Search

Nickel-Titanium alloys or Nitinol shape memory alloys have applications in various fields like biomedical, agriculture, pharmaceutical, civil, and mechanical engineering. Ferrous-based shape memory alloys (Fe-SMAs) do not possess excellent properties like that of Nitinol. In recent times, Fe-SMAs have been under consideration in the research field to enhance their shape memory properties because of their cost-effectiveness, corrosion resistance, biocompatibility, and high mechanical strength. Fe-SMAs are limited in their applications in the human body due to high austenite start temperature () even though they are biocompatible and being considered for temporary biodegradable implant applications. Several factors affect the phase transformation temperature of Fe-SMA, like alloy composition and heat treatment conditions. Some of the techniques that are useful to reduce the are quenching, solid solution treatment, ternary element addition, alloying, and thermal-mechanical treatment. In this study, the quenching with brine solution is used to reduce of Fe-15Mn-10 Cr-8Ni-4Si (wt. %). The phase transformation is observed using Differential Scanning Calorimetry (DSC). The best result from experimentation of all the samples collected as reduced from 93oC to 39.02oC which is very close to the temperature of the human body. The results of this study exhibit significant potential for advancement in the application of Fe-SMAs in the biomedical field for permanent implants in cardiovascular and orthopaedic applications.

The sustainability of the concrete industry is in jeopardy because it is one of the biggest consumers of natural resources. Environmental and monetary issues are the main difficulties the concrete industry is currently dealing with. In this study, the potential substitution of sawdust ash for cement in the production of concrete is explored. In this project, the potential substitution of sawdust ash for cement in concrete production was explored, a typical carpentry waste, and then we utilize several testing techniques to examine how it impacts the mechanical characteristics of concrete. In an experiment, the compressive, tensile, and flexural strengths of concrete samples made with various ratios of sawdust ash and cement were examined. The samples were made following ASTM C-109, ASTM C-496 and ASTM C-78 for compression, tensile and flexural testing. In place of cement, saw dust ash was added to the M-15 (M indicates ‘mix’ and 15 indicates compressive strength of 15MPA) sample in weight percentages of 5%, 10%, 15%, 20%, and 25%. The concrete samples were tested to ascertain their compressive, tensile, and flexural strengths after 14 days. Comparisons between the results and untreated concrete were done. In this study, the behavior of concrete was investigated when sawdust ash was replaced for cement to weight-based extents of 0%, 5%, 10%, 20%, and 25%. This could address the problem of how to dispose of sawdust ash while also enhancing the properties of concrete.

In order to achieve high strength in concrete composites, various fiber reinforcements in plain concrete composites (PCC) are used. Steel is widely used for this purpose but the main issue in using steel as a reinforcement member is rusting, which results into structure deterioration. So, the alternative is to use a durable, environment friendly, and cost-effective natural fiber. Jute Fiber is used in this study for the reinforcement of plain concrete composites while, the plain concrete composites were made with cement, sand, and bricks waste. The comparative study of using bricks waste as an alternative material to crush bricks is also presented in this research study. Bricks waste and its disposal is becoming an environmental challenge. This study proposes jute fibers as a reinforcement element and uses bricks waste as an alternative material to crushed stone. M20 grade concrete with various volumetric percentages (0.05%, 0.10%, 0.25%, 0.50%, and 1.00%) of 10 mm long jute fibers are produced. An increase of 39% and 37% in compressive strength and split tensile strength, respectively, was observed after the addition of jute fibers. Therefore, the use of jute fibers and bricks waste to replace crushed stone is an effective way to mitigate environmental pollution and sustainability issues.

Incorporating distributed renewable energy sources into heating, power, and cooling systems is facilitating the drive toward intelligent building energy solutions. However, the inherent uncertainties associated with controllable renewable resources pose challenges for the thermo-economic scheduling of smart buildings. Nonetheless, the flexibility inherent in smart buildings can be leveraged through various market mechanisms. Hence, this research introduces a sustainable energy-building system driven by an autonomous solar/dish Stirling engine (SDSE) and wind turbine combined with a sophisticated control strategy and battery storage, which is designed to provide heat and power requirements. The proposed control strategy is also devised to optimize energy generation, ensuring prudent conservation and minimal waste, thereby amplifying overall efficiency and financial viability. The meticulous design of the system is accomplished through advanced MATLAB/Simulink® modeling. A thorough technical sensitivity analysis meticulously hones design parameters, revealing optimal operational thresholds. Simulation outcomes unveil a consistent Stirling engine (SE) efficiency, achieving a pinnacle of 35 %, while the SDSE attains over 28 %, respectively. The horizontal axis wind turbine, encompassing 100 kW and 500 kW modules, demonstrates power coefficients spanning from 0.18 to 0.09, with corresponding land area requirements ranging from 4.22 m2 to 21.10 m2, emphasizing the pivotal roles of module power and land area optimization. This paper also casts a spotlight on the environmental repercussions of the system, illustrating its potential to avert up to 30,000 kg of CO2 emissions per kWh/year. Moreover, the levelized cost of electricity ranges from 0.13 to 0.16 $/kWh, accompanied by an hourly cost that fluctuates between 3 $/h and 40 $/h, respectively. Conclusively, the developed modeling can be regarded as a significant stride in the realm of hybrid renewable energy systems, replacing the conventional photovoltaic/wind models with cutting-edge solar/wind configurations managed by a sophisticated control strategy and battery storage system.

Formulating a mix design for 3D concrete printing (3DCP) is challenging, as it involves an iterative approach, wasting a lot of resources, time, and effort to optimize the mix for strength and printability. A potential solution is mix formulation through artificial intelligence (AI); however, being a new and emerging field, the open-source availability of datasets is limited. Limited datasets significantly restrict the predictive performance of machine learning (ML) models. This research explores data augmentation techniques like deep generative adversarial network (DGAN) and bootstrap resampling (BR) to increase the available data to train three ML models, namely support vector machine (SVM), artificial neural network (ANN), and extreme gradient boosting regression (XGBoost). Their performance was evaluated using R2, MSE, RMSE, and MAE metrics. Models trained on BR-augmented data showed higher accuracy than those trained on the DGAN-augmented data. The BR-trained XGBoost exhibited the highest R2 scores of 0.982, 0.970, 0.972, 0.971, and 0.980 for cast compressive strength, printed compressive strength direction 1, 2, 3, and slump flow respectively. The proposed method of predicting the slump flow (mm), cast, and anisotropic compressive strength (MPa) can effectively predict the mix design for printable concrete, unlocking its full potential for application in the construction industry.

Tissue engineering, a rapidly advancing field in medicine, has made significant strides with the development of artificial tissue substitutes to meet the growing need for organ transplants. Three-dimensional (3D) porous scaffolds are widely utilized in tissue engineering, especially in orthopedic surgery. This study investigated the fluidic properties of diamond and gyroid structures with varying porosity levels (50–80%) using Computational Fluid Dynamics (CFD) analysis. The pressure and velocity distributions were analyzed, and it was observed that the pressure decreased gradually, whereas the velocity increased in the central area of the surface structures. Specifically, the pressure drop ranged from 2.079 to 0.984 Pa for the diamond structure and from 1.669 to 0.943 Pa for the gyroid structure as the porosity increased from 50% to 80%. It was also found that the permeability increased as the porosity level increased, with values ranging from 2.424×10−9 to 5.122×10−9 m2 for the diamond structure and from 2.966×10−9 to 5.344×10−9 m2 for the gyroid structure. The wall shear stress (WSS) was also analyzed, showing a consistent decrease with increased porosity for both types of structures, with WSS values ranging from 9.903×10−2 to 9.840×10−1 Pa for the diamond structure and from 1.150×10−1 to 7.717×10−2 Pa for the gyroid structure. Overall, this study provides insights into the fluidic properties of diamond and gyroid structures, which can be useful in various applications such as tissue engineering.

