Tactile graphical display for the visually impaired information technology applications

This paper presents an interactive tactile graphical display, for the visually impaired information technology access applications. The display consists of a matrix of dots. Each dot is an electro rheological micro actuator. The actuator design and development process is presented in this paper. Prototype size 124x4 dots was manufactured. An advanced software tools and embedded system based on voltage matrix manipulation has been developed, to provide the display near real time control. The experimental tests carried out into the developed prototype showed that each actuator of the matrix was able to provide a vertical movement of 0.7 mm and vertical holding force of 100 to 200 mN. The stroke and dynamic response tests showed the practicability of the developed tactile display, for the visually impaired information technology applications

A vibration measurement system for deaf people’s emergency apparatus

A vibration measurement system for deaf people’s emergency warning equipment is presented in this paper. Deaf alarm devices are among the most supportive products that help to alert deaf people in various emergency circumstances. Applicable British standards recommend that acceptable working parameters for such products are frequency: 25 to 150 Hz, enough vibration strength, intensity and stable performance against lifetime. This paper presents research that has been conducted to test and validate the performance of a variety of alerting devices using the proposed vibration measurement system. It introduces the laboratory arrangements, practical measurements carried out and its compliance with the British standards. The products investigated were the Mk I, Mk II Deaf Alerters, the Deafgard alerting device and the C-TEC 24V Pillow Pad. Measurements were taken using an ADXL335 accelerometer, to determine the vibration strength. Other parameters considered were temperature variation, efficiency and lifetime of the products

Anorectal motility in patients with achalasia of the esophagus: recognition of an esophago-rectal syndrome

BACKGROUND: During my study of constipation, I encountered patients who had achalasia of the esophagus (AE) as well. The possibility of an existing relationship between the 2 conditions was studied. METHOD: Investigations to study the anorectal motility in 9 AE patients included: the intestinal transit time, anorectal manometry, rectoanal inhibitory reflex, defecography and electromyography (EMG) of external anal sphincter and levator ani muscle. Anorectal biopsy was done. The study comprised 8 healthy volunteers as controls. RESULTS: 6/9 AE patients had constipation presenting as strainodynia (excessive prolonged straining at stool). Rectocele was present in 4 of them. The 6 constipated patients showed significantly high rectal neck pressure (p < 0.05), absent rectoanal inhibitory reflex and aganglionosis in the anorectal biopsy. The EMG revealed diminished activity in 4 of the 6 constipated patients. The remaining 3 patients with AE had normal anorectal function. Heller's myotomy with Nissen's fundoplication improved the dysphagia, but not the constipation which was, however, relieved after performance of anorectal myectomy. CONCLUSION: The high incidence of constipation with AE postulates a relationship between the 2 conditions. Both have the same pathologic lesion which is aganglionosis. This study is preliminary and requires further studies on a larger number of patients

Computer Aided Design of Self-Learning Robotic System using Imitation Learning

Artificial intelligence (AI), imitation learning, big data, cloud and distributed computing, robotics cells, and information communication technology, are some of the key tools and elements of the future digital and smart manufacturing facility. There are a number of challenges that digital and smart manufacturing is facing, especially with the complication of AI (i.e., machine, deep and cognitive learning) algorithms, great amount of data to process, and essential complex coding required, which makes immediate changes needed in manufacturing facilities not straightforward. This is notable in small manufacturing cells which is an integrated part of future smart factories such manufacturing facilities are usually needed some annual and regular updates to meet the update in the design specifications of next generation of products. Imitation learning is offering a great opportunity to overcome these challenges and simplify such complications, where human skills, ability to perform specific tasks, knowledge, and talent could be transferred. This is conveying the knowledge, and skills transfer using imitation learning. However, smart manufacturing and industrial revolution needs robotics cells that has skills beyond this, especially when it comes to process optimisation. Therefore, deep imitation learning could come in to help in the development of self-learning robotic systems and cells. Off course with the powerful tools such as distributed computing, blockchain, cloud computing, edge computing, and 5G the collaboration between such self-learning robotic cells will be possible. This will certainly not eliminate human existence but will enhance the manufacturing environment. This paper is focused on presenting the outcomes of CAD simulation and modelling phase of the ongoing research programme that focused on developing a self-learning robotic system using imitation learning. CAD tools have been used and some initial results is presented

Risk factor assessment of digital eye strain during the COVID-19 pandemic: a cross-sectional survey

