A biological analysis of endocrine-disturbing chemicals in camel meat sector in Kazakhstan

Endocrine disrupting chemicals (EDCs) consist of a diverse group of industrial chemicals and pharmacological agents. The use of instrumental analyses as the first screening tool might not be cost-effective to identify the existence of enormous numbers of chemical contaminants in environments. Also, knowledge of the concentration of individual residues is difficult to use to evaluate biological impacts of contaminants to wildlife and humans. The primary objective of present paper is a biological analysis of camel meat status in Kazakhstan. After a post-independence decline linked to the restructuration of collective structures in agriculture and food sector, the camel sector increased regularly. The camel population increased annually by 0.5% on average since the independence, while camel meat production increased by 1.2%. The slaughtering rate appeared still high, but stable for 10 years. Camel meat represented 1% only of the total red meat consumed in the country but this proportion is increasing. Despite this growing interest for camel meat, the sector is not organized in Kazakhstan. Despite recent initiatives in big towns the breeding is still traditional, and the consumption is essentially rural. Moreover, there are very few processing and no standard regarding this meat. The perspectives of development require however, the establishment of formal rules

A biological analysis of endocrine-disturbing chemicals in camel meat sector in Kazakhstan

Endocrine disrupting chemicals (EDCs) consist of a diverse group of industrial chemicals and pharmacological agents. The use of instrumental analyses as the first screening tool might not be cost-effective to identify the existence of enormous numbers of chemical contaminants in environments. Also, knowledge of the concentration of individual residues is difficult to use to evaluate biological impacts of contaminants to wildlife and humans. The primary objective of present paper is a biological analysis of camel meat status in Kazakhstan. After a post-independence decline linked to the restructuration of collective structures in agriculture and food sector, the camel sector increased regularly. The camel population increased annually by 0.5% on average since the independence, while camel meat production increased by 1.2%. The slaughtering rate appeared still high, but stable for 10 years. Camel meat represented 1% only of the total red meat consumed in the country but this proportion is increasing. Despite this growing interest for camel meat, the sector is not organized in Kazakhstan. Despite recent initiatives in big towns the breeding is still traditional, and the consumption is essentially rural. Moreover, there are very few processing and no standard regarding this meat. The perspectives of development require however, the establishment of formal rules

Target prices for mass production of tyrosine kinase inhibitors for global cancer treatment

OBJECTIVE: To calculate sustainable generic prices for 4 tyrosine kinase inhibitors (TKIs). BACKGROUND: TKIs have proven survival benefits in the treatment of several cancers, including chronic myeloid leukaemia, breast, liver, renal and lung cancer. However, current high prices are a barrier to treatment. Mass production of low-cost generic antiretrovirals has led to over 13 million people being on HIV/AIDS treatment worldwide. This analysis estimates target prices for generic TKIs, assuming similar methods of mass production. METHODS: Four TKIs with patent expiry dates in the next 5 years were selected for analysis: imatinib, erlotinib, lapatinib and sorafenib. Chemistry, dosing, published data on per-kilogram pricing for commercial transactions of active pharmaceutical ingredient (API), and quotes from manufacturers were used to estimate costs of production. Analysis included costs of excipients, formulation, packaging, shipping and a 50% profit margin. Target prices were compared with current prices. Global numbers of patients eligible for treatment with each TKI were estimated. RESULTS: API costs per kg were $347–$746 for imatinib, $2470 for erlotinib,$4671 for lapatinib, and $3000 for sorafenib. Basing on annual dose requirements, costs of formulation/packaging and a 50$128–$216 for imatinib,$240 for erlotinib, $1450 for sorafenib, and$4020 for lapatinib. Over 1 million people would be newly eligible to start treatment with these TKIs annually. CONCLUSIONS: Mass generic production of several TKIs could achieve treatment prices in the range of $128–$4020 per person-year, versus current US prices of $75161–$139 138. Generic TKIs could allow significant savings and scaling-up of treatment globally, for over 1 million eligible patients

