Journal of Engineering Research最新文献

When sensor nodes in wireless sensor networks (WSNs) have limited energy, data minimization is crucial. Usually, data communications use up energy. A sensor node's lifespan can frequently be increased by sending and receiving the proper quantity of data. The PISAE (Partially Informed Sparse Autoencoder) is a state-of-the-art unmapped neural network architecture designed to reconstruct all sensor inputs from a limited number of sensors. It is presented in this publication. Use this architecture to create a system for selecting sensors. We now propose the cross-layer based opportunistic routing protocol (CORP) as an opportunistic routing mechanism for WSNs. In order to cut down on processing time and energy consumption and increase the dependability of data transfer, the best way is selected utilizing the CORP technique, which is suggested.For energy-efficient routing based combinatorial random sampling bat optimisation (ERFN-CSSBO), we also suggest using fuzzy neural networks. This enables you to conserve energy, increase the lifespan of your wireless sensor network, and keep your energy consumption in check. With CSSBO (Combined Random Sampling Prevosti Bat Optimisation), the best path is identified by integrating features (such as distance, energy, trust, and internode measurement connection stability) from all the bats.The key challenge in WSN is choosing cluster heads (CH). K-Medoid is used to enhance sensor node clustering. Important considerations include the effect on quality of service (QoS), sensor node position, proximity, and energy state needs. This study combines Hybrid BFO (Bacterial Foraging Optimization) and HSA (Harmony Search Algorithm), two well-known optimization techniques, to pick cluster heads in wireless sensor networks that are optimal in terms of distance and energy. On-task completion. The outcomes of the simulation indicate that the proposed technique improves QoS. Endpoints, throughput (1.0 Mbps), (98.5 % of packets are forwarded), and packet loss rate (1.5 %), and other QoS factors are all included in the performance statistics plotted. It performs better than conventional routing protocols in terms of network lifetime (6100 rounds), delay at both ends (1.5 s), and energy usage (30.35 mJ).

The major goal of the present contribution is to fully examine the linear stability analysis of two-dimensional nanofluid flow over a moving as well as a static wedge under the impact of an adverse or favorable pressure gradient. Using similarity variables, the velocity profiles for the mean flow are obtained by converting the mean flow equations into a nonlinear ODE that is numerically solved using the fourth-order Runge-Kutta method. The stability equations for the flow of nanofluids are solved using the spectral Chebyshev collocation technique. Numerous numerical simulations are performed to understand the impact and influence of various parameters such as the concentration of the selected nanoparticle, wedge velocity, pressure gradient, and types of nanofluid. The obtained outcomes indicate that the critical dimensionless value of Reynolds number of instability decreases significantly with increasing wedge velocity in the same direction of fluid motion, with increasing concentration of nanoparticles, and when the pressure is adverse, which makes the flow more stable. On the contrary, the flow becomes unstable when the wedge moves against the fluid flow's direction in the instance of a favorable pressure.

The study outlines the significant changes that have taken place in the housing market in response to mega events, migration flows, and laws governing real estate ownership. To gain a comprehensive understanding of the housing market in Greater Doha, a survey was conducted that involved in-person interviews with experts, urban planners, and real estate professionals. The primary objective of this survey is to examine the growth patterns of housing typologies. The study concludes that there will be an accelerated transition from villas to apartments, supported by governmental policies and economic diversification initiatives. Based on the statistical analysis and experts’ reviews, the concentration of apartments will be shifted from the downtown to suburban areas to accommodate the increasing number of migrants. A set of recommendations has been formulated, taking into consideration the housing preferences of both nationals and migrants, affordable housing options, and property ownership opportunities for migrants. The paper provides valuable guidelines for addressing the challenges and opportunities related to housing dynamics in rapid development cities. It can help policymakers to understand the relative importance of different factors in shaping the housing market in Greater Doha, and to develop appropriate strategies to manage housing affordability and availability.