Background: Shifting to online learning during the coronavirus pandemic has increased the number of individuals symptomatic of digital eye strain (DES). This study aimed to determine the frequency and potential risk factors of DES among university staff members and students in this pandemic era. Methods: A cross-sectional online survey was conducted during the pandemic, in May and June 2020. The online questionnaire was designed to collect data on DES-related ocular and extraocular manifestations. The survey was sent via social media to the previous year’s students and staff within the Faculty of Medicine, Tanta University, Tanta, Egypt. The responses were downloaded and analyzed. Results: Of the 412 participants completing the questionnaire, 34 (8.3%) were university staff members with a mean (standard deviation [SD]) age of 36.7 (6.6) years, and 378 (91.7%) were university students with a mean (SD) age of 20.8 (1.8) years. Participants with DES symptoms numbered 294 (71.4%) before the lockdown, increasing to 366 (88.8%) during the last month, with 84 developing new-onset DES. Most participants reported ocular symptoms associated with DES. After the lockdown, both students and staff had a significant prolongation of nighttime digital screen use and TV watching, an increase in the 4-item Patient Health Questionnaire (PHQ-4) severity scale scores, and anxiety and depression, with a reduced duration of daytime reading (all P &lt; 0.05). Students had a significant prolongation of daytime digital screen use and TV watching and an increase in the frequency of eye lubricant use and mean Perceived Stress Scale (PSS)-4 scores (all P &lt; 0.05). Multiple logistic regression analysis revealed that studenthood and increased nighttime screen use were independent risk factors for DES by odds ratios (95% confidence intervals) of 10.60 (2.12 – 53.00) and 3.99 (1.71 – 9.34), respectively (both P &lt; 0.05). Conclusions: Lockdown and closure of the university, with a shift to online learning, increased the exposure of staff and students to digital screens and the number of individuals with DES. Studenthood and prolonged nighttime digital screen use were independent risk factors for DES. Further studies investigating the prevalence and risk factors of DES, comparing similar data during and after the pandemic, may reveal other aspects of DES caused by virtual reality-based training

A fitoplankton produktivitása és diverzitása pikoplankton dominanciájú vízterekben = Productivity and diversity of phytoplankton in picoplankton dominated water bodies

Feltártuk a bakteriális méretű algákban (pikoplankton) gazdag turbid vizekben a pikoplankton tömegének, összetételének, dinamikájának és fotoszintézisének alapvető jellemzőit. A fitoplankton tömege alapján ezek a tavak hipertrófok (a-klorofill>100 micro g/l), ugyanakkor a fénylimitáció miatt elsődleges termelésük a vártnál alacsonyabb volt. Vizükben a pikoplankton abundanciája egy-két nagyságrenddel nagyobb volt, mint más tavakban, télen a pikoeukarióták nyáron a pikocianobaktériumok domináltak a Balatonhoz hasonlóan. A Balatonban a pikoalgák részesedése az összes produkcióból 23-54% között volt. Megállapítottuk, hogy a pikoplankton abundancia folyó vizekben egy nagyságrenddel kisebb, mint tavakban, azonos fitoplankton biomassza esetén. A Balatonból egy új pikoeukarióta, a szikes tavakból egy új pikocianobaktérium morfotaxont írtunk le. A természetes pikoplankton együttesek molekuláris vizsgálata bizonyította, hogy a Pannon Biogeográfiai Régió víztereinek Synechococcus flórája igen gazdag és diverz. Az izolált pikocianobaktérium törzsek genetikai karakterizálása is ezt az eredményt támasztotta alá. A Böddi-székből izolált törzsek a pikofitoplankton klád egy új, eddig tenyésztésbe nem vont csoportját képviselik. Pikoeukarióta és pikocianobaktérium törzsek ökofiziológiai vizsgálatával bizonyítottuk, hogy a Duna-Tisza közi szikes tavakban (és minden bizonnyal más tavakban is) a fény és a hőmérséklet változása együtt szabályozza a pikoalgák szezonális szukcesszióját. | The abundance, composition, dynamics and photosynthesis of the bacterium-sized algae (picoplankton) has been studied in Hungarian turbid waters, where the abundance of picoplankton is one or two order of magnitude higher than in other lakes. Based on the phytoplankton biomass these ponds were hypertrophic (chlorophyll-a > 100 micro g/l), however the primary production was lower than expected due to light limitation. Picoeukaryotes dominate in winter, while picocyanobacteria dominate in summer, similarly to Lake Balaton, where the proportion of the picoplankton in the total primary production was 23 and 54%. It has been stated, that the picoplankton abundance in running waters was one order of magnitude lower, than in shallow lakes having the same trophic state. One new eukaryotic and one cyanobacterial picoalgal morphotaxa has been described. The molecular characterization of phytoplankton assemblages showed that the Synechococcus flora of the Pannon Biogeographic Region is very diverse. The molecular identification of isolated picocyanobacterial strains also confirmed these results. The picocyanobacterial strains isolated from Böddi-szék pond formed a new, distinct group inside the picophytoplankton clade. The ecophysiological studies of picoeukaryotic and picocyanobacterial strains showed that the light and temperature control together the seasonal succession of the picoplankton in turbid soda lakes in the Danube-Tisza Interfluve (and presumably in other shallow lakes)

Impact of nitrogen regime on fatty acid profiles of Desmodesmus quadricaudatus and Chlorella sp. and ability to produce biofuel

