University of Engineering and Technology, Peshawar

Addressing environmental challenges such as pollution and resource depletion requires innovative industrial and municipal waste management approaches. Cement production, a significant contributor to greenhouse gas emissions, highlights the need for eco-friendly building materials to combat global warming and promote sustainability. This study evaluates the simultaneous use of Sugarcane Bagasse Ash (SCBA) and Stone Dust (SD) as partial replacements by volume for cement and sand, respectively, at varying ratios in eco-strength concrete mixes designed for 28 MPa (ES-28) and 34 MPa (ES-34), emphasizing their economic and environmental benefits. The influence of SCBA and SD on workability, mechanical properties, and durability were experimentally investigated. Results reveal that for ES-28, with 9% SCBA and 50% SD, compressive and tensile strengths were nearly equal to the control mix, while flexural strength improved by 6.86%. For ES-34, with 9% SCBA and 50% SD, compressive strength was enhanced by 10.16%, tensile strength by 11.68%, and flexural strength by 5.22%, compared to the control mix. This improvement is attributed to pozzolanic reactions, enhanced particle packing, and optimal curing conditions. However, water absorption increased significantly, with ES-28 showing a 31.61% rise and ES-34 a 22.32% rise when SCBA was 9% and SD was 50%. These results highlight the trade-offs between mechanical performance and durability. The optimized mix, derived from response surface analysis, demonstrates significant potential as a sustainable alternative to conventional concrete, aligning with environmental and structural performance objectives.

The widespread availability of networks has significantly increased the exposure of data to security threats, necessitating robust encryption methods. While existing symmetric block ciphers offer varying levels of security, they are susceptible to advancements in cryptanalysis. From this perspective, in this article, we presents an efficient approach to enhancing the security of these ciphers through the integration of chaos theory. The key to this enhancement is a newly developed adaptive chaotic map based on sine map and the dynamic interplay of two key parameters (r _n ,k _n ). Of course, the efficiency of the proposed chaotic map is demonstrated by the time evolution, the Bifurcation diagram, Permutation Entropy (PE), Fuzzy Correlation Dimension (FCD), and Lyapunov Exponent (LE) analyses and comparison with the existing reported maps. Thereafter, by applying chaos theory to address the limitations of default cipher parameters, and operating modes and to control S-boxes, permutation functions, key schedules, data masking, we introduce nonlinearity and diffusion, making the block ciphers more resistant to attacks. Through extensive simulations and evaluations, we demonstrate the effectiveness of our approach in strengthening the security of the enhanced ciphers against pattern, statistical, differential, key sensitivity, Linear, and Padding Oracle attacks. Additionally, we present a high-level design and implementation of a configurable cryptosystem using SystemC. The configurable design allows for flexibility in integrating different ciphers and adapting to evolving security needs.

This study examines the effects of fibre (0-3%) and cement (0-3%) additives on poorly graded Toyoura sand under consolidated undrained (CIU) compression and extension conditions. Compression tests revealed that dense sands exhibit increased peak strength, stiffness, and a steady decline from peak to post-peak strength. Cemented and fibre-cemented specimens demonstrated stiffer responses and strain-hardening post-peak behavior compared to pure sand. Adding fibres and cement significantly enhanced peak deviatoric stresses, while their impact under extension loading was less pronounced. Fibre and cement combinations, particularly at higher percentages, improved the strength of pure sand under extension conditions. Randomly oriented fibres increased friction angle, cohesion, and compressive strength, while cemented and fibre-cemented specimens exhibited higher secant moduli. The additives enhanced strength parameters, the slope of the critical state line, and the state parameter, with the stress ratio (q/p') rising for both peak and critical states.

