This study aims to identify the most suitable water treatment plant among various alternativesu,including,surface Wates, groundwater, RO seawater desalination, thermal desalination (MSF/MED) secondary and tertiary wastewater treatment, industrial, stormwater, and sludge or advanced reuse treatment plants. The evaluation considers key technical, economic, and environmental criteria such as water quality, CapitaLand maintenance costs, energy consumption, environment impact. waste generation, implementation time, operational efficiency, operational risk, and space requirements. The M-polar fuzzy soft set method is applied to systematically handle uncertainty and select the optimal water treatment plant

Keywords: Surface Water Treatment Plants, Operational Efficiency, Optimization, Desalination

AMS Classification: 03E72, 47S40

The phenomenon of big data has become a defining characteristic of the technological age. Consequently, new challenges arise, including the types of data and the scale of the information available; traditional relational databases frequently prove inadequate in addressing these demands, resulting in the proliferation of NoSQL databases. This research presents a comprehensive review of NoSQL-based systems and their methodologies for managing extensive data. Furthermore, this study examines the primary categories of NoSQL databases, their optimal attributes, and their strategies for addressing the contemporary "5Vs" of Big Data: volume, velocity, variety, value, and veracity. Additionally, the research highlights the opportunities presented by NoSQL technologies in emerging fields, such as the Internet of Things (IoT), artificial intelligence, and machine learning, alongside the associated risks, including trade-offs in consistency, security challenges, and the lack of standardization. By integrating classification, comparison, and analysis, this review offers a unified perspective that can aid researchers and practitioners in the effective selection and implementation of NoSQL solutions within Big Data contexts.

The rise of multidrug-resistant (MDR) bacteria poses a significant challenge to the antibacterial efficacy of the

produced chemical, (2-(2-(2-nitrophenyl) diazinyl) malononitrile), which is generated from malononitrile and a

diazo moiety. The molecule was synthesized using the diazotization of 2-nitroaniline, followed by coupling with

malononitrile, resulting in NPDAM with an 85% yield and a melting point of 123-125 °C. The substance at

concentrations of 1, 5, and 10 mg/mL was assessed against methicillin-resistant Staphylococcus aureus (MRSA)

(SA121) and Escherichia coli (EC49). The chemical exhibited efficacy against MRSA at varying doses, yielding

clear zones of 29.6, 32.6, and 33.0 mm, in contrast to E. coli at the same concentrations, which produced clear

zones of 10, 11.6, and 14.6 mm, respectively. The microdilution technique was employed to ascertain the

minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values. The compound

exhibited low minimum inhibitory concentration (MIC) values against MRSA at concentrations of 1 and 5 mg/mL

(19.5 and 4.89 μg/mL, respectively), in contrast to E. coli, which demonstrated inhibition at 39.0 and 9.78 μg/mL

at the same concentrations. Additionally, it yielded the lowest MIC values (0.97 μg/mL) against both bacteria at

a concentration of 10 mg/mL. The MBC values against MRSA ranged from 39.0 to 1.95 μg/mL, while against

E. coli, the MBC values were slightly lower, ranging from 78.1 to 1.95 μg/mL. In conclusion, all chemical

concentrations exhibited activity against the studied microorganisms, indicating their potential as antimicrobial

agents.

Keywords: Malononitrile, Diazo compounds, antimicrobial activity, MRSA, E. coli.

(E)-2-(naphthalen-1-yldiazenyl) malononitrile (NDMN) was synthesized via a diazotization–coupling reaction and evaluated for its antibacterial activity against methicillin-resistant Staphylococcus aureus (MRSA) and Escherichia coli. The compound showed strong, concentration-dependent antibacterial activity, with significantly higher efficacy against MRSA than E. coli, as confirmed by disc diffusion, MIC, and MBC assays. These results suggest that NDMN is a promising synthetic antimicrobial agent, particularly against Gram-positive resistant bacteria, and merits further biological and toxicity investigations.

This study reports the development of a new series of pyrazolo[4,3-b] pyridine and pyrazolo[1,5-a] pyrimidine derivatives designed as potential antischistosomal agents. A diverse set of fused nitrogen-containing heterocycles was successfully obtained and subjected to preliminary biological relevance assessment based on their structural features. These newly synthesized frameworks, belonging to scaffolds previously associated with notable antimicrobial and antiparasitic properties, demonstrate promising potential for further evaluation against Schistosoma species. The findings highlight the value of these heterocyclic systems as candidates for developing alternative therapies to complement existing schistosomiasis treatment strategies

Keywords: Pyrazolopyridines; Pyrazolopyrimidines; Heterocyclic synthesis; Condensation reactions

Book recommendation systems (BRSs) play a vital role in digital libraries, online bookstores, and e-learning platforms by assisting users in discovering relevant content from vast collections. Traditional methods, such as collaborative filtering (CF), content-based filtering (CBF), and hybrid techniques, have historically formed the foundation of BRSs; however, they suffer from limitations including the cold-start problem, data sparsity, and overspecialization. In recent years, deep learning–based approaches have emerged as powerful alternatives, leveraging architectures such as CNNs, RNNs, BERT, and Neural Collaborative Filtering (NCF) to capture complex user–item interactions and support multimodal integration. This survey is the first to systematically review book recommendation systems published between 2020 and February 2025, filling a critical gap left by earlier studies that did not comprehensively examine this recent period of accelerated research. The paper introduces a novel taxonomy of BRSs that classifies systems according to methodological foundations, approaches, datasets, and evaluation metrics, while also identifying recurring challenges and emerging trends. The findings reveal a clear methodological transition from similarity-driven approaches to neural representation learning, reflecting the increasing demand for intelligent, scalable, and adaptive solutions. Traditional methods, however, remain essential as baseline models for benchmarking and comparative evaluation.

The rapid growth of digital content has intensified the problem of information overload, making it challenging for users to access relevant resources. Recommender systems (RSs) address this issue by filtering data and providing suggestions, thereby improving decision-making and user satisfaction. This paper presents a comprehensive review of recommender systems (RSs), with particular emphasis on their methods, techniques, benefits, history, and applications. It examines traditional approaches, including collaborative filtering, content-based filtering, and hybrid strategies, before providing a classification of deep learning models in recommender systems and analyzing their impact on enhancing RS capabilities. In addition, the paper discusses evaluation methods used to assess recommendation performance and highlights their roles in measuring system effectiveness. Finally, it synthesizes the key challenges confronting recommender systems, including data sparsity, scalability, and cold-start issues.