Since the systematic approach of the processes and their interactions, the aim is to establish the configuration of a construction project for the housing of the Weenhayek indigenous people. Applied from the theoretical research of various authors on a group of methodologies, phases and tools for project management, through rational scientific methods, such as descriptive, analytical, comparative, analytical-synthetic, inductive-deductive, historical-logical, analogies, modeling, systemic-structural-functional, systematization; and empirical methods, such as interpretivism that involves inductive, qualitative, phenomenological and transversal research, and the interview technique; the way in which the implementation processes are organized, interacted and structured is established. This reveals an alternative for the detailed configuration of a construction project for Weenhayek houses, based on phases, activities, actions and work tasks with characteristics in accordance with the needs of the project.

Credit risk assessment is one of the most important aspects of financial decision-making processes. This study presents a systematic review of the literature on the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques in credit risk assessment, offering insights into methodologies, outcomes, and prevalent analysis techniques. Covering studies from diverse regions and countries, the review focuses on AI/ML-based credit risk assessment from consumer and corporate perspectives. Employing the PRISMA framework, Antecedents, Decisions, and Outcomes (ADO) framework and stringent inclusion criteria, the review analyses geographic focus, methodologies, results, and analytical techniques. It examines a wide array of datasets and approaches, from traditional statistical methods to advanced AI/ML and deep learning techniques, emphasizing their impact on improving lending practices and ensuring fairness for borrowers. The discussion section critically evaluates the contributions and limitations of existing research papers, providing novel insights and comprehensive coverage. This review highlights the international scope of research in this field, with contributions from various countries providing diverse perspectives. This systematic review enhances understanding of the evolving landscape of credit risk assessment and offers valuable insights into the application, challenges, and opportunities of AI and ML in this critical financial domain. By comparing findings with existing survey papers, this review identifies novel insights and contributions, making it a valuable resource for researchers, practitioners, and policymakers in the financial industry.

With the rising global consumer demand for green and healthy food, the tea industry is facing unprecedented competitive pressure. Therefore, how to build tea enterprises with sustainable competitiveness has become a key issue facing the industry. This paper firstly reviews the concept of traceability systems and their evolution and, based on the theory of enterprise competitive advantage, explores the influence mechanism of traceability as a strategic resource on the long-term competitiveness of tea enterprises; secondly, it analyzes the multi-dimensional role of traceability on enterprise competitiveness from five aspects, namely, quality and safety control and guarantee, brand image shaping and trust construction, market dynamics response and consumer feedback, risk response and product recall, as well as technological innovation and efficiency enhancement; finally, combined with the above analysis, this paper constructs a theoretical framework for the competitiveness of tea enterprises, integrates the impact of traceability in different dimensions, and proposes a multi-level competitiveness enhancement model. Through this framework, tea enterprises can more comprehensively understand and grasp the close relationship between traceability and the long-term competitive advantage of enterprises and then make strategic adjustments according to their own actual situation so as to realize sustainable competitiveness enhancement in the future market competition.

Every production day in Nigeria, and in other oil producing countries, millions of barrels of produced water is generated. Being very toxic, remediation of the produced water before discharge into environment or re-use is very essential. An eco-friendly and cost effective approach is hereby reported for remediative pre-treatment of produced water (PW) obtained from Nigerian oilfield. In this approach, Telfairia occidentalis stem extract-silver nanoparticles (TOSE-AgNPs) were synthesized, characterized and applied as bio-based adsorbent for treating the PW in situ. The nanoparticles were of average size 42.8 nm ± 5.3 nm, spherical to round shaped and mainly composed of nitrogen and oxygen as major atoms on the surface. Owing to the effect of addition of TOSE-AgNPs, the initially high levels (mg/L) of Total Dissolved Solids (TDS), Biological Oxygen Demand (BOD) and TSS of 607, 3.78 and 48.4 in the PW were reduced to 381, 1.22 and 19.6, respectively, whereas DO and COD improved from 161 and 48.4 to 276 and 19.6 respectively, most of which fell within WHO and US-EPA safe limits. Particularly, the added TOSE-AgNPs efficiently removed Pb (II) ions from the PW at temperatures between 25 ℃ to 50 ℃. Removal of TOSE-AgNPs occurred through the adsorption mechanism and was dependent contact time, temperature and dose of TOSE-AgNPs added. Optimal remediation was achieved with 0.5 g/L TOSE-AgNPs at 30 ℃ after 5 h contact time. Adsorption of Pb (Ⅱ) ions on TOSE-AgNPs was spontaneous and physical in nature with remediation efficiency of over 82% of the Pb (Ⅱ) ions in solution. Instead of discarding the stem of Telfairia occidentalis, it can be extracted and prepared into a new material and applied in the oilfield as reported here for the first time.

This paper presents an effective method for performing audio steganography, which would help in improving the security of information transmission. Audio steganography is one of the techniques for hiding secret messages within an audio file to maintain communication secrecy from unwanted listeners. Most of these conventional methods have several drawbacks related to distortion, detectability, and inefficiency. To mitigate these issues, a new scheme is presented which combines the techniques of image interpolation with LSB encoding. It selects non-seed pixels to allow reversibility and diminish distortion in medical images. Our technique also embeds a fragile watermarking scheme to identify any breach during transmission to recover data securely and reliably. A magic rectangle has also been used for encryption to enhance data security. This paper proposes a robust, low-distortion audio steganography technique that provides high data integrity with undetectability and will have wide applications in sectors like e-healthcare, corporate data security, and forensic applications. In the future, this approach will be refined for broader audio formats and overall system robustness.