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Annually, the thermal coal industries produce billion tons of fly-ash (FA) as a waste by-product. Which has been proficiently used for the manufacture of FA based geopolymer concrete (FGC). To accelerate the usage of FA in building industry, an innovative machine learning techniques namely gene expression programming (GEP) and multi expression programming (MEP) are employed for forecasting the compressive strength of FGC. The comprehensive database is constructed comprising of 311 compressive strength results. The obtained equations relate the compressive strength of FGC with eight most effective parameters i.e., curing regime (T), time for curing (t) in hours, age of samples (A) in days, percentage of total aggregate by volume (% Ag), molarity of sodium hydroxide (NaOH) solution (M), silica (SiO2) solids percentage in sodium silicate (Na2SiO3) solution (%S), superplasticizer (%P) and extra water (%EW) as percent FA. The accurateness and predictive capacity of both GEP and MEP model is assessed via statistical checks, external validation criteria suggested by different researcher and then compared with linear regression (LR) and non-linear regression (NLR) models. In comparison with MEP equation, the GEP equation has lesser statistical error and higher correlation coefficient. Also, the GEP equation is short and it would be easy to use in the field. So, the GEP model is further utilized for sensitivity and parametric study. This research will increase the re-usage of hazardous FA in the development of green concrete that would leads to environmental safety and monetarist reliefs.

The international development agenda was catalyzed into a higher gear during 2015, primarily through the establishment of the Sustainable Development Goals (SDGs), which cover a broad range of interlinked development issues. The overall development objectives of poverty eradication, universal health, universal education, removing hunger, and achieving gender equality squarely intersect with worldwide protection of water as a resource and its universal provision as a service. This chapter explores how water truly fits within this emerging international development agenda. Discussions revolve around the various water-related targets in the SDG framework and how human, institutional, and financial resources can be mobilized to achieve these targets. A number of emerging ethical dilemmas related to the scope of the targets, addressing complexity of targets and equitable mobilization of financial resources, are discussed. The chapter offers some insights into what the future might hold if the SDG framework is implemented in an imperfect way.

The purpose of this article is to demonstrate the potential of gene expression programming (GEP) in anticipating the compressive strength of circular CFRP confined concrete columns. A new GEP model has been developed based on a credible and extensive database of 828 data points to date. Numerous analyses were carried out to evaluate and validate the presented model by comparing them with those presented previously by different researchers along with external validation comparison. In comparison to other artificial intelligence (AI) techniques, such as Artificial Neural Networks (ANN) and the adaptive neuro-fuzzy interface system (ANFIS), only GEP has the capability and robustness to provide output in the form of a simple mathematical relationship that is easy to use. The developed GEP model is also compared with linear and nonlinear regression models to evaluate the performance. Afterwards, a detailed parametric and sensitivity analysis confirms the generalized nature of the newly established model. Sensitivity analysis results indicate the performance of the model by evaluating the relative contribution of explanatory variables involved in development. Moreover, the Taylor diagram is also established to visualize how the proposed model outperformed other existing models in terms of accuracy, efficiency, and being closer to the target. Lastly, the criteria of external validation were also fulfilled by the GEP model much better than other conventional models. These findings show that the presented model effectively forecasts the confined strength of circular concrete columns significantly better than the previously established conventional regression-based models.

Traditional object detection answers two questions; “what” (what the object is?) and “where” (where the object is?). “what” part of the object detection can be fine grained further i-e. “what type”, “what shape” and “what material” etc. This results in shifting of object detection task to object description paradigm. Describing object provides additional detail that enables us to understand the characteristics and attributes of the object (“plastic boat” not just boat, “glass bottle” not just bottle). This additional information can implicitly be used to gain insight about unseen objects (e.g. unknown object is “metallic”, “has wheels”), which is not possible in traditional object detection. In this paper, we present a new approach to simultaneously detect objects and infer their attributes, we call it Detectand- Describe (DaD) framework. DaD is a deep learning-based approach that extends object detection to object attribute prediction as well. We train our model on aPascal train set and evaluate our approach on aPascal test set. We achieve 97.0% in Area Under the Receiver Operating Characteristic Curve (AUC) for object attributes prediction on aPascal test set. We also show qualitative results for object attribute prediction on unseen objects, which demonstrate the effectiveness of our approach for describing unknown objects.

