Use of untreated wastewater in peri-urban agriculture in Pakistan: risks and opportunities

Water reuse / Waste waters / Water quality / Groundwater / Irrigation practices / Soil properties / Environmental effects / Conjunctive use / Pakistan / Haroonabad

Diagnosis and monitoring of Alzheimer's patients using classical and deep learning techniques

Machine based analysis and prediction systems are widely used for diagnosis of Alzheimer's Disease (AD). However, lower accuracy of existing techniques and lack of post diagnosis monitoring systems limit the scope of such studies. In this paper, a novel machine learning based diagnosis and monitoring of AD-like diseases is proposed. The AD-like diseases diagnosis process is accomplished by analysing the magnetic resonance imaging (MRI) scans using deep learning and is followed by an activity monitoring framework to monitor the subjects’ activities of daily living using body worn inertial sensors. The activity monitoring provides an assistive framework in daily life activities and evaluates vulnerability of the patients based on the activity level. The AD diagnosis results show up to 82% improvement in comparison to well-known existing techniques. Moreover, above 95% accuracy is achieved to classify the activities of daily living which is quite encouraging in terms of monitoring the activity profile of the subject

Changing with the time: New ventures’ quest for innovation

New ventures are often based on new ideas and innovation. For creating and improving the innovation new ventures can draw on internal and external resources, to which they often have limited access. Our study analyses how new ventures can improve their innovation search by entering collaborative workspaces, so-called co-working spaces. In our qualitative study, we use participative observation and analyze 8 cases of new ventures operating in a coworking-space. Key findings are that forms of innovation search differ with respect to the venture’s life cycle. The new ventures search focus alternates between internal and external search, depending on the current stage of the venture. In general, the co-location of ventures in collaborative workspaces offers rich opportunities for social interactions, information exchange, and collaboration which are especially important for early-stage ventures

Fourth-generation SM imprints in B -> K^*l^+l^- decays with polarized K^*

The implication of the fourth-generation quarks in the B -> K^*l^+l^- (l=mu,tau) decays, when K^* meson is longitudinally or transversely polarized, is presented. In this context, the dependence of the branching ratio with polarized K^* and the helicity fractions (f_{L,T}) of K^* meson are studied. It is observed that the polarized branching ratios as well as helicity fractions are sensitive to the NP parameters, especially when the final state leptons are tauons. Hence the measurements of these observables at LHC can serve as a good tool to investigate the indirect searches of new physics beyond the Standard Model.Comment: 13 pages, 10 figures, V2: some of the graphs are modified according to the new data from recent experiments. arXiv admin note: substantial text overlap with arXiv:1107.569

OSCA: a comprehensive open-access system of analysis of posterior capsular opacification

BACKGROUND: This paper presents and tests a comprehensive computerised system of analysis of digital images of posterior capsule opacification (PCO). It updates and expands significantly on a previous presentation to include facilities for selecting user defined central areas and for registering and subsequent merging of images for artefact removal. Also, the program is compiled and thus eliminates the need for specialised additional software. The system is referred to in this paper as the open-access systematic capsule assessment (OSCA). The system is designed to be evidence based, objective and openly available, improving on current systems of analysis. METHODS: Principal features of the OSCA system of analysis are discussed. Flash artefacts are automatically located in two PCO images and the images merged to produce a composite free from these artefacts. For this to be possible the second image has to be manipulated with a registration technique to bring it into alignment with the first. Further image processing and analysis steps use a location-sensitive entropy based texture analysis of PCO. Validity of measuring PCO progression of the whole new system is assessed along with visual significance of scores. Reliability of the system is assessed. RESULTS: Analysis of PCO by the system shows ability to detect early progression of PCO, as well as detection of more visually significant PCO. Images with no clinical PCO produce very low scores in the analysis. Reliability of the system of analysis is demonstrated. CONCLUSION: This system of PCO analysis is evidence-based, objective and clinically useful. It incorporates flash detection and removal as well as location sensitive texture analysis. It provides features and benefits not previously available to most researchers or clinicians. Substantial evidence is provided for this system's validity and reliability

Potentials of molecular based breeding to enhance drought tolerance in wheat (Triticum aestivum L.)