Owing to the massive expansion and intermittent nature of renewable power, green hydrogen production, storage, and transportation technologies with improved economic returns need to be developed. Moreover, the slowness of the dehydrogenation reaction is a primary barrier to the commercialization of liquid organic hydrogen carrier (LOHC) technology. The present study focused on increasing the speed of dehydrogenation, resulting in the proposal of a triple-loop dehydrogenation system comprising reaction, heating, and chilling loops. The reactor has a rotating cage containing a packed bed of catalyst pellets, which is designed to enhance both heat and mass transfer by helping to detach precipitated hydrogen bubbles from the catalyst surface. In addition, the centrifugal force aids in isolating the gas phase from the LOHC liquid. A dehydrogenation experiment was conducted using the reaction and chilling loops, which revealed that the average hydrogen production rate during the first hour was 52.6 LPM (liter per minute) from 26.3 L of perhydro-dibenzyl-toluene with 1.5 kg of 0.5 wt% Pt/Al2O3 catalyst. This was approximately 48% more than the value predicted with the reaction kinetics measured with a small-scale plug flow dehydrogenation reactor with less than 1.0 g of 5.0 wt% Pt/Al2O3 catalyst. The concept, construction methods, and results of the preliminary gas infiltration, flow visualization, and reactor pumping experiments are also described in this paper.

The limitations in the available reinforcing methods have accompanied the increasing popularity of 3D Concrete Printing (3DCP). Incorporating steel fibers as reinforcement is a promising approach to overcome these limitations. However, the impact of the printing process on the alignment of these fibers is not well understood. Therefore, the objective of this research is to quantitatively analyze the distribution of steel fiber alignment in 3D printed concrete. To achieve this, digital image analysis was employed to assess the influence of nozzle diameter, print speed, and fiber content on fiber alignment in both mold-cast and 3D-printed samples. UHPC matrix without fiber addition and fiber reinforced UHPC composites with brass-coated steel fiber contents of 1.5% and 3% by volume fraction were printed. Furthermore, Material nozzles ranging from 10 mm to 40 mm in size were employed and printing speeds of 15, 25, 35, and 45 mm/s were adjusted. Subsequently, the study examined the implications of fiber alignment on the hardened performance of printed specimens and compared them with conventionally mold-cast samples. The findings of the study demonstrated that increasing the fiber content and using smaller diameter nozzles during the printing procedure led to significant improvements in fiber orientation along the printing direction. As a result, the mechanical performance of the printed samples showed a substantial enhancement compared to the specimens produced through mold casting, primarily due to the improved fiber alignment.

Households in developing countries rely on traditional cooking methods despite low-efficiency and high risks of air pollution and health. Various improved biomass cookstove technologies have been developed to address this issue, but there is still room for improvement in biomass cooking technology. This research aims to enhance the biomass cookstove by investigating its performance under different air inlet conditions using computational fluid dynamics analysis. The impact of air flow rate and the position of the secondary air inlet on the thermal efficiency of the biomass cookstove is examined. The eddy dissipation combustion model and k-epsilon turbulence model are utilized in ANSYS Fluent to simulate the combustion process. The cookstove's performance is assessed at three different air flow rates (0.01, 0.02, and 0.03 kg/s) and at two different secondary air inlet positions. The highest thermal efficiency is observed at secondary air inlet located 50 mm below the outlet at a flow rate of 0.03 kg/s. The computational findings from this investigation show a significant connection between the air inlet conditions and the thermal efficiency of the biomass cookstove. Repositioning the secondary air inlet to a location with more available space for complete combustion improves thermal efficiency compared to traditional designs.

In recent times, there has been a substantial increase in the annual use of plastics, resulting in a major upward trend. As a result, recycling plastic waste has become a major global issue. The present research aims to explore the feasibility of utilizing Polyethylene terephthalate (PET) as a possible substitute for coarse aggregate in concrete. In addition, the integration of Graphene Nano-platelets (GNPs) into the concrete mixture was conducted using different quantities, aiming to improve its structural integrity. The study employed an experimental research design to carry out its examination. PET was included into concrete at varied ratios, namely 0 %, 5 %, 15 %, 20 %, and 25 %, serving as an additional ingredient alongside coarse aggregate. Additionally, GNPs were introduced into the mixture at various weight percentages, namely 0 %, 0.03 %, 0.05 %, 0.08 %, and 0.1 %. A series of mechanical tests were performed to evaluate various properties of the concrete, including compressive strength (CS), split tensile strength (STS), flexural strength (FS), modulus of elasticity (MoE), ultrasonic pulse velocity (UPV). In addition, an assessment of the concrete's environmental impact was conducted by analysing the carbon content and evaluating its eco-efficiency (ESE). The research study revealed that the incorporation of 5 % PET as a replacement for coarse aggregate, together with the inclusion of 0.1 % GNPs, resulted in the optimal enhancement of CS, STS, FS, MOE, UPV by 9 %, 12.21 %, 4.40 %, 4.40 % and 0.070 % respectively. The Response Surface Methodology (RSM) models were developed, and mathematical equations were generated in order to predict the expected results. The optimization process for all the models was carried out using a multi-objective optimization technique, followed by a subsequent validation process.

This study evaluates the earthquake-induced movement of geogrid earth-retaining (GER) walls. A thorough investigation was conducted on a GER wall model, utilizing a comprehensive finite element (FE) analysis. This research focuses on investigating and designing hollow prefabricated concrete panels and conventional gravity-type stone masonry GER walls. It also displays comparative studies such as the displacement of the wall, deflection of the wall, lateral pressure of the wall, settlement of the backfill reinforcement, vertical pressure of the backfill, lateral pressure of the backfill, vertical settlement of the foundation, and settlements of soil layers across the height and acceleration of the walls of the GER walls. The FE simulations used a three-dimensional (3D) nonlinear dynamic FE model of full-scale GER walls. The seismic performance of models has also been examined in terms of wall height. It was found that the seismic motion significantly impacts the height of the GER walls. In addition, the validity of the proposed study model was assessed by comparing it to the conventional reinforcement concrete and gravity-type GRE wall and ASSHTO guidelines using finite element (FE) simulation results. Based on the findings, the hollow prefabricated concrete panels were the most practical alternative due to their lower deflection and displacement. Based on the observation, it was also found that the hollow prefabricated GER wall is the most viable option, as the settlement and lateral pressure in the former type are high.

