The growing threat of windows malware poses an increasing risk to the security of computers and the sensitive information they hold. The exponential rise in malware threats targeting the windows platform necessitates robust and adaptive detection mechanisms. Machine learning (ML) techniques demonstrate effectiveness in identifying windows malware therefore, a thorough analysis of these techniques is essential. This paper presents a comprehensive review of machine learning based techniques which have been proposed by research community for detecting windows malware. The review begins by providing a comparison of this study with the existing reviews. Then, we provide details of different ML based malware detection techniques. These techniques have been assessed on multiple parameters including: dataset used for training and testing, availability of dataset, ML model used for classification, the type of extracted features, analysis type and the metrics employed to measure the effectiveness of technique. Furthermore, the paper highlights the limitations and challenges in this field and suggests potential future research directions. By providing a comprehensive overview and critical analysis of ML-based malware detection techniques proposed for the windows environment, this study aims to guide and inspire further research in handling evolving cyber threats.

This paper focuses on the challenges and opportunities of achieving sustainability in smart cities. It depends on maintaining the balance between energy efficiency and connectivity through energy-efficient protocols. The study surveys systems with energy-efficient protocols, emphasizing their strengths and limitations in maintaining sustainability in smart cities. The need for energy-efficient protocols in smart city settings is that these protocols dynamically adjust operations to optimize energy consumption within sensor nodes in communication networks while considering parameters: like heterogeneity, traffic patterns, and environmental factors. Evaluation against key performance metrics such as energy consumption, data delivery ratio, network lifetime, and throughput identify promising technologies to achieve an optimal balance between connectivity and energy efficiency in smart cities. It concludes by proposing future research directions and considerations to make the development of sustainable networking solutions rapidly tailored to the evolving landscape of smart cities. This paper aims to present a comprehensive overview of the pivotal role played by energy-efficient protocols in fostering sustainable networking architectures.

A new era of genetics dawned in the 2010s with the discovery of the revolutionary CRISPR/Cas technology, which enables the targeting and editing of specific genes or genomic loci with high precision [4] . Since then, CRISPR-Cas has become a household name, as it has quite literally democratized genome editing across the globe due to its versatility, flexibility, high efficiency, and relative ease of adoption in diverse biological systems. Conventional CRISPR-Cas9 and advanced tools like base editors and prime editors have become principal tools for disrupting gene functions, replacing alleles, creating precise insertions or deletions, and conducting large-scale chromosome engineering [11] . However, there are numerous challenges that must be addressed to fully realize the potential of this technology. Base Editing (BE), Prime Editing (PE) and Homology-Directed Repair (HDR) offer the capability for precise allele swapping. However, the efficiency of PE and HDR in higher eukaryotes, particularly in plants, is currently low and requires further enhancement to enable routine application in crop improvement [17] . The delivery of CRISPR reagents to specific cell types and their encapsulation within cargo vehicles pose significant challenges. Furthermore, the regeneration of edited plants remains problematic in many recalcitrant crops. Addressing the efficiency of editing in polyploid crops necessitates dedicated attention.

The minimum requirements for post-cellular evolution from prokaryotes towards multicellular eukaryotes are outlined. The main steps are: formation of a true nucleus harboring linear chromosomes with centromeres and telomeres, establishing of mitosis and of sex via meiosis and fertilization, endosymbiotic gain of mitochondria and plastids, and epigenetic differentiation of non-separated cells into tissues of multicellular organisms. Erroneous DNA double-strand break repair and cell fusion are postulated as main drivers of eukaryotic evolution. Advantages versus disadvantages of eukaryotes compared to prokaryotes are discussed.