Graphene nanostructures exhibit a wide range of remarkable properties suitable for many applications, including those in the field of biomedical engineering. In this work, plasma-enhanced chemical vapor deposition was utilized at different applied RF power for the fabrication of vertical graphene nanowalls on silicon and quartz substrates from an inherently volatile carbon precursor without the use of any catalyst. AFM confirmed the presence of very sharp exposed graphene edges, with associated high surface roughness. The hydrophobicity of the material increased with the power of deposition, reaching the water contact angle of 123 ˚ for 500 W. Confocal scanning laser microscopy demonstrated that the viability of gram-negative Escherichia coli and gram-positive Staphylococcus aureus cells were 33% and 37% when incubated on graphene samples, respectively, compared to controls (quartz) that showed the viability of 82% and 84%, respectively. SEM verified significant morphological damage to bacterial cell walls by the sharp edges of graphene walls, with cells appearing abnormal and deformed. The presented data clearly contributed to the current understanding of the mechanical-bactericidal mechanism of vertically oriented graphene nanowalls upon direct contact with microorganisms.

To avoid time-consuming, costly, and laborious experimental tests that require skilled personnel, an effort has been made to formulate the depth of wear of fly-ash concrete using a comparative study of machine learning techniques, namely random forest regression (RFR) and gene expression programming (GEP). A widespread database comprising 216 experimental records was constructed from available research. The database includes depth of wear as a response parameter and nine different explanatory variables, i.e., cement content, fly ash, water content, fine and coarse aggregate, plasticizer, air-entraining agent, age of concrete, and time of testing. The performance of the models was judged via statistical metrics. The GEP model gives better performance with R2 and ρ equals 0.9667 and 0.0501 respectively and meet with the external validation criterion suggested in the previous literature. The k-fold cross-validation also verifies the accurateness of the model by evaluating R2, RSE, MAE, and RMSE. The sensitivity analysis of GEP equation indicated that the time of testing is the influential parameter. The results of this research can help the designers, practitioners, and researchers to quickly estimate the depth of wear of fly-ash concrete thus shortening its ecological susceptibilities that push to sustainable and faster construction from the viewpoint of environmentally friendly waste management.

Introduction Ankle-brachial pressure indices (ABIs) continue to form the basis of diagnostics for lower extremity arterial disease (LEAD). However, there remains a paucity of data to support its accuracy. This study aims to evaluate its diagnostic sensitivity and specificity using established arterial-imaging modalities as a benchmark. Methods In this retrospective study, a regional, prospectively maintained, vascular laboratory database was interrogated to identify referred patients with arterial disease who underwent concomitant assessment with ABI and lower limb arterial duplex ultrasound (DUS). Duplex acted as the reference standard. Those who had peripheral computed tomography angiogram (CTA) within 3 months of initial assessment were included in a subgroup analysis to correlate ABI with CTA. The primary end point was the sensitivity and specificity of ABI compared with DUS as the reference standard. Results Concomitant assessment was performed in 438 limbs (250 patients) over a 27-month period. The ABI was normal (0.9 to 1.4) in 196 limbs (44.9%) and abnormal in the remaining 241 limbs (55.1%). False-positive results occurred in 83 out of 241 limbs (34.4%), and false-negative results occurred in 54 limbs out of 196 (27.5%). True-positive results were 158 out of 241 limbs (65.6%), whereas true-negative results were 142 out of 196 limbs (72.4%). ABI using DUS as a benchmark identified a sensitivity for peripheral artery disease of 72.3% and a specificity of 69.3%. Concomitant CTA imaging was available in 200 limbs. The sensitivity and specificity of ABI correlated with CTA were 65.5 and 68.8%, respectively. Conclusion ABIs have a moderate predictive value in the diagnosis of LEAD. Normal range outcomes cannot be taken to infer the absence of LEAD and, as such, further arterial imaging in the form of DUS or angiography should be strongly considered in those with suspected underlying disease requiring intervention. Further noninvasive tests such as exercise studies or pulse volume waveforms should be considered, if diagnostic uncertainty exists, in those requiring nonoperative intervention and risk factor control.