Internet of things-based framework for public transportation fleet management in the Free State

Thesis (Masters: Information Technology) -- Central University of Technology, Free State, 2019The poor service delivery by the Free State public transportation system inspired us to design a framework solution to improve the current system. This qualitative study focuses on improving the management of the public transportation fleet. One of the most recently developed technologies in Information and Communication Technology (ICT), namely the Internet of Things (IoT), was utilised to develop this framework. Existing problems were identified through research observations, analyses of the current system, analyses of the current problem areas, as well as participants’ questionnaire answers and recommendations, the participants being the passengers, drivers and vehicle owners. The framework was developed in two phases, namely a hardware phase that makes use of ICT sensors (e.g. RFID, GPS, GPRS, IR, Zigbee, WiFi), and a software phase that uses an internet connection to communicate with the different ICT devices. The software utilised a Graphic User Interface (GUI) to ensure that the software is user-friendly and addresses possible problems and barriers such as multiple language interfaces and different ICT skills levels. The newly designed framework offers different services and solutions to meet the participants’ needs, such as real-time tracking for public transport vehicles to help passengers manage their departure and arrival times, as well as for vehicle owners to monitor their own vehicles. In turn, vehicle arrival notifications will encourage passengers to be on time so that vehicles will not be delayed unnecessarily. Another feature is counting devices that can be installed inside the vehicles, which will inform vehicle owners how many passengers are being transported by a vehicle. The passenger pre-booking system will support the drivers when planning their trips/routes. Finally, the framework was designed to fulfil all the participants’ needs that were indicated in the questionnaires in order to achieve the goal of the research study

Examination of the factors that predict job satisfaction.

The purpose of this study was to determine the predictors of job satisfaction among three departments within an organization. The study used five predictors: leadership/top management communication with subordinates, feedback received from one\u27s supervisor, training opportunities for employees, career opportunities within the company, and teamwork or cooperation among employees. Using data from 608 participants, the present study examined the relationships between each of these five predictors and job satisfaction. Consistent with hypotheses, each of these predictors was significantly related to job satisfaction. Moreover, leadership/top management communication with subordinates (except for one department surveyed), career opportunities within the company, and teamwork or cooperation among employees contributed most to the prediction of job satisfaction for all the departments. Implications of the findings are discussed

Interactive, multi-purpose traffic prediction platform using connected vehicles dataset

Traffic congestion is a perennial issue because of the increasing traffic demand yet limited budget for maintaining current transportation infrastructure; let alone expanding them. Many congestion management techniques require timely and accurate traffic estimation and prediction. Examples of such techniques include incident management, real-time routing, and providing accurate trip information based on historical data. In this dissertation, a speech-powered traffic prediction platform is proposed, which deploys a new deep learning algorithm for traffic prediction using Connected Vehicles (CV) data. To speed-up traffic forecasting, a Graph Convolution -- Gated Recurrent Unit (GC-GRU) architecture is proposed and analysis of its performance on tabular data is compared to state-of-the-art models. GC-GRU's Mean Absolute Percentage Error (MAPE) was very close to Transformer (3.16 vs 3.12) while achieving the fastest inference time and a six-fold faster training time than Transformer, although Long-Short-Term Memory (LSTM) was the fastest in training. Such improved performance in traffic prediction with a shorter inference time and competitive training time allows the proposed architecture to better cater to real-time applications. This is the first study to demonstrate the advantage of using multiscale approach by combining CV data with conventional sources such as Waze and probe data. CV data was better at detecting short duration, Jam and stand-still incidents and detected them earlier as compared to probe. CV data excelled at detecting minor incidents with a 90 percent detection rate versus 20 percent for probes and detecting them 3 minutes faster. To process the big CV data faster, a new algorithm is proposed to extract the spatial and temporal features from the CSV files into a Multiscale Data Analysis (MDA). The algorithm also leverages Graphics Processing Unit (GPU) using the Nvidia Rapids framework and Dask parallel cluster in Python. The results show a seventy-fold speedup in the data Extract, Transform, Load (ETL) of the CV data for the State of Missouri of an entire day for all the unique CV journeys (reducing the processing time from about 48 hours to 25 minutes). The processed data is then fed into a customized UNet model that learns highlevel traffic features from network-level images to predict large-scale, multi-route, speed and volume of CVs. The accuracy and robustness of the proposed model are evaluated by taking different road types, times of day and image snippets of the developed model and comparable benchmarks. To visually analyze the historical traffic data and the results of the prediction model, an interactive web application powered by speech queries is built to offer accurate and fast insights of traffic performance, and thus, allow for better positioning of traffic control strategies. The product of this dissertation can be seamlessly deployed by transportation authorities to understand and manage congestions in a timely manner.Includes bibliographical references

