Dynamics and control of two-dimensional discrete-time biological model incorporating weak Allee's effect.

Incorporating a weak Allee effect in a two-dimensional biological model in ℜ2, the study delves into the application of bifurcation theory, including center manifold and Ljapunov-Schmidt reduction, normal form theory, and universal unfolding, to analyze nonlinear stability issues across various engineering domains. The focus lies on the qualitative dynamics of a discrete-time system describing the interaction between prey and predator. Unlike its continuous counterpart, the discrete-time model exhibits heightened chaotic behavior. By exploring a biological Mmdel with linear functional prey response, the research elucidates the local asymptotic properties of equilibria. Additionally, employing bifurcation theory and the center manifold theorem, the analysis reveals that, for all α1 (i.e., intrinsic growth rate of prey), ð1˙ (i.e., parameter that scales the terms yn), and m (i.e., Allee effect constant), the model exhibits boundary fixed points A1 and A2, along with the unique positive fixed point A∗, given that the all parameters are positive. Additionally, stability theory is employed to explore the local dynamic characteristics, along with topological classifications, for the fixed points A1, A2, and A∗, considering the impact of the weak Allee effect on prey dynamics. A flip bifurcation is identified for the boundary fixed point A2, and a Neimark-Sacker bifurcation is observed in a small parameter neighborhood around the unique positive fixed point A∗=(mð1˙-1,α1-1-α1mð1˙-1). Furthermore, it implements two chaos control strategies, namely, state feedback and a hybrid approach. The effectiveness of these methods is demonstrated through numerical simulations, providing concrete illustrations of the theoretical findings. The model incorporates essential elements of population dynamics, considering interactions such as predation, competition, and environmental factors, along with a weak Allee effect influencing the prey population.

Evaluating iodine deficiency and goiter in hilly areas of District Poonch Azad Jammu Kashmir.

Background & Objectives: Iodine deficiency is considered as a global challenge, even after decades of efforts to solve the issue. Our objective was to assess the iodine deficiency status and associated prevalence of goiter in population groups (school-age children and women) from rural areas of District Poonch, and to assess the determinants of iodine deficiency in the area. Methods: Cross-sectional study was conducted in District Poonch Azad Jammu and Kashmir from 25 April 2022 to 30 June 2023. A total of 150 goiter patients from different villages of the District Poonch was included. Their urinary iodine concentration and goiter prevalence was assessed. Using palpation techniques, a trained and experienced public health officer assessed the presence of goiters based on WHO/ UNICEF/ICCIDD criteria. Descriptive statistics were computed for continuous variables and presented in frequency and percentage, based on the distributional characteristics of the data. chi-square was used to check association between socio-demographic factors and goiter. It was a HEC Project No.: 20-16988/NRPU/R&D/HEC/2021. Results: Iodine status and associated goiter prevalence was high and 59.3% of them were severely iodine deficient. Within the district, the highest severe iodine deficiency was observed in 81.1% goiter patients of the Rawalakot subdivision, Hajira and Abbaspur subdivisions. Regarding goiter status 40% of the patients were classified with palpable-visible goiter and 56% were characterized with visible but nodular goiter. Conclusion: Study showed that there was a severe iodine deficiency and associated goiter prevalence in the area. Policymakers should take actions for future to overcome iodine deficiency in future.

Electrophoretically deposited Asphaltum punjabianum (Shilajit) coatings on polyvinylalcohol/carboxymethylcellulose hydrogels.

