A Novel Approach of Polyvinyl Alcohol/Acrylic Acid Based Hydrogels for Controlled Delivery of Diclofenac Sodium.

AIM AND OBJECTIVE: The aim of this study was to prepare polyvinyl alcohol/acrylic acid (PVA/AA) hydrogels for the controlled release of diclofenac sodium and to develop PVA/AA hydrogels as controlled release carriers to overcome not only the side effects of diclofenac sodium but also sustain its release for an extended period. BACKGROUND: Diclofenac sodium is employed for relieving pain and fever. The half-life of diclofenac sodium is very short (1-2 h). Hence, multiple intakes of diclofenac sodium are required to maintain a constant pharmacological action. Multiple GI adverse effects are produced as a result of diclofenac sodium intake. METHOD: A free radical polymerization technique was used for crosslinking PVA with AA in the presence of APS. EGDMA was used as a cross-linker. FTIR and XRD confirmed the preparation and loading of the drug by prepared hydrogels. An increase in the thermal stability of PVA was shown by TGA and DSC analysis. Surface morphology was investigated by SEM. Similarly, water penetration and drug loading were demonstrated by porosity and drug loading studies. The pH-sensitive nature of PVA/AA hydrogels was investigated at different pH values by swelling and drug release studies. RESULTS: The development and drug loading of PVA/AA hydrogels were confirmed by FTIR and XRD analysis. TGA and DSC indicated high thermal stability of prepared hydrogels as compared to unreacted PVA. SEM indicated a hard and compact network of developed hydrogels. The swelling and drug release studies indicated maximum swelling and drug release at high pH as compared to low pH values, indicating the pH-sensitive nature of prepared hydrogels. Moreover, we demonstrated that drug release was sustained for a prolonged time in a controlled pattern by prepared hydrogels by comparing the drug release of the developed hydrogels with the commercial product Cataflam. CONCLUSION: The results indicated that prepared PVA/AA hydrogels can be used as an alternative approach for the controlled delivery of diclofenac sodium.

Neuroprotective potential of Mentha piperita extract prevents motor dysfunctions in mouse model of Parkinson's disease through anti-oxidant capacities.

Parkinson's disease (PD) is the second most common neurodegenerative disease in the world. Neurodegeneration of the substantia nigra (SN) and diminished release of dopamine are prominent causes of this progressive disease. The current study aims to evaluate the protective potential of ethanolic extract of Mentha piperita (EthMP) against rotenone-mediated PD features, dopaminergic neuronal degeneration, oxidative stress and neuronal survival in a mouse model. Swiss albino male mice were assigned to five groups: control (2.5% DMSO vehicle), PD (rotenone 2.5 mg/kg), EthMP and rotenone (200mg/kg and 2.5mg/kg, respectively), EthMP (200 mg/kg), and Sinemet, reference treatment containing levodopa and carbidopa (20 mg/kg and rotenone 2.5mg/kg). Behavioral tests for motor functional deficit analysis were performed. Anti-oxidant capacity was estimated using standard antioxidant markers. Histopathology of the mid-brain for neurodegeneration estimation was performed. HPLC based dopamine level analysis and modulation of gene expression using quantitative real-time polymerase chain reaction was performed for the selected genes. EthMP administration significantly prevented the rotenone-mediated motor dysfunctions compared to PD group as assessed through open field, beam walk, pole climb down, stepping, tail suspension, and stride length tests. EthMP administration modulated the lipid peroxidation (LPO), reduced glutathione (GSH), and superoxide dismutase (SOD) levels, as well as glutathione-s-transferase (GST) and catalase (CAT) activities in mouse brain. EthMP extract prevented neurodegeneration in the SN of mice and partially maintained dopamine levels. The expression of genes related to dopamine, anti-oxidant potential and synapses were modulated in M. piperita (MP) extract treated mice brains. Current data suggest therapeutic capacities of MP extract and neuroprotective capacities, possibly through antioxidant capacities. Therefore, it may have potential clinical applications for PD management.

Insights into measles virus: Serological surveillance and molecular characterization.