Nanostructures of undoped zinc oxide and nickel-doped zinc oxide (Ni = Zn0.98Ni0.02O, Zn0.96Ni0.04O, and Zn0.94Ni0.06O) were synthesized by using the coprecipitation process, and their optical and dielectric properties were simultaneously investigated. The XRD results confirm the hexagonal structure having space group P63mc. By increasing nickel concentration, the particle size decreases, while the strain is increased. Fourier-transform infrared (FTIR) analysis was carried out in order to learn more about the phonon modes present in nickel-doped zinc oxide. UV-Vis spectroscopy further revealed that the optical band gap of nickel-doped samples varied from 3.18 eV to 2.80 eV. The SEM analysis confirms the rod shape morphology of the already synthesized samples. EDX analysis investigates the incorporation of nickel ions into the zinc oxide lattice. Using photoluminescence spectroscopy, we found that the synthesized materials had oxygen vacancies (Vo) and zinc interstitial (Zni) defects. Dielectric constant (εr) and dielectric loss (ε) are both improved in nickel-doped zinc oxide compared to undoped zinc oxide. Since more charge carriers enhanced after the nickel ions were exchanged for the Zn ions, the AC electrical conductivity (σa.c) improves by nickel doping compared to undoped zinc oxide.

Abstract Clean water and renewable energy sources are becoming increasingly important in the current era, as well as a future challenge, and one of the potential solutions is photocatalysis. In the current study, a simple one‐step hydrothermal technique is employed to fabricate the pure and La‐doped CuO (0%, 1%, 3%, 5%, and 7%) photocatalysts. The influence of varying La concentration on structure, morphology, and optical properties is determined by scanning electron microscope (SEM), X‐ray diffraction (XRD), ultraviolet (UV)–visible spectroscopy, and photoluminescence. SEM showed that synthesized nanostructures are irregularly spherical and transform into needle‐like nanostructures on increasing La concentration. XRD revealed the monoclinic phase with a crystallite size of 15–23 nm. The UV–visible spectrum exhibited a decrease in the band gap of La‐doped CuO needle‐like nanostructures from UV to visible light. The composition and purity of synthesized nanostructures are evaluated via the energy‐dispersive X‐ray spectrum which revealed that needle‐like nanostructures are pure without any impurity traces. The synthesized nanostructures were used as a photocatalyst against methylene blue dye to examine their photocatalytic activity. The synthesized CuO‐3La photocatalyst exhibited excellent photocatalytic performance of dye degradation and hydrogen production 95.3 μmol h−1 g−1 with more than 97% cyclic stability. Therefore, the synthesized La‐doped CuO nanostructures are potential candidates for photocatalytic water splitting and hydrogen evolution.

Global urbanization and increasing traffic volume have intensified traffic congestion throughout transportation infrastructure, particularly at toll plazas, highlighting the critical need to implement proactive transportation infrastructure solutions. Traditional toll plaza management approaches, often relying on manual interventions, suffer from inefficiencies that fail to adapt to dynamic traffic flow and are unable to produce preemptive control strategies, resulting in prolonged queues, extended travel times, and adverse environmental effects. This study proposes a proactive traffic control strategy using advanced technologies to combat toll plaza congestion and optimize traffic management. The approach involves deep learning convolutional neural network models (YOLOv7–Deep SORT) for vehicle counting and an extended short-term memory model for short-term arrival rate prediction. When projected arrival rates exceed a threshold, the strategy proactively activates variable speed limits (VSLs) and ramp metering (RM) strategies during peak hours. The novelty of this study lies in its predictive and adaptive capabilities, ensuring efficient traffic flow management. Validated through a case study at Ravi Toll Plaza Lahore using PTV VISSIMv7, the proposed method reduces queue length by 57% and vehicle delays by 47% while cutting fuel consumption and pollutant emissions by 28.4% and 34%, respectively. Additionally, by identifying the limitations of conventional approaches, this study presents a novel framework alongside the proposed strategy to bridge the gap between theory and practice, making it easier for toll plaza operators and transportation authorities to adopt and benefit from advanced traffic management techniques. Ultimately, this study underscores the importance of integrated and proactive traffic control strategies in enhancing traffic management, minimizing congestion, and fostering a more sustainable transportation system.

Human knee plays a vital role in performing day-to-day activities. For healthy person it is easy to perform locomotion activities but for people with transfemoral amputation, it is very difficult task. To overcome this issue prosthetic knees are developed. These prosthetic knees provide necessary function of gait cycle. In order to mimic gait cycle of human knee, it is very crucial to detect different gait phases in gait cycle. Mechanical sensors such as force and angle sensors are used to collect kinematic data and then with heuristic rule base system, the gait phases are detected. Rule-based system performs well but as number of gait phases are increased then it is difficult to identify them. This paper proposed machine learning based gait phase detection. Decision Trees, Liner discriminant analysis and Support Vector Machine, are applied to the kinematics data obtained from strain gauges and angle encoder. These algorithms are easy to implement on embedded hardware as they use low computational power. The Linear Discriminant analysis has highest validation accuracy of 95.6% and test accuracy of 95.40% while both Support Vector Machine and Decision Trees algorithm has 95.2% validation accuracy. The test accuracy of Support Vector Machine is 95.10% and for Decision Tree it is 95.05%.

Over the years, the allocation of public funds toward agriculture has consistently played a pivotal role in facilitating the modernization and commercialization of the agricultural industry. Similarly, the integration of technological breakthroughs plays a pivotal role in guaranteeing the sustainability of food production, not solely for the present populace but also for subsequent generations. The present study examines the impact of public investment in agriculture and farm management practices, specifically focusing on cultivated area, fertilizer use, pesticide application, total agricultural machinery, and rural labour force on wheat production in nine provinces of China. The study encompasses the period from 1995 to 2020. This study employs advanced econometric techniques, such as second-generation unit root procedures (CADF and CIPS) and the Westerlund cointegration method, to investigate the stationarity properties and cointegration of the variables. The findings derived from the AMG and CCEMG methods indicate that public investment plays a statistically significant influence on wheat production. In the context of production-related variables, long-term wheat production is statistically and substantially influenced by the total area under cultivation, fertilizer use, and pesticide application. Besides these results, the Dumitrescu and Hurlin causality test reveals a unidirectional causality from agricultural machinery power consumption to wheat production. Furthermore, bidirectional causality exists between public investment, cultivated area, fertilizer use, labour, and wheat production. These results provide vital implications and valuable insights for policymakers in China, which may furnish novel policymaking options for sustainable food production through strategic investments in research and development, irrigation systems, and technological advancements.

The centralized power grid bears a heavy burden in a time when consumers expect an uninterrupted reliable power supply, a reduction in carbon emissions, increased efficiency within the national grid, and power supplied to remote communities. One such structure is used to implement this type of control that is based on small- scale Distributed Generation (DGs), at a single house or a small building level: micro-grid (μG). A μG is an autonomous, sustainable hybrid framework that uses renewable and non-renewable energy resources to supply continuous electricity to the load. Taking this scenario into account, the three sources of generation that have been utilized in the proposed μG are Photovoltaic (PV) array, Fuel Cell (FC), and Wind Turbine (WT) in this research paper. The active and reactive power of all three generation resources has been controlled using various controllers, i.e., integral, proportional-integral, proportional derivative, proportional integral derivative, fractional-order proportional-integral, Fractional Order Proportional Integral Derivative (FOPID), and Sliding Mode Controller (SMC). An advanced optimization technique based on a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm has been utilized to optimize all these controllers. The integral square error has been taken as the objective function for both optimization algorithms. Finally, a graphical and tabular comparative analysis of all optimized controllers along with their control parameters and performance indexes has been evaluated to find the best optimal solution using MATLAB/Simulink. The performance of SMC has been surpassed the performance of all other optimized controllers for the power stability analysis. Moreover, a smart switching algorithm has been introduced for switching between the generation resources following the load demand and cost of the system to operate the μG more economically. Finally, a case study has been performed in which the smart switching algorithm has been utilized to switch to the best available generation resource in case of any fault at the generation side to provide uninterrupted power to the attached loads.