The lakes are facing anthropogenic pressure and losing its natural characteristics. Hence, the evaluation of lake water quality is a thrust area of research. The Nani-High Altitude Lake (N-HAL) was aimed to study during pre- and post-monsoon (March and October) period (2017–2022) using earth observation data based on false color composite (FCC) and spectral indices. The Sentinel-2 A/B data of 10 m spatial resolution was considered for monitoring and mapping of high-altitude lake. The FCC images were used to map and monitor changes in water spread and aquatic vegetation. Further, three spectral indices, namely, Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Turbidity Index (NDTI), were applied to delineate water bodies and aquatic macrophytic vegetation (recorded are the Potamogeton pectinatus, Potamogeton crispus, Polygonum glabrum, Polygonum amphibium and Polygonum hydropiper (Water pepper). Results reveal that the water surface extent has changed from 2017 to 2022, depending on the pre- and post-monsoon rainfall pattern during the study period. During a flood year on 18-20, October 2021, in the area resulting into a drastic reduction in the aquatic vegetation and suspended particles. This lake needs immediate conservation and management policies.

The Internet of Things (IoT) and Wireless Sensor Networks (WSNs) have rapidly spread in recent decades, leading to remarkable innovation and integrated possibilities. The switch from IPv4 to IPv6, made possible by advancements in networking technology and the use of nanodevices, has further improved connectivity. This move allows for connecting a wider range of devices to servers. Nevertheless, the increasing interconnectivity has brought about difficulties in efficiently overseeing and analysing the enormous amount of data produced throughout all levels of the IoT. The requirement of comprehensive security management is particularly concerning for IoT devices due to their large quantity and small size. Within the layered architecture of IoT, the network layer assumes pivotal importance in ensuring security, bearing responsibility for storing routing information and executing corresponding decisions. The Black Hole attack is a frequently encountered and significant concern among the security attacks addressed. This paper thoroughly examines the consequences of the Black Hole attack on IoT networks, carefully analyzing its impact. Furthermore, it presents a novel mitigation algorithm designed to counter such threats efficiently. The research employs NS2 and Simulink to run extensive simulations, enabling the evaluation of network throughput and Packet Delivery Ratio (PDR). Applying the proposed mitigation strategy to a network affected by the Black Hole attack results in a significant improvement in throughput, which closely resembles that of an unaffected network. The observed Packet Delivery Ratio (PDR) is measured at 98.21%. This highlights the algorithm’s effectiveness in mitigating the detrimental effects of the Black Hole attack on IoT networks.

The solid oxide fuel cells (SOFC) – proton exchange membrane fuel cells (PEMFC) and waste heat recovery utilization systems employing liquefied natural gas (LNG) as fuel are designed and integrated. The cold energy from LNG supply system is harvested and supported to the CO₂ capture system. The gas turbine (GT), organic Rankine cycle (ORC), steam Rankine cycle (SRC) are integrated to use LNG cold energy and waste heat of system and transfer it into useful work and power. PEMFC is constructed and coupled in an integrated system to address the disadvantage of SOFC's delay in starting and maneuvering period of vessel. To adapt regulations and standards from international and local authorities, the CO₂ capture system is designed and integrated. The ORC-SOFC-GT-PEMFC-ORC-SRC-CO₂ capture system is analyzed and investigated thermodynamically based on the first and second laws of thermodynamics. ASPEN HYSYS V12.1 are used to simulate and optimize the LNG SOFC integrated system combined CO₂ capture utilizing LNG cold energy. Laws of thermodynamics were employed to build up the thermodynamics equations and estimate the system performance indicators. The exergy destruction of major components was established and estimated to optimizing the designation and operation of suggested system. The energy and exergy efficiencies of proposed system was calculated at 68.76% and 33.58%, respectively. The waste heat recovery combined cycles generated more 2100.42 kW which equivalent to 35.6% of the total output of system. The innovated models were validated against experiment data from literature with good agreement. Furthermore, the parametric study show that the current density varied from 930 to 1930 A/m² influencing the main indicator which total energy efficiency varied by 33.18% from 3.29% to 5.11%. The ORC took both advantages on cold energy and high-temperature of waste heat results high efficiency of energy recovery. The use of exhaust gas boiler for waste heat recovery provides 9000 kg/h of superheated vapor at 151.5 $℃$and 498 kPa for accommodating of seafarers and heating the lubricating oil on board. The combination of harvesting LNG cold energy and exhaust gas waste heat recovery to the CO₂ capture system contributing to greener of exhaust gas adapting the regulations of International Maritime Organization (IMO) for emission from marine vessels.