BACKGROUND: Measles has been a significant public health concern in Pakistan, especially in the Khyber Pakhtunkhwa (KPK) province, where sporadic and silent epidemics continue to challenge existing control measures. This study aimed to estimate the prevalence and investigate the molecular epidemiology of the measles virus (MeV) in KPK and explore the vaccination status among the suspected individuals. METHODS: A cross-sectional study was conducted between February and October 2021. A total of 336 suspected measles cases from the study population were analyzed for IgM antibodies using Enzyme-Linked Immunosorbent Assay (ELISA). Throat swabs were randomly collected from a subset of positive cases for molecular analysis. Phylogenetic analysis of MeV isolates was performed using the neighbor-joining method. The vaccination status of individuals was also recorded. RESULTS: Among the suspected participants, 61.0% (205/336) were ELISA positive for IgM antibodies, with a higher prevalence in males (64.17%) compared to females (57.04%). The majority of cases (36.0%) were observed in infants and toddlers, consistent with previous reports. The majority of IgM-positive cases (71.7%) had not received any dose of measles vaccine, highlighting gaps in vaccine coverage and the need for improved immunization programs. Genetic analysis revealed that all MeV isolates belonged to the B3 genotype, with minor genetic variations from previously reported variants in the region. CONCLUSION: This study provides valuable insights into the genetic epidemiology of the MeV in KPK, Pakistan. The high incidence of measles infection among unvaccinated individuals highlights the urgency of raising awareness about vaccine importance and strengthening routine immunization programs.

A Hybrid Approach for Energy Consumption and Improvement in Sensor Network Lifespan in Wireless Sensor Networks.

In this paper, we propose an improved clustering algorithm for wireless sensor networks (WSNs) that aims to increase network lifetime and efficiency. We introduce an enhanced fuzzy spider monkey optimization technique and a hidden Markov model-based clustering algorithm for selecting cluster heads. Our approach considers factors such as network cluster head energy, cluster head density, and cluster head position. We also enhance the energy-efficient routing strategy for connecting cluster heads to the base station. Additionally, we introduce a polling control method to improve network performance while maintaining energy efficiency during steady transmission periods. Simulation results demonstrate a 1.2% improvement in network performance using our proposed model.

Artificial-Intelligence-Enhanced On-the-Fly Simulation of Nonlinear Time-Resolved Spectra.

Time-resolved spectroscopy is an important tool for unraveling the minute details of structural changes in molecules of biological and technological significance. The nonlinear femtosecond signals detected for such systems must be interpreted, but it is a challenging task for which theoretical simulations are often indispensable. Accurate simulations of transient absorption or two-dimensional electronic spectra are, however, computationally very expensive, prohibiting the wider adoption of existing first-principles methods. Here, we report an artificial-intelligence-enhanced protocol to drastically reduce the computational cost of simulating nonlinear time-resolved electronic spectra, which makes such simulations affordable for polyatomic molecules of increasing size. The protocol is based on the doorway-window approach for the on-the-fly surface-hopping simulations. We show its applicability for the prototypical molecule of pyrazine for which it produces spectra with high precision with respect to ab initio reference while cutting the computational cost by at least 95% compared to pure first-principles simulations.

Isolation, characterization, and cloning of thermostable pullulanase from Geobacillus stearothermophilus ADM-11.

This study aimed to identify thermo-stable pullulanase-producing bacteria in soil samples of potato fields and food-producing companies. Pullulan agar medium was used to screen 17 bacterial strains, which were incubated at 65 °C. The isolate with the maximum activity (375U/ml) was selected and recognized as Geobacillus stearothermophilus ADM-11 by morphological, biochemical characterization, and 16S rRNA gene sequencing. The pullulanase production required optimum pH of 7 and temperature of 75 °C, respectively. The electrophoresis of purified pullulanase on SDS-polyacrylamide gel revealed 83 kDa of a molecular weight that is active at 70 °C and pH 7.0. It was also stable at 90 °C but its activity was decreased by 10 % at 100 °C. The action of pullulanase was increased and stabilized by Ca+2 among the metal ions. Beta and gamma-cyclodextrins inhibited enzyme activity while ethylenediaminetetraacetate (EDTA) and phenylmethylsulfonyl fluoride (PMSF) have no significant effect on pullulanase activity. A full-length pullulanase gene was amplified from G. stearothermophilus ADM-11 using genomic DNA 2.1 kb of PCR product which was then purified and ligated in the cloning vector pTZ57R using the TA cloning technique. Colony PCR confirmed cloning on the positive clones after the pullulanase gene had been ligated and subjected to restriction digestion. It revealed 74 % similarity with the reported pullulanase gene from Geobacillus sp. 44C. The thermostability of pullulanase and its ability to degrade raw pullulan may therefore have wide-scale applications in starch processing, the detergent business, and new biotechnological applications.

