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  Subjects -> SCIENCES: COMPREHENSIVE WORKS (Total: 374 journals)
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Karbala International Journal of Modern Science
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  This is an Open Access Journal Open Access journal
ISSN (Print) 2405-609X - ISSN (Online) 2405-6103
Published by Digital Commons Homepage  [8 journals]
  • Investigations of the Eutectic Formation and Skin Rejuvenation by
           Hyaluronan - Kojic Acid Dipalmitate System

    • Authors: Syed Waqar Hussain Shah et al.
      Abstract: Eutectic phenomenon has been investigated in binary system based on biopolymer hyaluronan (HN) and kojic acid dipalmitate (KAD). Solid-liquid phase diagram showed a significant dependence of melting points on weight fraction of KAD up to KAD < 0.5. A negligible regain to melting temperature of pure KAD occurred later. Simulations of molecular mechanics using a four-unit segment of HN and KAD revealed the interaction between carbonyl of KAD with 4-OH on N-acetylglucosamine unit of oligomer. Infrared vibrational spectroscopy also endorsed the existence of a weakly interacting system. Such behavior was expected due to steric hinderance and rigidity of biopolymer. The thermal decomposition temperature of HN (i.e., 215 °C) was increased to 322 °C in HK50 having HN and KAD in 1:50 w/w. Bioelectric impedance analysis revealed that these green materials could promote skin health in humans.
      PubDate: Thu, 22 Feb 2024 23:02:32 PST
  • Synthesis and Characterization of Renewable Heterogeneous Catalyst ZnO
           Supported Biogenic Silica from Pineapple Leaves Ash for Sustainable
           Biodiesel Conversion

    • Authors: Nadila Pratiwi et al.
      Abstract: This study reports on the first case of the low-cost and environmentally friendly ZnO/SiO2 heterogeneous catalyst from pineapple leaves ash (PLA). Catalyst shows excellent performance in catalyzing the transesterification of waste cooking oil (WCO) with methanol for biodiesel conversion. This study focuses on assessing the influence of Zn content on physicochemical characteristics, using XRD, FTIR, SEM, and N2 adsorption-desorption methods. In addition, three different Zn content levels (20, 25, and 30 %wt) were applied. The results showed that all ZnO/SiO2 samples exhibited characteristics suitable for use as catalyst with an average crystallite size of 31.83-34.15 nm, and a surface area of 88.97 m2/g to 93.41 m2/g. Importantly, the sample shows efficient catalytic activity for conversion of biodiesel from WCO with the largest conversion using a ZnO/SiO2-30 catalyst of 96.17% with the carbon chain of C12-C20. The optimum reaction conditions used 3 g of ZnO/SiO2-30, reaction time 6 hours at 57.5 oC and WCO to methanol ratio of 3:1 with a yield of 98.62%. The resulting catalyst has extraordinary durability up to sixth cycles. It shows that the material has the potential to be a renewable catalyst from sustainable resources. The ZnO/SiO2 catalyst production using PLA for biodiesel production from WCO represents the potential for using agricultural waste into valuable materials in future energy.
      PubDate: Thu, 22 Feb 2024 23:02:29 PST
  • Butterworth filter to reduce reactivity fluctuations

    • Authors: Daniel Suescún-Díaz et al.
      Abstract: In this study, we introduce the calculation of reactivity in nuclear reactors. The proposed method uses the Euler-Maclaurin series to approximate the integral in the inverse equation of point kinetics. The approximation is done with the first three terms, the first term represents the approximation of a zero-order sum, the second term the trapezoidal rule and the third term the first Bernoulli number. These three terms improve the approximation, along with an estimate of the neutron density using the prompt jump approximation. To reduce neutron density fluctuations, a second-order Butterworth filter for the reactivity calculation was implemented, which offers the advantage of minimal delay based on only three data points. Whereas the methods reported in literature that consider noise in the neutron population, it is necessary to consider a much higher number of 225 samples, as in the case of the Savitzky-Golay filter and the first order delay low-pass filter. These filters reduce fluctuations in the calculation of reactivity but with a longer delay. To assess the accuracy of these enhancements a comparison was done for results obtained with different numerical simulations using a filter based on least squares fitting, varying the data window, the time step and fixing a polynomial of order d, using physical parameters for thermal reactors. The results of the numerical simulations indicate that the proposed method can be used to calculate the reactivity with high precision and with a high reduction of fluctuations by applying the Butterworth filter when the noise level increases.
      PubDate: Thu, 15 Feb 2024 04:27:25 PST
  • Machine Learning Model and Molecular Docking for Screening Medicinal
           Plants as HIV-1 Reverse Transcriptase Inhibitors

