The exponential growth of scientific publications creates a critical challenge for researchers attempting to navigate their fields. Manual literature reviews, once sufficient for identifying research opportunities, now consume disproportionate time and often lack comprehensiveness. This paper presents an automated research gap detection system that integrates natural language processing, citation network analysis, and ensemble machine learning to identify research gaps across scientific literature systematically. The proposed system uses transformer-based models (SciBERT, BioBERT) for semantic understanding, graph neural networks for citation structure analysis, and Support Vector Machines, Random Forests, and Gradient Boosting for gap classification. We implement a complete pipeline processing documents at scale, extracting semantic content, analyzing citation relationships, and identifying knowledge gaps through multiple complementary techniques. The system was designed to detect three categories of gaps: knowledge discrepancies (conflicting information), knowledge voids (completely missing information), and methodological limitations (inadequate research methods). Experimental evaluation across multiple scientific domains demonstrates that the system identifies research opportunities with accuracy comparable to expert assessments while processing millions of documents efficiently. The results show 78% accuracy in predicting emerging research areas up to two years in advance and 88% validation rate for identified gaps when reviewed by domain experts.
The urbanization in India particularly in the Delhi-NCR area has been very significant in promoting the demand of efficient, cost effective as well as sustainable building material. The classical masonry blocks such as fired clay bricks are associated with large embodied energy, natural resources prostrate and low performance in thermal conditions. This essay is an evaluation of the Autoclaved Aerated Concrete (AAC) blocks as some of the lightweight and environment friendly alternative to building urban construction. This is compared using secondary data, Indian Standards as well as Delhi-NCR market rates with some key parameters like the density, compressive strength, thermal conductivity, construction efficiency, and cost per unit wall area being the key ones in comparison. As per IS 2185 (Part 3): 1984, AAC blocks exhibit a density of 550–650 kg/m³ and compressive strength of 3–5 MPa. It is the findings that AAC blocks can reduce the structural dead load by approximately 55-60 percent particularly against the requirements of IS 875 (Part 1): 1987, and has 3-4 times higher thermal insulation appearance when compared to traditional bricks which, in turn, have the potential to save energy upto 25-30% by (Central Building Research Institute) Cost analysis indicates that although AAC blocks have a higher upfront material cost, the overall construction expenses remain comparable. This is primarily because AAC blocks require less mortar, reduce structural demands as per IS 456:2000, and accelerate the construction process.. The study arrives at a conclusion that the AAC blocks may be considered a promising and sustainable high-density urban construction in India.
The Arithmetic Logic Unit (ALU) is one of the most important units of the digital systems of the modern era, which performs arithmetic and logical operations. The efficiency of an inner adder architecture of an ALU has a significant effect on the performance of the latter. This paper is the design, FPGA implementation and comparative analysis of three Ripple Carry Adder (RCA) based 16-bit ALU architecture, Carry Look-Ahead Adder (CLA) based architecture, and Carry Select Adder (CSLA) architecture. The suggested designs are programmed in Verilog HDL and synthesized on a Xilinx Kintex-7 FPGA (xc7k70tfbv676-1) with Vivado 2023.1. In addition, a hybrid CLA-CSLA architecture is implemented to improve computational speed, and operand isolation is applied to reduce switching activity and enhance power efficiency. They have been experimentally demonstrated to be the smallest-area, consuming 73 LUTs, compared to 109 and 111 LUTs used by CSLA and CLA structures respectively. Nonetheless, Although the Simple ALU shows higher timing slack due to FPGA carry-chain optimization, CLA and CSLA architectures provide improved carry computation efficiency and better scalability for high-speed arithmetic operations. Power analysis shows that they all work at below 0.1 W power, with the CLA-based ALU having the lowest power consumption. The paper discusses various trade-offs between area, speed, and power, and shows that optimized adder architectures can be used to achieve a high improved performance of the ALU in an FPGA-led system.
Public sentiment, media influence, and investor psychology play a crucial role in shaping stock market behavior, making sentiment analysis an essential component for informed decision-making. In This paper focuses on analyzing and classifying market sentiment from real-time textual data sources such as financial news, social media posts, and market reports. The system collects live textual data and processes it using Natural Language Processing (NLP) techniques, including text cleaning, tokenization, stop-word removal, and normalization to ensure consistency and accuracy. The processed data is then transformed into numerical features using methods like TF-IDF or Bag-of-Words. A supervised machine learning classifier, such as Naïve Bayes or Logistic Regression, is applied to categorize the sentiment into positive, negative, or neutral classes. The proposed system provides fast, automated, and objective sentiment analysis, reducing human bias and manual effort. The results are displayed through a simple and user-friendly interface, enabling both beginners and experienced investors to easily understand market trends without requiring deep financial knowledge. Overall, the project offers a lightweight, efficient, and scalable solution for real-time sentiment analysis. Future enhancements may include integration of advanced NLP models, incorporation of additional data sources like social media streams, and the use of predictive analytics for improved market insights.
The paper discusses matrix formulations of NTRU to support algebraic structure, support a SIMD-friendly and hardware-efficient implementation and discuss the concept of security with respect to key and ciphertext operation expressed in terms of (block)-circular and Toeplitz matrices. We suggest a family of Matrix-NTRU (MaNTRU) schemes in which multiplication of rings in is put into operation as structured matrix-vector products and an additional step to Module-NTRU through stacking blocks. The matrix perspective of correctness/error growth, parameter selection, side-channel resistance and resistance to lattice attacks is examined. The resulting products are formalizations, resistance to BKZ-style attacks, and software (or optional FPGA) test models that are 1.3-2.0 times faster than the formal code of NTRU and have the same NIST security.
