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Mathematical Modeling of Concrete Fracture Energy of Notched Specimens Using Experimental Evidence
The tensile stiffness of concrete is an important parameter for crack initiation. The microcrack initiation and propagation regulate the stressstrain behavior and the failure mode of concrete. Therefore, fundamental awareness of the fracture mechanism in terms of fracture energy is a requisite to comprehend concrete behavior. There is research consensus that fracture energy alone does not suffice to characterize the ductility/brittleness and also the size dependency of concrete. Therefore, it is necessary to evaluate the fracture energy and the characteristic length for a realistic assessment of the fracture behavior of concrete. Towards this objective, this study examined the fracture energy of concrete by experimentation, and the fracture energy proposed by various models in the literature. Further, the characteristic length proposed by Hillerborg which depicted both the material influence and the size effect, has been computed. Based on the RILEM50 FM recommendations, 18 specimens with varying grades of concrete and notch depths have been tested and the fracture energy parameters have been evaluated. Also, two regression models with key fracture parameters as variables for two-notch ratios, have been formulated for the concrete fracture energy. The arguments have been supported by experimental evidence. The Author(s), under exclusive licence to Shiraz University 2024. -
Prediction of CFRP and NSM-Wrapped Composite Column Capacities Using Experimentation and an Ensemble Machine-Learning Approach with SHAP Interpretation
Column capacity is an essential parameter in structural design, and its accurate determination is critical for a safe load-Transfer mechanism in structures. Also, experimental and accurate model-based assessments are critical to the column capacity evaluation. The main objective of this study is to experimentally and analytically investigate near-surface mounted (NSM) wrapped columns of different configurations and compare their capacity enhancements with the capacity enhancements of carbon fiber-reinforced polymer (CFRP). The study is also focused on developing a statistical regression model and extreme gradient boost (XG Boost), an ensemble machine-learning (ML) approach-based model, and examining both models developed for the experimental results by Shapley additive explanations (SHAP) interpretations. Therefore, the study experimentally reviewed the behavior of 24 composite columns to gain insights into experimental and code-recommended column capacities, stress-strain responses, axial stiffness, ductility factors, and failure modes. NSM-wrapped columns gained 10% strength increments, and, in comparison, the full-wrapped CFRP columns achieved 22% strength enhancement. The structural columns in a structure typically require various levels or types of strengthening, depending on their loading conditions, geometry, and material properties. With a 10% increment, the NSM technique suits columns needing lesser strength enhancements. Therefore, a key finding of the study is that the contribution of NSM longitudinal wrapping to column capacity is significant and cannot be ignored. A statistical regression model is developed for column capacity with four key parameters: percentage steel reinforcement, the extent of epoxy adhesion, the weight of the specimen, and the concrete clear cover. A model based on XG Boost, an ensemble ML approach, is also developed for the same four key parameters. The models developed are evaluated by SHAP interpretations. The SHAP analysis technique interpreted this improved model for various input-output features. The XG Boost machine-learning algorithm, developed with a coefficient of determination of 0.99, is found to be a refined regression model. Also, the study establishes that the ensemble ML approach used in tandem with SHAP analysis is a robust prediction and model interpretation tool, highlighting the significance of the percentage of steel reinforcement and the extent of epoxy adhesion over the other variables for the experimental dataset. 2026 American Society of Civil Engineers. -
Comparative Analysis of Composite Column Capacity Estimation in International Codes