    • Authors: Muthia Rahayu Iresha et al.
      Abstract: The human immunodeficiency virus type 1 reverse transcriptase (HIV-1 RT) plays a significant role in viral replication and is one of the targets for anti-HIV. However, a mutation in viral strains rapidly developed the resistance of the com-pounds to the protein, reducing the effectiveness of the inhibitors. This work seeks to utilize machine learning-based quantitative structure-activity relationship (QSAR) analysis in combination with molecular docking simulations to forecast the presence of active compounds derived from medicinal plants. Specifically, the objective is to identify com-pounds that have the potential to operate as inhibitors of HIV-1 reverse transcriptase (RT), encompassing both wild-type and mutant variants. It is demonstrated that some substances are no longer suitable as inhibitors due to changes in the HIV-1 RT enzyme. Based on the screening results, four medicinal plants, Melissa officinalis, Punica granatum, Psidium guajava, and Curcuma longa, are worth further investigation. Nevertheless, the findings from the in vitro study suggest that extracts derived from pomegranate rind and guava leaves exhibit significant promise as HIV-1 RT inhibitors.
      PubDate: Mon, 12 Feb 2024 07:27:41 PST
  • Multi-Agent System For Portfolio Profit Optimization For Future Stock

    • Authors: Usha Devi et al.
      Abstract: Stock trading highly contributes to the economic growth of the country. The stock trading objective is to earn profits with buy/sell/hold decisions on the set of stocks in the portfolio. The portfolio optimization problem is finding the decision sequence that leads to higher profit and lower risk. Portfolio optimization is challenging due to complex price history patterns and an uncertain environment. Incorrect decisions in stock trading lead to massive losses. The proposed Multi-Agent System for Portfolio Profit Optimization (MASPPO) aims to optimize trading profit and reduce risk with accurate predictions. The proposed model integrates the Fuzzy c-means with the Deep reinforcement learning model. The experimental datasets contain stock price history with 14,562 records. The MASPPO model maximizes the portfolio profit, intending to reduce the error. The proposed model, MASPPO, showed a Root mean squared error of 9.48 and a Mean absolute error of 2.63 and outpaced the recent models in the literature. The results proved that MASPPO maximizes the portfolio profit and is reliable.
      PubDate: Sun, 04 Feb 2024 07:37:19 PST
  • YOLOv8-CAB: Improved YOLOv8 for Real-time object detection

    • Authors: Moahaimen Talib et al.
      Abstract: This study presents a groundbreaking approach to enhance the accuracy of the YOLOv8 model in object detection, focusing mainly on addressing the limitations of detecting objects in varied image types, particularly for small objects. The proposed strategy of this work incorporates the Context Attention Block (CAB) to effectively locate and identify small objects in images. Furthermore, the proposed work improves the feature extraction capability without increasing model complexity by increasing the thickness of the Coarse-to-Fine(C2F) block. In addition, Spatial Attention (SA) has been modified to accelerate detection performance. The enhanced YOLOv8 model (Namely YOLOv8-CAB) strongly emphasizes the performance of detecting smaller objects by leveraging the CAB block to exploit multi-scale feature maps and iterative feedback, thereby optimizing object detection mechanisms. As a result, the innovative design facilitates superior feature extraction, “especially the weak features,” contextual information preservation, and efficient feature fusion. Rigorous testing on the Common Objects in Context (COCO) dataset was performed to demonstrate the efficacy of the proposed technique. It is resulting in a remarkable improvement over standard YOLO models. The YOLOv8-CAB model achieved a mean average precision of 97% of detecting rate, indicating a 1% increase compared to conventional models. This study highlights the capabilities of our improved YOLOv8 method in detecting objects, representing a breakthrough that sets the stage for advancements in real-time object detection techniques.
      PubDate: Wed, 24 Jan 2024 06:22:50 PST
  • Genome-wide profiling of novel conserved Zea mays microRNAs along with
           their key biological, molecular and cellular targets and validation using
           an RT-PCR platform

