Health monitoring and fault diagnosis of rolling bearings are crucial for the continuous and effective operation of mechanical equipment. In order to improve the accuracy of BP neural network in fault diagnosis of rolling bearings, a feature model is established from the vibration signals of rolling bearings, and an improved genetic algorithm is used to optimize the initial weights, biases, and hyperparameters of the BP neural network. This overcomes the shortcomings of BP neural network, such as being prone to local minima, slow convergence speed, and sample dependence. The improved genetic algorithm fully considers the degree of concentration and dispersion of population fitness in genetic algorithms, and adaptively adjusts the crossover and mutation probabilities of genetic algorithms in a non-linear manner. At the same time, in order to accelerate the optimization efficiency of the selection operator, the elite retention strategy is combined with the hierarchical proportional selection operation. Using the rolling bearing dataset from Case Western Reserve University in the United States as experimental data, the proposed algorithm was used for simulation and prediction. The experimental results show that compared with the other seven models, the proposed IGA-BPNN exhibit superior performance in both convergence speed and predictive performance.

The diagnosis of leukemia is a serious matter that requires immediate and accurate attention. This research presents a revolutionary method for diagnosing leukemia using a Capsule Neural Network (CapsNet) with an optimized design. CapsNet is a cutting-edge neural network that effectively captures complex features and spatial relationships within images. To improve the CapsNet\'s performance, a Modified Version of Osprey Optimization Algorithm (MOA) has been utilized. Thesuggested approach has been tested on the ALL-IDB database, a widely recognized dataset for leukemia image classification. Comparative analysis with various machine learning techniques, including Combined combine MobilenetV2 and ResNet18 (MBV2/Res) network, Depth-wise convolution model, a hybrid model that combines a genetic algorithm with ResNet-50V2 (ResNet/GA), and SVM/JAYA demonstrated the superiority of our method in different terms. As a result, the proposed method is a robust and powerful tool for diagnosing leukemia from medical images.

Oleaginous fungi have attracted a great deal of interest for their potency to accumulate high amounts of lipids (more than 20% of biomass dry weight) and polyunsaturated fatty acids (PUFAs), which have a variety of industrial and biological applications. Lipids of plant and animal origin are related to some restrictions and thus lead to attention towards oleaginous microorganisms as reliable substitute resources. Lipids are traditionally biosynthesized intra-cellularly and involved in the building structure of a variety of cellular compartments. In oleaginous fungi, under certain conditions of elevated carbon ratio and decreased nitrogen in the growth medium, a change in metabolic pathway occurred by switching the whole central carbon metabolism to fatty acid anabolism, which subsequently resulted in high lipid accumulation. The present review illustrates the bio-lipid structure, fatty acid classes and biosynthesis within oleaginous fungi with certain key enzymes, and the advantages of oleaginous fungi over other lipid bio-sources. Qualitative and quantitative techniques for detecting the lipid accumulation capability of oleaginous microbes including visual, and analytical (convenient and non-convenient) were debated. Factors affecting lipid production, and different approaches followed to enhance the lipid content in oleaginous yeasts and fungi, including optimization, utilization of cost-effective wastes, co-culturing, as well as metabolic and genetic engineering, were discussed. A better understanding of the oleaginous fungi regarding screening, detection, and maximization of lipid content using different strategies could help to discover new potent oleaginous isolates, exploit and recycle low-cost wastes, and improve the efficiency of bio-lipids cumulation with biotechnological significance.

The RIME optimization algorithm is a newly developed physics-based optimization algorithm used for solving optimization problems. The RIME algorithm proved high-performing in various fields and domains, providing a high-performance solution. Nevertheless, like many swarm-based optimization algorithms, RIME suffers from many limitations, including the exploration-exploitation balance not being well balanced. In addition, the likelihood of falling into local optimal solutions is high, and the convergence speed still needs some work. Hence, there is room for enhancement in the search mechanism so that various search agents can discover new solutions. The authors suggest an adaptive chaotic version of the RIME algorithm named ACRIME, which incorporates four main improvements, including an intelligent population initialization using chaotic maps, a novel adaptive modified Symbiotic Organism Search (SOS) mutualism phase, a novel mixed mutation strategy, and the utilization of restart strategy. The main goal of these improvements is to improve the variety of the population, achieve a better balance between exploration and exploitation, and improve RIME\'s local and global search abilities. The study assesses the effectiveness of ACRIME by using the standard benchmark functions of the CEC2005 and CEC2019 benchmarks. The proposed ACRIME is also applied as a feature selection to fourteen various datasets to test its applicability to real-world problems. Besides, the ACRIME algorithm is applied to the COVID-19 classification real problem to test its applicability and performance further. The suggested algorithm is compared to other sophisticated classical and advanced metaheuristics, and its performance is assessed using statistical tests such as Wilcoxon rank-sum and Friedman rank tests. The study demonstrates that ACRIME exhibits a high level of competitiveness and often outperforms competing algorithms. It discovers the optimal subset of features, enhancing the accuracy of classification and minimizing the number of features employed. This study primarily focuses on enhancing the equilibrium between exploration and exploitation, extending the scope of local search.