The primary function of a building, bridge or any structural system is to transmit loads safely from the superstructure to the foundation. The columns play a critical role in this function, and any inaccuracies in load prediction can lead to catastrophic damage. Hence, the evaluation of a column's strength assumes considerable importance. Upon this premise, this research aimed to investigate the strength prediction of M40 grade concrete columns subjected to uniaxial compression loading. The experimental loading capacities of various columns were compared with the evaluated loads as per the Indian Standard code (IS 456:2000), British Standard (BS 8110-1:1997), American Concrete Institute (ACI 318-14) and European Standard (EN 1994-1-1 (2004)). It was observed that the partial safety factors and design philosophies in these codes were different. The experimental results suggested that the load-carrying capacities experimentally determined of the tested columns compare well with the capacities recommended by the IS code and the BS code for columns. In contrast, the other two codes have vastly different column capacity assessments due to higher partial safety factors. It is concluded that all four codes have evolved based on different design philosophies and, hence, have varying partial safety factors. Thus, a direct code comparison is not appropriate. The Authors, published by EDP Sciences, 2025. -
Leveraging Agentic RAG toReduce Hallucinations inLarge Language Models
Large Language Models (LLMs) have revolutionized language generation and comprehension. However, a notable issue remains, which is their sensitivity to hallucination, which may lead them to generate inaccurate or irrelevant content. Context dependency, or the capacity to use and understand ones environment, is crucial for surviving hallucinations. The agentic RAG framework offers a feasible solution, leveraging intelligent agents to strengthen contextual knowledge. Through the evaluation of entity roles and relationships, agentic RAG aids LLMs in understanding the variation of context, spotting inconsistencies and generating more precise and balanced replies. This research explores the establishment of Agentic RAG into LLMs to refine their reliability and efficiency by overcasting hallucinations and lifting contextual awareness. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Machine Learning Classifiers for Credit Risk Analysis
The modern world is a place of global commerce. Since globalization became popular, entrepreneurs of small and medium enterprises to large ones have looked up to banks, which have existed in various forms since antiquity, as their pillars of support. The risk of granting loans in various forms has significantly increased as a consequence of this, the businesses face financing difficulties. Credit Risk Analysis is a major aspect of approving the loan application that is done by analyzing different types of data. The goal is to minimize the risk of approving the loan for the Individuals or businesses who might not pay back on time. This research paper addresses this challenge by applying various machine learning classifiers to the German credit risk dataset. By evaluating and comparing the accuracy of these models to identify the most effective classifier for credit risk analysis. Furthermore, it proposes a contributory approach that combines the strengths of multiple classifiers to enhance the decision-making process for loan approvals. By leveraging ensemble learning techniques, such as the Voting Ensemble model, the aim is to improve the accuracy and reliability of credit risk analysis. Additionally, it explores tailored feature engineering techniques that focus on selecting and engineering informative features specific to credit risk analysis. 2024 Sudiksha et al., licensed to EAI. -
Encountering risk with resilience for experiences: a case study on tourism in a conflicted tourist destination
Purpose: This paper aims to unravel how tourists balance their novel experiences with risk perceptions, psychological resilience and behavioral intentions. Additionally, it explores how tourists' personalities moderate the relationship between experiences and travel intentions. Design/methodology/approach: A total of 234 self-administered questionnaires were distributed to a diverse group of tourists who recently explored the Srinagar region to capture their perspectives. The data obtained was analyzed using Smart PLS-SEM. Findings: This study revealed that the impact of perceived terror risk on behavioral intentions is not statistically significant. Instead, tourists' experiences significantly influence psychological resilience and behavioral intentions. Tourists with higher resilience are inclined to perceive these experiences as aiding in managing negative feelings. Research limitations/implications: The study's focus is confined to one conflict zone within the country due to research constraints, excluding other areas. Practical implications: This research provides practical insights for destination management authorities and highlights areas for improvement for tourism service providers and the government in the Srinagar region, as well as other conflict regions. Emphasizing mutual respect between locals and tourists can foster community-based tourism, enhancing the region's appeal and promoting positive intentions