    • Authors: Abdul Baqi et al.
      Abstract: MicroRNAs (miRNAs), which are typically non-coding RNAs that start off as endogenous molecules and regulate post-transcriptional levels of gene expression by mRNA degradation or translational repression. They are 18–26 nucleotides long, evolutionarily conserved and essential for predicting novel miRNAs in a variety of plants. Maize (Zea mays) is a significant food and forage crop in the globe today. In the present study, many maize miRNAs have been found to be associated with both plant development and responses to stress. In this study, 66 unique conserved maize miRNAs from 65 different miRNA families were predicted using several genomics-based methods and then verified using RT-PCR after the preparation of randomly chosen primers. Accordingly, using the psRNA Target method, 10294 distinct protein targets of these recently expected miRNAs have been attained. These targets contain 55 specific GO terms that have important biological, cellular and molecular targets. Likewise, the newly found maize miRNAs like zma-miR6224a is engaged in the reproduction process. Several novel maize miRNAs, such as zma-miR11339, 2922 and 6253 function as regulators and target genes that influence ageing, chemotaxis and karyogamy respectively. The newly predicted maize miRNAs including zma-miR1439 and 9774 are intended to inhibit the activities of voltage-gated ion channels and carbon-sulfur lyase respectively. Therefore, the results of the novel maize miRNAs target a variety of important genes that aid in controlling the environment for maize to improve crop output.
      PubDate: Fri, 19 Jan 2024 10:13:19 PST
  • The role of Cu (0-0.03) and Zn (0.02) substitution on the structural,
           optical and magnetic properties of MgO nanoparticles

    • Authors: S. Naseem Shah et al.
      Abstract: The co-precipitation method was employed to prepared Cu (0-0.03) and Zn (0.02) dual doped MgO nanoparticles. The secondary phases of CuO and Cu2O were observed along with the cubical phase of MgO. The doping induced effect was noticed for the crystallite size variations (14.39-19.89 nm). The morphological transformation from spherical to rice-like shape were observed. The estimated values of optical bandgap (4.66-4.45 eV) were well correlated with the crystallite size and dopant concentrations. The ferromagnetic ordering was observed at room temperature and the enchantment in the coercivity (142.27 Oe) with Zn (0.02) doping was noticed. Such type of ferromagnetism at room temperature in the samples can be used for future multibit spintronic and data storage devices.
      PubDate: Fri, 19 Jan 2024 10:13:15 PST
  • Future Frame Prediction using Generative Adversarial Networks

    • Authors: Nishtha Jatana et al.
      Abstract: The ability of a human to anticipate what is going to happen in the near future given the current situation helps in making intelligent decisions about how to react in that situation. In this paper, we have developed multiple Deep Neural Network models, intending to generate the next frame in a sequence given previous frames. In recent years, Generative Adversarial Networks (GAN) have shown promising results in the field of image generation. Hence, in this paper, we aim to create and compare two Generative Adversarial Models created for Future Frame Prediction by combining GANs with convolutional neural networks, Long Short-Term Memory Networks, and Convolutional LSTM networks. Based on the state-of-the-art, we have tried to improve the results of our model both visually and numerically. The paper is summarized by comparing the outputs of our two models and then finally comparing them with previously developed models for this purpose and providing future scope for research. Both the models presented in this work perform well based on certain aspects of future frame prediction. The results presented in this paper are crucial in the field of future prediction, in fields such as robotics, autonomous driving, and autonomous agent development.
      PubDate: Fri, 12 Jan 2024 10:28:56 PST
  • Treatment of Oil Refinery Wastewater Polluted by Heavy Metal Ions via
           Adsorption Technique using Non-Valuable Media: Cadmium Ions and Buckthorn
           Leaves as a Study Case