The brittleness of ordinary concrete lead reinforced concrete (RC) moment-resisting frames susceptible to earthquake-induced shaking. Engineered cementitious composites (ECC) are excellent alternatives to ordinary concrete because of their ductile nature. However, the assessment of ECC at the structural level has not been fully investigated yet. Thus, this study evaluates the performance of ECC special moment resisting frames (SMRF) using a non-linear numerical model. Cyclic results of single-story revealed that compared to the RC frame, ECC frame improves the average peak base shear capacity approximately by 17%. Fragility curves under incremental dynamic analyses were derived for two-story RC and ECC code-compliant and non-compliant SMRF structures using a probabilistic-based methodology. Overall, non-compliant RC and ECC frames were more fragile than RC and ECC code-compliant frames respectively. However, ECC compliant and non-compliant frames outclassed RC compliant and non-compliant frames respectively in terms of less damage for each slight, moderate, heavy and near collapse damage limit. The probability of non-compliant RC and ECC frames exceeding the near collapse damage states under 0.67 g was 29% and 18% respectively. Moreover, the probability of compliant RC and ECC frames exceeding the near collapse damage state under 1.37 g was 22% and 7% respectively. Furthermore, vulnerability loss curves showed that ECC frames reduce economic losses as opposed to RC frames. The loss ratio of RC non-compliant and ECC non-compliant under 0.57 g was noted 37% and 22% respectively. While the loss ratio of RC compliant and ECC compliant under 1.27 g was noted 36% and 17% respectively.

Despite seemingly inexorable imminent risks of food insecurity that hang over the world, especially in developing countries like Pakistan where traditional agricultural methods are being followed, there still are opportunities created by technology that can help us steer clear of food crisis threats in upcoming years. At present, the agricultural sector worldwide is rapidly pacing towards technology-driven Precision Agriculture (PA) approaches for enhancing crop protection and boosting productivity. Literature highlights the limitations of traditional approaches such as chances of human error in recognizing and counting pests, and require trained labor. Against such a backdrop, this paper proposes a smart IoT-based pest detection platform for integrated pest management, and monitoring crop field conditions that are of crucial help to farmers in real field environments. The proposed system comprises a physical prototype of a smart insect trap equipped with embedded computing to detect and classify pests. To this aim, a dataset was created featuring images of oriental fruit flies captured under varying illumination conditions in guava orchards. The size of the dataset is 1000+ images categorized into two groups: (1) fruit fly and (2) not fruit fly and a convolutional neural network (CNN) classifier was trained based on the following features: (1) Haralick features (2) Histogram of oriented gradients (3) Hu moments and (4) Color histogram. The system achieved a recall value of 86.2% for real test images with Mean Average Precision (mAP) of 97.3%. Additionally, the proposed model has been compared with numerous machine learning (ML) and deep learning (DL) based models to verify the efficacy of the proposed model. The comparative results indicated that the best performance was achieved by the proposed model with the highest accuracy, precision, recall, F1-score, specificity, and FNR with values of 97.5%, 92.82%, 98.92%, 95.00%, 95.90%, and 5.88% respectively.

Expression of concern for ‘Optimized Cu-doping in ZnO electro-spun nanofibers for enhanced photovoltaic performance in perovskite solar cells and photocatalytic dye degradation’ by Kang Hoon Lee et al., RSC Adv., 2024, 14, 15391–15407, https://doi.org/10.1039/D4RA01544D.

In the n-tier framework, data generated by sensors requires immediate execution. The processing elements need powerful resources to entertain incoming requests. Fog computing, unlike cloud computing, provides low latency for real-time applications. However, data generated by real-time Internet of Things (IoT) devices significantly impacts fog devices. The data generated must be processed by fog devices with quick response time, minimum delay, and energy consumption and send it back to the end-users with high reliability and success rate. However, devices fail due to damage or internal state of a fog device which measures incorrectly or causes destruction which badly affects the overall system performance. The end-to-end transmission requests from IoT devices require immediate response with minimal delay, execution cost, and energy consumption in spite the occurrence of fog devices failure. In this article, we propose a novel energy efficient task scheduling algorithm based on reactive fault tolerance in an n-tier fog computing framework for IoT applications to enhance the overall fog computing performance. In case of fog device failure, the assigned task is rescheduled to other executable fog nodes without further delay. The proposed framework is based on modified particle swarm optimization and is designed and evaluated in iFogSim. The main objective of the proposed technique is to reduce energy consumption, latency, network bandwidth utilization and increase system reliability and success rate. Several experiments have been carried out by taking a maximum of 10 iterations based on which it is concluded that the proposed technique reduces energy consumption by 3%, latency by 5%, network bandwidth utilization by 3% and increases the system reliability by 2% and success rate by 8%.