Molecular identification and genotyping of hepatitis E virus from Southern Punjab, Pakistan.

Hepatitis E is a global health concern. Hepatitis E virus (HEV) infection is endemic in Pakistan. HEV has four genotypes: HEV-1 through HEV-4. The genotypes HEV-1 and HEV-2 are associated with infection in humans, especially in countries with poor sanitation. The genotypes HEV-3 and HEV-4 are zoonotic and human infection takes place by consuming undercooked meat or being in contact with animals. The present study was designed to ascertain the presence of HEV in the Southern Punjab region of Pakistan. First, blood samples (n = 50) were collected from patients suspected of infection with the hepatitis E virus from the Multan District. The serum was separated and the samples were initially screened using an HEV IgM-ELISA. Second, the ELISA-positive samples were subjected to PCR and were genetically characterized. For PCR, the RNA extraction and complementary DNA synthesis were done using commercial kits. The HEV ORF2 (Open Reading Frame-2, capsid protein) was amplified using nested PCR targeting a 348 bp segment. The PCR amplicons were sequenced and an evolutionary tree was constructed using MEGA X software. A protein model was built employing the SWISS Model after protein translation using ExPASy online tool. The positivity rate of anti-HEV antibodies in serum samples was found as 56% (28/50). All Pakistani HEV showed homology with genotype 1 and shared common evolutionary origin and ancestry with HEV isolates of genotype 1 of London (MH504163), France (MN401238), and Japan (LC314158). Sequence analysis of motif regions assessment and protein structure revealed that the sequences had a similarity with the reference sequence. These data suggest that genotype 1 of HEV is circulating in Pakistan. This finding could be used for the diagnosis and control of HEV in the specific geographic region focusing on its prevalent genotype.

MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows.

Machine learning (ML) is increasingly becoming a common tool in computational chemistry. At the same time, the rapid development of ML methods requires a flexible software framework for designing custom workflows. MLatom 3 is a program package designed to leverage the power of ML to enhance typical computational chemistry simulations and to create complex workflows. This open-source package provides plenty of choice to the users who can run simulations with the command-line options, input files, or with scripts using MLatom as a Python package, both on their computers and on the online XACS cloud computing service at XACScloud.com. Computational chemists can calculate energies and thermochemical properties, optimize geometries, run molecular and quantum dynamics, and simulate (ro)vibrational, one-photon UV/vis absorption, and two-photon absorption spectra with ML, quantum mechanical, and combined models. The users can choose from an extensive library of methods containing pretrained ML models and quantum mechanical approximations such as AIQM1 approaching coupled-cluster accuracy. The developers can build their own models using various ML algorithms. The great flexibility of MLatom is largely due to the extensive use of the interfaces to many state-of-the-art software packages and libraries.

Hydrogeochemistry and prediction of arsenic contamination in groundwater of Vehari, Pakistan: comparison of artificial neural network, random forest and logistic regression models.

