Microwave parameters for bitumen emulsion and its application in highway engineering

Bitumen emulsion is used as a bonding material between two layers and partially acts as a water proofing agent; it gained more importance when there were environmental concerns with cutbacks bitumen. A very few properties of bitumen emulsions are known so far from its physical and chemical prospective. However, a new method to measure its dielectric properties i.e. permittivity by applying electromagnetic waves (microwaves) with open ended coaxial probe (OCP) method was investigated in this study at different temperatures and frequency of 8 to 12 GHz. After examining the physical properties of certain bitumen emulsion a correlation was established on the basis of dielectric constant (permittivity) with the existing conventional property viscosity at temperature of 25, 40, 50 and 60°C. On the basis of these properties different characteristics of the bitumen emulsion were found and hence, a correlation was established to predict its behavior. A good correlation factor was found for the four types of samples used in the study which were 0.98 for SS-1K, 0.99 for MS-1K, 1.00 for RS-1K and 0.99 for K1-40. This study has provided an effective parameter to measure and predict the behavior of bitumen emulsion at different temperature, which can be applied in highway industry

ANALYSIS AND DESIGN OF FINANCIAL STATEMENT ACCOUNTABILITY MANAGEMENT INFORMATION SYSTEM

This research conducted in Lampung Province, Indonesia, aims to investigate the factors influencing the issuance of opinions on the financial statements of district governments. The study utilizes interviews processed through to analyse and design of  the Financial Statement Accountability Management Information System (SIMALK) application to analyze the impact of technology utilization and oversight on financial reporting. Findings reveal that the Reasonable Without Exception (WTP) opinion by BPK RI is supported by the internal control system within SIMALK and oversight by the Inspectorate, rather than the presence of the SPI. The flowchart of SIMALK illustrates a systematic data processing and decision-making workflow. Financial supervision involves audits by district and provincial inspectorates, ensuring compliance with budgetary allocations and financial accountability. The study underscores the importance of public sector financial reporting in promoting transparency and accountability, highlighting the need for continuous training, technological infrastructure, and commitment from local governments to enhance governance practices and stakeholder trust

BRCA2 Polymorphisms and Breast Cancer Susceptibility: a Multi-Tools Bioinformatics Approach

Background/Aims: The main focus of this investigation is to identify deleterious single nucleotide polymorphisms (SNPs) located in the BRCA2 gene through in silico approach, thereby, providing an understanding of potential consequences regarding the susceptibility to breast cancer. Methods: The GenomAD database was used to identify SNPs. To determine the potential adverse consequences, our study employed various prediction tools, including SIFT, PolyPhen, PredictSNP, SNAP2, PhD-SNP, and ClinVar. The pathogenicity associated with the deleterious snSNPs was evaluated bu MutPred and Fathmm. Additionally, I-Mutant and MuPro were used to assess the stability, followed by conservation and protein-protein interaction analysis using robust computational tools. The 3D structure of BRCA2 protein was generated by SwissModel, followed by validation using PROCHECK and Errat. Results: The GenomAD database was used to identify a total of 7, 921 SNPs, including 1940 missense SNPs. A set of 69 SNPs predicted by consensus to be damaging across all platforms was identified. Mutpred and Fathmm identified 48 and 38 SNPs, respectively to be associated with cancer. While I- Mutant and MuPro assays suggested 22 SNPs to decrease protein stability. Additionally, these 22 SNPs reside within highly conserved regions of the BRCA2 protein. Domain analysis, utilizing InterPro, pinpointed 18 deleterious mutations within crucial DNA binding domains and one in the BRC repeat region. Conclusion: This study establishes a foundation for future experimental validations and the creation of breast cancer-targeted treatment approaches

Impact of Overall injustice on Employee Performance: Moderating Effect of Supportive Leadership Style