Macrocyclic peptides possess unique features, making them highly promising as a drug modality. However, evaluating their bioactivity through wet lab experiments is generally resource-intensive and time-consuming. Despite advancements in artificial intelligence (AI) for bioactivity prediction, challenges remain due to limited data availability and the interpretability issues in deep learning models, often leading to less-than-ideal predictions. To address these challenges, we developed PepExplainer, an explainable graph neural network based on substructure mask explanation (SME). This model excels at deciphering amino acid substructures, translating macrocyclic peptides into detailed molecular graphs at the atomic level, and efficiently handling non-canonical amino acids and complex macrocyclic peptide structures. PepExplainer\'s effectiveness is enhanced by utilizing the correlation between peptide enrichment data from selection-based focused library and bioactivity data, and employing transfer learning to improve bioactivity predictions of macrocyclic peptides against IL-17C/IL-17 RE interaction. Additionally, PepExplainer underwent further validation for bioactivity prediction using an additional set of thirteen newly synthesized macrocyclic peptides. Moreover, it enabled the optimization of the IC50 of a macrocyclic peptide, reducing it from 15 nM to 5.6 nM based on the contribution score provided by PepExplainer. This achievement underscores PepExplainer\'s skill in deciphering complex molecular patterns, highlighting its potential to accelerate the discovery and optimization of macrocyclic peptides.

Streptococcus pyogenes Cas9 (SpCas9) is the most popular tool in gene editing; however, off-target mutagenesis is one of the biggest impediments in its application. In our previous study, we proposed the HH theory, which states that sgRNA/DNA hybrid (hybrid) extrusion-induced enhancement of hydrophobic interactions between the hybrid and REC3/HNH is a key factor in cleavage initiation. Based on the HH theory, we analyzed the interactions between the REC3 domain and hybrid and obtained 8 mutant sites. We designed 8 SpCas9 variants (V1-V8), used digital droplet PCR to assess SpCas9-induced DNA indels in human cells, and developed high-fidelity variants. Thus, the HH theory may be employed to further optimize SpCas9-mediated genome editing systems, and the resultant V3, V6, V7, and V8 SpCas9 variants may be valuable for applications requiring high-precision genome editing.

Fuzzy hybrid models are efficient mathematical tools for managing unclear and vague data in real-world scenarios. This research explores the q-rung orthopair fuzzy soft set (q-ROFSS), which presents incomplete and ambiguous details in decision-making problems. The main intention of this study is to describe and evaluate the characteristics of the correlation coefficient (CC) and weighted correlation coefficient (WCC) for q-ROFSS. Also, the technique for order preference should be enhanced by similarity to the ideal solution (TOPSIS) with extended measures in q-ROFSS settings. Furthermore, we integrated mathematical formulations of correlation obstructions to confirm the consistency of the planned technique. It helps handle difficulties involving multi-attribute group decision-making (MAGDM). Moreover, a numerical illustration is presented to clarify how the advocated decision-making methodology can be implemented in evaluating suppliers in green supply chain management (GSCM). As a result, each alternative is assessed using multiple criteria, such as quality and reliability, capacity and scalability, compliance and certifications, and sustainability practices. The technique proposed in this study retains the selected research\'s specific structure more effectively than current techniques. A comparative analysis further substantiates the feasibility and effectiveness of the proposed approach over other decision-making techniques.