Arsenic contamination in the groundwater occurs in various parts of the world due to anthropogenic and natural sources, adversely affecting human health and ecosystems. The current study intends to examine the groundwater hydrogeochemistry containing elevated arsenic (As), predict As levels in groundwater, and determine the aptness of groundwater for drinking in the Vehari district, Pakistan. Four hundred groundwater samples from the study region were collected for physiochemical analysis. As levels in groundwater samples ranged from 0.1 to 52 µg/L, with an average of 11.64 µg/L, (43.5%), groundwater samples exceeded the WHO 2022 recommended limit of 10 µg/L for drinking purposes. Ion-exchange processes and the adsorption of ions significantly impacted the concentration of As. The HCO3- and Na+ are the dominant ions in the study area, and the water types of samples were CaHCO3, mixed CaMgCl, and CaCl, demonstrating that rock-water contact significantly impacts hydrochemical behavior. The geochemical modeling indicated negative saturation indices with calcium carbonate and other salt minerals, encompassing aragonite, calcite, dolomite, and halite. The dissolution mechanism suggested that these minerals might have implications for the mobilization of As in groundwater. A combination of human-induced and natural sources of contamination was unveiled through principal component analysis (PCA). Artificial neural networks (ANN), random forest (RF), and logistic regression (LR) were used to predict As in the groundwater. The data have been divided into two parts for statistical analysis: 20% for testing and 80% for training. The most significant input variables for As prediction was determined using Chi-squared analysis. The receiver operating characteristic area under the curve and confusion matrix were used to evaluate the models; the RF, ANN, and LR accuracies were 0.89, 0.85, and 0.76. The permutation feature and mean decrease in impurity determine ten parameters that influence groundwater arsenic in the study region, including F-, Fe2+, K+, Mg2+, Ca2+, Cl-, SO42-, NO3-, HCO3-, and Na+. The present study shows RF is the best model for predicting groundwater As contamination in the research area. The water quality index showed that 161 samples represent poor water, and 121 samples are unsuitable for drinking. Establishing effective strategies and regulatory measures is imperative in Vehari to ensure the sustainability of groundwater resources.

Machine learning framework for precise localization of bleached corals using bag-of-hybrid visual feature classification.

Corals are sessile invertebrates living underwater in colorful structures known as reefs. Unfortunately, coral's temperature sensitivity is causing color bleaching, which hosts organisms that are crucial and consequently affect marine pharmacognosy. To address this problem, many researchers are developing cures and treatment procedures to restore bleached corals. However, before the cure, the researchers need to precisely localize the bleached corals in the Great Barrier Reef. The researchers have developed various visual classification frameworks to localize bleached corals. However, the performance of those techniques degrades with variations in illumination, orientation, scale, and view angle. In this paper, we develop highly noise-robust and invariant robust localization using bag-of-hybrid visual features (RL-BoHVF) for bleached corals by employing the AlexNet DNN and ColorTexture handcrafted by raw features. It is observed that the overall dimension is reduced by using the bag-of-feature method while achieving a classification accuracy of 96.20% on the balanced dataset collected from the Great Barrier Reef of Australia. Furthermore, the localization performance of the proposed model was evaluated on 342 images, which include both train and test segments. The model achieved superior performance compared to other standalone and hybrid DNN and handcrafted models reported in the literature.

Genome wide identification, structural characterization and phylogenetic analysis of High-Affinity potassium (HAK) ion transporters in common bean (Phaseolus vulgaris L.).

BACKGROUND: High-Affinity Potassium ions represent one of the most important and large group of potassium transporters. Although HAK genes have been studied in a variety of plant species, yet, remain unexplored in common bean. RESULTS: In the current study, 20 HAK genes were identified in common bean genome. Super-family "K_trans" domain was found in all PvHAK genes. Signals for localization of PvHAK proteins were detected in cell membrane. Fifty three HAKs genes, across diverse plant species, were divided into 5 groups based on sequential homology. Twelve pairs of orthologs genes were found in various plant species. PvHAKs genes were distributed unequally on 7 chromosomes with maximum number (7) mapped on chromosome 2 while only 1 PvHAK found on each chromosome 1, 4, and 6. Tandem gene duplication was witnessed in 2 paralog pairs while 1 pair exhibited segmental gene duplication. Five groups were made in PvHAK gene family based on Phylogeny. Maximum PvHAKs (10) were detected in Group-V while group-II composed of only 1 PvHAK gene. Variation was witnessed in number and size of motifs, and structure of PvHAKs associated with different groups. Light and hormone responsive elements contributed 57 and 24% share, respectively, to cis regulatory elements. qRT-PCR based results revealed significant increase in expression of all 4 PvHAK genes under low-potassium stress. CONCLUSION: The current study provides valuable information for further functional characterization and uncovering the molecular mechanism associated with Potassium transportation in plants.