for all involved parties. Social implications: This study examines how local communities in conflict-affected areas adjust to and manage the presence of tourists, with an emphasis on building resilience and support systems. Additionally, it explores how travel decisions and behaviors are influenced by tourists' perceptions of safety and how these perceptions can influence broader societal attitudes toward areas affected by conflict. Evaluating the local population's economic reliance on tourism may result in changed social dynamics, as well as possible exploitation or over-reliance on industry. Promoting mutual understanding and cultural interchange between locals and visitors may have a positive impact on efforts to promote social cohesion and peacebuilding. Originality/value: This study broadens the scope of the existing literature on destination attributes in conflict zones, offering a unique perspective on the intrinsic features of this issue. The solutions proposed in this study contribute a novel dimension to the current literature. 2024, International Tourism Studies Association. -
An Efficient HOG-Centroid Descriptor for Human Gait Recognition
Automatic recognition of human gait have gained much attention nowadays. Histogram of Oriented Gradient (HOG) is a widely adopted descriptor for object's shape analysis. In this paper, combination of HOG descriptor with silhouette centroid for human gait recognition is proposed. The resultant descriptor, namely HOG-Centroid, achieves better recognition performance on comparison with HOG descriptor individually as well as other existing gait recognition methods. Experiments are carried out with CASIA gait dataset B and cumulative matching scores of 95.3%, 98.1% and 99.2% are obtained for rank 1, rank 5 and rank 10 respectively. 2019 IEEE. -
Discriminative Gait Features Based on Signal Properties of Silhouette Centroids
Among the biometric recognition systems, gait recognition plays an important role due to its attractive advantages over other biometric systems. One of the crucial tasks in gait recognition research is the extraction of discriminative features. In this paper, a novel and efficient discriminative feature vector using the signal characteristics of motion of centroids across video frames is proposed. These centroid based features are obtained from the upper and lower regions of the gait silhouette frames in a gait cycle. Since gait cycle contains the sequence of motion pattern and this pattern possesses uniqueness over individuals, extracting the centroid features can better represent the dynamic variations. These variations can be viewed as a signal and therefore the signal properties obtained from the centroid features contains more discriminant information of an individual. Experiments are carried out with CASIA gait dataset B and the proposed feature achieves 97.3% of accuracy using SVM classifier. 2019, Springer Nature Singapore Pte Ltd. -
An overlap-based human gait cycle detection
Identification of a person by his/her style of walking is referred as gait recognition. Gait is one among the biometric used for human identification. In gait recognition, an inevitable step for accurate feature extraction is gait cycle detection. In this paper, a novel gait cycle detection algorithm based on the concept of overlap between legs during locomotion is proposed. To identify overlap, zero-crossing counts of silhouette frames as well as bottom halves of silhouette frames are considered. The efficiency of this algorithm is tested using normal walking sequence of subjects with 90 viewing angle from CASIA B as well as TUM-IITKGP human gait databases. The results obtained shows that gait cycle can be easily and efficiently detected with zero-crossing count of silhouette frames. Further zero-crossing counts taken from bottom halves of silhouette frames gives better performance. Copyright 2019 Inderscience Enterprises Ltd. -
Inter Frame Statistical Feature Fusion for Human Gait Recognition
Researches showed that gait is unique for individuals and human gait recognition gained much attention nowadays. The sequence of gait silhouettes extracted from the video sequences has its own significance for gait recognition performance. In this paper, a novel inter frame feature discriminating the individual gait characteristics is proposed. Consecutive frames within a gait cycle are divided into equal number of blocks and corresponding block differences are calculated. It can preserve the minute temporal variations of the different body parts within each block and the cumulative difference provide a unique feature capable of discriminating individuals. To avoid synchronization problems, secondary statistical features are extracted from the primary inter frame variations. Finally, feature level fusion schemes are applied on these statistical features with existing features extracted from CEI representation. The efficiency of the proposed feature is evaluated on widely adopted CASIA gait dataset B using subspace discriminant analysis. The experimental results show that our proposed feature has better recognition accuracy in comparison with existing features. 2019 IEEE. -
