Performance analysis of multiuser mmWave DCT-spread CP-Less OFDM communication system

In this paper, we propose a framework for multiuser mmWave DCT-Spread CP-less OFDM communication system and analyze it comprehensively. Due to excessive cyclic prefix (CP) usage in conventional multicarrier systems, spectral efficiency reduces and increases transmission latency. Our proposed system enhances spectral efficiency and reduces transmission latency. We introduce an image encryption algorithm based on DNA encoding combined with a chaotic map is introduced to enhance physical layer security (PLS). The impact of block diagonalization (BD) channel precoding for multiuser interference (MUI) reduction and designed subcarrier mapping with time-domain Tukey windowing for out-of-band (OOB) power emission reduction are investigated. In addition, the paper studies the applicability of discrete cosine transforms (DCT)-Spreading for peak-to-average power ratio (PAPR) reduction. Simulation results demonstrate that Turbo and Repeat and Accumulate (RA) channel coding and minimum mean square error (MMSE) and Cholesky decomposition (CD)-based zero-forcing (ZF) signal detection schemes improve bit error rate (BER) performance of the proposed system for different users

Green analytical comparison and central composite design optimization for simultaneous estimation of pain management drugs using RP-liquid chromatography

The Central Composite Design method was utilized to validate a precise RP-HPLC method for concurrently determining the quantities of Paracetamol (PC), Diclofenac Sodium (DS), and Eperisone Hydrochloride (EH) in tablet compositions. By employing Design of Experiment (DOE), the experimental parameters were fine-tuned, resulting in an optimized eluent consisting of methanol: water (90:10) with 0.1 % orthophosphoic acid at a eluent velocity of 1 mL/min. The method exhibited exceptional purities: PC (100.83 % ± 0.85), DS (102.01 % ± 0.90), and EH (100.49 % ± 1.29). Regression equations were formulated for PC, DS, and EH as follows: y = 479762x + 151907, y = 2182788x + 2409442, and y = 777144x − 1146334, respectively. The analytical method underwent comprehensive validation, including tests for: Accuracy, Precision, Linearity and Robustness. To assess the method’s environmental impact, several Green Analytical Chemistry (GAC) tools were employed. These tools provided a multifaceted evaluation of the method’s sustainability and eco-friendlines

Interfacial synergy of pre-lithiation silicon anodes and GNP/MnO2/S cathodes for lithium polysulfides in silicon–sulfur batteries studied via DFT

The development of innovative electrodes with outstanding high-rate cycling performance for the next generation of sulfur-based batteries has emerged as a key area of research. This study presents a straightforward approach for designing silicon/graphene nanoplates as an anode material using a one-step hydrothermal process. Additionally, to reduce the shuttle effect, the GNP/MnO2/S cathode is investigated. In this study, MnO2 particles are grown in situ on the surface of the GNP. The pre-lithiation Si/GNP anode and the MnO2/GNP/S and GNP/S cathodes are evaluated at a current density of 1000 mA g−1. The findings reveal an impressive capacity retention of 1048 mA h g−1 after 200 cycles, indicating remarkable cycling performance for the cell with the pre-lithiation Si/GNP anode and the MnO2/GNP/S cathode. The capacity retention observed in thicker electrodes highlights the synergistic effect of the effective chemical absorption of lithium polysulfides by MnO2/GNP/S when used as sulfur hosts. Additionally, DFT calculations suggest that MnO2 has a significant tendency to adhere to the surface of polysulfides, aligning well with our findings regarding cycle performance, rate performance, and discharge capacity. The novel electrode configuration introduced in this study provides a novel pathway for the large-scale production of high-performance pre-lithiation Si-S batteries. © 2025 The Royal Society of Chemistry

Industrial Untapped Rotational Kinetic Energy Assessment for Sustainable Energy Recycling