Elucidating growth and biochemical characteristics of rice seedlings under stress from chromium VI salt and nanoparticles.

Plants are usually provoked by a variety of heavy metal (HM) stressors that have adverse effects on their growth and other biochemical characterizations. Among the HMs, chromium has been considered the most toxic for both plants and animals. The present study was conducted to compare the phytotoxic effects of increasing chromium (VI) salt and nanoparticles (NPs) concentrations on various growth indexes of rice (Oryza sativa L. var. swat 1) seedlings grown in a hydroponic system. The 7-day rice seedlings were exposed to Cr (VI) salt and NPs hydroponic suspensions which were adjusted to the concentration of 0, 50, 100, 150, 200 and 250 mg/L. Both the Cr (VI) salt and NPs with lower concentrations (up to 100mg/L) exerted minimum inhibitory effects on the growth performance of rice seedlings. However, a significant decrease in shoot and root length and their fresh and dry weight was recorded at higher doses of Cr (VI) salt (200 mg/L) and NPs (250 mg/L). The stress induced by Cr (VI) salt has drastically affected the roots, whereas, Cr (VI) NPs significantly affected the shoot tissues. Photosynthetic pigments decreased significantly in a dose-dependent manner, and the reduction was more pronounced in rice seedlings exposed to Cr (VI) NPs compared to Cr (VI) salt. Cr (VI) NPs enhanced the membrane permeability in shoots and roots as compared to that of Cr (VI) salt, which resulted in higher concentration of reactive oxygen species (ROS) and increased lipid peroxidation. The activities of antioxidant enzymes superoxide dismutase (SOD), peroxidase (POD), catalase (CAT) and ascorbate peroxidase (APX) increased significantly in shoot/root tissue following exposure to higher doses of Cr (VI) salt (200 mg/L) and NPs stress (250 mg/L), while minor changes in CAT and APX activities were observed in root and shoot tissues after exposure to higher concentration of Cr (VI) NP. Furthermore, the increasing concentrations of Cr (VI) NPs increased the length of stomatal guard cells. Conclusively, Cr (VI) salt and NPs in higher concentrations have higher potential to damage the growth and induce oxidative stress in rice plants.

Effect of probiotics on hematological parameters of male and female Wistar rats.

In the present study, the effect of probiotics on the hematology of Wistar rats was examined. Locally isolated Lactobacillus plantarum MZ707748 (Pro 1), L. plantarum MZ710117 (Pro 2), Weisella confusa MZ727611 (Pro 3), and L. plantarum MZ735961 (Pro 4) were used. One strain of probiotic, L. acidophilus-14 (Pro 5), was purchased commercially. Different groups were designed as G1, G2, G3, G4, and 5, G5/PC consisting only pro 5 and NC & 0 day were untreated. Different groups have different probiotics like G1 containing Pro 1 and Pro 2, G2 comprising Pro 3 and Pro 4, G3 containing Pro 2, Pro 3 and Pro 5, G4 having Pro 1-5, and G5 containing Pro 5. A complete count of blood, serum chemistry, fecal analysis, and histopathological examination of the thymus and liver were done. Statistical differences were seen in the complete blood count parameters (p < 0.05). No difference was observed in AST, ALT, bilirubin, albumin, IL-6, and IgA (p > 0.05) except for TP, creatinine, and globulin (p < 0.05). Fecal strains of probiotic groups were antibiotic-resistant. In males, Lactobacillus helveticus OQ152020, Enterococcus lactis OQ1519891, E. faecium OQ152017, L. gasseri OQ152017, and E. lactis OQ152019 were isolated from positive control, G1, G2, G3, and G4 respectively. In females, Enterococcus sp. OP800231, Limosilactobacillus fermentum OQ151985, E. lactis OP800267, L. plantarum OP800244, and E. faecium OQ151988 were isolated from positive control, G1, G2, G3 and G4, respectively. It was concluded that all probiotic strains were safe to use and had beneficial effects on the hematology of Wistar rats.