The purpose of this paper is to study the impact of overall injustice on the performance of employees working in the Private sectors and to investigate how supportive leadership style in supervisors can increase employee performance when they are under high stress due to injustice perceptions. Data was collected through questionnaires that were designed and distributed to the employees working in private sectors. Sample size of 250 was equally distributed in the two sectors. This measured the perceived level of injustice related stress and its possible effect on employee performance. Supportive leadership style has a significant effect on performance of employee and increases the performance but injustice may or may not affect employee performance. Injustice is sometimes not given much importance due to low magnitude or external causes of injustice, so it is not always negatively related to employee performance. The research expands our knowledge of supportive leadership and tended to focus that how supportive leadership style in supervisors can increase employee performance working in private sectors. Public sector organizations should also be studied and sample size should be increased to cover large number of organizations. By expanding the range of organizations in the study would add credibility to the findings. Supportive leadership style plays an important role in the overall performance of an employee. Organizations need to improve leadership skills in supervisors to achieve positive outcomes and increased productivity

Impact of Overall injustice on Employee Performance: Moderating Effect of Supportive Leadership Style

The purpose of this paper is to study the impact of overall injustice on the performance of employees working in the Private sectors and to investigate how supportive leadership style in supervisors can increase employee performance when they are under high stress due to injustice perceptions. Data was collected through questionnaires that were designed and distributed to the employees working in private sectors. Sample size of 250 was equally distributed in the two sectors. This measured the perceived level of injustice related stress and its possible effect on employee performance. Supportive leadership style has a significant effect on performance of employee and increases the performance but injustice may or may not affect employee performance. Injustice is sometimes not given much importance due to low magnitude or external causes of injustice, so it is not always negatively related to employee performance. The research expands our knowledge of supportive leadership and tended to focus that how supportive leadership style in supervisors can increase employee performance working in private sectors. Public sector organizations should also be studied and sample size should be increased to cover large number of organizations. By expanding the range of organizations in the study would add credibility to the findings. Supportive leadership style plays an important role in the overall performance of an employee. Organizations need to improve leadership skills in supervisors to achieve positive outcomes and increased productivity

MRI Imaging, Comparison of MRI with other Modalities, Noise in MRI Images and Machine Learning Techniques for Noise Removal : A Review

Medical imaging is to assume greater and greater significance in efficient and precise diagnosis process. It is a set of various methodologies which are used to capture internal or external images of human body and organs for clinical and diagnosis needs to examine human form for various kind of ailments [1]. Computationally intelligent machine learning techniques and their application in medical imaging can play a significant role in expediting the diagnosis process and making it more precise. This review presents an up-to-date coverage about research topics which include recent literature in the areas of MRI imaging, comparison with other modalities, noise in MRI and machine learning techniques to remove the noise

Digital Images Enhancement using Tiny Character Adjustment and Referenced Image Approach

Digital image enhancement has been a hot topic during the past decades. In this paper, we have established a new approach for local gray contrast adjustment of tiny characters and global referenced base contrast enhancement approach to improve the contrast of degraded images. The proposed approach initially adjusts the contrast of tiny characters and then enhances the contrast of whole image by finding out some vital information from the histogram of the referenced image. The experimental results show that the proposed algorithm can adjust the tiny characters and increase the image contrast efficiently

Op2Vec: An Opcode Embedding Technique and Dataset Design for End-to-End Detection of Android Malware

Android is one of the leading operating systems for smart phones in terms of market share and usage. Unfortunately, it is also an appealing target for attackers to compromise its security through malicious applications. To tackle this issue, domain experts and researchers are trying different techniques to stop such attacks. All the attempts of securing Android platform are somewhat successful. However, existing detection techniques have severe shortcomings, including the cumbersome process of feature engineering. Designing representative features require expert domain knowledge. There is a need for minimizing human experts' intervention by circumventing handcrafted feature engineering. Deep learning could be exploited by extracting deep features automatically. Previous work has shown that operational codes (opcodes) of executables provide key information to be used with deep learning models for detection process of malicious applications. The only challenge is to feed opcodes information to deep learning models. Existing techniques use one-hot encoding to tackle the challenge. However, the one-hot encoding scheme has severe limitations. In this paper, we introduce; (1) a novel technique for opcodes embedding, which we name Op2Vec, (2) based on the learned Op2Vec we have developed a dataset for end-to-end detection of android malware. Introducing the end-to-end Android malware detection technique avoids expert-intensive handcrafted features extraction, and ensures automation. Some of the recent deep learning-based techniques showed significantly improved results when tested with the proposed approach and achieved an average detection accuracy of 97.47%, precision of 0.976 and F1 score of 0.979