Blockchain-Based Service Oriented Privacy-Preserving Data Sharing over Distributed Data Streams in Asynchronous Environment
Innovative city applications use information and communication technologies to function various operations efficiently. The widespread use of the Internet of Things (IoT) can be viewed in several applications like smart cars, smart cities, e-commerce, and cyber-physical systems. The huge amount of data produced and transmitted by these systems is handled by cloud-based storage services, which are vulnerable to multiple threats risking the privacy and security features of the application. Cloud storage services employ encryption algorithms to ensure data confidentiality, but it fails to address the privacy issues. Apart from the privacy risks, in these systems, the identity of a user who shares and accesses the data is traceable, as it is required to verify user eligibility before providing access. Also, a vast amount of daily data is stored on a centralized system that processes service requests from multiple users, posing considerable risks to the system's stability during peak periods. To address these challenges faced during the data sharing process in a centralized system, Service Oriented Privacy-Preserving Data Sharing (SOPPDS) platform based on a blockchain framework is proposed. The modified Key Policy-Attribute-based Encryption (MKP-ABE) technique is applied to securely share the data between the service owners and the service consumers. It was evident from the performance evaluation of the proposed SOPPDS platform that the encryption process takes lesser time than the decryption process. Also, the cryptographic operations performed on the prime order sets exhibited increased latency and computational cost. It was observed that comparatively, cryptographic operations performed on composite order sets could overcome the issues in prime order sets. SOPPDS platform works well in preserving the users' privacy, ensuring anonymity in the data sharing process, and maintaining the confidentiality of the data shared in the system. 2023, Ismail Saritas. All rights reserved. -
Optimized Metamaterial Loaded Square Fractal Antenna for Gain and Bandwidth Enhancement
This paper presents a report on the enhanced performance of an optimized metamaterial loaded square fractal antenna (OMSFA). The design and simulation of the antenna was carried out using Electronic Desk Top HFSS version 18.2 software. The antenna layer spreads over an area of 23 square millimeter on a FR4 substrate whose dielectric permittivity is 4.4. The substrate size measures an area of 46 mm X 28 mm, with 1.6 mm thickness. Also the design includes a microstrip feed and truncated ground. The antenna resonates well with a deep return loss of-38.9 dB in a broad bandwidth of 3.2 GHz (128 %) between 2 GHz and 5.2 GHz. The OMSFA produces enhanced gain of 9.8 dB at 2.5 GHz. The radiation is more focused due to the effect of metamaterial loading. The proposed antenna is recommended for wireless application in the lower region (S band) of the microwave spectrum. 2018 IEEE. -
Integration of 0.1 GHz to 40 GHz RF and microwave anechoic chamber and the intricacies
The aim of this paper is to highlight and elaborate the construction and establishment of a rectangular anechoic chamber (AC) of dimensions 7 m 4 m 3 m working from 0.1 GHz to 40 GHz. It is an informative checklist giving an insight on the reckoning of chamber dimensions and selection of appropriate absorbers as per the required specifications. It briefs the key features of validation of an anechoic chamber, namely, shielding effectiveness and reflectivity (quiet zone). It describes the intricacies of the integration of systems such as vector network analyzer (VNA), antenna mounting stands, three-axes motorized antenna rotation control circuitry, and customized software. The validation of the established chamber is accomplished for overall shielding effectiveness of ?80 dB and reflectivity of ?40 dB in one cubic meter area at the receiving antenna or the antenna under test (AUT) region far away from transmitter say, at 5.5 m separation. This paper covers the measurement results of three broadband horn antennas which can be used as reference antennas for characterization of other antennas in the chosen frequency range. The entire report will certainly be a guideline for any reader or aspirant who is interested in the development of a similar anechoic chamber and looking for complete intricacies. 2020, Electromagnetics Academy. All rights reserved. -