Abstract Microalgae have emerged as one of the most promising sources for fatty acid production. Since the various fatty acid profiles (chain length, degree of unsaturation, and branching of the chain) of the different sources influence biodiesel fuel properties, it is important to possess data on how the presence of NaNO3 as nitrogen source can influence the profile of produced fatty acids from algae. The fatty acid profiles of Desmodesmus quadricaudatus and Chlorella sp. were detected in pure batch cultures experiments. BG-11 nitrogen free medium and the medium contained 1.5 g NaNO3 l−1 were used in this investigation. At late stationary growth phase in nitrogen free medium, Chlorella sp. produced 58.39% saturated fatty acids and 41.60% unsaturated fatty acids. While in medium contained 1.5 g NaNO3 l−1Chlorella sp. produced 62.08% saturated fatty acids and 37.92% unsaturated fatty acids. In nitrogen free medium D. quadricaudatus produced 66.92% saturated fatty acids and 33.07% unsaturated fatty acids. While in cultures contained 1.5 g NaNO3 l−1D. quadricaudatus produced 51.62% saturated fatty acids and 48.37% unsaturated fatty acids. The fatty acid profile of Chlorella sp. and D. quadricaudatus that isolated from Egyptian water body and grown in nitrogen free medium may be suitable for biodiesel production. The results discussed and compared to fatty acid profiles produced by other algal species

Capacitive Sensors based on Recycled Carbon Fibre (rCF) Composites

Recycled carbon fibre (rCF) composites are increasingly being explored for applications such as strain sensing, manufacturing of automobile parts, assistive technologies, and structural health monitoring, due to their properties, economic and environmental benefits. The high conductivity of carbon and its wide application for sensing, makes rCF very attractive for integrating sensing into passive structures. In this paper, capacitive sensors have been fabricated using rCF compo-sites of varying compositions. First, we investigated the suitability of recycled carbon fibre pol-ymer composites for different sensing applications. As a proof of concept, we fabricated five touch/proximity sensors and three soil moisture sensors, using recycled carbon fibre composites and their performances compared. The soil moisture sensors were realized using rCF as elec-trodes. This makes them corrosion-resistant and more environmental-friendly compared to conventional soil moisture sensors realized using metallic electrodes. The results of the touch/proximity sensing show an average change in capacitance (ΔC/C~34) for 20mm and (ΔC/C~5) for 100mm, distances of a hand, from the active sensing region. The results of the soil moisture sensors show a stable and repeatable response, with a high sensitivity of ~116pF/ml of water in the linear region. These results demonstrate their respective potential for touch/proximity sensing, as well as smart and sustainable agriculture

A New Servo Control Drive for Electro Discharge Texturing System Industrial Applications Using Ultrasonic Technology

This paper presents a new ultrasonic servo control drive for electro discharge texturing system industrial applications. The new drive is aiming to overcome the current teething issues of the existing electro discharge texturing system, servo control drive level of precision, processing stability, dynamic response and surface profile of the machined products. The new ultrasonic servo control drive consists of three main apparatuses, an ultrasonic motor, electronic driver and control unit. The ultrasonic motor consists of three main parts, the stator, rotor and sliding element. The motor design process, basic configuration, principles of motion, finite element analysis and experimental examination of the main characteristics is discussed in this paper. The electronic driver of the motor consists of two main stages which are the booster and piezoelectric amplifier. The experimental test and validation of the developed servo control drive in electro discharge texturing platform is also discussed and presented in this paper. The initial results showed that the ultrasonic servo control drive is able to provide: a bidirectional of motion, a resolution of &lt;50μm and a dynamic response of &lt;10msec. The electron microscopic micro examination into the textured samples showed that: a clear improvement in machining stability, products surface profile, a notable reduction in the processing time, arcing and short-circuiting teething phenomena

Machine Learning for Run-Time Energy Optimisation in Many-Core Systems

In recent years, the focus of computing has moved away from performance-centric serial computation to energy-efficient parallel computation. This necessitates run-time optimisation techniques to address the dynamic resource requirements of different applications on many-core architectures. In this paper, we report on intelligent run-time algorithms which have been experimentally validated for managing energy and application performance in many-core embedded system. The algorithms are underpinned by a cross-layer system approach where the hardware, system software and application layers work together to optimise the energy-performance trade-off. Algorithm development is motivated by the biological process of how a human brain (acting as an agent) interacts with the external environment (system) changing their respective states over time. This leads to a pay-off for the action taken, and the agent eventually learns to take the optimal/best decisions in future. In particular, our online approach uses a model-free reinforcement learning algorithm that suitably selects the appropriate voltage-frequency scaling based on workload prediction to meet the applications’ performance requirements and achieve energy savings of up to 16% in comparison to state-of-the-art-techniques, when tested on four ARM A15 cores of an ODROID-XU3 platform