    • Authors: Salem Jawad Alhamd et al.
      Abstract: This study focuses on the removal of cadmium ions generated by oil refinery wastewater, employing an agricultural by-product. Buckthorn leaves, sourced from Baghdad and Diyala provinces, underwent preparation, including washing, drying, crushing, and sieving before being utilized in experiments. Batch experiments were conducted using simulated solutions to assess the impact of six key adsorption design parameters: pH, cadmium concentration, agitation speed, contact time, adsorbent dosage, and temperature. The highest adsorption efficiency, reaching 94.4367%, was directly correlated with contact time, adsorbent dosage, pH value, and agitation speed, and inversely related to other variables. Morphological studies on the treated adsorbent, indicated structural changes during the adsorption process, manifested as shifts in FTIR and XRD peaks, and observed pore alterations through SEM analysis. The BET test revealed a surface area of 36 m²/g, with less than 68% utilization through adsorption. Adsorption behavior was analyzed in three parts: isothermal analysis, exhibiting a strong fit to the Langmuir model; kinetic study, favoring the pseudo-second-order model; and thermodynamic characterization as exothermic, of low entropy, and spontaneous. The study also investigated the regeneration of spent adsorbent, highlighting physical activation as the more effective method, providing four reuse cycles compared to chemical activation's two. The paper extended its investigation to real oil refinery wastewater, assessing the ability of the adsorbent to compete with other contaminants. Buckthorn leaves exhibited an efficiency of 50-75% in remediating real wastewater, similar to simulated solutions. Consequently, this research proposes an environmentally sound, cost-effective means of sustainably repurposing agricultural waste to achieve zero-residue levels.
      PubDate: Fri, 12 Jan 2024 10:28:52 PST
  • Electrochemical Degradation of Methylene Blue with Seawater and Pb/PbO2
           Electrodes from Battery Waste

    • Authors: Gunawan Gunawan et al.
      Abstract: Electrochemical degradation of methylene blue (MB) dye with seawater electrolyte using lead and lead oxide (Pb/PbO2) electrodes from waste batteries has been successfully conducted. Characterization of battery waste, the effectiveness of dye degradation, sodium hypochlorite (NaOCl) concentration, dissolved oxygen (DO) level, reaction mechanism, the effect of time variation (15, 30, 45, and 60 minutes), and voltage variation (0, 1, 2, 3, 4, and 5 volts) were observed. Characterization showed results by the characteristics of Pb and PbO2 confirmed by X-ray diffractometer (XRD) result-ing in 2θ peaks of Pb at 31.36, 36.38, 52.26, 62.36, 65.38º and 2θ (β-PbO2) at 25.4, 32.0, 36.2, 49.1, 52.2, 59.0, 62.5, 66.9º. The electrode had a hollow granular morphology with lead (Pb) and Oxygen (O) composition that matched the standards of scanning electron microscope-energy dispersive X-ray spectrometer (SEM-EDX) and X-ray fluorescence (XRF). Electrode effectiveness on dye degradation measured using UV-Vis spectrophotometer, iodometric titration, and dissolved oxygen (DO) meter showed that dye degradation goes along with increasing NaOCl concentration, DO, elec-trolysis time, and voltage with optimal results obtained at a potential of 5 volts for 60 minutes can degrade MB by 92.68% or about 4.61 mg/L. Atomic absorption analysis confirmed the stability of the electrodes and the release of ions (Pb2+) that were much lower than the safe standard values. Degradation of dyes occurs through demethylation, hydrox-ylation, and ozonation reactions due to electron attack from hypochlorite oxidizer (OCl-), hydroxyl groups (∙OH), and ozone oxygen radicals (O3, ∙O) from the seawater electrolysis process with Pb/PbO2 electrode media against reactive groups and ring binding on MB. These results show the potential of the Pb/PbO2 electrode system from battery waste and seawater as a hypochlorite (OCl-) electrolyte generator to overcome dye waste in water.
      PubDate: Fri, 10 Nov 2023 23:22:37 PST
  • Green synthesized silver nanoparticles-based sensing for monitoring water
           pollution: an updated review