Subcontractor's Selection Practice in the Gaza Strip

Construction contracting involves subcontracting. The evolution of subcontractors has a substantial impact on the construction process. An increasing amount of building construction projects are contracted to subcontractors. Despite the significant role that is played by subcontractors, little attention has been paid to the selection process of specialty contractors. The aim of this study is to identify and evaluate the main effective factors considered by general contractors in the selection of subcontractors. This study is based on a questionnaire survey of thirty-one main contractors in the Gaza Strip. The results indicate the most important effective factors in subcontractors' selection are project size and complexity, applying specification and quality, compliance with programming and quality, experience of subcontractor, and nature and specialty of subcontractors

Rapid detection of the avian influenza virus H5N1 subtype in Egypt

Influenza A virus continue to cause widespread morbidity and mortality. The unprecedented spread of highly pathogenic avian influenza virus subtype H5N1 in Egypt is threatening poultry and public health systems. Effective diagnosis and control management are needed to control the disease. To this end, polyclonal antibodies (PAbs) were developed against the H5N1 avian influenza virus (AIV) and implemented an enzyme-linked immunosorbent assay (ELISA) to detect the H5 viral antigen. A group of rabbits was immunized with viral vaccine (H5N1) therefore, purified antibodies from rabbit serum, which secrete immunoglobulin G (IgG) was served as the detector antibody after conjugation with horse radish peroxidase and fluorescent isothiocyanate (FITC). The reactivity of the obtained peroxidase and fluorescent conjugated PAb of influenza virus revealed that they are specifically recognized H5N1 virus antigen. Specimens containing AIV subtypes collected from different Governments in Egypt yielded specific and strong signals with hemagglutination test. The detection limit of ELISA using the prepared peroxidae conjugated PAbs was 1:100000, while using fluorescent conjugated PAb was 1:10000. Reconstituted clinical samples consisting of H5 AIVs mixed with pharyngeal-tracheal mucus from healthy chickens also yielded positive signals in ELISA, and the results were confirmed using reference virus antigens. This investigation enhanced the usage of these PAbs in the surveillance and diagnosis of H5N1 AIV in Egypt.Keywords: Avian influenza virus, H5N1, fluorescent antibody enzyme-linked immunosorbent assay (ELISA)technique, polyclonal antibodiesAfrican Journal of Biotechnology Vol. 12(19), pp. 2748-275

Benefits of Electric Road System with charging from below technology to battery electric vehicles

This study uses GPS-logged movement patterns of 412 private conventional cars in Sweden to model the potential impacts and benefits of implementing an Electric Road System (ERS) that is designed to be usable also to passenger Battery Electric Vehicles (BEVs). The study inspects the possibility of eliminating stationary charging stations and allowing smaller batteries in BEVs and its economic benefit. The study examines different ERS placement scenarios, charging options and BEV shares. The results show that an ERS makes possible a drastic reduction in the required battery capacities to complete all driving. With only charging at home, the mean reduction in battery capacity is 75%, or to about 14 kWh. An average battery capacity of 87 kWh could eliminate all stationary charging. In Sweden, with an ERS net economic benefits of 29-45 billion € are expected from reduced battery capacities alone, depending on the stationary charging pattern, percentage of cars switching to BEV, ERS placement and ERS charging rate