This paper presents RIFD-DLT, an advanced near-lossless image compression algorithm that combines Delta Encoding with the original rounding the intensity followed by division (RIFD) method. The RIFD method first minimizes the image intensities, which makes the next compression stages more efficient. Subsequently, Delta Encoding subtracts neighboring rows in each of the image's three-color matrices, using the proximity of pixel values in adjacent rows to further reduce the image intensity. Extensive investigations show that RIFD-DLT outperforms the state-of-the-art algorithms and benchmarks with respect to compression ratio and processing time. More specifically, RIFD-DLT compresses data from 11520 KBs to 2131 KBs with an 81.93% reduction and a 43.7% improvement over original RIFD-Huffman when compressing Kodak Image set. When comparing the RIFD-DLT with LICA algorithm, the total file size is reduced by 71.2%, representing a 10.73% improvement for RIFD-DLT. Also, RIFD-DLT shows notable speed gains over RIFD-Huffman, requiring only 34.62 seconds to compress and decompress all images from three datasets (Waterloo, Kodak and EPFL), as compared to 50.99 seconds for RIFD-Huffman. As for the image quality, the proposed algorithm achieved an average PSNR values of 58.51 dB, 51.3 dB, and 52.22 dB for the EPFL, Kodak, and Waterloo image sets, respectively, demonstrate the excellent image quality that persists after decompression, with a minimal distortion that is imperceptible to the human visual system and identically to the RIFD-Huffman PSNR. These findings show that, while preserving excellent image quality, RIFD-DLT provides an incredibly efficient and fast method of image compression.

The most significant impacts on the environment and society of corporate operations occur in the supply chain. Environmental impacts occur at different stages of the environmental life cycle and relate to the extraction of raw materials from the earth, the processing and manufacturing of building materials, the construction process, operation or use, maintenance and repair, repair and eventual disposal or reuse of materials. Environmental phenomena of climate change or global warming are the result of human practice. Global warming causes an increase in ocean temperatures and surface of the book, causing melting of polar ice caps and rising sea levels. Another phenomenon of waste and sewage problems has become a national and international problem. Every supply chain activity in a series of production activities has an impact on the environment. Supply chains can have different organizational structures that reflect business relationships or the level of collaboration between chain companies. This study examines the relationship of Life Cycle Assessment (LCA) with Environment Supply Chain Management (ESCM) and Sustainability Supply Chain in Manufacture in Indonesia.

The study investigates the firm’s specific characteristics of manipulated firms in East Asian emerging and developed markets using hand-collected 244 manipulated cases between 2001 and 2017. The empirical analysis is conducted using panel logistic regression to identify which stocks are more likely to be manipulated. Result shows that large and highly liquid firms were more likely to be manipulated in both emerging and developed markets. Additionally, marginal effect shows that firms with high free float and market capitalization had a higher probability of being manipulated in these markets. On the contrary, profitable firms were less likely to be manipulated in both developed and emerging markets. Limited studies have been conducted to empirically identify the characteristics of the manipulated stocks across the developed and emerging markets. The regulator can use these results to identify possible and expected manipulation and to design enforcement rules, accordingly. Further, investors can take into consideration these characteristics of manipulated stocks while designing their portfolio in order to reduce the portfolio risk.

This research aims to examine how data privacy concerns influence DOI in Digital Marketing and investigates how artificial intelligence trust mechanism integration modulates that effect. Primary data was collected by accessing the structured questionnaire targeting ZAIN Telecom employees as the chosen study site. The PLS-SEM technique was used in this research to analyze data privacy, digital marketing effectiveness, and AI-driven trust constructs. According to this study, digital marketing processes significantly focus on data privacy and, ultimately, AI-driven trust. Further, powerful AI trust systems will be required to reinforce data privacy and systematically configure and drive digital marketing enterprises. Thus, such systems could allow firms a sustainable competitive advantage in this new Battle for data security age. The findings discovered that data privacy concerns significantly impede AI-driven trust, diminishing digital marketing effectiveness. Therefore, they contribute to the literature by offering empirical support for AI-powered trust mediating between factors within a model and offering practical implications and extensions of the theoretical models. This literature helps industry practitioners and policymakers build trust in AI interventions to alleviate data privacy risks and improve support for digital marketing strategies.

Supply chain management is essential for enhancing an organisation’s position among its competitors by improving its efficiency and effectiveness. The fundamental goal of this study is to recognize the effect of the supply chain responsiveness, flexibility, and its adaptation within the Saudi Arabian business industry. The data in this study were collected from a sample of 390 participants in senior business positions, and then analysed using the multiple regression technique. The results indicated that both the measurements and model have satisfactory fit indices and showed that the three hypotheses raised in this study could be accepted. Additionally, the results demonstrated that both the responsiveness and adaptation of the supply chain exert a positive effect on how the supply chain performs. So, the faster the organisation responds and adapts to the supply chain activities, the higher performance they tend to get.