In this work, we explore the transient response of an electric circuit using probabilistic analysis when the precise value of the characteristic parameter of a circuit element is not known and is instead constrained to a range of values. To accomplish this, a combination of probability theory and traditional circuit analysis techniques is employed. In this approach, the circuit element’s parameter is treated as a random variable, and probability methods are used to derive the probability distribution of the voltage or current across it. To demonstrate this approach, we apply it to a resistor capacitor (RC) circuit, where the capacitance parameter of the capacitor is regarded as a random variable. We derive probability distributions for the transient voltage across the capacitor and the voltage at the output terminals to illustrate the method’s effectiveness.

For over a century, enzyme immobilisation has been proven to be a superior strategy to improve catalytic activity and reusability and ensure easy separation, easy operation, and reduced cost. Enzyme immobilisation allows for an easier separation of the enzyme from the reaction mixture, thus simplifying downstream processing. This technology protects the enzyme from degradation or inactivation by harsh reaction conditions, making it more robust and suitable to be used in various applications. Recent strategies of immobilisation methods, such as adsorption, cross-linking, entrapment or encapsulation, and covalent bonding, were critically reviewed. These strategies have shown promising results in improving enzyme stability, activity, and reusability in various applications. A recent development in enzyme immobilisation in nanomaterials and agrowaste renewable carriers is underlined in the current review. Furthermore, the use of nanomaterials and agrowaste carriers in enzyme immobilisation has gained significant attention due to their unique properties, such as high surface area, high mass transfer, biocompatibility, and sustainability. These materials offer promising outcomes for developing more efficient and sustainable immobilised enzymes. This state-of-the-art strategy allows for better control over enzyme reactions and enhances their reusability, leading to more cost-effective and environmentally friendly processes. The use of renewable materials also helps to reduce waste generation and promote the utilisation of renewable resources, further contributing to the development of a circular economy.

Dynamic heterogeneous graphs comprise different types of events with temporal labels. In many real-world scenarios, the temporal order of different types of events possibly implies causal relationships between these event types. However, existing methods designed to model dynamic heterogeneous graphs neglect the underlying causal relationships between event types. For instance, the determination of the occurrence of a new event is misled by irrelevant historical events considering the type and could lead to performance degradation. First, this paper explicitly defines the causality of event types by the heterogeneous causality graph to utilize such causality from the perspective of the graph structure to tackle the aforementioned issue. Second, this paper proposes the event type causality based continuous-time heterogeneous attention network (ECHN) to model dynamic heterogeneous graphs. ECHN aggregates features based on the strength of different causal relationships between event types in the prediction process to utilize the causality of event types from the perspective of the modeling algorithm. The utilities of event type causality weaken the negative effect of irrelevant events. Experimental results demonstrate that ECHN outperforms state-of-the-arts in the link prediction task. The authors believe that this paper is the first study to model the causality of event types in dynamic heterogeneous graphs explicitly.

Ultradensification using different types of small cells (SCs) is one of the key enabling solutions to meet the multiple stringent requirements of 5G cellular networks. However, radio resource management (RRM) in ultra-dense heterogeneous networks (HetNets) is not easy due to interferences in multi-tiered architecture and dynamic network conditions. Interferences in 5G HetNets can be efficiently managed only through the techniques which are adaptive and self-organizing to handle dynamic conditions in 5G HetNets. In this article, a machine learning (ML) based self-adaptive resource allocation scheme is proposed based on the combination of independent and cooperative learning and evaluated for ultra-dense 5G HetNets. The proposed scheme aims to improve the QoS of all users associated with different network tiers in ultra-dense HetNets simultaneously. The proposed solution adaptively optimizes the SCs transmit power either through independent learning or cooperative learning based on the varying density of small cells to minimize the interferences and ensure minimum QoS requirements for all users in different network tiers. The proposed scheme not only maintains the minimum required capacities for QoS provision to all users simultaneously but has also shown a significant improvement in the capacities of users in different network tiers in high interference scenarios as compared to the use of a single learning scheme.

The controllable assembly of plasmonic nanoparticles has developed as one of the most significant approaches for surface enhanced Raman spectroscopy (SERS) applications. This study developed a simple approach to improve a large-scale ordered assembly of gold nanorods (GNRs) by controlling the droplet evaporation mode on hydrophobic substrates. The hydrophobic substrate was efficiently produced by spin coating the silicone oil onto the glass slides and annealing them. The analyte molecule rhodamine (R6G) was employed as a surface-enhanced Raman scattering probe to demonstrate the potential effects of the synthesized arrays. This hydrophobic platform enables the concentration and delivery of analyte molecules into the surface enhanced Raman spectroscopy sensitive site while suppressing the coffee ring effect generated by the smooth contraction motion of the base contact radius of the droplet without any pinning. Thus, the limit of detection (LOD) of the R6G analyte was lowered to 10−10 M and the homogenous dispersion of surface enhanced Raman spectroscopy hotspots within the self-assembly reproducible surface enhanced Raman spectroscopy signal. This new method enables a broad range of packing patterns and mechanisms by changing the host nanoparticles in the dispersion.

This study deals with numerical solution of momentum and heat transfer of fractional ordered Maxwell fluids within a coaxial cylinder. It is well known that the complex dynamics of flow regime can be well-described by the fractional approach. In this paper, a fractional differentiation operator Dtα of Caputo was applied for fractional modeling of magneto-hydro-dynamic (MHD) fluid. A set of appropriate transformations was applied to make the governing equations dimensionless. The finite differences were calculated by the discretization of momentum profile ur,t and heat profile Tr,t. The results obtained for ur,t and Tr,t were plotted against different physical parameters, such as Prandtl number Pr, the square of Hartmann number Ha, thermal Grashof number Gr, thermal radiation parameter Nr, and heat source/sink parameter Q0. The results were verified by comparing data from the proposed method with MAPLE built-in command results. Subjecting the system to a strong magnetic field led to increasing Tr,t and decreasing ur,t. It was found that increasing Gr and Pr increased the velocity and temperature profiles. Addition of Cu nanoparticles to a base fluid of H2O enhanced its heat transfer capability. Also, increasing the angular frequency of inner cylinder velocity resulted in a high velocity profile of fractional Maxell nano-fluids within a coaxial region (cylinder).

In-Vessel Retention (IVR) is one of the primary technique to mitigate severe accidents. This enables retaining the corium inside the Reactor Pressure Vessel (RPV) through external cooling. However, it is important to assess the validity and success of this technique, though actual validation is impossible. In this work, we have developed a model of selected case study and used MATLAB 2018b to simulate it. Developed model has been tested using two sources; constant temperature source and constant heat flux. These two sources were applied at the internal surface of RPV separately and heat transfer through RPV lower plenum was monitored. Main purpose of this analysis is to obtain temperature profiles along the RPV lower plenum external surface. Results show that the technique is successful because external surface temperature is lower than melting point of RPV in both the cases discussed. Sensitivity analysis showed that external temperature can be further decreased by increasing heat transfer, which can be changed by thickness of RPV and sub-cooling of inlet water. However, both parameters have their own limitations. Results obtained through MATLAB simulations are also comparable to MELCOR1.8.5 code simulation results and both found in agreement.

