Baru Debtera

Aqueous zinc‐metal batteries (ZMBs) offer high capacity, intrinsic safety, and low cost but remain challenged by dendrite growth, hydrogen evolution, and interfacial instability. Here, we develop a solvation‐engineered hybrid electrolyte comprising 3 M Zn(OTf) ₂ in water, methanol, and dimethyl sulfoxide (3:1:1, v/v/v). Methanol disrupts the hydrogen‐bond network, while DMSO stabilizes Zn ²⁺ coordination, collectively forming a unique solvation structure as confirmed by isothermal titration calorimetry, density functional theory, and Raman spectroscopy. This environment promotes preferential triflate anion decomposition, yielding an in situ fluorine‐rich solid electrolyte interphase (SEI) that passivates zinc, reduces water activity, and suppresses hydrogen evolution. As a result, Zn||Zn cells cycle stably for over 4000 h at 0.5 mA cm ⁻² , Zn||Cu cells achieve 99.8% coulombic efficiency over 2000 cycles, and Zn||MnO ₂ cells deliver > 150 mAh g ⁻¹ at 1C with sustained cathode stability. This work demonstrates a simple, scalable solvation‐engineering strategy that stabilizes zinc interfaces without complex additives or highly concentrated electrolytes, advancing the practical deployment of aqueous ZMBs.

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Avocado seed starch was extracted to prepare a biodegradable plastic film with enset cellulosic reinforcement and glycerol plasticiser. The characterisations were made in detail. Fourier transform infrared (FTIR) spectroscopy, differential scanning calorimeter (DSC), scanning electron microscopy (SEM), X-ray diffraction (XRD), rheometer, and Rapid Visco Analyser (RVA) techniques were employed to characterise the physicochemical properties of avocado seed starch, enset cellulosic, and the blend. Starch and enset cellulosic extraction yields are 18.3 ± 0.02% and 67.5 ± 0.08%, respectively. As a result, avocado seed starch and enset cellulosic blend can be used as alternative raw materials to develop biodegradable plastics.

Humans have traditionally found it simple to identify emotions from facial expressions, but it is far more difficult for a computer system to do the same. The social signal processing subfield of emotion recognition from facial expression is used in a wide range of contexts, particularly for human-computer interaction. Automatic emotion recognition has been the subject of numerous studies, most of which use a machine learning methodology. The recognition of simple emotions like anger, happiness, contempt, fear, sadness, and surprise, however, continues to be a difficult topic in computer vision. Deep learning has recently drawn increased attention as a solution to a variety of practical issues, including emotion recognition. In this study, we improved the convolutional neural network technique to identify 7 fundamental emotions and evaluated several preprocessing techniques to demonstrate how they affected the CNN performance. This research focuses on improving facial features and expressions based on emotional recognition. By identifying or recognising facial expressions that elicit human responses, it is possible for computers to make more accurate predictions about a person's mental state and to provide more tailored responses. As a result, we examine how a deep learning technique that employs a convolutional neural network might improve the detection of emotions based on facial features (CNN). Multiple facial expressions are included in our dataset, which consists of about 32,298 photos for testing and training. The preprocessing system aids in removing noise from the input image, and the pretraining phase aids in revealing face detection after noise removal, including feature extraction. As a result, the existing paper generates the classification of multiple facial reactions like the seven emotions of the facial acting coding system (FACS) without using the optimization technique, but our proposed paper reveals the same seven emotions of the facial acting coding system.

The whole world is fighting as one against a deadly virus. COVID-19 cases are upon us in waves, with subsequent waves turning out to be worse than the previous one. Scores of human lives are lost while the post-COVID-19 complications are on a rise. Monitoring the behaviour of people in public places and offices is necessary to mitigate the transmission of COVID-19 among humans. In this work, a low-cost, lightweight two-stage face mask detection model is proposed. In the first stage, the model checks if a face mask is worn. In the second stage, it detects if the mask is worn appropriately, by classifying and labelling them. The proposed models are trained to detect faces with and without masks for varied inputs such as images, recorded videos, and live streaming videos where it can efficiently detect multiple faces at once. The efficacy of the proposed approach is tested against conventional datasets as well as our proposed dataset, which includes no masks, surgical masks, and nonsurgical masks. In this work, multiple CNN models like MobileNetV2, ResNet50V2, and InceptionV3 have been considered for training and are evaluated based on transfer learning. We further rely on MobileNetV2 as the backbone model since it has an accuracy of 98.44.