The ultrafine fly ash (FA) is a hazardous material collected from coal productions, which has been proficiently employed for the manufacturing of geopolymer concrete (GPC). In this study, the three artificial intelligence (AI) techniques, namely, artificial neural network (ANN), adaptive neuro-fuzzy interface (ANFIS), and gene expression programming (GEP) are used to establish a reliable and accurate model to estimate the compressive strength (f′c) of fly ash–based geopolymer concrete (FGPC). A database of 298 instances is developed from the peer-reviewed published work. The database consists of the ten most prominent explanatory variables and f′c of FGPC as a response parameter. The statistical error checks and criteria suggested in the literature are considered for the verification of the predictive strength of the models. The statistical measures considered in this study are MAE, RSE, RMSE, RRMSE, R, and performance index (ρ). These checks verify that the ANFIS predictive model gives an outstanding performance followed by GEP and ANN predictive models. In the validation stage, the coefficient of correlation (R) for ANFIS, GEP, and ANN model is 0.9783, 0.9643, and 0.9314, respectively. All three models also fulfill the external verification criterion suggested in the literature. Generally, the GEP predictive model is ideal as it delivers a simplistic and easy mathematical equation for future use. The k-fold cross-validation (CV) of the GEP model is also conducted, which verifies the robustness of the GEP predictive model. Furthermore, the parametric study is carried via proposed GEP expression. This confirms that the GEP model accurately covers the influence of all the explanatory variables used for the prediction of f′c of FGPC. Thus, the proposed GEP equation can be used in the preliminary design of FGPC.

Leaves are the primary food‐producing organs for a plant that carry out photosynthesis and contribute to biomass and grain yield. Leaf senescence is a developmentally regulated physiological process but early leaf senescence is known to negatively affect plant yield. The cuticle is an outer waxy protective layer on the leaf surface which protects plants from pathogens attack as well as dehydration. Our understanding of the molecular mechanisms underlying cuticle development and leaf senescence is still immature. The present study reports the role of the DEGENERATED PANICLE AND PARTIAL STERILITY 1 (DPS1) gene encoding a cystathionine β‐synthase (CBS) domain‐containing protein in cuticle development and leaf senescence in rice. The dps1 loss‐of‐function mutant showed leaf senescence phenotype with twisted leaves, significantly reduced chlorophyll content and degenerated chloroplasts characterized by a reduced number of starch granules and an abundance of osmiophilic bodies. Furthermore, dps1 leaves displayed defective cuticle development, reduced wax and cutin compounds, and lower relative water content as compared with wild type. Physiological assays showed significantly higher accumulation of reactive oxygen species (ROS) accompanied by enhanced DNA fragmentation in dps1 leaves, which could be associated with chloroplast degeneration and defective cuticle development. Transcriptome analysis revealed altered expression of several critical genes related to photosynthesis and wax/cutin pathway. This study revealed a crucial role of DPS1 in regulating leaf cuticle development and senescence by affecting the expression of several genes. Thus, a moderate expression of DPS1 is necessary for better plant growth and productivity.

For the production of geopolymer concrete (GPC), fly-ash (FA) like waste material has been effectively utilized by various researchers. In this paper, the soft computing techniques known as gene expression programming (GEP) are executed to deliver an empirical equation to estimate the compressive strength of GPC made by employing FA. To build a model, a consistent, extensive and reliable data base is compiled through a detailed review of the published research. The compiled data set is comprised of 298 experimental results. The utmost dominant parameters are counted as explanatory variables, in other words, the extra water added as percent FA (), the percentage of plasticizer (), the initial curing temperature (), the age of the specimen (), the curing duration (), the fine aggregate to total aggregate ratio (), the percentage of total aggregate by volume (), the percent SiO2 solids to water ratio () in sodium silicate (Na2SiO3) solution, the NaOH solution molarity (), the activator or alkali to FA ratio (), the sodium oxide (Na2O) to water ratio () for preparing Na2SiO3 solution, and the Na2SiO3 to NaOH ratio (). A GEP empirical equation is proposed to estimate the of GPC made with FA. The accuracy, generalization, and prediction capability of the proposed model was evaluated by performing parametric analysis, applying statistical checks, and then compared with non-linear and linear regression equations.