In recent years, there has been growing interest in using plant fibers to reinforce materials in modern manufacturing. This study focuses on the development of a novel biocomposite made from an enset fiber (EF) and polylactic acid (PLA) matrix using compression molding at a hot-pressing temperature of 170 °C and pressure of 7 MPa for 7 min. Before preparing the biocomposites, the fibers were chemically modified with different concentrations of sodium hydroxide (NaOH) and cut into shorter fibers with a 40 mm average length in size. Then, the extent of modifications on the mechanical properties, dynamic mechanical behavior, morphology, and water absorption were investigated. The tensile, flexural, and Charpy impact tests were carried out to evaluate the mechanical properties of the samples as per ASTM standards. Moreover, dynamic mechanical analysis (DMA) and the water absorbency of the biocomposites were investigated, and the results were graphically shown and explained. The results indicate that the biocomposite treated with 5% NaOH exhibited significant improvements in tensile strength, flexural strength, and impact strength compared to the untreated composite. The tensile modulus and flexural modulus of 5% NaOH-modified enset fiber biocomposite were also enhanced by 55.8% and 70.3% compared to untreated enset fiber biocomposite. The highest tensile strength, flexural strength, and impact strength found for the PLA composite reinforced by EF treated with 5% w/v NaOH solution were 20.16 MPa, 30.21 MPa, and 12.02 kJ/m2, respectively. In general, the modification of natural fibers improves adhesion at the interface and therefore decreases the water absorption and improves the dynamic mechanical properties of biocomposites.

Electrical machines are prone to various faults and require constant monitoring to ensure safe and dependable functioning. A potential fault in electrical machinery results in unscheduled downtime, necessitating the prompt assessment of any abnormal circumstances in rotating electrical machines. This paper provides an in-depth analysis as well as the most recent trends in the application of condition monitoring and fault detection techniques in the disciplines of electrical machinery. It first investigates the evolution of traditional monitoring techniques, followed by signal-based techniques such as spectrum, vibration, and temperature analysis, and the most recent trends in its signal processing techniques for assessing faults. Then, it investigates and details the implementation and evolution of modern approaches that employ intelligence-based techniques such as neural networks and support vector machines. All these applicable and state-of-art techniques in condition monitoring and fault diagnosis aid in predictive maintenance and identification and have the highly reliable operation of a motor drive system. Furthermore, this paper focuses on the possible transformational impact of electrical machine condition monitoring by thoroughly analyzing each of the monitoring techniques, their corresponding pros and cons, their approaches, and their applicability. It offers strong and useful insights into proactive maintenance measures, improved operating efficiency, and specific recommendations for future applications in the field of diagnostics.

Continuous efforts are being made to improve plain concrete compressive strength and ductility by applying carbon, glass fiber, or hybrid-reinforced epoxy resin composites. The investigation centers on analyzing the axial compressive strength and strain, compressive stress–strain behavior, failure morphology, and crack evolution of the reinforced cylinders. Besides the experiments, non-linear finite element analysis was performed using the finite element (FE) package ABAQUS 2021. The test results indicate that carbon fiber triaxial woven fabric (TWF-C) confinement result in the most significant improvement of 118% in compressive stress than the concrete specimens. On the other hand, glass fiber triaxial woven fabric (TWF-G) confinement shows the highest enhancement of 161% in ductility. The mechanical properties of the sample utilizing glass fiber as the weft yarn and carbon fiber as the warp yarn (TWF-GC2) exhibit superior improvements of 22% in compressive stress and 8% in axial strain compared to the sample using glass fiber as the warp yarn and carbon fiber as the weft yarn (TWF-CG2). Samples with glass fiber as weft yarn show gradual cracks during loading, while carbon fiber as weft yarn show instantaneous damage. The numerical finite element models accurately predict the experimental results of the tested specimens in this study. There were 1.2~3% and 5~10% discrepancies for compressive stress and axial strain, respectively, between experimental and FE results. Overall, the results suggest that Triaxial woven fabric confinement is a valuable technique to improve the strength and strain of concrete and that the type of fibers used could be tailored for appropriate performance characteristics.

The quest for sustainable and environmentally friendly fuel feedstocks has led to the exploration of green solvents for the extraction of bio-oil from various biomass sources. This review paper provides a comprehensive analysis of the challenges and future research outlooks for different categories of green extraction solvents, including bio-based solvents, water-based solvents, supercritical fluids, and deep eutectic solvents (DES). The background of each solvent category is discussed, highlighting their potential advantages and limitations. Challenges such as biomass feedstock sourcing, cost fluctuations, solvent properties variability, limited compatibility, solute solubility, high costs, and potential toxicity are identified and examined in detail. To overcome these challenges, future research should focus on alternative and abundant feedstock sources, the development of improved solubility and separation techniques, optimization of process parameters, cost-effective equipment design, standardization of DES compositions, and comprehensive toxicological studies. By addressing these challenges and advancing research in these areas, the potential of green extraction solvents can be further enhanced, promoting their widespread adoption and contributing to more sustainable and environmentally friendly industrial processes.

Due to their efficiency and control capabilities, induction motors fed with inverters have become prevalent in various industrial applications. However, ensuring the reliable operation of the motor and diagnosing faults on time are crucial for preventing unexpected failures and minimizing downtime. This paper systematically analyzes condition monitoring and practical diagnostic techniques for inverter-fed motor drive systems. This study encompasses a thorough evaluation of different methods used for condition monitoring and diagnostics of induction motors, with the most crucial faults in their stator, rotor, bearings, eccentricity, shaft currents, and partial discharges. It also includes an assessment of their applicability. The presented analysis includes a focus on the challenges associated with inverter-fed systems, such as high-frequency harmonics, common-mode voltages causing the bearing currents, and high voltage gradients (dv/dt) due to fast switching frequency, which can impact the motor operation, as well as its faults analysis. Furthermore, this research explores the usefulness and efficiency of various available diagnostic methods, such as motor current signature analysis and other useful analyses using advanced signal processing techniques. This study aims to present findings that provide valuable insights for developing comprehensive condition monitoring strategies, and practical diagnostic techniques that enable proactive maintenance, enhanced system performance, and improved operational reliability of inverter-fed motor drive systems.

Reduction in friction ensures fuel economy, control on emissions and durability of components in internal combustion engines. A modern gasoline internal combustion engine was instrumented to determine the friction values at the cam–roller interface considering the effects of surface treatment and engine operating state. A series of tests under different operating speeds and lubricant inlet temperatures were undertaken using both an original surface roller and a Wonder Process Craft (WPC) surface-treated engine roller. The results clearly revealed a substantial reduction in friction magnitude for the WPC surface-treated engine roller in comparison to the original roller while operating under similar conditions, indicating their strong potential for employment in engines. An increase in friction with the rise in temperature was also observed for both types of rollers, whereas increased lubricant entraining velocity due to higher operating speed had the opposite impact. A considerable reduction in frictional drive torque ranging from 8% to 28% was observed by employing the WPC-treated roller in comparison to original/untreated roller at various operating conditions, which signifies the strong potential for employment of WPC surface treatment in the roller/follower valve train engines.