The present study aims to develop Asphaltum punjabianum (namely Shilajit) coated Polyvinyl alcohol (PVA)/Carboxymethyl cellulose (CMC) hydrogels and examine their structural, morphological, degradation, and biological properties. Hydrogels were produced at two different concentrations: 70:30 PVA/CMC and 90:10 PVA/CMC. Following that, Shilajit was applied to the synthesized hydrogels using electrophoretic deposition for a duration of 3 min at 30 V. The scanning electron microscopy images showed that the hydrogel's surface had a regular distribution of irregular Shilajit particles. Fourier transform infrared spectroscopy (FTIR) analysis demonstrated the presence of hydrogen bonding between PVA and CMC hydrogels and Shilajit, indicating the successful deposition of Shilajit on the hydrogel. The hydrogels coated with Shilajit exhibited strong antimicrobial activity, resulting in an inhibition zone measuring 34 mm against Escherichia coli (E. coli) and 41 mm against Staphylococcus aureus (S. aureus). The hydrogels exhibited a cell viability of 80 % with mesenchymal stem cells (MSCs), and the release of collagen II also increased. Furthermore, the PVA/CMC/Shilajit hydrogel exhibited a lower degradation rate compared to the PVA/CMC hydrogel. The results of the swelling, degradation, and drug release studies indicate that the shilajit coating is appropriate for the long-term process of tissue and cartilage regeneration.

Effect of supplementing epinephrine in maturation media on in-vitro developmental competence of cattle and buffalo oocytes.

During in-vitro maturation, the oocyte experiences stressful conditions that likely compromise its development. Epinephrine is a catecholamine that plays a vital role during cellular stress by scavenging free radicals. The hypothesis is that epinephrine addition in maturation media improves the developmental competence of oocytes in cattle and buffalo. The objectives of the experiments were to investigate the effect of epinephrine addition in maturation media on nuclear maturation, developmental competence, and oocyte mRNA abundance of genes related to antioxidants and growth pathways in cattle and buffalo. In experiment 1, cattle oocytes were matured for 24 h in maturation media supplemented with increasing concentrations of epinephrine 0, 0.01, 1.0, and 100 µM. Oocytes were cultured to assess cleavage at 48 h and blastocyst on day 7 of the culture. The cumulus-oocyte complexes (COCs) expansion, nuclear maturation, and oocyte mRNA abundance of genes (SOD1, GPX4, GDF9, CASP9) were evaluated. In experiment 2, buffalo oocytes were matured and assessed for development and mRNA abundance as described for cattle. In addition, the blastomere number was counted in the hatched blastocyst. The data were analyzed using GLIMMIX and MIXED procedures of SAS. Results revealed that the supplementation of epinephrine increased (P ≤ 0.03) the COCs expansion, nuclear maturation, and developmental competence of oocytes in cattle. Interestingly, all the responses were maximized (quadratic effect; P ≤ 0.08) at 1 µM concentrations. The mRNA abundance of genes in cattle oocytes was not affected by the treatment. The experiment in buffalo revealed that epinephrine increased blastocyst formation without affecting COCs expansion, and nuclear maturation. The higher blastocyst was achieved at 0.01 µM concentrations of epinephrine. Interestingly, the addition of epinephrine increased the mRNA abundance of genes related to antioxidant pathways (SOD1, GPX4). Moreover, supplementation of epinephrine increased the blastomere count of the hatched blastocyst in buffalo. In conclusion, epinephrine addition in maturation media benefited oocyte development in cattle and blastocyst yield in buffalo at 1 and 0.01 µM concentrations, respectively. It appears that the addition of epinephrine affected different cellular pathways, COCs expansion, and nuclear maturation in cattle and increased antioxidant genes for buffalo.

Dynamics and control of a plant-herbivore model incorporating Allee's effect.

