervous system. The question remains of how the flow of information between the patient and the prosthetic device should be designed to provide a safe, effective system that maximizes the patient’s access to the outside world. Much recent work by other investigators has focused on using decoded neural signals as low-level commands to directly control the trajectory of screen cursors or robotic end-effectors. Here we review results that show that high-level, or cognitive, signals can be decoded from planned arm movements. These results, coupled with fundamental limitations in signal recording technology, motivate an approach in which cognitive neural signals play a larger role in the neural interface. This proposed paradigm predicates that neural signals should be used to instruct external devices, rather than control their detailed movement. This approach will reduce the effort required of the patient and will take advantage of established and on-going robotics research in intelligent systems and human-robot interfaces

Effect of zero tillage and different weeding methods on grain yield of durum wheat in semi-arid regions

Received: September 28th, 2020 ; Accepted: December 1st, 2020 ; Published: December 10th, 2020 ; Correspondence: [email protected] high grain yield of wheat is limited by the dominance of weeds, particularly wild oat. Therefore, to improve wheat yield under these conditions, a field experiment was carried out in Maru Agricultural Research Station, Jordan during 2015–2016 and 2016–2017 to investigate yield response of two wheat varieties (Triticum durum L.) to different tillage and weeding treatments. The experimental design used was a split-split arrangement in a randomized complete block design with three replicates. Two-tillage treatments (conventional vs. zero tillage) were applied to the main plot, two wheat varieties to sub-plot, and five weeding methods (hand weeding, broadleaf + narrow leaf herbicide, broadleaf herbicide, narrow leaf herbicide, and controls) as a sub-sub-plot. The variety ‘Umqais’ had higher plant height, biological, grain, and straw yield than the variety ‘Sham’. Hand weeding slightly increased grain yield compared with mixed herbicides (the 2,4-D plus Antelope Clodinatop- propagyl). Furthermore, mixed herbicides presented a higher grain yield than using either single herbicide. The interaction between tillage systems and weeding methods was significant in both years. The highest (P < 0.05) straw yield (5,990 kg ha-1 ) was obtained by hand weeding under conventional tillage in the first season while the highest grain yield (2,005 kg ha-1 ) was obtained by hand weeding under zero tillage in the second season. Under all weed control treatments, the variety ‘Umqais’ had higher biological, grain, and straw yields than the variety ‘Sham’ in the second season indicating that variety ‘Umqais’ performed better under dry conditions. Our results confirmed the superior of zero tillage for increasing the grain yield of the variety ‘Umqais’, and for increasing the biological and straw yields of the variety ‘Sham’ under semi-arid rainfed conditions of Jordan

H

An H-function with complex parameters is defined by a Mellin-Barnes type integral. Necessary and sufficient conditions under which the integral defining the H-function converges absolutely are established. Some properties, special cases, and an application to integral transforms are given

Parietal Reach Region Encodes Reach Depth Using Retinal Disparity and Vergence Angle Signals

Performing a visually guided reach requires the ability to perceive the egocentric distance of a target in three-dimensional space. Previous studies have shown that the parietal reach region (PRR) encodes the two-dimensional location of frontoparallel targets in an eye-centered reference frame. To investigate how a reach target is represented in three dimensions, we recorded the spiking activity of PRR neurons from two rhesus macaques trained to fixate and perform memory reaches to targets at different depths. Reach and fixation targets were configured to explore whether neural activity directly reflects egocentric distance as the amplitude of the required motor command, which is the absolute depth of the target, or rather the relative depth of the target with reference to fixation depth. We show that planning activity in PRR represents the depth of the reach target as a function of disparity and fixation depth, the spatial parameters important for encoding the depth of a reach goal in an eye centered reference frame. The strength of modulation by disparity is maintained across fixation depth. Fixation depth gain modulates disparity tuning while preserving the location of peak tuning features in PRR neurons. The results show that individual PRR neurons code depth with respect to the fixation point, that is, in eye centered coordinates. However, because the activity is gain modulated by vergence angle, the absolute depth can be decoded from the population activity

Recording advances for neural prosthetics

An important challenge for neural prosthetics research is to record from populations of neurons over long periods of time, ideally for the lifetime of the patient. Two new advances toward this goal are described, the use of local field potentials (LFPs) and autonomously positioned recording electrodes. LFPs are the composite extracellular potential field from several hundreds of neurons around the electrode tip. LFP recordings can be maintained for longer periods of time than single cell recordings. We find that similar information can be decoded from LFP and spike recordings, with better performance for state decodes with LFPs and, depending on the area, equivalent or slightly less than equivalent performance for signaling the direction of planned movements. Movable electrodes in microdrives can be adjusted in the tissue to optimize recordings, but their movements must be automated to be a practical benefit to patients. We have developed automation algorithms and a meso-scale autonomous electrode testbed, and demonstrated that this system can autonomously isolate and maintain the recorded signal quality of single cells in the cortex of awake, behaving monkeys. These two advances show promise for developing very long term recording for neural prosthetic applications

Monolithic Silicon Probes with Flexible Parylene Cables for Neural Prostheses

This work presents the first parylene-insulated silicon probes, which are used for neural prostheses to record high-level cognitive neural signals. With parylene technology, our probes have several advantages compared with the current devices. First, instead of inorganic materials (e.g. silicon dioxide, silicon nitride), the electrodes and conduction traces on the probes are insulated by parylene, an easily-deposited polymer with mechanical flexibility and biocompatibility. As a result, the probes exhibit better electrical and mechanical properties. Second, flexible parylene cables are monolithically integrated with the probes, which arm the probes with very high flexibility to be easily assembled to a high density 3-D array and at the same time provide an ideal method to transmit neural signals through skull during chronic recording. The all dry fabrication process and a 4 X 4 probe array (64 electrodes) were demonstrated. The probes were successfully tested electrically and mechanically in rat cortex. Neural signals were properly recorded

A New Multi-Site Probe Array with Monolithically Integrated Parylene Flexible Cable for Neural Prostheses

This work presents a new multi-site probe array applied with parylene technology, used for neural prostheses to record high-level cognitive neural signals. Instead of inorganic materials (e.g. silicon dioxide, silicon nitride), the electrodes and conduction traces on probes are insulated by parylene, which is a polymer material with high electrical resistivity, mechanical flexibility, biocompatibility and easy deposition process. As a result, the probes exhibit better electrical and mechanical properties. The all dry process is demonstrated to fabricate these probe arrays with monolithically integrated parylene flexible cables using double-side-polished (DSP) wafers. With the parylene flexible cables, the probes can be easily assembled to a high density 3-D array for chronic implantation

Low-Power Circuits for Brain–Machine Interfaces

This paper presents work on ultra-low-power circuits for brain–machine interfaces with applications for paralysis prosthetics, stroke, Parkinson’s disease, epilepsy, prosthetics for the blind, and experimental neuroscience systems. The circuits include a micropower neural amplifier with adaptive power biasing for use in multi-electrode arrays; an analog linear decoding and learning architecture for data compression; low-power radio-frequency (RF) impedance-modulation circuits for data telemetry that minimize power consumption of implanted systems in the body; a wireless link for efficient power transfer; mixed-signal system integration for efficiency, robustness, and programmability; and circuits for wireless stimulation of neurons with power-conserving sleep modes and awake modes. Experimental results from chips that have stimulated and recorded from neurons in the zebra finch brain and results from RF power-link, RF data-link, electrode- recording and electrode-stimulating systems are presented. Simulations of analog learning circuits that have successfully decoded prerecorded neural signals from a monkey brain are also presented