Numerical Verification of Empirically Designed Support for a Headrace Tunnel

In this paper, we used two empirical rock classification systems of rock mass rating (RMR) and rock quality tunnelling index (Q-system) for the support design of a tunnel in District Battagram, Khyber Pakhtunkhwa, Pakistan. Along the tunnel route, the rocks of Precambrian namely Gandaf Formation, Karora Formation and Besham Complex were exposed. During the field investigations, two shear zones were marked in the schist of Karora Formation. The discontinuities parameters collected during the field investigations, results of laboratory testing and material constants determined from RocData version 5.0 software were used during the empirical classification and numerical modelling. The support was designed for the rock mass units from RMR and Q. The quantification of the thickness of plastic zone and total displacement around the tunnel were achieved by the numerical modelling of RS2 9.0 software in both unsupported and supported conditions. The empirically designed support was installed in the model prepared in the RS2 software. According to the results, the empirically designed support when installed in models prepared in RS2 significantly reduced the plastic zone around the tunnel. The reduction in the plastic zone and displacement around the tunnel verified the support design by empirical methods. The present research concludes that empirical designed support can be used for the complex geology of Pakistan

Association of Gibberellic Acid (GA3) with Fruit Set and Fruit Drop of Sweet Orange

To determine the association of Gibberellic acid with fruit set and fruit drop of sweet orange, a research study was conducted at Agricultural Research Institute Tarnab, Peshawar, Pakistan during the year 2012. Three different concentrations(10, 20 and 30ppm excluding control) of Gibberellic acid (GA­3) were applied as foliar spray at full bloom stage of three different sweet orange cultivars namely Blood Red, Musambi and Succari. Fruit set as well as fruit drop at different developmental stages of fruit were calculated. The results of the experiment revealed that 30 ppm GA3 application significantly reduced the percent fruit drop, percent June drop and increased yield tree-1. While fruit set branch-1, pre harvest fruit drop and fruit weight was significantly affected by 10 ppm GA3 application. It was concluded from the research study that the foliar application of 30 ppm GA3 at blooming stage could be applied in order to improve fruit set, control fruit drop and to increase the yield of sweet orange. Keywords: Gibberellic acid, sweet orange cultivars, Fruit set, June drop, fruit retention, Musambi, Succari, Blood Red

Impact of FATA merger with Khyber Pakhtunkhwa

This studyis an attempt to know the impacts of federally administered tribal areas merger with Khyber Pakhtunkhwa. These areas of Pakistan are adjacent with Afghanistan. Federally administered tribal areas have been remained under developed economically and educationally due to the mode of governance, which is the root cause of insurgency in the area.The study was based on twotypesof data, First one primary data that were collected through questionnaire from the most educated college community and the second was secondary data, collected from official records. The analysis of the primary data reveals the fact that these areas need to be integrated. More than triple foldof the respondents’ favors this merger; consider the Khyber Pakhtunkhwa’s government to manage it and asthesepeople shares analogous culture andlanguage. Majority of the respondents’thoughtsthat this merger will improve infrastructure of the area, flourish the economic condition of the people and the area will besecure. The analysis of the secondary data suggests that this merger will increase the number of people representatives’ of Khyber Pakhtunkhwa in the national assembly. It can be conclude from highlighting these factors that merger will have positive impact on people of both FATA and Khyber Pakhtunkhwa

Fabrication and in vitro evaluation of chitosan-gelatin based aceclofenac loaded scaffold