To reduce the environmental pollution caused by cement manufacturing and to save both the economy and resources, it is required to use alternative waste materials of pozzolanic nature to partially replace cement in the construction industry. Among different cement replacement materials being used now-a-days, marble dust is the one on which limited research has been done especially on the performance of structural members likeRC slabs. In this paper, marble dust has been used in different ratios as partial replacement of cement in concrete mix. RC one-way slabs as well as plain concrete standard samples were cast to determine their mechanical properties i.e. compressive, tensile and flexural strengths, stiffness, first and ultimate load, crack patterns, modulus of elasticity, maximum deflection and durability etc. It was found that by increasing the dosage of marble dust in plain concrete samples, workability was decreased. Compressive and tensile strengths of concrete increased up to 7.5% replacement but decreased on further replacement. The permeability of concrete decreased with the addition of marble dust up to 10% replacement, then it increased. The behaviour of one-way slabs showed an increase in the first crack load and ultimate failure capacity. Moreover, decrease in the deflection of slabs was observed with the increasing replacement of cement with marble dust. The resistance of concrete against corrosion of steel reinforcement also increased due to less migration of chloride ions. Ultrasonic Pulse Velocity (UPV) tests showed improved homogeneity and stiffness of concrete structurewith the replacement of cement by marble dust.

Recently, the need of high living standards and developments in modern technology have significantly increased the usage of throwaway glass products. The use of these waste glass products in the production of construction materials (especially bricks) is ultimately very wholesome approach towards sustainable development. In this study, wasted glass up to 20% by weight was added to observe its impacts on bricks. The bricks containing powder glass were burnt along with ordinary bricks (with no powder glass) in same kiln and under same condition (type of fuel, temperature, duration etc.). The results illustrated that the properties of bricks vividly improved such as areal density, water absorption and efflorescence. The 20% increase of waste glass in the brick samples resulted in 14% decrease in areal density. The addition of fine powder glass filled the pores and reduced porosity of bricks. The substantial reduction in water absorption of 26.14% were observed with 20% addition of waste glass content. Also efflorescence was less in specimens having less quantity of waste glass burnt at higher temperatures. The present study presented a comprehensive analysis of waste glass as a mixing component for producing light weight sustainable bricks with improved water absorption properties.

Anisopus mannii from the family Asclepiadaceae is a perennial herb that is currently utilized in the traditional Northern Nigerian medicinal system. The study document comprehensive In Vitro biological evaluation and chemical profiling of A. mannii whole plant crude extracts. Qualitative phytochemical, antioxidants, anti-inflammation, anti-hyperglycemia and chemical profiling was determined. The results revealed the presence of phenols, alkaloids, tannins, glycosides flavonoids, and saponins. The plant has the inhibition capacity to convert Fe3+ to Fe2 at 18.9 mmol/gm, scavenging at 75.5, injured tissues 28.4 75.5, -amylase 62.7 and α-Glucosidase 89.5 µg /mL respectively. 9-Octadecenoic acid, methyl and Lup-20(29)-en-3-ol, acetate, (3. beta) are the major compounds found in the whole plant extract. The current groundbreaking research revealed ethanolic extract from the whole plant is an effective medicinal agent. It provides the possibility for different therapy regimens to be developed from it. Research is needed to discover and purify the active chemicals that are responsible for therapeutic efficacy.

The use of switch mode power supply (SMPS) load such as computer, laptop, etc in residential as well as commercial areas give rise to harmonics pollution in the electrical power distribution system which causes the quality of power poor and the function of equipment connected to the electrical system is affected. Many electrical appliances have an SMPS design that characteristically draws a distorted current that flows back to the input supply and causes distortion in the voltage waveform. To ensure the safe functioning of various electrical devices, there is a need for the implementation of effective harmonics mitigating techniques. Passive filters have been used for mitigating voltage and current harmonics. This paper proposed an analytical approach to designing the passive filter instead of trial and error or a complex approach for harmonics mitigation. The Simulation Model of SMPS is developed in MATLAB/Simulink and used for validating the proposed approach. Simulation results show the attractive findings of the proposed approach for harmonic mitigation with the improvement of the power factor.

Over the past decade, ultrasonic cleaners have been widely used in many industries. Now, this technology is finding its way into homes for vegetable, fruit, and clothes cleaning. In widely used ultrasonic cleaners, piezoelectric transducers are externally attached to the steel tank to generate ultrasonic waves inside the tank. Based on the impedance data of the piezoelectric transducers, the driving circuit was tuned to generate the required frequencies inside the cleaning tank. This paper discusses the design, development, and validation of an 800 mL tank capacity ultrasonic cleaner driven with a piezoelectric disc actuator. To achieve an optimum cleaning action without surface abrasion, several characteristics need to be considered in this complex relationship. The placement of transducers has been investigated according to the pressure distribution inside the liquid medium. The optimized ultrasonic cleaner design, along with a class-D half-bridge circuit, was developed to drive the ultrasonic transducer in the resonance frequency range. To validate the optimal design and driving frequency, the acoustic spectrum generated inside the tank was measured using a piezoelectric sensor and FFT analysis was performed. To validate the cleaning effect, a qualitative test based on aluminuim foil perforations was performed. The perforation area in the foils was quantitatively measured using image processing based on the YOLO V5 technique. The proposed image processing technique has an accuracy of 97 % in the detection of perforation areas in the aluminuim foil test.

The additive manufacturing of concrete, also known as 3D-printed concrete, is produced layer by layer using a 3D printer. The three-dimensional printing of concrete offers several benefits compared to conventional concrete construction, such as reduced labor costs and wastage of materials. It can also be used to build complex structures with high precision and accuracy. However, optimizing the mix design of 3D-printed concrete is challenging, involving numerous factors and extensive hit-and-trail experimentation. This study addresses this issue by developing predictive models, such as the Gaussian Process Regression model, Decision Tree Regression model, Support Vector Machine model, and XGBoost Regression models. The input parameters were water (Kg/m3), cement (Kg/m3), silica fume (Kg/m3), fly ash (Kg/m3), coarse aggregate (Kg/m3 & mm for diameter), fine aggregate (Kg/m3 & mm for diameter), viscosity modifying agent (Kg/m3), fibers (Kg/m3), fiber properties (mm for diameter and MPa for strength), print speed (mm/sec), and nozzle area (mm2), while target properties were the flexural and tensile strength of concrete (MPa data from 25 literature studies were collected. The water/binder ratio used in the dataset ranged from 0.27 to 0.67. Different types of sands and fibers have been used, with fibers having a maximum length of 23 mm. Based upon the Coefficient of Determination (R2), Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE) for casted and printed concrete, the SVM model performed better than other models. All models’ cast and printed flexural strength values were also correlated. The model’s performance has also been checked on six different mix proportions from the dataset to show its accuracy. It is worth noting that the lack of ML-based predictive models for the flexural and tensile properties of 3D-printed concrete in the literature makes this study a novel innovation in the field. This model could reduce the computational and experimental effort required to formulate the mixed design of printed concrete.