Futuristic requirements of mobile communication systems ask for discovering new possibilities and dimensions to enable ultrafast and reliable communication. This work relates to the advancements in jointly decoded iterative multimedia communication systems for the H.264 source standard. The beneficial technique of Over-Complete Mapping (OCM) is utilized for introducing redundancy in the highly compressed source encoded video streams. Differential Space–Time Spreading (DSTS) is invoked along with Rate-3/4 Recursive Systematic Convolutional (RSC) as intermediate encoder in the proposed three-stage setup. More specifically, the RSC encoder is beneficial in attaining the iterative gain as it divides the decoder into two parts. The DSTS is invoked with the Sphere Packing (SP) modulation which is highly desirable for the non-coherent detection schemes without requiring any information from the channel. The proposed system comprises of three main components, namely OCM, Rate-3/4 RSC and SP modules, thus we call this as three-stage system. The iterative decoding enhances the Bit-Error Rate (BER) and Peak Signal-to-Noise Ratio (PSNR) performances of the joint source-channel systems. Furthermore, to expeditiously predict the convergence pattern of the proposed three-stage iteratively decoded systems, EXtrinsic Information Transfer (EXIT) chart analysis is performed. Moreover, the effect of variations in the minimum Hamming distance (${d}_{H,min}$) is observed for the proposed three-stage system. The ${{DSTS}-{SP}-{RSC}-{OCM}}_{[5 15]}$ having ${d}_{H,min}=6$ exhibits a gain of 20 dB against the benchmarker ${{DSTS}-{SP}-{RSC}-{OCM}}_{[2 6]}^{*}$ with ${d}_{H,min}=1$ at the PSNR degradation point of 1 dB over a correlated Rayleigh fading channel.

Background/Objectives: Accurate liver and tumor detection and segmentation are crucial in diagnosis of early-stage liver malignancies. As opposed to manual interpretation, which is a difficult and time-consuming process, accurate tumor detection using a computer-aided diagnosis system can save both time and human efforts. Methods: We propose a cascaded encoder–decoder technique based on self-organized neural networks, which is a recent variant of operational neural networks (ONNs), for accurate segmentation and identification of liver tumors. The first encoder–decoder CNN segments the liver. For generating the liver region of interest, the segmented liver mask is placed over the input computed tomography (CT) image and then fed to the second Self-ONN model for tumor segmentation. For further investigation the other three distinct encoder–decoder architectures U-Net, feature pyramid networks (FPNs), and U-Net++, have also been investigated by altering the backbone at the encoders utilizing ResNet and DenseNet variants for transfer learning. Results: For the liver segmentation task, Self-ONN with a ResNet18 backbone has achieved a dice similarity coefficient score of 98.182% and an intersection over union of 97.436%. Tumor segmentation with Self-ONN with the DenseNet201 encoder resulted in an outstanding DSC of 92.836% and IoU of 91.748%. Conclusions: The suggested method is capable of precisely locating liver tumors of various sizes and shapes, including tiny infection patches that were said to be challenging to find in earlier research.

In this work, polystyrene (PS) nanocomposites were synthesized by incorporating layered double hydroxide (LDH) functionalized with graphene (G) and phosphonium-based ionic liquid (PCL). These nanofillers were added to PS at varying weight contents of 5%, 10%, and 15% to evaluate their effect on thermal stability and mechanical properties. X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), and thermogravimetric analysis (TGA) of the nanomaterial exhibited the successful intercalation of graphene and PCL into the ZnAl-LDH matrix. The PS nanocomposite exhibited improved thermal stability and mechanical properties, attributed to the synergy between the functionalized LDH and graphene. The addition of a mere 5% of nanofillers improved the limiting oxygen index (LOI) from 18.51 to 19.11 for pure PS and PS-LDH-G-PCL, respectively. Although the enhancement in the LOI was modest, the nanocomposite (PCL-G-LDH) demonstrated excellent char-forming abilities and increased graphitization during combustion, contributing to flame retardancy and less smoke production and dripping. The findings demonstrate the successful intercalation of graphene and PCL into ZnAl-LDH interlayers and their potential applications as flame retardants for PS.