Four-Dimensional-Spacetime Atomistic Artificial Intelligence Models.

We demonstrate that AI can learn atomistic systems in the four-dimensional (4D) spacetime. For this, we introduce the 4D-spacetime GICnet model, which for the given initial conditions (nuclear positions and velocities at time zero) can predict nuclear positions and velocities as a continuous function of time up to the distant future. Such models of molecules can be unrolled in the time dimension to yield long-time high-resolution molecular dynamics trajectories with high efficiency and accuracy. 4D-spacetime models can make predictions for different times in any order and do not need a stepwise evaluation of forces and integration of the equations of motions at discretized time steps, which is a major advance over traditional, cost-inefficient molecular dynamics. These models can be used to speed up dynamics, simulate vibrational spectra, and obtain deeper insight into nuclear motions, as we demonstrate for a series of organic molecules.

The impact of economic growth, tourism, natural resources, technological innovation on carbon dioxide emission: evidence from BRICS countries.

The main objective of this manuscript was to investigate the relationships among economic development, tourism, the use of natural resources, technical advancement, and carbon dioxide emissions in the BRICS group of nations. Data from the panel was gathered from 1995 to 2018. Modern methodology tools including the CS-ARDL tests, Westerlund cointegration tests, and panel data unit root tests have been used in this study. Results of the models show that all the variables were transformed to the first difference to make it stationary. The Westerlund model test results suggest that dependent and independent variables have robust cointegration. Results of the CS-ARDL models reveal that all the variables signed, and significance are aligned with the economic theory. It indicates that except for tourism, the rest of the variables like technical innovation, natural resources, and economic growth have positive and significant effects on carbon dioxide emissions both in the short and long runs. Additionally, a 1% rise in economic growth, technical innovation, and natural resources over the long term would raise carbon dioxide emissions in the BRICS economies by 1.79%, 0.15%, and 0.10%, respectively. However, a 1% increase in tourism would result in a 0.39% decrease in carbon dioxide emissions among the nations in the panel data set. Therefore, the promotion of sustainable tourism and advancement in technological innovation is highly important in these countries, so the high impact of environmental degradation pressure may reduce to some extent. An in-addition comprehensive set of policies should be made on encouraging low-carbon transportation, promoting sustainable tourism certification, boosting local produce, reducing waste management, and provide education and awareness campaigns to tourists.

Genome wide and evolutionary analysis of heat shock protein 70 proteins in tomato and their role in response to heat and drought stress.

Heat shock protein 70 (HSP70) proteins play a crucial role in mitigating the detrimental effects of abiotic stresses in plants. In the present study, 21 full length non-redundant SlHSP70 genes were detected and characterized in tomato (Solanum lycopersicum L.). The SlHSP70 genes were classified into four groups based on phylogenetic analysis. Similarities were observed in gene features and motif structures of SlHSP70s belonging to the same group. SlHSP70 genes were unevenly and unequally mapped on 11 chromosomes. Segmental and tandem duplication are the main events that have contributed to the expansion of the SlHSP70 genes. A large number of groups and sub-groups were generated during comparative analysis of HSP70 genes in multiple plant species including tomato. These findings indicated a common ancestor which created diverse sub-groups prior to a mono-dicot split. The selection pressure on specific codons was identified through a maximum-likelihood approach and we found some important coding sites in the coding region of all groups. Diversifying positive selection was indirectly associated with evolutionary changes in SlHSP70 proteins and suggests that gene evolution modulated the tomato domestication event. In addition, expression analysis using RNA-seq revealed that 21 SlHSP70 genes were differentially expressed in response to drought and heat stress. SlHSP70-5 was down-regulated by heat treatment and up-regulated by drought stress. Furthermore, the expression of some of the duplicate genes was partially redundant, while others showed functional diversity. Our results indicate the diverse role of HSP70 gene family in S. lycopersicum under drought and heat stress conditions and open the gate for further investigation of HSP70 gene family functions, especially under drought and heat stress.