Scaffold development is a nascent field in drug development. The scaffolds mimic the innate microenvironment of the body. The goal of this study was to formulate a biocompatible and biodegradable scaffold, loaded with an analgesic drug, aceclofenac (Ace). The bioscaffold is aimed to have optimum mechanical strength and rheology, with drug released in a sustained manner. It was prepared via chemical cross-linking method: a chitosan (CS) solution was prepared and loaded with Ace; gelatin (GEL) was added and the mixture was cross-linked to get a hydrogel. 20 formulations were prepared to optimize different parameters including the stirring speed, drug injection rate and crosslinker volume. The optimal formulation was selected based on the viscosity, drug solubility, homogeneity, porosity and swelling index. A very high porosity and swelling index were attained. In vitro release data showed sustained drug delivery, with effective release at physiological and slightly acidic pH. SEM analysis revealed a homogeneous microstructure with highly interconnected pores within an extended polymer matrix. FT-IR spectra confirmed the absence of polymer-drug interactions, XRD provided evidences for efficient drug entrapment within the scaffold. Rheological analysis corroborated the scaffold injectability. Mathematical models were applied to in-vitro data, and the best fit was attained with Korsmeyer-Peppas

Maximum Power Extraction from a Standalone Photo Voltaic System via Neuro-Adaptive Arbitrary Order Sliding Mode Control Strategy with High Gain Differentiation

In this work, a photovoltaic (PV) system integrated with a non-inverting DC-DC buck-boost converter to extract maximum power under varying environmental conditions such as irradiance and temperature is considered. In order to extract maximum power (via maximum power transfer theorem), a robust nonlinear arbitrary order sliding mode-based control is designed for tracking the desired reference, which is generated via feed forward neural networks (FFNN). The proposed control law utilizes some states of the system, which are estimated via the use of a high gain differentiator and a famous flatness property of nonlinear systems. This synthetic control strategy is named neuroadaptive arbitrary order sliding mode control (NAAOSMC). The overall closed-loop stability is discussed in detail and simulations are carried out in Simulink environment of MATLAB to endorse effectiveness of the developed synthetic control strategy. Finally, comparison of the developed controller with the backstepping controller is done, which ensures the performance in terms of maximum power extraction, steady-state error and more robustness against sudden variations in atmospheric conditions

Effect of fat contents of buttermilk on fatty acid composition, lipolysis, vitamins and sensory properties of cheddar-type cheese

Cheddar-type cheese produced from buttermilk had softer texture than standard cheddar cheese due to lower fat content of buttermilk. Fat is extremely important for the functional characteristics and optimum textural attributes of cheese. The effect of different fat contents of buttermilk on chemical characteristics of cheddar-type cheese is not previously investigated. This investigation was conducted to know the effect of different fat contents of buttermilk on fatty acids composition, organic acids, vitamins, lipolysis and sensory characteristics of cheddar-type cheese. Cheddar-type cheese was produced from buttermilk having 1, 1.75, 2.50 and 3.25% fat contents (control, T1, T2 and T3). Fat content of control, T1, T2 and T3 were 9.81, 16.34, 25.17 and 31.19%. Fatty acids profile was determined on GC–MS, organic acids and vitamin A and E were determined on HPLC. Free fatty acids, peroxide value and cholesterol were determined. Cheddar-style cheese produced from buttermilk (1% fat) showed that it had softer texture and lacking typical cheese flavor. Gas chromatography–mass spectrometry (GC–MS) analysis showed that long-chain unsaturated fatty acids in control, T1, T2 and T3 samples were 45.88, 45.78, 45.90 and 46.19 mg/100 g. High Performance Liquid Chromatography (HPLC) analysis showed that lactic acid, propionic acid, citric acid and acetic acid gradually and steadily increased during the storage interval of 90 days. At the age of 90 days, lactic acid in control, T1, T2 and T3 was 4,789, 5,487, 6,571 and 8,049 ppm, respectively. At the end of ripening duration of 90 days, free fatty acids in control, T1, T2 and T3 were 0.29, 0.31, 0.35 and 0.42% with no difference in peroxide value. Stability of vitamin A after 90 days storage control, T1, T2 and T3 was 87.0, 80.0, 94.0 and 91.0%. Flavor score of cheddar-type cheese produced from butter milk having 1.0, 2.5 and 3.25% fat content was 81, 89 and 91% of total score (9). Hence, it is concluded that cheddar-type cheese can be produced from buttermilk having 2.5 and 3.25% fat contents with acceptable sensory attributes. Application of buttermilk for the production of other cheese varieties should be studied

