On the structure and bionomics of Ptinus tectus (Boield.) : with experiments on its respiration and vitality

The material on which the following notes and descriptions are made was originally supplied by Dr. R. S. MacDougall in June 1929. The origin of the material is unknown. The insects were in ground nut cake (Arachis hypogea) and had been lying in Edinburgh for three or four years.This material supplied my stock during all the experiments and no difficulty was experienced in getting any of the stages of development at any time of the year.For the purpose of. making observations the insects were placed in circular glass topped tin boxes (3 -4" dian. x 1" deep) . These were more satisfactory than cardboard boxes of the same description as the insects can bite holes through cardboard.The Ptinids are principally stored.produce pests having world -wide distribution through being transported in articles of commerce.Niptus holóleucus and Ptinus fur are well known cosmopolitan insects and are very destructive pests, doing a great deal of damage to stored products and textiles.In common with other members of the family, the species under consideration is a stored product pest capable of doing considerable damage both in the adul and larval stages due to the fact that it can adapt itself to a wide variety of food material of animal as well as of vegetable origin.Being a comparatively recent introduction to Europe, its economic importance has.notyet been full ascertained, although it, is reported from various quarters doing considerable damage.So far no work on the life- history and habits of Ptinus tectus, either. in Britain or elsewhere, has been published. Several isolated records and observations are to be found in the literature, however.As a thorough knowledge of the biology and life - history of a pest is essential for_its successful con trol, it is to be hoped that the following investigation on the structure and biology of Ptinus tectus will contribute to this end

Attitudes and Perceptions of Healthcare Providers and Medical Students Towards Clinical Pharmacy Services in United Arab Emirates

Purpose: To explore healthcare providers' (HCPs) and medical students’ attitudes to, and perceptions of the pharmaceutical services that clinical pharmacists can provide in United Arab Emirates.Methods: A total of 535 participants (265 HCPs and 270 medical students) were asked to complete a questionnaire over a period of three months (January through March 2009). Results: Almost three quarters of the students perceived that the clinical pharmacist is an important part of the healthcare team while 82% believed that clinical pharmacists can help improve the quality of medical care in hospitals. Eighty one percent of medical students expressed confidence in the ability of clinical pharmacists to minimize medication errors. Although slightly more than half of the respondents (53%) reported that they did not have clinical pharmacy services in their institutions, there was substantial willingness among physicians and nurses to cooperate with clinical pharmacists. The majority of physicians (92%) and nurses (87%) expressed the view that the clinical pharmacist is an important integral part of the healthcare team. Conclusion: The HCPs and medical students in the study setting valued the role of clinical pharmacists in healthcare delivery. However, new developments in pharmacy services in the UAE hospital setting is recommended for adoption in hospitals.Key words: Clinical pharmacy services, Pharmaceutical care, Perception, Healthcare providers

Political Mediatization of Blasphemy News

The independence of media after the reformation era in Indonesia was followed by the rise of the new media from various ownership backgrounds. Partisan media emerged as a means of delivering political messages to the public. Metro TV and TV One are two national television medias with different objectives, the direction of reporting and ownership. The case of religious blasphemy by Ahok in 2016 is inseparable from the political conditions in Jakarta which at the time held DKI Jakarta Governor Election 2017. The different framing of coverage of this case broadcasted by Metro TV and TV One indicated that there were many political messages veiled in order to achieve the goals of certain parties. The political mediatization shown by these two media by their power in directing the public and political institutions in following the logic of the media. The power of media ownership that has the of political parties background, ultimately eroded the independence and neutrality of the media itself. The political interests become important objectives thus overriding the interests of the public in getting information. The sustainability of media operations was supported by media capitalism becomes an endless economic target. Advertising from various sources are contested as a source of income for media. This study uses a descriptive qualitative research method with a case study of news on a blasphemy case by Ahok on Metro TV and TV One

Exact solutions of equations for the Burgers hierarchy

Some classes of the rational, periodic and solitary wave solutions for the Burgers hierarchy are presented. The solutions for this hierarchy are obtained by using the generalized Cole - Hopf transformation

