Fabrication of novel hybrid composite La2‐xCoxCuO4 electrode for high performance supercapacitors

Hybrid composites La_2‐xCo_xCuO₄ (x = 0, 0.1, 0.2, and 0.3) are prepared using one‐step simple hydrothermal route as electrodes for supercapacitors. The effect of varying cobalt content on morphological, structural, and electrochemical properties has been explored using X‐ray diffraction, scanning electron microscopy, and cyclic voltammetry, respectively. The structural parameters obtained by X‐ray diffraction showed tetragonal phase of hybrid composite without any evident impurity phases. The analysis of morphological properties suggested a strong correlation with electrochemical properties, for instance, a relationship between fabric porous structures and electrochemically active sites for redox reactions and intercalation/de‐intercalation processes. The hybrid composite electrodes demonstrated high specific capacitance of the order of 1304 F/g at 10 mV/s scan rate and exhibited decreasing trend on increasing scan rate. Hybrid composites were also tested for their ability as an electrode of high performance supercapacitors in different aqueous electrolytes, i. e, KOH, H₂SO₄, and Na₂SO₄ to optimize the best compatible electrolyte. The composite electrode material showed excellent cyclic stability and 98% capacitance retention for 1 A/g after 2000 cycles. The remarkable performance of hybrid composite electrode entails its potential for commercial applications of supercapacitors.     

NS-2 based simulation framework for cognitive radio sensor networks

In this paper, we propose a simulation model for cognitive radio sensor networks (CRSNs) which is an attempt to combine the useful properties of wireless sensor networks and cognitive radio networks. The existing simulation models for cognitive radios cannot be extended for this purpose as they do not consider the strict energy constraint in wireless sensor networks. Our proposed model considers the limited energy available for wireless sensor nodes that constrain the spectrum sensing process—an unavoidable operation in cognitive radios. Our model has been thoroughly tested by performing experiments in different scenarios of CRSNs. The results generated by the model have been found accurate which can be considered for realization of CRSNs.     

The suboccipital cavernous sinus

The authors studied the microsurgical anatomy of the suboccipital region, concentrating on the third segment (V3) of the vertebral artery (VA), which extends from the transverse foramen of the axis to the dural penetration of the VA, paying particular attention to its loops, branches, supporting fibrous rings, adjacent nerves, and surrounding venous structures. Ten cadaver heads (20 sides) were fixed in formalin, their blood vessels were perfused with colored silicone rubber, and they were dissected under magnification. The authors subdivided the V3 into two parts, the horizontal (V3h) and the vertical (V3v), and studied the anatomical structures topographically, from the superficial to the deep tissues. In two additional specimens, serial histological sections were acquired through the V3 and its encircling elements to elucidate their cross-sectional anatomy. Measurements of surgically and clinically important features were obtained with the aid of an operating microscope. This study reveals an astonishing anatomical resemblance between the suboccipital complex and the cavernous sinus, as follows: venous cushioning; anatomical properties of the V3 and those of the petrous-cavernous internal carotid artery (ICA), namely their loops, branches, supporting fibrous rings, and periarterial autonomic neural plexus; adjacent nerves; and skull base locations. Likewise, a review of the literature showed a related embryological development and functional and pathological features, as well as similar transitional patterns in the arterial walls of the V3 and the petrous-cavernous ICA. Hence, due to its similarity to the cavernous sinus, this suboccipital complex is here named the "suboccipital cavernous sinus." Its role in physiological and pathological conditions as they pertain to various clinical and surgical implications is also discussed.     

Open‐Shell Donor–Acceptor Conjugated Polymers with High Electrical Conductivity

Conductive polymers largely derive their electronic functionality from chemical doping, processes by which redox and charge‐transfer reactions form mobile carriers. While decades of research have demonstrated fundamentally new technologies that merge the unique functionality of these materials with the chemical versatility of macromolecules, doping and the resultant material properties are not ideal for many applications. Here, it is demonstrated that open‐shell conjugated polymers comprised of alternating cyclopentadithiophene and thiadiazoloquinoxaline units can achieve high electrical conductivities in their native “undoped” form. Spectroscopic, electrochemical, electron paramagnetic resonance, and magnetic susceptibility measurements demonstrate that this donor–acceptor architecture promotes very narrow bandgaps, strong electronic correlations, high‐spin ground states, and long‐range π‐delocalization. A comparative study of structural variants and processing methodologies demonstrates that the conductivity can be tuned up to 8.18 S cm^?1. This exceeds other neutral narrow bandgap conjugated polymers, many doped polymers, radical conductors, and is comparable to commercial grades of poly(styrene‐sulfonate)‐doped poly(3,4‐ethylenedioxythiophene). X‐ray and morphological studies trace the high conductivity to rigid backbone conformations emanating from strong π‐interactions and long‐range ordered structures formed through self‐organization that lead to a network of delocalized open‐shell sites in electronic communication. The results offer a new platform for the transport of charge in molecular systems.     

