Search

Land is a pricy and scarce resource, particularly in urban areas. If it is utilized effectively, suitable sites can be allocated for urban wastewater safe disposal through effluents treatment and reuse. This study used multicriteria decision-making approach known as weighted linear combination to map the suitability of an on-site natural wastewater treatment system in the urban and industrial environments—selecting Faisalabad City in Pakistan as the study area. Location of the study region in a water-scarce zone increases the scope of the untreated wastewater disposal into land and water bodies as well as its reuse for irrigation. Data on climate parameters (i.e., temperature, rainfall, and evapotranspiration), geophysical parameters, (i.e., including soil, topography, depth to groundwater table, and groundwater electrical conductivity), and socioeconomic parameters (i.e., land cover maps and proximity from roads/railways and rivers/canals) are used for the development of weighted linear combination based suitability index maps. Analytical hierarchical process-based heuristic approach is adapted to estimate the relative importance of all the parameters. Weights with less than 0.10 consistency ratio are used in the preparation of the final suitability map. The land area required for the proposed on-site wastewater treatment system is estimated as a function of climatic factors and wastewater characteristics. Adoption of the proposed multicriteria framework helped identifying four potentially environment-friendly and economically viable sites. The results show that geospatial multicriteria framework could efficiently help urban planners in sustainable wastewater management, particularly in developing countries where climate-change-induced water scarcity and associated health issues are critical. • Better wastewater management in cities is critical for socio-ecological well-being. • A spatial framework for siting an on-site wastewater treatment plant is proposed. • The framework identifies potential ergonomic sites in a decentralized manner. • The framework acts as a decision-support-system for urban planning/design. • The application of the spatial framework is equally useful for other urban regions. [ABSTRACT FROM AUTHOR]

A green approach was employed for the synthesis of heterostructures of g-C 3 N 4 @MoO 3 and characterized by XRD, UV–vis, SEM and FTIR techniques to evaluate the structural, morphological, and optical features of the heterostructures. The XRD analysis revealed the high crystallinity and intensity of g-C 3 N 4 /MoO 3 higher than the g-C 3 N 4 and MoO 3. The g-C 3 N 4 shows a crumpled layered arrangement and a MoO 3 rod-like structure. The refractive index (n), bandgap energy (E g) and extinction coefficient (k) (optical properties) are determined by UV–vis spectroscopy. The g-C 3 N 4 /MoO 3 optical properties like k (2.15), n (2.37), dielectric coefficient (0.43), electronegativity (0.739) and bandgap energy (2.75 eV) were enhanced significantly. In visible region, the g-C 3 N 4 /MoO 3 demonstrated excellent performance for the degradation of RhB dye. The mobility and detachment of electron-hole pairs in type-II heterojunction semiconductors are responsible for the enhancement of photocatalytic activity (PCA) in visible region, which can be utilized for the dyes remediation in effluents under natural sunlight irradiation. [Display omitted] • A green route was employed for graphatic-C 3 N 4 @MoO 3 heterostructure synthesis. • Characterization was done by XRD, UV–vis, SEM and FTIR techniques. • The optical properties enhanced of g-C 3 N 4 @MoO 3 versus g-C 3 N 4 and MoO 3. • g-C 3 N 4 @MoO 3 showed promising catalytic activity under visible light irradiation. [ABSTRACT FROM AUTHOR]