Ground Truncated Broadband Slotted Circular Microstrip Antenna
In this growing era of wireless technology, large sized devices have become obsolete. In response to the increasing demand for miniaturization over the past decades, microstrip antennas have drawn attention due to its various features like light weight, low cost, small in size and its greater resistivity to shock and vibrations. These can also easily get conformed to any surface. These antennas are also capable of operating at high frequencies, providing large bandwidth and gain by using various techniques slots and truncation of shapes. This report describes the design, simulation, fabrication and measurement results of a microstrip fed Slotted Circular Microstrip Antenna for broadband applications. The antenna was designed for an operating frequency of 2.45 GHz on a double side printed FR4 substrate measuring 55 mm x 55 mm x 1.6 mm with ?r of 4.4. It measured a very large resonant band of 1.3 - 9.05 GHz at a return loss level as low as -36.5 dB at 7.98 GHz. A maximum gain of 2.46 dB was achieved at 2.33 GHz. The enhancement in bandwidth was achieved by truncation in ground and inclusion of thin circular slot. The HFSS version 18.2 software and VNA model Anritsu SA20E were used for simulation and measurement respectively. It is found that the simulation and measurement results agree. 2018 IEEE. -
ANN based pattern generation, design and simulation of broadband fractal antenna for wireless applications
The synthesis of microstrip antenna(MSA) remains complex and time consuming from convenient design point of view. The Artificial Neural Network (ANN) on the other hand provides quicker and accurate solutions while multiple parameters controlling MSA designs. This paper proposes a new type of square fractal antenna (SFA) structure iterated and optimized by ANN developed using Advanced C and simulated using HFSS for optimum resonance characteristics covering 1.6-6.6 GHz frequency range. The motivation behind this work is size reduction of MSAs through FA concept with broadband resonance. It is suggested that the proposed antenna can be a right choice for various wireless applications because of its broadband functionality. 2016 IEEE. -
Experimental Verification of Gain and Bandwidth Enhancement of Fractal Contoured Metamaterial Inspired Antenna
The performance of any antenna cannot be completely assessed purely on the basis of simulation results. All simulations are made by assuming an ideal environment where the fabrication tolerances and practical losses are not accounted for. Therefore, evidencing the performance experimentally becomes a crucial step. In this work, the experimental validation of a fractal contoured square microstrip antenna with four ring metamaterial structure, hereon referred to as optimized metamaterial inspired square fractal antenna has been presented. It is an extension to previously designed antenna and aims to experimentally verify the enhanced gain and bandwidth of this antenna. The design and simulation of the proposed antenna was accomplished by using Ansys HFSS v18.2. The end-to-end antenna spread area is 23 mm x 23 mm on a 46 mm x 28 mm x 1.6 mm FR4 substrate with ?r of 4.4. The simulated design was fabricated using Nvis 72 Prototyping Machine and measured in an anechoic chamber facility using vector network analyzer. The antenna resonates with the deepest S11 of-39.5 dB in a broad bandwidth of 2.53 GHz from 2.265 GHz to 4.79 GHz with experimental verification. The proposed antenna provides an enhanced gain of 8.81 dB at the most popularly used frequency of 2.5 GHz. The simulation and experimental results of resonance, gain and radiation pattern are found to agree maximally. The fractional bandwidth offered by this proposed antenna is 72.28%. The experimental validation confirms enhanced gain-bandwidth performance in a wide resonance band. Hence, this antenna is well recommended for wireless, energy harvesting rectenna and sub-6 GHz (2.5 GHz to 4.20 GHz) 5G applications. 2022, Advanced Electromagnetics. All rights reserved. -
Enablers and Outcomes of Supply Chain Collaboration for Sustainable Growth