This paper investigates the bending of aluminum tubes under different fluid pressure and heat treatment conditions. The application of multi-criteria decision-making (MCDM) methods was evaluated for selecting the best forming condition considering cross-section ovality, thinning, and thickening of the bent tubes. Analytic hierarchy process (AHP) was used for criteria weighting, and technique for order of preference by similarity to ideal solution (TOPSIS) and multi-objective optimization on the basis of ratio analysis (MOORA) were used for alternative ranking. A combination of various types of AA6063 tubes, including as-received (R), annealed (O), and artificial aged (T6), with different fluid pressures (0, 1, 1.8, 3.2, and 3.6 MPa) were assumed to be as the alternatives (forming conditions). The results demonstrate that the ovality and thickening of the bent tubes decrease by increasing the internal fluid pressure. On the other hand, increasing the internal fluid pressure gives rise to an increase in thinning of bent tubes. The O sample has the lowest ovality and thickening while the T6 sample has the lowest thinning. MCDM analysis revealed that the O samples at higher pressures show better bendability.

The scattering of SH waves by interfacial crack in piezoelectric ceramic polymer composites is investigated. The mixed boundary condition are reduced to dual integral equations using Fourier transforms, and the series and Schmidt methods are used to solve an unknown quantity in crack displacement, and the expression of stress intensity factor at crack tip is obtained. The method was verified by degrading the piezoelectric ceramic polymer to the piezoelectric ceramic material. Finally, the crack propagation under different conditions such as frequency and angle are analyzed, as well as the influence of different piezoelectric ceramic polymer materials on crack propagation. It is of great significance for fracture propagation. This method can also be used as a guide for SH wave scattering of different interface cracks in real life, such as multi-cracks, rectangular crack and penny-shaped crack, so it can also be used as a starting point for future research.

This study assesses the strength capacity of brick columns under various confinement materials, including fiber-reinforced polymer (FRP), fiber-reinforced cementitious matrix (FRCM), and steel-reinforced grout (SRG) using gene expression programming (GEP) and artificial neural networks (ANN) models. To achieve this, a comprehensive database of masonry column test results from existing scientific literature is compiled. The models' performance is evaluated using statistical errors like the coefficient of linear correlation (R²), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Additionally, sensitivity analysis is carried out to assess the significance of individual parameters in the models. The findings reveal that ANN predictions closely match empirical data, demonstrating a strong correlation coefficient of 0.95. The accuracy of the ANN approach is reasonably high, with only 26% of the predicted values deviating by more than 20% from the actual data. Based on the statistical analyses, the correlation coefficient between the actual and estimated data was 0.88, for GEP method. Also, the GEP model yields outcomes, with roughly 43% of the predicted values differing by 20–50% from the actual data. In a comparison of the two models, the ANN model outperforms the GEP model, displaying a 40% reduction in error when estimating the compressive strength of masonry columns. The data estimated by the GEP were sparser than those estimated by the ANN. Nevertheless, the GEP model still maintains an acceptable correlation coefficient and error rate, making it a viable choice for precise predictions. It offers a user-friendly formula and meets the needs of both customers and builders.

Counting-based secret-sharing (CBSS) is a current cybersecurity technique that provide applicable authentication for multi-participants collaborative trust access control utilization. The CBSS originally assigns its main confidential target key as secret random automatically selected to generate all participants private shares fulfilling trap-door function. CBSS is normally not allowing to entertain user preference in personalizing the passwords causing severe remembrance reliability difficulty. This research addressed this retention personalized challenge by adjusting the CBSS procedure commencing its process by user password selection choice, as for secret shares extraction to generate the target-key, securing the cybersecurity collaborative system. We proposed several algorithms for user-preference secret shares combination based on text and images alternatives, further verified by personal email convenience authentication. The proposals tested this adjusted CBSS reliability comparing overall security, systems number of shares possibilities, and variations functional time complexity as well as PSNR comparing to others for proper validation, noting its novel relevance attraction. The analysis remarked very interesting contribution comparison commented highest randomization secrecy of 95% for proposed model A passwords followed by 39%, 21%, 19% and 5%, for other different images-based passwords models B, C, D, and E, respectively. The work remarked attractive contribution compared to other works providing applicable CBSS real-life current utilization, opening door for promising progressive advancements to come.