The advancement of both the fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has enabled the development of effective automatic systems for analyzing human behavior. It is possible to recognize gestures, which are frequently used by people to communicate information non-verbally, by studying hand movements. So, the main contribution of this research is the collected dataset, which is taken from open-source videos of the relevant subjects that contain actions that depict confidence levels. The dataset contains high-quality frames with minimal bias and less noise. Secondly, we have chosen the domain of confidence determination during social issues such as interviews, discussions, or criminal investigations. Thirdly, the proposed model is a combination of two high-performing models, i.e., CNN (GoogLeNet) and LSTM. GoogLeNet is the state-of-the-art architecture for hand detection and gesture recognition. LSTM prevents the loss of information by keeping temporal data. So the combination of these two outperformed during the training and testing process. This study presents a method to recognize different categories of Self-Efficacy by performing multi-class classification based on the current situation of hand movements using visual data processing and feature extraction. The proposed architecture pre-processes the sequence of images collected from different scenarios, including humans, and their quality frames are extracted. These frames are then processed to extract and analyze the features regarding their body joints and hand position and classify them into four different classes related to efficacy, i.e., confidence, cooperation, confusion, and uncomfortable. The features are extracted using a combination framework of customized Convolutional Neural Network (CNN) layers with Long Short-Term Memory (LSTM) for feature extraction and classification. Remarkable results have been achieved from this study representing 90.48% accuracy with effective recognition of human body gestures through deep learning approaches.

Automatic modulation classification (AMC) is a vital process in wireless communication systems that is fundamentally a classification problem. It is employed to automatically determine the type of modulation of a received signal. Deep learning (DL) methods have gained popularity in addressing the problem of modulation classification, as they automatically learn the features without needing technical expertise. However, their efficacy depends on the complexity of the algorithm, which can be characterized by the number of parameters. In this research, we presented a deep learning algorithm for AMC, inspired by residual learning, which has remarkable accuracy and great representational ability. We also employed a squeeze-and-excitation network that is capable of exploiting modeling interconnections between channels and adaptively re-calibrates the channel-wise feature response to improve performance. The proposed network was designed to meet the accuracy requirements with a reduced number of parameters for efficiency. The proposed model was evaluated on two benchmark datasets and compared with existing methods. The results show that the proposed model outperforms existing methods in terms of accuracy and has up to 72.5% fewer parameters than convolutional neural network designs.

High lift devices play a vital role in dictating the accelerated performance of an aircraft for different flight phases such as takeoff, landing, and aerobatic maneuvers. The aerodynamic design of high lift devices for any particular aircraft is an iterative process and is achieved through extensive aerodynamic Analysis of the aircraft for various flap configurations. Computational Fluid Dynamics (CFD) and Wind tunnel testing are highly effective techniques for performing the required Analysis, yet they have high computational costs and time. To overcome this shortcoming, a robust framework based on potential flow solver (PFS) and geometry parameterization is required without compromising the fidelity of the Analysis. This research aims to develop a highly robust aerodynamic analysis framework based on the Vortex Lattice Method (VLM) coupled with Polhamus Suction Analogy and parametric modeling of high lift devices. The fidelity of the framework is validated through experimental testing and is quantified by developing a fidelity assessment matrix. It is established that the computational cost of CFD has been reduced three times with only a 10% to 20% loss in accuracy when the developed framework is used. The developed PFS framework gives results from 80% to 90%. The framework results for a reference aircraft are thoroughly compared with CFD analyses. The framework provides values that agree with corresponding CFD analyses in a fraction of the time.

Ideally, electric supplying companies are projected to convey sinusoidal voltage having the frequency of constant rate, while the consumers are expected to extract the current in sinusoidal forms with power factor nearly equal to unity. But extensive applications of equipment causing the production of harmonics in domestics and industrial appliances causes some problems in the smooth flow of current and voltage. Especially the usage of non-linear loads is causing the distortion of electric voltage and currents in the power system. Due to these non-sinusoidal flows of voltage and current (harmonics), the performance of overall system is disturbed. So, the detection and mitigation of harmonics is essential. In this research work detection and mitigation of harmonics voltage and currents caused by nonlinear loads using DFIG has been investigated. Multi-reference theory has been used for this purpose.

Project-based organizations need to procure different commodities, and the failure/success of a project depends heavily on procurement management. Companies must refine and develop methods to simplify and optimize the procurement process in a highly competitive environment. This paper presents a methodology to help managers of project-based organizations analyze procurement processes to determine the optimal framework for simultaneously addressing multiple objectives. These goals include minimizing the time between the generation and required approval for a purchase, identifying unnamed activities, and allocating the budget efficiently. In this paper, we apply process mining algorithms to a dataset consisting of event logs on Oracle Financials-based enterprise resource planning (ERP) procurement processes in ERP systems and demonstrate interesting results leading to project procurement intelligence (PPI). The provided log data is the real-life data consisting of 180,462 events referring to seven activities within 43,101 cases. The logged procurement processes are filtered and analyzed using the open-source process mining frameworks PrOM and Disco. As a result of the process mining activities, a simulation of the discovered process model derived from the event log of the entire procurement process is presented, and the most frequent potential behaviors are identified. This analysis and extraction of frequent processes from corporate event logs help organizations understand, adapt, and redesign procurement operations and, most importantly, make them more efficient and of higher quality. This study shows that after the successful formulation of guiding principles, data refinement, and process structure optimization, the case study results are considered significant by the organization’s management.

Microbial fuel cell (MFC) technology is anticipated to be a practical alternative to the activated sludge technique for treating domestic and industrial effluents. The relevant literature mainly focuses on developing the systems and materials for maximum power output, whereas understanding the fundamental electrochemical characteristics is inadequate. This experimental study uses a double-chamber MFC having graphite electrodes and an anion-exchange membrane to investigate the electrochemical process limitations and the potential of bioelectricity generation and dairy effluent treatment. The results revealed an 81% reduction in the chemical oxygen demand (COD) in 10 days of cell operation, with an initial COD loading of 4520 mg/L. The third day recorded the highest open circuit voltage of 396 mV, and the maximum power density of 36.39 mW/m2 was achieved at a current density of 0.30 A/m2. The electrochemical impedance spectroscopy analysis disclosed that the activation polarization of the aerated cathode was the primary factor causing the cell’s resistance, followed by the ohmic and anodic activation overpotentials.

In this study, the natural frequencies and roots (Eigenvalues) of the transcendental equation in a cantilever steel beam for transverse vibration with clamped free (CF) boundary conditions are estimated using a long short-term memory-recurrent neural network (LSTM-RNN) approach. The finite element method (FEM) package ANSYS is used for dynamic analysis and, with the aid of simulated results, the Euler–Bernoulli beam theory is adopted for the generation of sample datasets. Then, a deep neural network (DNN)-based LSTM-RNN technique is implemented to approximate the roots of the transcendental equation. Datasets are mainly based on the cantilever beam geometry characteristics used for training and testing the proposed LSTM-RNN network. Furthermore, an algorithm using MATLAB platform for numerical solutions is used to cross-validate the dataset results. The network performance is evaluated using the mean square error (MSE) and mean absolute error (MAE). Finally, the numerical and simulated results are compared using the LSTM-RNN methodology to demonstrate the network validity.

