Jaypee University of Engineering and Technology (JUET), formerly Jaypee Institute of Engineering and Technology, is a private engineering University located at Raghogarh, Guna, Madhya Pradesh, India.
In the present situation, a lot of research has been directed towards the potency of plants. These natural resources contain characteristics valuable in combat against a number of diseases. But due to lack of familiarity of these plants among human beings, an appropriate advantage of their significance cannot be drawn away. Plants also shares the certain similar characteristics of leaves like color, texture, shape or size, making them hard to classify them among others. So, to eradicate this problem, a deep learning model has been used for the purpose for classification of different plants species captured in real-time using internet of things practice. Six different plants namely Ashwagandha, Black Pepper, Garlic, Ginger, Basil, and Turmeric has been selected for this purpose. Our proposed convolutional neural network (CNN) model achieved higher performance with an accuracy of 99
Numerous novel technologies, including self-powered implanted sensors and wireless sensor networks (WSNs) have evolved over the past decade. Due to the complications related to charging and maintaining batteries, these devices generally include a continuous power supply to operate consistently and safely. A viable solution may involve piezoelectric energy harvesting derived from vibrations produced by artificial technology, human motion and environmental factors. This paper presents the inaugural integration of piezoelectric polyvinylidene fluoride yarn-braid within the FRPC structure. The developed smart composite demonstrates its multifunctional capabilities, encompassing structural reinforcement, vibration attenuation, and energy harvesting. Testing subjected to cyclic loading circumstances of 5 to 22.75 Hz produces a power density of 3.1 mW/cm³ and an AOV of 3.8 V when strains are below 0.27
A systematic comparative investigation of polycrystalline Bi-0.La-9(0).1Fe1-xMxO3 (M = Mn, Nd, Co, Ni) synthesized via citrate combustion and solid-state reaction routes is presented. X-ray diffraction with Rietveld refinement shows that La substitution stabilizes the rhombohedral R3c structure and suppresses Bi-rich secondary phases, while B-site doping introduces lattice distortion and microstrain. These structural changes modify Fe-O-Fe bonding, as reflected in Raman spectra. Magnetic measurements reveal the emergence of weak ferromagnetism in doped compositions, likely associated with modification of the magnetic spin structure. Co-doped samples exhibit the highest value of magnetization (M-max similar to 3.75 emu g(-1) at 5 T) and an increased Neel temperature (similar to 740 K), whereas Mn substitution leads to large coercivity (similar to 7000 Oe) associated with enhanced magnetic anisotropy and domain-wall pinning. Ni doping results in moderate magnetization with T-N similar to 650 K, while Mn doping lowers T-N to similar to 622 K. For all compositions, citrate-derived samples show a stronger magnetic response than solid-state samples, primarily due to smaller crystallite size and enhanced contributions from uncompensated surface spins. These results demonstrate that combined chemical substitution and synthesis-route control effectively tune the structural distortion and magnetic behaviour of La-doped BiFeO3.
Rock mass behavior under sustained loading at the edge of slope for discontinuous joints is difficult to assess under unconfined condition. The current study is conducted on rock mass with orthogonal joint sets with one continuous and other discontinuous joint. Rock mass joint sets angles varies from 30° to 90° with the increment of 15° up to 90°. Experimentation shows failure pattern as well as load carrying capacity in such conditions. Mode of failure is the most essential parameter which governs the load carrying capacity of the rock mass specimen. The load intensities were calculated analytically for joint angles of 90°, 75°, and 60° using Euler’s method, resulting in percentage errors with experimental results of 7.93
As global competition intensifies, most manufacturing companies strive to improve their production methods to gain a competitive edge. One such advancement is the adoption of Flexible Manufacturing Systems (FMS), which enable the efficient production of various products in specified quantities with minimal lead times. These systems offer adaptability and efficiency, allowing manufacturers to leverage modern technologies to improve operational performance. However, evaluating or selecting an appropriate FMS involves considering numerous conflicting criteria. To address this complexity, Multi-criteria Decision Making (MCDM) methods are employed. This study conducts a comparative evaluation of eight FMS alternatives using the Evaluation based on the Distance from the Average Solution (EDAS) method, integrated with Shannon Entropy for objective weight determination. Key performance indicators, including production cost, system flexibility, energy efficiency, and operational reliability, are used in the assessment. The Shannon Entropy method ensures unbiased, data-driven weight assignment, while the EDAS method provides a robust framework for ranking alternatives based on their deviation from an average solution. To test the robustness of the ranking, we compared the ranking with other MCDM methods and also conducted a sensitivity analysis using equal weighting criteria. We found that the first and last rankings remained unchanged when we changed the criteria, although there were slight changes in the rankings of some alternatives. The findings highlight the effectiveness of integrating EDAS with Shannon Entropy in selecting the best flexible manufacturing systems, offering valuable insights for manufacturers and decision-makers.