This study aims to assess the success of implementing Lean Manufacturing (LM) practices in food companies in Indonesia through the mediating role of Manufacturing Agility Competencies (MAC) to improve Operational Performance (OP). The method used is survey method. The study uses a positivist paradigm to see the effect of Lean manufacturing on operational performance. Testing used Structural Equation Modeling (SEM) by the Partial Least Square (PLS) approach. Finding of this study reveals that lean manufacturing practices have no positive effect on operational performance in Indonesian food industries. Meanwhile, the mediating role of Manufacturing Agility Competencies has succeeded in increasing the effect of Lean Manufacturing on operational performance. The originality of the research lies in the process of forming Manufacturing Agility Competencies as a strategy with a long process based on the experiences and observations made in food companies in Indonesia.

The accurate and efficient classification of leukemia images is crucial for early diagnosis and effective treatment planning. Traditional methods often face challenges in handling the complexity and variability of medical images. To address these challenges, we propose a novel approach that leverages the Gray Level Co-occurrence Matrix (GLCM) and statistical feature-based segmentation techniques. In this paper, we present a comprehensive framework for the automated classification of leukemia images using advanced image processing techniques. The methodology involves six key stages: input of leukemia images, preprocessing to enhance image quality, segmentation to isolate relevant features, feature extraction using texture analysis, classification using multiple distance metrics Euclidean, Manhattan, Canberra, and Chebyshev, and performance evaluation. Our results demonstrate significant improvements in classification accuracy, sensitivity, specificity, and error rates across various metrics and feature sets. For instance, using the Chebyshev distance, we achieved an average accuracy of 82.69%, sensitivity of 85.95%, and specificity of 82.77%. The Canberra distance provided optimal performance with 65 features, yielding an accuracy of 85.18%, sensitivity of 86.39%, and specificity of 86.31%. These findings underscore the efficacy of our approach in distinguishing between healthy and leukemic cells, thereby contributing to early diagnosis and effective treatment planning for leukemia.

Recently, Lean Six Sigma (LSS) has attracted much attention from both researchers and practitioners. In fact, many published papers used a quantitative research methodology to build and test theory in the field of LSS implementation in manufacturing area. Very few studies have been reported about the critical success factors influencing of six sigma implementations. The aim of this study is to present the results, from an empirical evidence, of Six Sigma implementation in Tunisian manufacturing. Therefore, fourteen main factors have been identified through literature review and questionnaire survey. Interpretive structural modeling (ISM) has been used to develop a hierarchical structure for analyzing the interactions among the key factors. By using the MICMAC technique, the barriers were classified into four groups according to their driving power and dependence power. Our finding provides important insight to managers and practitioners about factors in the LSS implementation process.

The Kingdom of Saudi Arabia has witnessed a significant surge in online shopping in recent years, fueled by factors like growing internet penetration, smartphone adoption, and government initiatives supporting e-commerce growth. This rise in online activity has led to a corresponding increase in the utilization of online courier services, playing a crucial role in ensuring timely and efficient delivery of goods In this context, understanding public perception of online courier services becomes crucial for businesses to improve their offerings, address customer concerns, and maintain a competitive edge. Social media platforms have emerged as a valuable source of customer feedback and user-generated content, offering insights into customer experiences and opinions. This paper presents a sentiment analysis on online couriers in Saudi Arabia using natural language processing techniques combined with Decision Tree and Support Vector Machine (SVM) classifiers of machine learning. A dataset on customers’ sentiments was created by a crawling process from X social media. Both classifiers perform well, with Decision Tree classifier performs slightly better on accuracy, i.e. 95.01% compared to 93.60% of the Support Vector Machine. Other metrics support the robustness of the classification.

The rice industry policy must be comprehensive with a scope of policy from upstream to downstream. In other words, it must cover all supply chains of the rice industry consisting of five levels, such as, the level of farmers who process rice, grain traders, rice traders, rice traders in production areas and rice traders in urban markets.The purposes of this research are (1) to identify and model the current supply chain system of the rice industry; (2) analyze the simulation of the rice industry supply chain system policy; and (3) recommend rice industry policies which provide incentives for all rice industry supply chain actors. The research method used is the case study method. The aim is to understand a phenomenon in the rice industry supply chain system in depth in West Java Province – Indonesia. The results show that the rice industry supply chain system model is a closed cycle consisting of material flow feedback in the form of grain, rice, money and information flow in the form of demand that occurs in the interaction of actors from farmers, grain traders, rice milling units (RMU), rice traders in production centers to rice traders in urban wholesale markets in Bandung and Jakarta. Every businessman in the rice industry has the same goal, which is to maximize the profits. Thus, it can potentially lead to a conflict of interest which is manifested in the desire of every businessman to sell as much as they produce at the highest possible price but this will not happen because of the limited resources they have, such as capital and the market demand they receive. The strategy of increasing production is often carried out by the government at this time which it cannot be done partially without considering other rice industry supply chain instruments. The impacts of this partial policies are farmers, collectors, rice traders and rice mills gain unstable profits and the benefits received are lower than before the production strategy and policy were implemented. The recommended policy strategy is the rice industry supply chain system should be able to guarantee the availability of sufficient rice and capable of guaranting the welfare of farmers with a policy that integrates rice farming and agro-industry production strategies, financing strategies and accessible to all levels of actors in the rice industry supply chain, human resource development strategies and rice business risk management strategies, simultaneously. The integrated policy strategy needs to be carried out since rice supply chain management is a coordinating system of material flow in the form of grain, rice, money, facilities and information flow in the form of orders or requests, knowledge and innovation.