This study explores the intricate dynamics, challenges, and potential benefits of supply chain collaboration, emphasizing its pivotal role in achieving sustainability goals. Modern Supply Chain Collaboration (SCC) projects focus on sustainability-related activities, fostering interdependence between partners and driving sustained competitive advantage. The study introduces a comprehensive framework encompassing specific enablers (e.g., Joint Decision Making, Technology Integration) and outcomes (e.g., Social, Economic, and Environmental Sustainability) of supply chain collaboration. It contributes to practical guidelines for businesses seeking to enhance collaboration strategies and delves into theoretical paradigms such as the Cooperative Advantage concept, Triple Bottom Line Theory, Resource-Based View Theory, and Network Theory. The Triple Bottom Line Theory serves as an integrated theory of sustainability, emphasizing economic advantages, environmental impact minimization, and societal benefits. The Resource-Based View Theory underscores the role of internal resources in gaining competitive advantages, aligning with sustainability goals. Network Theory explores collaborative dynamics among competing entities, emphasizing resource sharing. The study's findings offer practical implications, enabling companies to assess and improve the sustainability of their supply chain management. It advocates for the integration of supply chain collaboration into organizational missions, emphasizing the importance of trust-building through standardized guidelines. The insights gained from this study are applicable across sectors, aiding legislators in developing flexible regulations and refining collaboration processes. Additionally, the study highlights the potential cultural variations in supply chain collaboration, paving the way for future research. 2024, Iquz Galaxy Publisher. All rights reserved. -
Skill sets required to meet a human-centered Industry 5.0: A systematic literature review and bibliometric analysis
The first industrial revolution, known as Industry 1.0, was primarily concerned with mechanical engineering and water and steam. Electric power systems and mass production assembly lines were established during the second industrial revolution (Industry 2.0). The third industrial revolution (Industry 3.0) was defined as automatic manufacturing and the incorporation of electronics, computers, and information technology into manufacturing. The fourth industrial revolution (Industry 4.0) is automating business operations and advancing manufacturing to a level based on connected devices, smart factories, cyberphysical systems (CPS), and the internet of things (IoT), where machines will change how they interact with one another and carry out specific tasks. Industry 5.0, with all modern technologies, is aimed to be a harmonious balance between human and machine interaction, and has an emphasis on sustainable growth. The present study uses an interpretive-qualitative research method to review the skill sets required to meet a human-centered Industry 5.0. 2024, IGI Global. All rights reserved. -
Enhancing the job scheduling procedure to develop an efficient cloud environment using near optimal clustering algorithm
In this internet era, cloud computing and there are various problems in the cloud computing, where the consumers as well as the service providers facing in their day to day cloud activities. Job scheduling problem plays a vital role in the cloud environment. To provide an efficient job scheduling environment, it is necessary to perform efficient resource clustering. In this regard, the proposed system, concentrated on the resource clustering methodology by proposing an efficient resource clustering algorithm named identicalness split up periodic node size (ISPNS) in the cloud environment. The algorithm proposed helps in forming resource clusters with the help of cloud environment. The proposed system is compared with the existing systems to justify the performance of the proposed resource clustering algorithm and it produces near optimal solution for the resource clustering problem which helps to provide an efficient job scheduling in cloud environment. Copyright 2023 Inderscience Enterprises Ltd. -
High-performance 2D photonic crystal sensor for simultaneous detection of chemical and biological analytes
This work proposes a two-dimensional (2D) photonic crystal based sensor for multi-analyte detection across biochemical and biological domains. The proposed sensor can detect sulfuric acid, hydrogen peroxide, salt concentrations in seawater, alcohol detection, cancer cell detection, and aberrant bone tissue without any external modification to the sensor configuration. The intriguing aspect of the proposed sensor is that its structural parameters are optimised to detect refractive index values in the range of 12, and the resonant wavelength is therefore shifted to a longer wavelength. Based on the shift, the performance parameters of the analyte are observed. The proposed sensor offers excellent performance metrics, including a high transmission efficiency of 100%, a high-quality factor of 1,600, a sensitivity of 315nm/RIU, and a maximum detection limit of 0.09. The footprint of the proposed design is 16?m. This makes the sensor suitable for photonic integrated circuits and lab-on-chip applications. It offers a promising platform for next-generation optical sensing technologies. The Author(s), under exclusive licence to The Optical Society of India 2025.