With the global population on the rise, an escalating interest exists in environmentally sustainable and friendly protein sources. Insects have emerged as multifaceted resources, viewed not only as potential food items, but also as sources of traditional medicines and proteins. This study utilized response surface methodology (RSM) to ascertain the optimal extraction conditions for proteins from Musca domestica used in toad feeding, denoted as MDPs-T. The yield of MDPs-T was elevated to 18.3% ± 0.2% under these optimized conditions. Subsequently, the particle size, ζ-potentials, and structures of MDPs-T were analyzed and compared with the proteins derived from Musca domestica fed on a normal diet (MDPs-ND). This comparative analysis utilized a range of advanced techniques, involving UV spectroscopy, Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), high-performance gel permeation chromatography (HPGPC), and scanning electron microscopy (SEM). The outcomes have revealed a marginal disparity in the physical and chemical properties between MDPs-T and MDPs-ND. Derosination led to a reduction in the particle size of the MDPs by 10.98% to 62.81%. MDPs-T exhibited a higher proportion of low-molecular-weight components relative to MDPs-ND. Additionally, in a comparative analysis of amino acids, MDPs-T displayed a greater abundance of essential and total amino acids relative to MDPs-ND. Consequently, MDPs-T holds potential as a valuable food supplement for human consumption or as a nutrient-rich feed supplement for animals.

This study aimed to investigate the efficacy of supercritical CO2 (SC-CO2) extraction in enhancing the extraction rate, purity, and antioxidant activity of Indocalamus latifolius (Keng) McClure (Poaceae) leaf terpenoids (ILLTs). Crude extracts obtained from leaves were subjected to qualitative and quantitative analyses, revealing neophytadiene, phytol, β-sitosterol, β-amyrone, squalene, and friedelin as the primary terpenoid constituents, identified through gas chromatography-mass spectrometry (GC-MS). Compared with steam distillation extraction (SD), simultaneous distillation extraction (SDE), ultra-high pressure-assisted n-hexane extraction (UHPE-Hex), ultra-high pressure-assisted ethanol extraction (UHPE-EtOH), ultrasound-assisted n-hexane extraction (UE-Hex), and ultrasound-assisted ethanol extraction (UE-EtOH), SC-CO2 exhibited a superior ILLT extraction rate, purity, and antioxidant activity. Scanning electron microscopy (SEM) observations of the residues further revealed more severe damage to both the residues and their cell walls after SC-CO2 extraction. Under optimal parameters (4.5 h, 26 MPa, 39 °C, and 20% ethyl alcohol), the ILLT extraction rate with SC-CO2 reached 1.44 ± 0.12 mg/g, which was significantly higher than the rates obtained by the other six methods. The subsequent separation and purification using WelFlash C18-l, BUCHI-C18, and Sephadex LH-20 led to an increase in the purity of the six terpenoid components from 12.91% to 93.34%. Furthermore, the ILLTs demonstrated cytotoxicity against HepG2 cells with an IC50 value of 148.93 ± 9.93 μg/mL. Additionally, with increasing concentrations, the ILLTs exhibited an enhanced cellular antioxidant status, as evidenced by reductions in both reactive oxygen species (ROS) and malondialdehyde (MDA) levels.

Cameroon is currently grappling with a significant energy crisis, which is adversely affecting its economy due to cost, reliability, and availability constraints within the power infrastructure. While electrochemical storage presents a potential remedy, its implementation faces hurdles like high costs and technical limitations. Conversely, generator-based systems, although a viable alternative, bring their own set of issues such as noise pollution and demanding maintenance requirements. This paper meticulously assesses a novel hybrid energy system specifically engineered to meet the diverse energy needs of Douala, Cameroon. By employing advanced simulation techniques, especially the Hybrid Optimization Model for Electric Renewable (HOMER) Pro program, the study carefully examines the intricacies of load demands across distinct consumer categories while accommodating varied pricing models. The paper offers a detailed analysis of the proposed grid-connected PV/Diesel/Generator system, aiming to gauge its performance, economic feasibility, and reliability in ensuring uninterrupted energy supply. Notably, the study unveils significant potential for cost reduction per kilowatt-hour, indicating promising updated rates of $0.07/kW, $0.08/kW, and $0.06/kW for low, medium, and high usage groups, respectively. Furthermore, the research underscores the importance of overcoming operational challenges and constraints such as temperature fluctuations, equipment costs, and regulatory compliance. It also acknowledges the impact of operational nuances like maintenance and grid integration on system efficiency. As the world progresses towards renewable energy adoption and hybrid systems, this investigation lays a strong foundation for future advancements in renewable energy integration and energy management strategies. It strives to create a sustainable energy ecosystem in Cameroon and beyond, where hybrid energy systems play a pivotal role in mitigating power deficiencies and supporting sustainable development.