The ability of plant to sustain itself in limited water conditions is crucial in the world of agriculture. To breed for drought tolerance in wheat, it is essential to clearly understand drought tolerant mechanisms. Conventional breeding is time consuming and labor intensive being inefficient with low heritability traits like drought tolerance. Recent progress made in the field of genomics enabling us to access genes linked with drought tolerance has enhanced our understanding of this complex phenomenon. The purpose of this review paper was to briefly overview the accomplishments in molecular breeding for drought tolerance in wheat. Thus, by knowing the genetics of drought tolerance and identifying quantitative trait loci (QTLs) linked with DNA markers will help wheat breeders to develop high yielding drought tolerant cultivars.Key words: Triticum aestivum L, drought tolerance, QTLs, marker assisted selection (MAS)

Diagnosis and monitoring of Alzheimer's patients using classical and deep learning techniques

Machine based analysis and prediction systems are widely used for diagnosis of Alzheimer's Disease (AD). However, lower accuracy of existing techniques and lack of post diagnosis monitoring systems limit the scope of such studies. In this paper, a novel machine learning based diagnosis and monitoring of AD-like diseases is proposed. The AD-like diseases diagnosis process is accomplished by analysing the magnetic resonance imaging (MRI) scans using deep learning and is followed by an activity monitoring framework to monitor the subjects’ activities of daily living using body worn inertial sensors. The activity monitoring provides an assistive framework in daily life activities and evaluates vulnerability of the patients based on the activity level. The AD diagnosis results show up to 82% improvement in comparison to well-known existing techniques. Moreover, above 95% accuracy is achieved to classify the activities of daily living which is quite encouraging in terms of monitoring the activity profile of the subject

Unveiling the Efficiency of Vermicompost Derived from Different Biowastes on Wheat (Triticum aestivum L.) Plant Growth and Soil Health

The present study was conducted to explore the role of different types of vermicomposts (VCs) prepared from different substrates to improve soil health (physical and chemical properties) and wheat plant growth under field conditions. Different combinations of vermicompost prepared from different substrates (cow dung, paper waste, and rice straw) and inorganic fertilizers were applied in soil using wheat as a test plant. The impact of three different VCs on physico-chemical characteristics and nutrient availability in soil was evaluated to examine their efficacy in combination with chemical fertilizers. Temporal trends in vermicomposting treatments at various stages showed significant improvement in physico-chemical attributes of the VCs substrates. All the plant physiological attributes showed significant response where N: P: K 100:50:50 kg ha?1 + 10 t ha?1 cow dung vermicompost was applied. In addition, post-harvest analysis of soil not only revealed that different combinations of the vermicomposting treatments improved the soil health by improving the physico-chemical attributes of the soil. Conclusively, application of cow dung vermicompost along with recommended NPK not only improved crop yield, soil health, reduced insect (aphid) infestation but also fortified grains with Zn and Fe. © 2019 by the authors.7527, 9017; Provozn? Ekonomická Fakulta, ?eská Zem?d?lská Univerzita v Praze: 2019B0011; Higher Education Commision, Pakistan, HECFunding: This work was financially supported by Higher Education Commission of Pakistan (NRPU project # 7527 to Dr. Zubair Aslam and 9017 to Dr. Safdar Bashir). This work was supported by the Internal Grant Agency (IGA) of the Faculty of Economics and Management, Czech University of Life Sciences Prague, grant no. 2019B0011 “Economic analysis of water balance of the current agricultural commodities production mix in the Czech Republic” (Ekonomická anal?za vodní bilance stávajícího produk?ního mixu zem?d?lsk?ch komodit v ?R) to Dr. Mansoor Maitah