TRA-954: SOLUTION MODEL FOR URBAN TRAFFIC CONGESTION: EGYPTIAN CASE STUDY

Traffic congestion is a major problem in many cities around the world resulting in massive delays, increased fuel wastage, environmental impact and other negative consequences affecting the daily life of each individual. From a transportation engineering point of view, making the correct decision to eliminate such congestion problems can be very difficult for decision-makers who carry the burden of analyzing large quantities of data which could be vague and conflicting in nature. Therefore, an effective and consistent system is required to simplify the decision-making process of the traffic congestion control. Traffic simulation could be that tool. The seriousness of traffic congestion in Egypt is the main motive for the study presented herein. The study aims at developing a model that sets guidelines on how to approach an urban congested traffic area, be able to tackle the problem and choose the effective engineering solution in terms of either geometry and/or structure. Sectors of El Nasr Road, Cairo with a total length of 2.4 km is chosen as a typical example of an urban area with traffic congestion hotspots. It serves as an excellent location to implement the traffic solution model on. The model is developed and implemented as follows: collection of traffic data, diagnosis of the congestion problems in terms of social, commercial, cultural, and behavioral aspects. The analysis of the data finger points out the flag areas by conducting total and peak traffic volume counts, simulation of the existing traffic conditions to get the delayed travel times of vehicles in that area. The analysis of the output would finally help decide whether such problem would be solved by geometric adjustments of the surface or the problem requires a multi-layered intersection

Fredholm-Volterra integral equation of the first kind with potential kernel

A series method is used to separate the variables of position and time for the Fredholm-Volterra integral equation of the first kind and the solution of the system in L_2 [0,1] × C[0,T], 0 ≤ t ≤ T < ∞ is obtained, the Fredholm integral equation is discussed using Krein's method. The kernel is written in a Legendre polynomial form. Some important relations are also, established and discussed

Optimising deep learning at the edge for accurate hourly air quality prediction

Accurate air quality monitoring requires processing of multi-dimensional, multi-location sensor data, which has previously been considered in centralised machine learning models. These are often unsuitable for resource-constrained edge devices. In this article, we address this challenge by: (1) designing a novel hybrid deep learning model for hourly PM2.5 pollutant prediction; (2) optimising the obtained model for edge devices; and (3) examining model performance running on the edge devices in terms of both accuracy and latency. The hybrid deep learning model in this work comprises a 1D Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) to predict hourly PM2.5 concentration. The results show that our proposed model outperforms other deep learning models, evaluated by calculating RMSE and MAE errors. The proposed model was optimised for edge devices, the Raspberry Pi 3 Model B+ (RPi3B+) and Raspberry Pi 4 Model B (RPi4B). This optimised model reduced file size to a quarter of the original, with further size reduction achieved by implementing different post-training quantisation. In total, 8272 hourly samples were continuously fed to the edge device, with the RPi4B executing the model twice as fast as the RPi3B+ in all quantisation modes. Full-integer quantisation produced the lowest execution time, with latencies of 2.19 s and 4.73 s for RPi4B and RPi3B+, respectively. View Full-Text 10 selecting the best model, we optimise the model for edge devices, using Raspberry Pi 3 Model B+ 11 (RPi3B+) and Raspberry Pi 4 Model B boards (RPi4B). The lite version produced 4 times smaller 12 file size compared to the original version. From the lite version, further size reduction can be 13 achieved by implementing different post-training quantisations. About a 47% reduction can be 14 achieved by dynamic range quantisation, about 45% by full integer quantisation, and about 35% 15 by float16 quantisation. A total of 8272 hourly samples were continuously executed directly at the 16 edge. The RPi4B executed these data two times faster compared to the RPi3B+ in all quantisation 17 modes. Full-integer quantisation produced the most effective time execution, with latencies of 18 2.19 seconds and 4.73 seconds for RPi4B and RPi3B+, respectively

Estimation of missing air pollutant data using a spatiotemporal convolutional autoencoder

A key challenge in building machine learning models for time series prediction is the incompleteness of the datasets. Missing data can arise for a variety of reasons, including sensor failure and network outages, resulting in datasets that can be missing significant periods of measurements. Models built using these datasets can therefore be biased. Although various methods have been proposed to handle missing data in many application areas, more air quality missing data prediction requires additional investigation. This study proposes an autoencoder model with spatiotemporal considerations to estimate missing values in air quality data. The model consists of one-dimensional convolution layers, making it flexible to cover spatial and temporal behaviours of air contaminants. This model exploits data from nearby stations to enhance predictions at the target station with missing data. This method does not require additional external features, such as weather and climate data. The results show that the proposed method effectively imputes missing data for discontinuous and long-interval interrupted datasets. Compared to univariate imputation techniques (most frequent, median and mean imputations), our model achieves up to 65% RMSE improvement and 20–40% against multivariate imputation techniques (decision tree, extra-trees, k-nearest neighbours and Bayesian ridge regressors). Imputation performance degrades when neighbouring stations are negatively correlated or weakly correlated