Cropping bioenergy and biomaterials in marginal land: The added value of the biorefinery concept

A biorefinery is an integrated pattern of farming and conversion activities capable to provide bioenergy and biomaterials as alternative to fossil-based refineries, increasing jobs and income in rural areas. Considering the need to avoid competition with food production in arable land, non-food cropping on marginal land is being explored worldwide focusing on lignocellulosic crops (“second-generation” substrates). The viability of bioenergy and biochemicals from non-food crops in marginal land of Southern Italy was explored, using Brassica carinata as a test crop. An LCA-consistent, integrated evaluation method, named SUMMA (Sustainability multi-scale multi-method Approach) was applied for joint assessment of material, embodied energy, environmental support (emergy) and economic flows and performance. Two hypotheses were tested: (a) cropping for bioenergy (biodiesel + heat); (b) bioenergy and biomaterials within a biorefinery framework. In addition to biodiesel production from seeds, the first hypothesis assumes the conversion of residues (cake meal and straw) into heat for local industrial use, while the second one is based on a lignocellulose-to-chemicals biorefinery. Cropping for bioenergy provides a small net energy yield with no economic return. Instead, converting lignocellulosic residues to high added value biochemicals definitely improves the process performance from both energetic and economic points of view.     

Laminar mixed convection boundary layers induced by a linearly stretching permeable surface

The boundary layer flow on a linearly moving permeable vertical surface is studied when the buoyancy force assists or opposes the flow. Similarity and local similarity solutions are obtained for the boundary layer equations subject to power law temperature and velocity variation. The effect of various governing parameters, such as Prandtl number Pr, injection parameter d, and the mixed convection parameter λ=Gr_x/Re_x², which determine the velocity and temperature distributions, the heat transfer coefficient, and the shear stress at the surface are studied. The heat transfer coefficient increases as λ assisting the flow for all d for uniformly or linearly heated surface and as Pr increases it becomes almost independent of λ. However, as the temperature inversely proportional to the distance up the surface, the buoyancy has no effects on the heat transfer coefficient. Critical buoyancy parameter values are obtained for vanished shear stress and for predominate natural convection. Critical values are also presented for predominate buoyancy shear stress at the surface for assisting or opposing flow. A closed form analytical solution is also presented as a special case of the energy equation.     

Preparing thick, defect-free films of anatase titania for dye-sensitized solar cells

Anatase titania (TiO₂) nanoparticle films were prepared on fluorine-doped tin oxide (FTO) and tin-doped indium oxide (ITO) substrates. The films were characterized by X-ray diffraction, scanning electron microscopy, profilometry, Raman spectroscopy, and optical microscopy. The results show that defects are initiated during the sintering step and continue to propagate once the film is cooled. The sintering and annealing steps were controlled by reducing the pressure and the rate of temperature change. These steps reduced the stresses generated during film preparation, allowing thick titania films on both FTO and ITO substrates to be prepared with minimal defects. Using the optimized conditions for film preparation, 20 μm thick films of titania on FTO and ITO substrates were obtained with calculated defect densities of 2.5 and 7.8%. Films as thick as 25 μm were prepared on FTO substrates with a defect density of only 6.0%. Dye-sensitized solar cells (DSSCs) were fabricated using the titania films prepared by both standard and vacuum sintering methods. DSSCs made with 20 μm titania films sintered at intermediate pressures show improvements to short-circuit current, open-circuit voltage, and device efficiency.     

Optimal Deep Learning Based Ransomware Detection and Classification in the Internet of Things Environment

With the advent of the Internet of Things (IoT), several devices like sensors nowadays can interact and easily share information. But the IoT model is prone to security concerns as several attackers try to hit the network and make it vulnerable. In such scenarios, security concern is the most prominent. Different models were intended to address these security problems; still, several emergent variants of botnet attacks like Bashlite, Mirai, and Persirai use security breaches. The malware classification and detection in the IoT model is still a problem, as the adversary reliably generates a new variant of IoT malware and actively searches for compromise on the victim devices. This article develops a Sine Cosine Algorithm with Deep Learning based Ransomware Detection and Classification (SCADL-RWDC) method in an IoT environment. In the presented SCADL-RWDC technique, the major intention exists in recognizing and classifying ransomware attacks in the IoT platform. The SCADL-RWDC technique uses the SCA feature selection (SCA-FS) model to improve the detection rate. Besides, the SCADL-RWDC technique exploits the hybrid grey wolf optimizer (HGWO) with a gated recurrent unit (GRU) model for ransomware classification. A widespread experimental analysis is performed to exhibit the enhanced ransomware detection outcomes of the SCADL-RWDC technique. The comparison study reported the enhancement of the SCADL-RWDC technique over other models.     