This paper evaluates the feasibility of bioethanol production from onion waste by Saccharomyces cerevisiae and Pichia pastoris and their novel co-culture through fermentation. The process parameters were optimized for each strain and their combination to observe the synergistic effect of co-fermentation. A dinitro salicylic acid (DNS) test was conducted to study the reducing sugar content of samples at different time intervals. Fourier transform infrared (FTIR) spectroscopic analysis was used to compare results for functional groups of samples before and after fermentation, and gas chromatography with flame ionization detection (GC-FID) analysis was performed to measure the bioethanol concentration obtained at different combinations of pH (5, 5.5, 6), temperature (20 °C, 30 °C, 40 °C), and time (24–110 h). The maximum bioethanol concentration was achieved through a monoculture of Saccharomyces cerevisiae, i.e., 30.56 g/L. The ethanol productivity was determined based on the ethanol concentration and fermentation time ratio. The energy content was determined using the obtained ethanol value and the specific energy content of ethanol, i.e., 30 kJ/g. The productivity and energy of bioethanol obtained at this maximum concentration were 0.355 g/L h and 916.8 kJ/L, respectively, after 86 h of fermentation at 30 °C and pH 5. Pichia pastoris produced a maximum of 21.06 g/L bioethanol concentration with bioethanol productivity and energy of 0.264 g/L h and 631.8 kJ/L, respectively, after 72 h of fermentation at 30 °C and pH 5. The coculture fermentation resulted in 22.72 g/L of bioethanol concentration with bioethanol productivity and energy of 0.264 g/L h and 681.6 kJ/L, respectively, after 86 h of fermentation at 30 °C and pH 5. The results of reducing sugars also supported the same conclusion that monoculture fermentation using Saccharomyces cerevisiae was the most effective for bioethanol production compared to Pichia pastoris and co-culture fermentation.

In image classification, various techniques have been developed to enhance the performance of principal component analysis (PCA) dimension reduction techniques with guiding weighting features to remove redundant and irrelevant features. This study proposes the statistically weighted dimension technique based on three distribution-related class behaviors; collection-class, inter-class, and intra-class to enhance the feature-extraction ability before using PCA for feature selection. The data from the statistics-weighted dimension spaces is utilized to reduce dimensionality by reducing the large index data into smaller index data using PCA. The new principal component from the weighted training part by an unlabeled dataset is constructed and then the image is classified efficiently. Additionally, the weighting direction investigates the pros and cons of promoting and demoting to determine the worst or best option utilizing the exponents of three proposed weighted scheme. The experiment is conducted using three datasets, MNIST, E-MNIST, and F-MNIST, along with three image classification algorithms, logistic Regression, KNN, and SVM (RBF). The results clearly demonstrate that the statistically weighted dimension feature can improve the conventional classification accuracy in lower dimensions with an appropriate combination of weighting nearly 3% for the best solution on dimensionality reduction by more than 50%.

The reuse of lignocellulosic biomaterials as a source of clean energy has been explored in recent years due to the large amount of waste that involves human activities, such as those related to agriculture and food. The anaerobic digestion (AD) of plant-based biomass for bioenergy production poses a series of challenges that new technologies are attempting to solve. An improved decomposition of recalcitrant lignocellulose together with an increase in biogas production yield are the main objectives of these new approaches, which also seek the added value of being environmentally friendly. Recent research has reported significant progress in this regard, offering promising outcomes on the degradation of lignocellulose and its subsequent transformation into biomethane by specialized anaerobic microorganisms, overcoming the drawbacks inherent to the process and improving the yield of methane production. The future of the agri–food industry seems to be heading towards the implementation of a circular economy through the introduction of strategies based on the optimized use of lignocellulosic residues as a source of clean and sustainable energy.

A novel technique for conversion of DC power to AC power is introduced using solar powered sine wave generation system is presented. DC power extracted from solar system is made utilitarian for driving the BLDC (brushless DC) motor with constant speed constraints. The back EMF signals of each phase of the BLDC motor facilitated the development of technique for the sine wave generation from solar system without battery backup. This motor is coupled with AC generator, which yields the AC power on demand. The constant speed constraint indispensable for fixed electrical frequency at the output of generator. A new control technique is rendered for reference tracking speed control of BLDC motor using Lyapunov theory by changing switching frequency, and also maintaining the voltage level at the output of converter driving BLDC by regulating the switching frequency of the zeta converter. The proposed methodology for generation of AC power from solar power system is also tested on prototype. The comparison of conventional technique for generation of sine wave power and proposed scheme is mentioned at later section of this paper for describing the efficacy of newly presented scheme.

This paper proposed a novel method to find the Optimal Feeder Reconfiguration (OFR) of radial distribution network along with optimal site and size of Distributed Generation (DG) with an objective of power loss reduction. OFR and DG allocation problems are highly non-linear and complex optimization problems, complexity of problem is further increased with the addition of distribution operation constraints. In the last two decades, Evolutionary Algorithms (EAs) have been implemented successfully to find the OFR and DG allocation considering different objective functions. However, search space adopted by EAs are unconstrained, therefore numerous methodologies are adapted to discard infeasible solutions. The penalty approach is most widely used in the literature, it requires appropriate selection of penalty parameters using a tedious trial and error method. In this paper, a new EA Constrained Composite Differential Evolution (C2oDE) is proposed find optimal capacity and site of DG along with reconfiguration problem. In the proposed algorithms three trial vectors are generated to strike balance among exploration and exploitation. Furthermore, to find the feasible solutions in the decision space two most widely used constraint handling techniques that include feasibility rule and ɛ-constrained method are added to select the trail vector and individuals respectively for the next generation. Proposed C2oDE method has been validated by considering IEEE 33-bus, 69-bus and 119-bus distribution networks at five different cases. Simulation results obtained shows better performance of proposed method against the most recent literature to find both OFR and DG allocation

Online academic learning throughout the world has gained much importance and this is mainly due to the presence of Information and Communication Technologies. The introduction of online learning has led to the vast usage of personal computers (PCs), tablets, laptops, and smartphones. Even so, due to the COVID-19 pandemic situation, all physical academic activities stopped in educational institutes and the delivery of education was shifted to an online learning platform. From this standpoint, many educational institutes used various types of online applications such as Microsoft teams, Zoom, Skype, Whatsapp, and so on, to continue their studies which is an important aspect, particularly when there are no physical classes. These online applications have gained popularity because they offer an opportunity to connect with people anywhere, at any time. These online applications have provided a platform to share knowledge and experience with people around the globe. In education, these online applications play a vital role in strengthening the knowledge of students, teachers, and trainees. This paper focuses on the use of Microsoft teams as an online application in education and evaluates the effects of its use on university students’ performance in education. For this purpose, the Technology Acceptance Model (TAM) has been used to investigate the acceptance of Microsoft teams as an online source in education. For the study, data has been collected from the undergraduate and postgraduate students of Quaid-e-Awam University of Engineering, Science, and Technology, Nawabshah. Key parameters like ease of use and perceived usefulness have been considered for measuring the performance of students. The findings revealed that students had a positive impression of Microsoft Teams. Students expect that Microsoft teams will be utilized during online learning with some modifications so that learning may be done more successfully and efficiently.

Researchers have conducted numerous investigations on various repair techniques to restore the mechanical properties of fire-damaged concrete. However, most of the techniques are either expensive or require high technical skills. As a continuous work, this study involved an experimental investigation of the effects of various low-cost repair techniques to restore the mechanical properties of fire-damaged concrete. Sixty cylindrical concrete samples were subjected to heat ranging from 400°C to 800°C in a propane gas furnace. The samples were categorized into six groups, including the following: undamaged, fire-damaged, and fire-damaged and repaired using water curing; fire-damaged and repaired using cement-based slurry injection; re-curing fire-damaged and repaired with steel wire mesh along with epoxy resin wrapping; and fire-damaged and repaired using epoxy injection. The samples were tested for non-destructive and destructive tests, including ultrasonic pulse velocity, rebound hammer, and compressive strength. The results showed that the use of cement-based slurry injection along with the water-curing repair technique could regain almost 90% of its ultimate strength and secant stiffness, making it the most effective low-cost repair technique for fire-damaged concrete exposed up to 700°C.