    • Authors: Muhamad Allan Serunting et al.
      Abstract: Water is a basic human need and has been heavily contaminated. Therefore, it becomes a concern to remove the pollutant and monitor its quality. The removal methods include precipitation, filtration, adsorption, and photodegradation. Meanwhile, the monitoring can be done by measuring and analyzing the contaminant using spectrophotometry and chromatography. Nevertheless, those methods usually need a complicated preparation, and are expensive. Thus, a simple method is necessary to overcome these drawbacks by developing a sensor. In recent years, the sensor performance has been enhanced by using nanomaterials, such as silver nanoparticles (AgNPs). AgNPs can be synthesized using plant extracts through a green synthesis approach. Extracts of leaves, stems, roots, and fruits from various plants have been successfully used as reducing agents in synthesis process and stabilizing the AgNPs. More importantly, the published articles also reported that the green synthesized nanoparticles can be applied as the sensor component for monitoring various water pollutants (both organic and inorganic). We have critically reviewed the potential of plant extract as a reducing agent of silver ions using different green synthetic methods. The characterizations of silver nanoparticles include the initial characterization (UV-visible spectrophotometry) and the advanced characterizations (FTIR, XRD, SEM, EDS, TEM, and DLS). This review also provides information about applications of AgNPs in sensor for monitoring water pollution. Therefore, this review article delivers a point of view on the silver nanoparticle development in recent decades, and it can be a reference for further study, especially for sensors.
      PubDate: Fri, 03 Nov 2023 04:52:54 PDT
  • Synthesis and characterization of zirconium oxide nanoparticles using Z.

    • Authors: M. J. Tuama et al.
      Abstract: Abstract The dramatic rise in bacterial infections and increased resistance to conventional antibiotics has led to the exploration of biologically derived nanomaterials to counteract bacterial activity. Nanotechnology, which deals with materials at the atomic or molecular level, is a promising way to achieve this goal. Zirconium oxide nanoparticles (ZrO2NPs) have shown strong antibacterial effects due to the increased surface-to-volume ratio at the nanoscale. This study focused on the production of ZrO2NPs in an environmentally friendly manner, which included extracts from Zingiber officinale (ginger), where G-ZrO2NPs were produced, and Syzygium aromaticum (clove), which produced S-ZrO2NPs. Various techniques were used, such as X-ray diffraction (XRD) for structural examination, while for morphological properties, field emission scanning electron microscopy (FESEM) was used and energy dispersive X-ray (EDX) form composition. Differential reflection spectroscopy (DRS) was employed to determine the energy gap of prepared samples. In contrast, the knowledge of the organic chemical bonds and their association with zirconium ions were distinguished by Fourier transform infrared (FTIR), and the zeta potential was used to identify the surface charge of the nanoparticles. G-ZrO2NPs showed monoclinic and tetragonal phases, with crystallite sizes of approximately 14.28 nm and 16.80 nm, respectively, whereas tetragonal structure was revealed for S-ZrO2NPs with a crystallite size of about 13.69 nm. Spherical nanoparticle morphology with some agglomeration was shown in G-ZrO2NPs. The prepared G-ZrO2NPs and S-ZrO2NPs samples have direct energy gaps of 4.98 eV and 4.84 eV, respectively. Zeta potential measurements indicated -15.3 mV for G-ZrO2NPs and -25.8 mV for S-ZrO2NPs.This research confirms the potential of manufactured and environmentally friendly ZrO2NPs as effective agents against bacterial pathogens. Notably, gram-negative and gram-positive bacteria experienced significant effects, with S-ZrO2NPs showing more effective bactericidal activity than G-ZrO2NPs.
      PubDate: Fri, 03 Nov 2023 04:52:51 PDT
  • Modification of Chitosan Using Glycidyl Methacrylate-grafted Cellulose
           (GMAgCell/ Chi) for Methylene Blue Adsorption

    • Authors: Haya Fathana et al.
      Abstract: In this study, a glycidyl methacrylate-grafted cellulose/chitosan (GMA-g-Cell/Chi) film was successfully prepared and characterized. GMA-g-Cell was obtained from the grafting process of cellulose derived from sugarcane bagasse using glycidyl methacrylate (GMA). The cellulose grafting process was obtained using 20% GMA for 4 hours at 60oC. The percentage of grafting (PG) and grafting efficiency (GE) values for these parameters were 516 and 60.28%, respectively. Chitosan was modified with GMA-g-Cell and has higher adsorption capacity and tensile strength than chitosan. The adsorption kinetics tend to follow the pseudo-first-order adsorption kinetics model, with Qe and k1 being 7 mg/g and 0.067 g/mg. minute. The maximum adsorption capacity (Qmax) as a consequence was 182.37 mg/g. Thermodynamic data showed that the methylene blue adsorption process occurred physically and spontaneously.
      PubDate: Thu, 26 Oct 2023 08:12:58 PDT
  • Fortifying IoT against crimpling cyber-attacks: a systematic review