Securing Arabic Contents Algorithm for Smart Detecting of Illegal Tampering Attacks

The most common digital media exchanged via the Internet is in text form. The Arabic language is considered one of the most sensitive languages of content modification due to the presence of diacritics that can cause a change in the meaning. In this paper, an intelligent scheme is proposed for improving the reliability and security of the text exchanged via the Internet. The core mechanism of the proposed scheme depends on integrating the hidden Markov model and zero text watermarking techniques. The watermark key will be generated by utilizing the extracted features of the text analysis process using the third order and word level of the Markov model. The Embedding and detection processes of the proposed scheme will be performed logically without the effect of the original text. The proposed scheme is implemented using PHP with VS code IDE. The simulation results, using varying sizes of standard datasets, show that the proposed scheme can obtain high reliability and provide better accuracy of the common illegal tampering attacks. Comparison results with other baseline techniques show the added value of the proposed scheme.     

Artificial Intelligence Based Data Offloading Technique for Secure MEC Systems

Mobile edge computing (MEC) provides effective cloud services and functionality at the edge device, to improve the quality of service (QoS) of end users by offloading the high computation tasks. Currently, the introduction of deep learning (DL) and hardware technologies paves a method in detecting the current traffic status, data offloading, and cyberattacks in MEC. This study introduces an artificial intelligence with metaheuristic based data offloading technique for Secure MEC (AIMDO-SMEC) systems. The proposed AIMDO-SMEC technique incorporates an effective traffic prediction module using Siamese Neural Networks (SNN) to determine the traffic status in the MEC system. Also, an adaptive sampling cross entropy (ASCE) technique is utilized for data offloading in MEC systems. Moreover, the modified salp swarm algorithm (MSSA) with extreme gradient boosting (XGBoost) technique was implemented to identification and classification of cyberattack that exist in the MEC systems. For examining the enhanced outcomes of the AIMDO-SMEC technique, a comprehensive experimental analysis is carried out and the results demonstrated the enhanced outcomes of the AIMDO-SMEC technique with the minimal completion time of tasks (CTT) of 0.680.     

A Joint Algorithm for Resource Allocation in D2D 5G Wireless Networks

With the rapid development of Internet technology, users have an increasing demand for data. The continuous popularization of traffic-intensive applications such as high-definition video, 3D visualization, and cloud computing has promoted the rapid evolution of the communications industry. In order to cope with the huge traffic demand of today’s users, 5G networks must be fast, flexible, reliable and sustainable. Based on these research backgrounds, the academic community has proposed D2D communication. The main feature of D2D communication is that it enables direct communication between devices, thereby effectively improve resource utilization and reduce the dependence on base stations, so it can effectively improve the throughput of multimedia data. One of the most considerable factor which affects the performance of D2D communication is the co-channel interference which results due to the multiplexing of multiple D2D user using the same channel resource of the cellular user. To solve this problem, this paper proposes a joint algorithm time scheduling and power control. The main idea is to effectively maximize the number of allocated resources in each scheduling period with satisfied quality of service requirements. The constraint problem is decomposed into time scheduling and power control subproblems. The power control subproblem has the characteristics of mixed-integer linear programming of NP-hard. Therefore, we proposed a gradual power control method. The time scheduling subproblem belongs to the NP-hard problem having convex-cordinality, therefore, we proposed a heuristic scheme to optimize resource allocation. Simulation results show that the proposed algorithm effectively improved the resource allocation and overcome the co-channel interference as compared with existing algorithms.     