Electrical energy can be harvested from the rotational kinetic energy of moving bodies, consisting of both mechanical and kinetic energy as a potential power source through electromagnetic induction, similar to wind energy applications. In industries, rotational bodies are commonly present in operations, yet this kinetic energy remains untapped. This research explores the energy generation characteristics of two rotational body types, disk-shaped and cylinder-shaped under specific experimental setups. The hardware setup included a direct current (DC) motor driver, power supply, DC generator, mechanical support, and load resistance, while the software setup involved automation testing tools and data logging. Electromagnetic induction was used to harvest energy, and experiments were conducted at room temperature (25○ C) with controlled variables like speed and friction. Results showed the disk-shaped body exhibited higher energy efficiency than the cylinder-shaped body, largely due to lower mechanical losses. The disk required only two bearings, while the cylinder required four, resulting in lower bearing losses for the disk. Additionally, the disk experienced only air friction, whereas the cylinder encountered friction from a soft, uneven rubber material, increasing surface contact losses. Under a 40 W resistive load, the disk demonstrated a 17.1% energy loss due to mechanical friction, achieving up to 15.55 J of recycled energy. Conversely, the cylinder body experienced a 48.05% energy loss, delivering only 51.95% of energy to the load. These insights suggest significant potential for designing efficient energy recycling systems in industrial settings, particularly in manufacturing and processing industries where rotational machinery is prevalent. Despite its lower energy density, this system could be beneficially integrated with energy storage solutions, enhancing sustainability in industrial practices

Identification of Depression Patients Using LIF Spiking Neural Network Model From the Pattern of EEG Signals

Interpreting electroencephalography signals and the abnormality of the signals can help to find the specific pattern for specific diseases like depression. A Spiking Neural Network is a machine learning approach that emphasizes the data value and manipulates the value to find the particular signal feature. Finding the specific abnormal features of electroencephalography signals can help to detect depression patients. Since a vast number of individuals are suffering from depression and the treatment of depression is possible by detecting depression patients earlier, different deep learning and conventional machine learning approaches were proposed. But speed, accuracy, and reality with less time and space complexity are essential factors in detecting depression patients in our society. We have proposed a leaky integrate and fire spiking neural network model for interpreting the electroencephalography signals of depression patients. The electroencephalography signals of a sixty-channel dataset of 121 subjects are taken for the experiment where frequency for each channel of a subject is recorded for 2 mins in 2-second time intervals, and the dataset contains 4,35,600 data with 121 instances and 3600 attributes. A leaky integrate and fire model is applied to the electroencephalography signals to find the spike sequences and potentials. Then, a three-layered neural network approach is stacked to generate a classifier. The performance of the classifier is shown to be approximately 98% accuracy. Generating a noble classifier and implementing it with a mask of metal disk benefited society for easily and quickly detecting a depression patient, and corresponding treatment can be started. Besides, more experiments are needed on different and more depression datasets with spiking neural network models to identify depression patients and finalize a robotic classifier

Dynamic ReLab: A Binary Path-Based Labeling Scheme for Dynamic XML Data

Dynamic updates in XML data present significant challenges for maintaining efficient query performance, particularly in large-scale and dynamic environments. Existing labeling schemes, such as ReLab, Dietz encoding, and region numbering, fail to address these challenges effectively due to their reliance on re-labeling entire subtrees during updates, leading to significant computational and memory overhead. These limitations hinder their applicability in dynamic scenarios where frequent updates are required. This study introduces Dynamic ReLab, a novel binary path-based labeling scheme explicitly designed to overcome the inefficiencies of traditional approaches in handling dynamic XML data. By integrating binary path encoding with subtree-based labeling, Dynamic ReLab enables efficient label generation and maintenance, ensuring the quick determination of structural relationships, such as ancestor-descendant and parent-child, without extensive re-labeling. The proposed scheme is particularly advantageous in scenarios where XML data undergo frequent updates, as it significantly reduces the time and memory required for label maintenance, thereby improving overall performance. Experimental results on real-world XML datasets demonstrate that although Dynamic ReLab incurs higher overhead during initial label generation, it substantially outperforms traditional schemes in update processing efficiency. This improvement is achieved through innovative techniques, such as hierarchical bit-masking and binary path concatenation, which streamline the update process and ensure the integrity of the XML structure. These results highlight Dynamic ReLab’s relevance for modern applications requiring real-time adaptability and high-performance query processing in dynamic XML data environments

Empowering Social Sciences with Data Visualization - An Insights and Explorations into Behavioral Patterns and Urban Dynamics