Achieving Sustainable Development Goal (SDG)-4 prerequisites quality education provisioning. In this context, we present important insights and references for educational interventions/investments to be tailored to local necessities in Pakistan. Several spatial statistical models such as the Global Moran's I-based spatial autocorrelation, multivariate clustering, and the Cluster and Outlier model are used to explore geographic heterogeneities and patterns. Additionally, significant determinants among several socio-economic, spatio-environmental, and infrastructural variables are identified for education status (EdS) using regression. As a result, a large geographic inequality regarding EdS is found in Pakistan. While a strong spatial association is evident, the districts in northern Punjab are identified as significant hotspots—higher EdS clusters (∼22% of total districts, 95% confidence). Majority of the 44% poorly performing districts belong to Balochistan province (95% confidence). Overall, the educational status in Punjab is higher as compared with other provinces. We find that four out of seven potential factors (i.e., poverty, urbanization, electricity accessibility, and school infrastructure) are statistically significant determinants of EdS. Among these, poverty is the most strongly associated (mean coefficient value −18.848) factor to control EdS. The results have important implications to decision-making for immediate or gradual actions in the context of spatially equitable provisioning of quality education through an informed prioritization (i.e., low performing districts). Based on the findings, while rigorous measures are needed for low performing regions and the identified determinants to improve education status, this study sheds light on the mechanisms to achieve SDG4, consequently promoting human well-being through educating communities. • Pakistan's EdS is analyzed at the district-level. • Spatial-statistical techniques are coupled for EdS profiling. • While 44% districts show very poor EdS, poverty is the most significantly associated. • Lowest EdS is observed for districts in Balochistan province. • Results provide important references for policy, decisions, and actions to achieve SDG4. [ABSTRACT FROM AUTHOR]

[Display omitted] • Lumbrokinases are group of serine proteases. • Serine found at position 214 in lumbrokinase improved the activity of lumbrokinase. • In silico tools were used for characterization of mutant Lk-6 proteins. • Serine-Valin214 substitution were direct activation of breaking Arg561-Val562 bond. • The results of this study are step forward towards engineering smart lumbrokinases. Lumbrokinases (LKs) belong to the group serine proteases capable to prevent thrombosis through the proteolysis of both plasminogen-bound and plasminogen-free fibrin molecules. The article presents improved activity of Lumbrokinase-6 (Lk-6) by suggesting the substitution of a Serine found at position 214 (Lk-6) with three other amino acids namely Glutamic acid, Proline and Valine. To characterize the stability, enzyme-substrate interaction and improved activity of three mutant Lk-6 proteins (Lk-Glu214, Lk-Pro214, Lk-Val214) In Silico tools were utilized. Subsequently, Lk-6 wild type and three mutant proteins were subjected to structure prediction, molecular modeling, phylogeny, molecular docking and Protein-Protein Interaction (PPI) using the In Silico tools. Collection and analysis of results revealed that substituted mutation at Ser214 with Valine214 can appreciably stabilize the overall structure of Lk-6 protein and makes its interaction with plasminogen activator physically powerful for higher plasmin activation. Similarly, Serine214 to Valine214 substitution resulted the direct activation of plasmin breakage at the Arg561-Val562 bond. The Arg-Val at position 561–562 in plasminogen and its connection at catalytic site have significantly shown that the predicted residue Valine214 could be further examined through genetic engineering of Lk-6 protein. Therefore, such results are potential steps towards the engineering of smart and active Lks. [ABSTRACT FROM AUTHOR]

• A novel technique to enhance the proficiency of deep face recognition system. • Improving softmax by margining both multiplicative angular and additive cosine margin. • Most significant way to use these margins by not dividing into piece-wise function. • For testing, datasets have massive variation on age, pose and expression in face images. • Results has achieved on widely used benchmark datasets with low computational power. Face recognition in digital images or video frames has several real-world applications in the modern zone of computer vision. Loss function plays a vital role in deep face recognition. Recently, several loss functions have been proposed in classification techniques to reduce the number of model errors. Among several loss functions, softmax loss implemented either multiplicative angular or additive cosine margin. These individual margins have less capacity to reduce the model's errors. To fill this gap; we proposed a hybrid angularly discriminative features by combining multiplicative angular and additive cosine margin to improve the efficiency of angular softmax loss and large margin cosine. We trained proposed model using CASIA-WebFace dataset and testing has been performed on Labeled Face in the Wild (LFW), YouTube Faces (YTF), VGGFace1 and VGGFace2. The experimental result shows 99.77% accuracy on LFW dataset whereas 96.40% accuracy achieved on YTF dataset which is higher than the existing similar state-of-the-art techniques. [ABSTRACT FROM AUTHOR]