    • Authors: Usman Tariq et al.
      Abstract: The rapid growth and increasing demand for Internet of Things (IoT) devices in our everyday lives create exciting opportunities for human involvement, data integration, and seamless automation. This fully interconnected ecosystem considerably impacts crucial aspects of our lives, such as transportation, healthcare, energy management, and urban infrastructure. However, alongside the immense benefits, the widespread adoption of IoT also brings a complex web of security threats that can influence society, policy, and infrastructure conditions. IoT devices are particularly vulnerable to security violations, and industrial routines face potentially damaging vulnerabilities. To ensure a trustworthy and robust security framework, it is crucial to tackle the diverse challenges involved. This survey paper aims to aid researchers by categorizing attacks and vulnerabilities based on their targets. It provides a detailed analysis of attack methods and proposes effective countermeasures for each attack category. The paper also highlights case studies of critical IoT applications, showcasing security solutions. In addition to traditional cryptographic approaches, this work explores emerging technologies like Quantum Crypto Physical Unclonable Functions (QC-PUFs) and blockchain, discussing their pros and cons in securing IoT environments. The research identifies and examines attacks, vulnerabilities, and security measures and endeavors to impact the overall understanding of IoT security. The insights and findings presented here will serve as a valuable resource for researchers, guiding the development of resilient security mechanisms to ensure the trustworthy and safe operation of IoT ecosystems.
      PubDate: Sat, 14 Oct 2023 02:07:28 PDT
  • Smart Service Function Chain System for Dynamic Traffic Steering using
           Reinforcement Learning (CHRL)

    • Authors: Ahmed Nadhum et al.
      Abstract: The rapid development of the Internet and network services coupled with the growth of communication infrastructure necessitates the employment of intelligent systems. The complexity of the network is heightened by these systems, as they offer diverse services contingent on traffic type, user needs, and security considerations. In this context, a service function chain offers a toolkit to facilitate the management of intricate network systems. However, various traffic types require dynamic adaptation in the sets of function chains. The problem of optimizing the order of service functions in the chain must be solved using the proposed approach, along with balancing the network load and enhancement of net-work security. In addition, the delay issue must be resolved by selecting an optimal path to establish a connection. The proposed system provides a set of intelligent function chains that can adaptively optimize the network performance while considering dynamic traffic demands using SDNs and Q-learning. The proposed system can significantly improve the overall efficiency, scalability, and adaptability of the network while also providing a better quality of service to end-users. Compared with traditional software-defined networks, the simulation results of the proposed system showed an improvement in throughput of up to 76%, accompanied by a reduction in the level of link congestion. The results also exhibit an improvement of up to 54% compared with state-of-the-art load balancing. In particular, in terms of the FTP performance, our proposed system outperforms existing approaches by up to 20%.
      PubDate: Sat, 14 Oct 2023 02:07:25 PDT
  • New Quantum Genetic Algorithm Based on Constrained Quantum Optimization

    • Authors: Mohammed R. Almasaoodi et al.
      Abstract: In the past decades, many quantum algorithms have been developed. The main obstacle that prevents the widespread implementation of these algorithms is the small size of the available quantum computer in terms of qubits. Blind Quantum Computation (BQC) holds the promise of handling this issue by delegating computation to quantum remote devices. Here, we introduce a novel Constrained Quantum Genetic Algorithm (CQGA) that selects the optimum extreme (minimum or maximum) value of a constrained goal function (or a vast unsorted database) with very low computational complexity. Since the convergence speed to the optimal solution for the Constrained Classical Genetic Algorithm (CCGA) is highly dependent on the level of quality of the initially selected potential solutions, the CCGA's heuristic initialization stage is replaced by a quantum one. This is achieved by exploiting the strengths of the Constrained Quantum Optimization Algorithm (CQOA) and the BQC. The proposed CQGA is applied as an embedded computational infrastructure for the uplink multi-cell massive MIMO system. The algorithm maximizes the energy efficiency (EE) of the uplink massive MIMO while considering different users target bit rate classes. Simulation results show that the suggested CQGA maximizes energy efficiency through careful computation of the optimal transmit power for each active user using fewer computational steps than the CCGA. We demonstrated that when the overall transmit power set or the overall number of active users increases, the CQGA keeps executing a smaller number of generation steps compared to the CCGA. For instance, if we consider a scenario where the overall number of active users () is set to 18, the CQGA finds the optimal solution with a smaller number of generation steps equal to 6, while the CCGA takes a larger number of generation steps, reaching 65.
      PubDate: Sat, 14 Oct 2023 02:07:22 PDT
  • Recent advances in CRISPR/Cas9-assisted gene therapy