IoMT-Based Smart Monitoring Hierarchical Fuzzy Inference System for Diagnosis of COVID-19

The prediction of human diseases, particularly COVID-19, is an extremely challenging task not only for medical experts but also for the technologists supporting them in diagnosis and treatment. To deal with the prediction and diagnosis of COVID-19, we propose an Internet of Medical Things-based Smart Monitoring Hierarchical Mamdani Fuzzy Inference System (IoMTSM-HMFIS). The proposed system determines the various factors like fever, cough, complete blood count, respiratory rate, Ct-chest, Erythrocyte sedimentation rate and C-reactive protein, family history, and antibody detection (lgG) that are directly involved in COVID-19. The expert system has two input variables in layer 1, and seven input variables in layer 2. In layer 1, the initial identification for COVID-19 is considered, whereas in layer 2, the different factors involved are studied. Finally, advanced lab tests are conducted to identify the actual current status of the disease. The major focus of this study is to build an IoMT-based smart monitoring system that can be used by anyone exposed to COVID-19; the system would evaluate the user’s health condition and inform them if they need consultation with a specialist for quarantining. MATLAB-2019a tool is used to conduct the simulation. The COVID-19 IoMTSM-HMFIS system has an overall accuracy of approximately 83%. Finally, to achieve improved performance, the analysis results of the system were shared with experts of the Lahore General Hospital, Lahore, Pakistan.     

Efficient Bifunctional Catalytic Electrodes with Uniformly Distributed NiN2 Active Sites and Channels for Long‐Lasting Rechargeable Zinc–Air Batteries

Freestanding bifunctional electrodes with outstanding oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) properties are of great significance for zinc–air batteries, attributed to the avoided use of organic binder and strong adhesion with substrates. Herein, a strategy is developed to fabricate freestanding bifunctional electrodes from the predeposited nickel nanoparticles (Ni‐NCNT) on carbon fiber paper. The steric effect of monodispersed SiO₂ nanospheres limits the configuration of carbon atoms forming 3D interconnected nanotubes with uniformly distributed NiN₂ active sites. The bifunctional electrodes (Ni‐NCNT) demonstrate ideal ORR and OER properties. The zinc–air batteries assembled with Ni‐NCNT directly exhibit extremely outstanding long term stability (2250 cycles with 10 mA cm^?2 charge/discharge current density) along with high power density of 120 mV cm^?2 and specific capacity of 834.1 mA h g^?1. This work provides a new view to optimize the distribution of active sites and the electrode structure.     

Metallurgical Investigation of the Collapsed Front Structure of a Dragline in a Coal Mine

Journal of Failure Analysis and Prevention - A dragline is the largest mobile equipment on earth, and it is called the “kingpin” of any mine site. In this present investigation, a case...     

Optimal Implementation of Photovoltaic and Battery Energy Storage in Distribution Networks

Recently, implementation of Battery Energy Storage (BES) with photovoltaic (PV) array in distribution networks is becoming very popular in overall the world. Integrating PV alone in distribution networks generates variable output power during 24-hours as it depends on variable natural source. PV can be able to generate constant output power during 24-hours by installing BES with it. Therefore, this paper presents a new application of a recent metaheuristic algorithm, called Slime Mould Algorithm (SMA), to determine the best size, and location of photovoltaic alone or with battery energy storage in the radial distribution system (RDS). This algorithm is modeled from the behavior of SMA in nature. During the optimization process, the total active power loss during 24-hours is used as an objective function considering the equality and inequality constraints. In addition, the presented function is based on the probabilistic for PV output and different types of system load. The candidate buses for integrating PV and BES in the distribution network are determined by the real power loss sensitivity factor (PLSF). IEEE 69-bus RDS with different types of loads is used as a test system. The effectiveness of SMA is validated by comparing its results with those obtained by other well-known optimization algorithms.     

A Machine Learning Based Algorithm to Process Partial Shading Effects in PV Arrays

Solar energy is a widely used type of renewable energy. Photovoltaic arrays are used to harvest solar energy. The major goal, in harvesting the maximum possible power, is to operate the system at its maximum power point (MPP). If the irradiation conditions are uniform, the P-V curve of the PV array has only one peak that is called its MPP. But when the irradiation conditions are non-uniform, the P-V curve has multiple peaks. Each peak represents an MPP for a specific irradiation condition. The highest of all the peaks is called Global Maximum Power Point (GMPP). Under uniform irradiation conditions, there is zero or no partial shading. But the changing irradiance causes a shading effect which is called Partial Shading. Many conventional and soft computing techniques have been in use to harvest solar energy. These techniques perform well under uniform and weak shading conditions but fail when shading conditions are strong. In this paper, a new method is proposed which uses Machine Learning based algorithm called Opposition-Based-Learning (OBL) to deal with partial shading conditions. Simulation studies on different cases of partial shading have proven this technique effective in attaining MPP.