The present study addresses the numerical solution of two-dimensional steady-state heat conduction problems with nonlocal multi-point boundary conditions across three distinct domains: a unit rectangle with a quarter-circle cutout of radius 0.5, an irregular domain, and a Cassini curve. Dirichlet boundary conditions are imposed on specific segments, while nonlocal boundary conditions are applied to the remaining portions. The Kansa method is employed to solve the steady-state heat conduction equation, utilizing three types of radial basis functions (RBFs) to explore the influence of the shape parameter on accuracy and matrix conditioning. These include the inverse multiquadric RBF, a modified inverse mul-tiquadric RBF proposed here for the first time, and a hybrid RBF [1]. As a meshless method, the Kansa approach eliminates the need for mesh generation or node connectivity within local subdomains. To evaluate accuracy and performance, the L∞ error norm is employed. The results demonstrate the effectiveness of the proposed techniques in solving the 2D steady-state heat conduction problem. A comparative analysis is conducted to assess the accuracy and computational efficiency of the methods.

High-capacity communication networks are built to provide high throughput and low latency to accommodate the growing demand for bandwidth. However, the provision of these features is subject to a robust underlying network, which can provide high capacity with maximum reliability in terms of the system’s connection availability. This work optimizes an existing 2D spectral–spatial optical code division multiple access (OCDMA) passive optical network (PON) to maximize connection availability while maintaining desirable communication capacity and capital expenditure. Optimization is performed by employing ring topology at the feeder level, which is used to provide a redundant path in case of connection failures. Furthermore, high transmission capacity is ensured by utilizing a pseudo-3D double-weight zero cross-correlation (DW-ZCC) code. The analysis is performed with Optisystem simulations to observe the performance of the system in terms of bit error rate (BER), received power, and eye openings. It is observed that the introduction of ring topology at the feeder level of the PON does not impact the overall transmission capacity of the system. The system can still support maximum transmission capacity at receiver sensitivities of up to −19 dB. Reliability analysis also shows that the optimized ring-based architecture can provide desirable connection availability compared to the existing system.

The resilient modulus (MR) of different pavement materials is one of the most important input parameters for the mechanistic-empirical pavement design approach. The dynamic triaxial test is the most often used method for evaluating the MR, although it is expensive, time-consuming, and requires specialized lab facilities. The purpose of this study is to establish a new model based on Long Short-Term Memory (LSTM) networks for predicting the MR of stabilized base materials with various additives during wet-dry cycles (WDC). A laboratory dataset of 704 records has been used using input parameters, including WDC, ratio of calcium oxide to silica, alumina, and ferric oxide compound, Maximum dry density to the optimal moisture content ratio (DMR), deviator stress (σd), and confining stress (σ3). The results demonstrate that the LSTM technique is very accurate, with coefficients of determination of 0.995 and 0.980 for the training and testing datasets, respectively. The LSTM model outperforms other developed models, such as support vector regression and least squares approaches, in the literature. A sensitivity analysis study has determined that the DMR parameter is the most significant factor, while the σd parameter is the least significant factor in predicting the MR of the stabilized base material under WDC. Furthermore, the SHapley Additive exPlanations approach is employed to elucidate the optimal model and examine the impact of its features on the final result.