    • Authors: Apeksha Srivastava et al.
      Abstract: CRISPR/Cas9 (Clustered regularly interspaced short palindromic repeats) is an exponentially growing tool with wide-spread applications in therapeutics like gene modifications that focus on altering the hereditary material to repair or eliminate any defective gene-causing diseases like cancer, AIDS (Acquired immunodeficiency syndrome), etc. It also includes the identification of the target sequence with the help of sgRNA followed by the substitution of a malfunction-ing gene with a normal version. It offers high efficiency, specificity, and post-gene-editing efficacy, but have also some off-target impressions, and immunogenic effects. The contribution of CRISPR/Cas9 has already been proved primarily in in-vitro studies using animal germ cell lines but translation in in-vivo models is still not much supported due to ethi-cal considerations. The recent advances include studies and clinical trials focusing on the treatment of various diseases of genetic origin. For instance, CRISPR gene knock-in technique was applied for in-vivo Leber Congenital Amaurosis 10 treatment, where CRISPR components were delivered via sub-retinal injection to correct the mutation in CE9290. The current paper recapitulates the capability of CRISPR/Cas9 in in-vivo gene therapy for various disorders like cancer, AIDS, sickle cell disease and the most recent COVID-19. The insights presented herein are poised to contribute signifi-cantly to the advancement of the field, fostering a deeper understanding of CRISPR/Cas9 technology and accelerating its clinical transition. Ultimately, this review paper serves as a valuable resource for researchers, clinicians, and policy-makers invested in the continued evolution of gene therapy and responsible utilization of CRISPR/Cas9 for human welfare
      PubDate: Tue, 10 Oct 2023 05:53:08 PDT
  • Optical, Structural, and Electrochemical Properties of P3HT:Y6 Photoactive
           Layer for Organic Photovoltaics

    • Authors: Hassan Tarikhum B. et al.
      Abstract: Recently, organic photovoltaics (OPV) have emerged as a promising technology for environmentally friendly energy production. Achieving high performance in OPV requires the discovery of novel compounds. This article aims to study the optical, structural, electrochemical, and electrical properties of a P3HT:Y6 blend. The UV-visible spectra of the blend provided insight into how composition affects the optoelectronic properties of OPV devices. The optimised P3HT:Y6 ratio (0.6:1) resulted in the largest redshift and the highest absorption intensity. Raman and X-ray diffraction tests showed low aggregation and low crystallinity of the polymer P3HT as Y6 content increased. In the OPV devices, the best performance was recorded at a 0.6:1 ratio, with an effi-ciency of 2.84%.
      PubDate: Tue, 10 Oct 2023 05:53:05 PDT
  • Estimation of water quality of Dikhu River of Nagaland through a
           combination of water quality index and principal component analysis

    • Authors: Lanuyanger Longchar et al.
      Abstract: Monitoring the physico-chemical attributes of water is essential for determining water quality. The present study explores the effect of various anthropogenic factors on the quality of Dikhu River, Nagaland. Water samples from three sampling stations were compared against three standards: Indian Council of Medical Research, Bureau of Indian Standards, and World Health Organization. Although all parameters were within the permissible limits, the Water Quality Index categorized all sites during the rainy season as "poor quality", highlighting anthropogenic impacts. A principal component analysis created a Minimum Data Set (MDS) explaining 100%, 93.27%, and 96.26% of the total variance for the three sites. The MDS creation will enable sustainable, rapid, and cost-effective monitoring of the Dikhu River.
      PubDate: Tue, 10 Oct 2023 05:53:02 PDT
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School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
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