Visual data is an integral and essential aspect of any science, a powerful tool for description and exploration in social sciences005. Abstract: In this study, we apply new advanced ways of visualization to bring light on steps helping policy makers better understand multifaceted social phenomena using datasets publicly available. Through combining geospatial, temporal and network visualizations, the framework provides an intimate understanding of urban growth patterns of i-Social networks and behavior characteristics. Findings show that attractive visualizations make interpreting and revealing otherwise hidden patterns easier and provide insight into policy. Such an approach would finally close the gap between data complexity and actionable insights, offering social scientists a solid foundation for research with real impact

Measuring the Performance of AI-Generated Animations: Strengths, Weaknesses, and Future Directions

Generative AI has rapidly advanced in producing visually appealing animations, but its ability to capture the subtle complexities of traditional animation techniques remains insufficiently explored. This study provides a detailed evaluation of AI-generated animations through the lens of the 12 classical principles of animation, which are essential for creating lifelike and engaging motion. Utilizing expert evaluations combined with robust statistical methods, including Chi-square tests and logistic regression, we quantitatively compare AI-generated animations to traditional hand-crafted ones. Our findings reveal that while AI-generated animations perform well in static principles such as Staging, Solid Drawing, and Appeal, they consistently fail to replicate dynamic principles like Squash and Stretch, Follow-Through, and Anticipation. This analysis represents the first quantitative benchmark of AI's performance in animation, offering critical insights into its strengths and shortcomings. We conclude with specific recommendations for improving generative AI models, particularly in motion dynamics, and outline future research directions to bridge the gap between AI-driven and traditional animation techniques

The role of ICT, human capital and economic growth on sustainable forest management: evidence from panel cointegration and Fourier causality tests

Forests provide a critical role in many ecosystem services such as biodiversity conservation, soil erosion prevention, land conservation and climate change mitigation. However, forest degradation and deforestation have been increasing globally in recent years, causing serious environmental and social problems. The main objective of this study is to analyze the impacts of information and communication technologies (ICT), human capital (HC) and economic growth on forest degradation and deforestation and to assess the effectiveness of ICT and HC policies in sustainable forest management. For this purpose, data from 28 countries covering the period 1993‐2020 were analyzed with Fourier-based panel cointegration and causality tests. The findings show that economic growth increases the footprint of forest products in the long run, whereas ICT and human capital reduce the footprint of forest products. Moreover, a causal relationship from ICT and human capital to forest products footprint was found to be valid in countries with high forest density. On the other hand, a causal relationship from economic growth to forest product footprint was found to hold in countries with relatively low forest density. These results provide important clues for the development of sustainable forest management policies and suggest that promoting ICT and human capital should be part of a sustainable forest management agenda. In this context, it is recommended that policymakers prioritize ICT-based monitoring and warning systems in countries with high forest cover, and it is recommended that they prioritize training and capacity building efforts to develop human capital in countries with low forest cover

Problems, Effects, and Methods of Monitoring and Sensing Oil Pollution in Water: A Review

: Oil pollution in water bodies is a substantial environmental concern that poses severe risks to human health, aquatic ecosystems, and economic activities. Rising energy consumption and industrial activity have resulted in more oil spills, damaging longterm ecology. The aim of the review is to discuss problems, effects, and methods of monitoring and sensing oil pollution in water. Oil can destroy the aquatic habitat. Once oil gets into aquatic habitats, it changes both physically and chemically, depending on temperature, wind, and wave currents. If not promptly addressed, these processes have severe repercussions on the spread, persistence, and toxicity of oil. Effective monitoring and early identification of oil pollution are vital to limit environmental harm and permit timely reaction and cleanup activities. Three main categories define the three main methodologies of oil spill detection. Remote sensing utilizes satellite imaging and airborne surveillance to monitor large-scale oil spills and trace their migration across aquatic bodies. Accurate real-time detection is made possible by optical sensing, which uses fluorescence and infrared methods to identify and measure oil contamination based on its particular optical characteristics. Using sensor networks and Internet of Things (IoT) technologies, wireless sensing improves early detection and response capacity by the continuous automated monitoring of oil pollution in aquatic settings. In addition, the effectiveness of advanced artificial intelligence (AI) techniques, such as deep learning (DL) and machine learning (ML), in enhancing detection accuracy, predicting leak patterns, and optimizing response strategies, is investigated. This review assesses the advantages and limits of these detection technologies and offers future research directions to advance oil spill monitoring. The results help create more sustainable and efficient plans for controlling oil pollution and safeguarding aquatic habitats