An environmentally responsible and sustainable replacement for finite fossil fuels is biodiesel. Because of its amazing qualities, biodiesel is becoming more and more popular as a renewable fuel around the globe. The many approaches, feedstocks, catalysts, comparison standards, reaction kinetics, final product analysis, and final product characterization of biodiesel are covered in this review article. Researchers have used a variety of techniques to produce biodiesel throughout history, with transesterification emerging as the most effective approach in more recent times. Numerous studies on biodiesel feedstock and catalysts to produce high biodiesel yields have been published; nevertheless, it should be highlighted that the type of feedstock must be considered while choosing a catalyst. The review paper highlights the significance of several parameters that are crucial to the manufacture of biodiesel, without which achieving a high yield would be challenging. The literature has also discussed the limitations and advantages of different catalysts, and scientists are currently working to identify the ideal catalyst within certain optimal parameters for the manufacture of biodiesel. Homogeneous reaction‐based biodiesel synthesis has a number of drawbacks, though, such as water content, a laborious purification procedure, and a low tolerance for free fatty acids. To address these issues, scientists have started investigating heterogeneous reactions involving solid catalysts. A large pore network, a moderate‐to‐high density of strong acid sites, a hydrophobic surface, and the ability to control surface hydrophobicity to avoid deactivation are all desirable characteristics of an ideal solid catalyst. Ion exchange resins, sulfated oxides, heterogeneous base catalysts, boron group‐based heterogeneous catalysts, alkaline earth metal oxides, mixed metal oxides, alkali metal oxides, heterogeneous catalysts derived from waste materials, and different approaches to biodiesel synthesis that employ enzymes, carbon‐based heterogeneous catalysts, and ionic liquids as catalysts are among the categories of catalysts that can be used in the production of biodiesel. The finest benchmarks to compare the quality of biodiesel with European and American Society for Testing Material standards. For detailed characterization of the finished product, gas chromatography and nuclear magnetic resonance are the most effective methods.

Automating mineral delineation and rock type analysis using remote sensing imaging data is a critical application of machine learning. Traditional machine learning methods often struggle with accuracy and precise map generation. This study aims to enhance performance through a refined deep learning model. In this work, we present a deep learning pipeline to map the mineral deposits in the study area. Initially, we apply a deep convolutional neural network (CNN) to a specialized mineral dataset to map mineral deposits within the study area. Subsequently, we build a hybrid model combining deep CNN layers with a support vector machine (SVM). This merger significantly improves classification accuracy from an initial 92.7% to 95.3%. In our approach, CNN layers function as feature extractors while the SVM serves as the classification model. Moreover, we conduct an evaluation of the SVM using polynomial kernels of degrees 3, 6, 9, and 12. The results indicate that the SVM with a degree of 12 achieved the highest classification accuracy, followed by degrees 9, 6, and 3. Experimental results demonstrate the effectiveness of our proposed method for classifying remote sensing imaging data, showcasing its potential for advancing mineral delineation and rock type analysis.

An antenna array having a size of 45 $\:\times\:$ 40 cm ² (5.7 $\:\times\:$ 5 $\:{\lambda\:}_{0}$ ² ) and consisting of four pairs of printed U-shaped dipoles positioned above a metal reflector, for 5G Sub-6 GHz base station applications, is designed and tested. The array consists of eight excitation ports, one port for each dipole. Four parasitic square patches are etched on the bottom side of the dipole arms for producing radiations in 2.2 GHz and 3.8 GHz bands. The size of the reflector and height of the dipoles are optimized in order to enhance antenna gain up to 11.5 dB at 2.2 GHz and 14.5 dB at 3.8 GHz. Beam steering up to 20 \:^\circ\: is achieved, using phase shifted simultaneous excitation of different ports. The proposed antenna array not only fulfills 5G base station requirements but is also simple and compact as it only requires eight ports to achieve dual-band, high-gain and beam steering operation in a single design. It also offers a unique feature of dual-sector coverage per panel, which results in an increased coverage capacity of the base station without increasing the system resources.

Ultra-high Molecular Weight Polyethylene (UHMWPE) is a highly versatile polymer known for its exceptional mechanical properties, however, its limited life as an implant material for Total Joint Replacement (TJR) necessitates surface modification to extend its lifespan. This study aims to enhance the surface properties of UHMWPE through application of ceramic coatings. Magnetron sputtering method was used to deposit thin film of white Titania (TiO2) on the material’s surface. To evaluate the surface characteristics, such as surface roughness, uniformity and structure, coated and uncoated samples were analyzed through Atomic Force Microscopy (AFM), Scanning Electron Microscopy (SEM) and X-ray Diffraction Analysis (XRD). The material performance in relation to biological context was investigated through Contact Angle measurement. A comparative analysis of coated and uncoated samples was then performed. The coated samples showed better wettability compared to uncoated sample. This fact highlights the hydrophilic nature of film. The results of the coated UHMWPE suggest that this surface modification technique could significantly extend the lifespan of UHMWPE implants in TJR, potentially addressing the current limitations associated with their longevity.