Background Grain size is a key agronomic trait that is directly associated with grain yield in rice. Although several genes related to grain size in rice have been identified, our understanding of the mechanism of grain development is still limited. Results In this study, we reported the characterization of a novel seed size mutant mini seed 2 (mis2), in which the grain showed reduced length, width and thickness along with wrinkled surface. Microscopic analysis revealed that the spikelet epidermal cell size was reduced but the cell number was increased in the mis2 mutant, suggesting that MIS2 controls grain size by coordinately regulating epidermal cell size and cell number. Map-based cloning revealed that MIS2 encodes a receptor-like kinase CRINKLY4 (CR4) which showed the highest expression in developing panicles. The MIS2 protein is localized primarily on the plasma membrane along with the endosome. However, the Arg258Gln mutation located in extracellular domain in the mis2 mutant disturbed its subcellular localization. Additionally, three major haplotypes of MIS2 were identified in the japonica, indica and aus rice cultivars. The 18-bp InDel (insertion and deletion) in the 5′-UTR (untranslated region) caused different expression level of MIS2 in haplotypes. Conclusions We reported a key role of OsCR4 in controlling grain size and shape by coordinately regulating epidermal cell size and cell number. The Arg258 in the extracellular seven-repeat domain is essential for the correct subcellular behavior and function of the OsCR4 protein.

Plants adapt to harsh environments particularly high temperature stress by regulating their physiological and biochemical processes, which are key tolerance mechanisms. Thus, identification of heat-tolerant rice genotypes and reliable selection indices are crucial for rice improvement programs. Here, we evaluated the response of a rice mutant population for high-temperature stress at the seedling and reproductive stages based on agronomic, physiological and molecular traits. The estimate of variance components revealed significant differences (POsSRFP1expression under heat stress, suggesting their key role in regulating heat tolerance in rice. The heat-tolerant mutants identified in this study could be used in breeding programs and the development of mapping populations to unravel the underlying genetic architecture for heat-stress adaptability.Summary text for table of contentsHeat stress probably due to changing climate scenario has become a serious threat for global rice production. On the other side, efforts to develop high yielding cultivars have led to the reduced genetic variability to withstand harsh environmental conditions. This study aimed to identify novel heat tolerant mutants developed through gamma irradiation which will provide a unique genetic resource for breeding programs. Further, we have identified reliable selection indices for screening heat-tolerant rice germplasm at early growth stages.

Anemia is considered a severe public health problem by World Health Organization. The purpose of this study was to determine the prevalence of Anemia with associated risk factors in pregnant women. This study was done in OPD of gynaecology department of Allied hospital and DHQ hospital Faisalabad by using cross-sectional study. The study was conducted from 15th June 2014 to 15th August 2014 with the sample size of 30 pregnant women, selected through convenient sampling. Data was collected through questionnaire comprising of close ended questions including history of haemorrhage, intake of iron supplements, green leafy vegetables and meat, status of pregnancy either prim gravida or multigravida. Anemia was more prevalent in 78% women who are uneducated, had poor sanitary condition, less intake of iron and meat, had history of heavy menstruation and postpartum haemorrhage. Anemia in pregnant women is a public health problem, coexisting with iron, folate and vitamin B12 deficiency. Hence to overcome the prevalence of Anemia, Anemia control programs with reasonable care should be recommended. Keywords: Anemia, pregnancy, postpartum haemorrhage, iron supplements.

