ASRIC Journal of Natural Sciences 2023 v3-i1
ISSN: 2795-3610
EISSN: 2795-3610
ASRIC Journal of Natural Sciences 2023 v3-i1
ASRIC Journal of Natural Sciences 2023 v3-i1
Published: 2023-12-29
Articles
Development Of Novel Commercial Production Protocol for High-Yielding Oyster Mushrooms (Pleurotus Species)
Adebayo Elijah Adegoke
The main difficulties in growing mushrooms in Nigeria continue to be the low bio-efficiency of the most frequently utilized substrates and the stress that substrate composting experiences. This study assesses how well palm kernel fruit stalk (PKFS) wastes work for mushroom cultivation and makes additional attempts to determine how sterilization affects substrate composting and mushroom yield. Pleu
Exploring the Efficacy of a Potent Corrosion Suppressant Derived from Trandescantia Pallida (Purpple Heart) Leaves: A Qualitative Assessment
Cornelius C. Ahanotu* Kenneth C. Madu, Cynthia A. Ugochukwu
The corrosion inhibiting effect of Tradescantia pallida leaf extract (TPLE) on low carbon steel in a sulphuric acid medium was investigated. Weight loss experiments were conducted at two temperatures, 27°C and 60°C, using six low carbon steel coupons of known dimensions, compositions, and weights over a 3-day immersion period. The results revealed that TPLE exhibited significant corrosion inhibiti
Cytotoxic Evaluation of the Aqueous Extract of some Selected Medicinal Plants Combinations on Lung Carcinoma Epithelial Cells A549 and Human Cervix Carcinoma HeLa S330194
Tossou Sandra Bénédicta Kadoukpè*, Luka Carrol Domkat, Emmanuel Adeyemi, Jeffrey Matthew, Taiwo Emmanuel Alemika
Many are the anti-cancer drugs currently in use but unfortunately, they fail to differentiate cancer cells from healthy cells. The aim of this study is to find an alternative way based only on medicinal plants to treat cancer by enhancing the immune system without damaging the healthy cells for the well-being of the patient. This study investigated the cytotoxic effect(s) of (04) Combined Plants
Ctgan Adversarial Attack on Network Intrusion Detection Based on Lstm Algorithm
Ahmad Abubakar Yunusa, Fatima Umar. Zambuk, Badamasi Imam. Ya’u, Abubakar Umar, Abdulkadir Hassan Disina
Deep neural networks have proven successful in the intrusion detection domain. Cyber security experts and designers must develop a variety of network intrusion detection systems to secure networks and computers from black hackers who might breach the network system and steal or damage important data from databases. Regrettably, recent studies revealed that adversarial samples can affect deep neura
Issues