SOFT β W-HAUSDORFF SPACE IN SOFT BI TOPOLOGICAL SPACES

In this article the concept of Soft β W-?2 structure in soft bi topological spaces is introduced in different ways. Fleix Hausdorff was a German Mathematician who is supposed to be the forefather of up-to-the-minute Topology. There are many topological structures in soft topology but Hausdorff topological structure is interesting and more practical, that is why it catches our attention to the best

Mechanical Performance of Polymeric ARGF-Based Fly Ash-Concrete Composites: A Study for Eco-Friendly Circular Economy Application

At present, low tensile mechanical properties and a high carbon footprint are considered the chief drawbacks of plain cement concrete (PCC). At the same time, the combination of supplementary cementitious material (SCM) and reinforcement of fiber filaments is an innovative and eco-friendly approach to overcome the tensile and environmental drawbacks of plain cement concrete (PCC). The combined and individual effect of fly ash (FA) and Alkali resistance glass fiber (ARGF) with several contents on the mechanical characteristics of M20 grade plain cement concrete was investigated in this study. A total of 20 concrete mix proportions were prepared with numerous contents of FA (i.e., 0, 10, 20, 30 and 40%) and ARGF (i.e., 0, 0.5, 1 and 1.5%). The curing of these concrete specimens was carried out for 7 and 28 days. For the analysis of concrete mechanical characteristics, the following flexural, split tensile, and compressive strength tests were applied to these casted specimens. The outcomes reveal that the mechanical properties increase with the addition of fibers and decrease at 30 and 40% replacement of cement with fly ash. Replacement of cement at higher percentages (i.e., 30 and 40) negatively affects the mechanical properties of concrete. On the other hand, the addition of fibers positively enhanced the flexural and tensile strength of concrete mixes with and without FA in contrast to compressive strength. In the end, it was concluded that the combined addition of these two materials enhances the strength and toughness of plain cement concrete, supportive of the application of an eco-friendly circular economy. The relationship among the mechanical properties of fiber-reinforced concrete was successfully generated at each percentage of fly ash. The R-square for general relationships varied from (0.48–0.90) to (0.68–0.96) for each percentage of FA fiber reinforced concrete. Additionally, the accumulation of fibers effectively boosts the mechanical properties of all concrete mixes.publishedVersio

Augmenting the Robustness and Efficiency of Violence Detection Systems for Surveillance and Non-Surveillance Scenarios

Violence detection holds immense significance in ensuring public safety, security, and law enforcement in various domains. With the increasing availability of video data from surveillance cameras and social media platforms, the need for accurate and efficient violence detection algorithms has become paramount. Automated violence detection systems can aid law enforcement agencies in identifying and responding to violent incidents promptly, thereby preventing potential threats and ensuring public protection. This research focuses on violence detection in large video databases, proposing two keyframe-based models named DeepkeyFrm and AreaDiffKey. The keyframes selection process is critical in violence detection systems, as it reduces computational complexity and enhances accuracy. EvoKeyNet and KFCRNet are the proposed classification models that leverage feature extraction from optimal keyframes. EvoKeyNet utilizes an evolutionary algorithm to select optimal feature attributes, while KFCRNet employs an ensemble of LSTM, Bi-LSTM, and GRU models with a voting scheme. Our key contributions include the development of efficient keyframes selection methods and classification models, addressing the challenge of violence detection in dynamic surveillance scenarios. The proposed models outperform existing methods in terms of accuracy and computational efficiency, with accuracy results as follows: 98.98% (Hockey Fight), 99.29% (Violent Flow), 99% (RLVS), 91% (UCF-Crime), and 91% (ShanghaiTech). The ANOVA and Tukey tests were performed to validate the statistical significance of the differences among all models. The proposed approaches, supported by the statistical tests, pave the way for more effective violence detection systems, holding immense promise for a safer and secure future. As violence detection technology continues to evolve, our research stands as a crucial stepping stone towards achieving improved public safety and security in the face of dynamic challenges