In this work, the business Computational Fluid Dynamics (CFD) is used for the numerical simulations of an air-solid fluidized bed container in the solid phase involving a multi-fluid Eulerian multiphase model and the Kinetic Theory of Granular Flow (KTGF). The height of the fluidized bed setup is 1.5 m while its diameter is 0.2 m. With it, a series of experimentations were obtained using Helium tracer to determine the Residence Time Distribution (RTD) at various normalized velocities, that is, mixing of air-solids at different degrees. 2 and 3 dimensions of the fluidized bed container are simulated. The main purpose of this study is to understand the hydrodynamic behavior of air-solid fluidized bed container through a framework of Eulerian multiphase model and to analyze the hydrodynamic behavior of the air-solids mixing. As a first approach, the CFD model is validated using the experimental results of the residence time study. The numerical results of RTD corresponded well with the experimental findings. This shows that the CFD modeling might be used to indicate the performance of a fluidized bed reactor. Key words: Computational fluid dynamics, fluidized bed, residence time distribution (RTD), gas-solids mixing, turbulence. In this work, the business Computational Fluid Dynamics (CFD) is used for the numerical simulations of an air-solid fluidized bed container in the solid phase involving a multi-fluid Eulerian multiphase model and the Kinetic Theory of Granular Flow (KTGF). The height of the fluidized bed setup is 1.5 m while its diameter is 0.2 m. With it, a series of experimentations were obtained using Helium tracer to determine the Residence Time Distribution (RTD) at various normalized velocities, that is, mixing of air-solids at different degrees. 2 and 3 dimensions of the fluidized bed container are simulated. The main purpose of this study is to understand the hydrodynamic behavior of air-solid fluidized bed container through a framework of Eulerian multiphase model and to analyze the hydrodynamic behavior of the air-solids mixing. As a first approach, the CFD model is validated using the experimental results of the residence time study. The numerical results of RTD corresponded well with the experimental findings. This shows that the CFD modeling might be used to indicate the performance of a fluidized bed reactor. Key words: Computational fluid dynamics, fluidized bed, residence time distribution (RTD), gas-solids mixing, turbulence.

In a mobile network, nodes are placed in infrequent manner, which are moved along network in abruptly. Communication flaw between sender node and accepting node in path, the node having restricted energy and restricted transmission rate. It does not provide perfect route for communication among mobile nodes. It increases the packet drop rate and minimizes the lifetime of network. This work has proposed enhanced path routing with buffer allocation (IPBA) scheme which is implemented to obtain better communication; it protects the node from packet loss, and the buffer is used to maintain the temporary details of nodes and data packets that are ready to broadcast and receive. The coupling node selection algorithm is constructed to offer the path which frequently communicates data packets in normal case; the two efficient nodes are coupled with each other, and this type of nodes is selected to perform communication. It reduces the packet loss rate and increases network lifetime. End-to-end delay, communication overhead, throughput, network lifetime, packet loss, and energy consumption are the parameters considered for performance evaluations.

Skin disease is the major health problem around the world. The diagnosis of skin disease remains a challenge to dermatologist profession particularly in the detection, evaluation, and management. Health data are very large and complex due to this processing of data using traditional data processing techniques is very difficult. In this paper, to ease the complexity while processing the inputs, we use multilayered perceptron with backpropagation neural networks (MLP-BPNN). The image is collected from the devices that contain nanotechnology sensors, which is the state-of-art in the proposed model. The nanotechnology sensors sense the skin for its chemical, physical, and biological conditions with better detection specificity, sensitivity, and multiplexing ability to acquire the image for optimal classification. The MLP-BPNN technique is used to envisage the future result of disease type effectively. By using the above MLP-BPNN technique, it is easy to predict the skin diseases such as melanoma, nevus, psoriasis, and seborrheic keratosis.

This paper proposes a solar collector that utilizes supercritical CO₂ as the working fluid to detect implicit water heating and boost the collectors heating rate efficiency. Solar water heating system efficiency, cost, and environmental friendliness all depend on the working fluid used. CO₂ is a possible natural refrigerant replacement. Even a little increase in temperature or pressure may have a big impact on the density of CO₂ at the critical point. Because of this, solar heating can readily generate a spontaneous convection flow of supercritical carbon dioxide. The most basic collector characteristics, such as CO₂ pressure and temperature, were determined by building and testing an experimental setup using a CO₂-based solar collector. Due to solar radiation, liquid, gas, or supercritical CO₂ pressures and temperatures change throughout the test. There was a 50 time average collector efficiency (ηcol) and a 30 heat recovery efficiency (ηRE). Solar thermal collectors based on supercritical CO₂ have now been shown in this paper. Since the results show that even though the solar energy is low, the CO₂ temperature, pressure, and supercritical stress remain constant, this is distinct from conventional liquid-based solar collectors.

Malware has grown in popularity as a method of conducting cyber assaults in former decades as a result of numerous new deception methods employed by malware. To preserve networks, information, and intelligence, malware must be detected as soon as feasible. This article compares various attribute extraction techniques with distinct machine learning algorithms for static malware classification and detection. The findings indicated that merging PCA attribute extraction and SVM classifier results in the highest correct rate with the fewest possible attributes, and this paper discusses sophisticated malware, their detection techniques, and how and where to defend systems and data from malware attacks. Overall, 96% the proposed method determines the malware more accurately than the existing methods.