Objective: We aimed this research to assess the serum levels of calcium (Ca), potassium (K) and sodium (Na) in SAM cases and also compared their association in the absence and presence of diarrhoea. Methods and material: Our cross-sectional research was carried out at Services Hospital, Lahore from March 2016 to July 2017. We included children in the age bracket of 6 – 59 months who were diagnosed with SAM and also hospitalized. Two groups were made out of the total research participants as Group – I and II having respectively diarrhoea cases and non-diarrhoea cases. Blood samples were also drained for the serum of calcium, sodium and potassium. Results: A total of one hundred hospitalized children were included in the research with a mean age of (23.56 ± 13.80) months. Male children had dominance over females as males were 62% and females were 38%. Mean values of sodium, potassium and calcium serums was respectively (138.46 ± 4.14) mmol/L, (3.961 ± 0.691) mmol/L and (8.359 ± 0.61) mg/dl. Diarrhoea cases were included in Group – I (67) and Non-diarrhea cases were included in Group – II (33). In Group I and II the mean level of sodium serum was respectively (138.51 ± 4.22) and (138.36 ± 4.05) mmol/L with a significant P-value of (0.10). The level of potassium serum was (3.89 ± 0.67) and (4.1 ± 0.72) respectively in Group – I and II with a significant P-value as (0.009). The level of calcium serum was (8.39 ± 0.6) and (8.34 ± 0.73) in Group – I and II with a P-Value of (0.71). Isolated hyponatremia was reported in Group – I and II respectively as 10.4% & 9.1% (P-Value = 0.83); whereas, an isolated hypokalemia in Group – I and II was respectively 26.9% & 18.2% with a significant P-value of (0.34). Conclusion: SAM patients whether with diarrhoea or without diarrhoea were commonly reported for electrolytes disturbances. Keywords: Diarrhea, SAM (Severe Acute Malnutrition), Electrolytes, Sodium (Na), Potassium (K), Calcium (Ca), Hypokalemia and Hyponatremia.

Background: Salinity is among the most damaging abiotic stresses for rice production which limits its growing area. The present research was conducted to evaluate five rice varieties for salinity tolerance at seedling stage. Methods: Experiment was conducted in triplicate and in two sets. One set was grown as a control (non-stress) and other as salt stressed. Salt stress of 15 dS/m was applied to one set of rice seedlings under controlled conditions. Data for different growth related morpho-physiological traits, i.e. germination percentage, root and shoot length, seedling fresh and dry weight, Na+ and K+ uptake were recorded after 15 days of seedling emergence under control as well as salinity condition. Results: Significant differences were observed among the genotypes under both the treatments and interaction of the evaluated traits suggested a significant variability among the rice genotypes under salt stress. NIAB-IRRI-9, Basmati-198 and KSK-133 were proved to be relatively salt tolerant varieties as they showed good performance for the recorded parameters. However, Basmati-385 was observed a salt sensitive variety due to highest reduction in seedling fresh and dry weight along with the maximum Na+ uptake. Conclusion: Based on obtained results, it was concluded that the evaluated morpho-physiological traits were useful to screen rice cultivars for salinity stress. In addition, NIAB-IRRI-9, Basmati-198 and KSK-133 can be used in breeding programs as tolerant check and Basmati-385 can be used as sensitive check.

A 72-year-old woman underwent left wide local excision and an axillary node sampling in 2005 for a T2, N0, M0 invasive ductal carcinoma followed by adjuvant chemotherapy, radiotherapy and hormonal therapy. In 2011, she developed spontaneous blue breast discolouration. Initial examination revealed global breast firmness, skin thickening and eechymosis. Despite no evidence of recurrent disease, the patient was scheduled for fortnightly review. This review demonstrated marked worsening discolouration and new nipple areolar complex blood-filled blisters. Despite concurrent antiplatelet therapy as a possible cause, the breast clinician9s clinical concern of angiosarcoma led to a skin punch biopsy, revealing only non-specific lymphocytic infiltrate. Interval mammographic assessment demonstrated non-specific architectural distortion. Regardless of these benign findings, a breast MRI was performed demonstrating possible breast angiosarcoma. Ultrasound-guided biopsy demonstrated low-grade breast angiosarcoma. The patient underwent completion mastectomy, for a 150 mm high-grade postradiotherapy angiosarcoma. Annual clinical review and contralateral mammography is planned.