Does Globalization Cause Greenhouse Gas Emissions in Pakistan? A Promise to Enlighten the Value of Environmental Quality.

Global environmental issues such as environmental degradation, climate change, and global warming have posed a threat to the global economy, including Pakistan. The primary source of these problems is greenhouse gas emissions. These emissions are the result of human activity. The objective of the study was to investigate the symmetric and asymmetric relationship between globalization and greenhouse gas emissions in Pakistan. The ARDL modern econometric techniques of the time series model were used. Firstly, the stationarity test favors the use of the ARDL model in this study. The BDS test result confirmed that the ARDL model has a non-linearity issue. As a result, the ARDL approach was used to test both the symmetric and asymmetric effect. The results of the asymmetric ARDL model are more robust and reliable than those of the symmetric ARDL model. According to the results of the symmetric ARDL, economic, social, and political globalization have a positive relationship with greenhouse gas emissions in both the short and long run. Furthermore, the long-run results of the asymmetric ARDL model show that positive and negative shocks of economic and political globalization have positive and negative shock effects on greenhouse gas emissions. In the long run, however, the positive shock of social globalization has a negative relationship with greenhouse gas emissions. According to the results of impulse response functions, economic globalization has a significantly more relationship with greenhouse gas emissions than social and political globalization. A policy should be developed that allows only the positive effects of globalization while prohibiting the negative effects of globalization.

One-Shot Trajectory Learning of Open Quantum Systems Dynamics.

Nonadiabatic quantum dynamics is important for understanding light-harvesting processes, but its propagation with traditional methods can be rather expensive. Here we present a one-shot trajectory learning approach that allows us to directly make an ultrafast prediction of the entire trajectory of the reduced density matrix for a new set of such simulation parameters as temperature and reorganization energy. The whole 10-ps-long propagation takes 70 ms as we demonstrate on the comparatively large quantum system, the Fenna-Matthews-Olsen (FMO) complex. Our approach also significantly reduces time and memory requirements for training.

Improvement for tasks allocation system in VM for cloud datacenter using modified bat algorithm.

Since its inception, cloud computing has greatly transformed our lives by connecting the entire world through shared computational resources over the internet. The COVID-19 pandemic has also disrupted the traditional learning and businesses and led us towards an era of cloud-based activities. Virtual machine is one of the main elements of virtualization in cloud computing that represents physical server into the virtual machine. The utilizations of these VM's are important to achieved effective task scheduling mechanism in cloud environment. This paper focuses on improvment of the task distribution system in VM for cloud computing using load balancing technique. For that reason modification took place at Bat algorithm fitness function value this section used in load balancer section. When algorithm iteration are complete then time to distribute the task among different VM therefore in this section of algorithm was modified. The second modification took place at the search process of Bat at dimension section. The proposed algorithm is known as modified Bat algorithm. Four parameter are used to check the performance of the system which are throughput, makespan, degree of imbalance and processing time. The proposed algorithm provides efficient result as compaire to other standard technique. Hence the proposed algorithm improved cloud data center accuracy and efficiency.

Predicting the future of excitation energy transfer in light-harvesting complex with artificial intelligence-based quantum dynamics.

Exploring excitation energy transfer (EET) in light-harvesting complexes (LHCs) is essential for understanding the natural processes and design of highly-efficient photovoltaic devices. LHCs are open systems, where quantum effects may play a crucial role for almost perfect utilization of solar energy. Simulation of energy transfer with inclusion of quantum effects can be done within the framework of dissipative quantum dynamics (QD), which are computationally expensive. Thus, artificial intelligence (AI) offers itself as a tool for reducing the computational cost. Here we suggest AI-QD approach using AI to directly predict QD as a function of time and other parameters such as temperature, reorganization energy, etc., completely circumventing the need of recursive step-wise dynamics propagation in contrast to the traditional QD and alternative, recursive AI-based QD approaches. Our trajectory-learning AI-QD approach is able to predict the correct asymptotic behavior of QD at infinite time. We demonstrate AI-QD on seven-sites Fenna-Matthews-Olson (FMO) complex.