Optimization and feasibility analysis of a microscale pin-fins heat sink of an ultrahigh concentrating photovoltaic system

Nowadays, the most recent optical configuration based on Cassegrain and Fresnel lens designs of concentrator photovoltaic(CPV) has shown a race to achieve the ultrahigh concentration ratio. Still, none of those has experimentally shown an optical concentration ratio (GC) beyond 2000 suns. This is because their energy concentration ratios are challenged by the excessive temperature raised throughout the optical stages, which diminishes the efficiency of the solar cell. In this context, this research work aims to numerically investigate a microscale pin-fins heat sink configuration to enhance the thermal performance and the cost-competitivity of ultrahigh CPV thermal receiver. The impacts of the solar cell area, cell efficiency, and heat sink's material have been analyzed and discussed. The results showed that a circular pin-fins heat sink could accomplish a drop of 23.28% in the maximum operating cell temperature at 10 000 suns for cell area of 1 × 1 mm² relatively compared to the conventional flat-plate heat sink. Furthermore, for a circular pin-fins heat sink with a cell area of 2 × 2 mm², the cell temperature started exceeding the safe operating range of temperature (80°C) at 8000 suns with an average temperature of 96.1°C and reaching a maximum of 113.91°C at 10 000 suns. A gradient temperature on the planar direction of the aluminum circular pin-fins heat sink was about 1.187°C at 10 000 suns whereas 0.703°C was recorded in the case of a copper circular pin-fins heat sink. The circular pin-fins heat sink showed the highest thermal performance resulting in maintaining the solar cell temperature within its safe operating range even beyond 10 000 suns. From an economic point of view, aluminum circular pin-fins heat sink has been found to be less costly than the copper one. Finally, it was found that at 8000 suns, the flat-plate heat sink cost is more expensive than the traditional pin-fins heat sink by 14.7%, where the flat-plate heat sink becomes the worst economic configuration at 10 000 suns. At that concentration ratio, the cost has increased by 43.38%, 5.75%, and 10.61% compared to the traditional pin-fins heat sink, cylindrical pin-fins heat sink, and circular pin-fins heat sink, respectively.     

Metaheuristic Resource Allocation Strategy for Cluster Based 6G Industrial Applications

The emergence of Beyond 5G (B5G) and 6G networks translated personal and industrial operations highly effective, reliable, and gainful by speeding up the growth of next generation Internet of Things (IoT). Industrial equipment in 6G encompasses a huge number of wireless sensors, responsible for collecting massive quantities of data. At the same time, 6G network can take real-world intelligent decisions and implement automated equipment operations. But the inclusion of different technologies into the system increased its energy consumption for which appropriate measures need to be taken. This has become mandatory for optimal resource allocation in 6G-enabled industrial applications. In this scenario, the current research paper introduces a new metaheuristic resource allocation strategy for cluster-based 6G industrial applications, named MRAS-CBIA technique. MRAS-CBIA technique aims at accomplishing energy efficiency and optimal resource allocation in 6G-enabled industrial applications. The proposed MRAS-CBIR technique involves three major processes. Firstly, Weighted Clustering Technique (WCT) is employed to elect the optimal Cluster Heads (CHs) or coordinating agents with the help of three parameters namely, residual energy, distance, and node degree. Secondly, Decision Tree-based Location Prediction (DTLP) mechanism is applied to determine the exact location of Management Agent (MA). Finally, Fuzzy C-means with Tunicate Swarm Algorithm (FCM-TSA) is used for optimal resource allocation in 6G industrial applications. The performance of the proposed MRAS-CBIA technique was validated and the results were examined under different dimensions. The resultant experimental values highlighted the superior performance of MRAS-CBIR technique over existing state-of-the-art methods.     

Influence of Filler on the Microstructure,Mechanical Properties and Residual Stresses in TIG Weldments of Dissimilar Titanium Alloys

The influence of titanium alloy (Ti-5Al-2.5Sn) and commercially pure titanium (cpTi) as fillers on dissimilar pulsed tungsten inert gas weldments of Ti-5Al-2.5Sn/cpTi was investigated in terms of microstructure,mechanical/nano-mechanical proper-ties,and residual stresses.A partial martensitic transformation was observed in the weldments for all the welding conditions due to high heat input.The microstructure evolved in the FZ/cpTi interfacial region was observed to be the most sensitive to the proportion of α stabilizer in the filler alloy.Furthermore,the addition of filler alloy improved the tensile properties and nano-mechanical response of the weld joint owing to the increased volume of metal in the weld joint.As compared to the Ti-5Al-2.5Sn wire,the use of cpTi filler wire proved to be better in terms of energy absorbed during tensile and impact tests,tensile strength and ductility of the dissimilar welds.An asymmetrical residual stresses profile was observed close to the weld centerline,with high compressive stresses on the Ti-5Al-2.5Sn side for both the weldments obtained with and without filler wires.This was attributed to mainly the low thermal conductivity of Ti-5Al-2.5Sn.The presence of residual stresses also influenced the nano-hardness profile across the weldments.