This research focuses on the interaction between the grape borer and grapevine using a discrete-time plant-herbivore model with Allee's effect. We specifically investigate a model that incorporates a strong predator functional response to better understand the system's qualitative behavior at positive equilibrium points. In the present study, we explore the topological classifications at fixed points, stability analysis, Neimark-Sacker, Transcritical bifurcation and State feedback control in the two-dimensional discrete-time plant-herbivore model. It is proved that for all involved parameters ς1,ϱ1,γ1 and ϒ1, discrete-time plant-herbivore model has boundary and interior fixed points: c1=(0,0), c2=(ς1-1ϱ1,0) and c3=(ϒ1(1-γ1)2γ1-1,γ1(2ς1+ϱ1ϒ1-2)-ϱ1ϒ1+1-ς12γ1-1) respectively. Then by linear stability theory, local dynamics with different topological classifications are investigated at fixed points: c1=(0,0), c2=(ς1-1ϱ1,0) and c3=(ϒ1(1-γ1)2γ1-1,γ1(2ς1+ϱ1ϒ1-2)-ϱ1ϒ1+1-ς12γ1-1). Our investigation uncovers that the boundary equilibrium c2=(ς1-1ϱ1,0) experiences a transcritical bifurcation, whereas the unique positive steady-state c3=(ϒ1(1-γ1)2γ1-1,γ1(2ς1+ϱ1ϒ1-2)-ϱ1ϒ1+1-ς12γ1-1) of the discrete-time plant-herbivore model undergoes a Neimark-Sacker bifurcation. To address the periodic fluctuations in grapevine population density and other unpredictable behaviors observed in the model, we propose implementing state feedback chaos control. To support our theoretical findings, we provide comprehensive numerical simulations, phase portraits, dynamics diagrams, and a graph of the maximum Lyapunov exponent. These visual representations enhance the clarity of our research outcomes and further validate the effectiveness of the chaos control approach.

Iron-Carbon Nanospheres as Promising Material for Magnetic Assisted Adsorption and Separation of Impurities from a Liquid Phase.

The composites containing various iron compounds and highly microporous carbon spheres were produced and investigated for structural and magnetic properties. Iron citrate, nitrate and chloride were used to prepare samples and the obtained products contained iron, iron carbide or magnetite. All the produced samples were characterized by high porosity and good magnetic properties. The coupling of the high porosity of carbon spheres with magnetic properties of iron compounds provides a potential application of the composites to removal of impurities from water, followed by a magnetic separation of the sorbent.

SEINN: A deep learning algorithm for the stochastic epidemic model.

Stochastic modeling predicts various outcomes from stochasticity in the data, parameters and dynamical system. Stochastic models are deemed more appropriate than deterministic models accounting in terms of essential and practical information about a system. The objective of the current investigation is to address the issue above through the development of a novel deep neural network referred to as a stochastic epidemiology-informed neural network. This network learns knowledge about the parameters and dynamics of a stochastic epidemic vaccine model. Our analysis centers on examining the nonlinear incidence rate of the model from the perspective of the combined effects of vaccination and stochasticity. Based on empirical evidence, stochastic models offer a more comprehensive understanding than deterministic models, mainly when we use error metrics. The findings of our study indicate that a decrease in randomness and an increase in vaccination rates are associated with a better prediction of nonlinear incidence rates. Adopting a nonlinear incidence rate enables a more comprehensive representation of the complexities of transmitting diseases. The computational analysis of the proposed method, focusing on sensitivity analysis and overfitting analysis, shows that the proposed method is efficient. Our research aims to guide policymakers on the effects of stochasticity in epidemic models, thereby aiding the development of effective vaccination and mitigation policies. Several case studies have been conducted on nonlinear incidence rates using data from Tennessee, USA.

Data-Driven Deep Learning Neural Networks for Predicting the Number of Individuals Infected by COVID-19 Omicron Variant.

Infectious disease epidemics are challenging for medical and public health practitioners. They require prompt treatment, but it is challenging to recognize and define epidemics in real time. Knowing the prediction of an infectious disease epidemic can evaluate and prevent the disease's impact. Mathematical models of epidemics that work in real time are important tools for preventing disease, and data-driven deep learning enables practical algorithms for identifying parameters in mathematical models. In this paper, the SIR model was reduced to a logistic differential equation involving a constant parameter and a time-dependent function. The time-dependent function leads to constant, rational, and birational models. These models use several constant parameters from the available data to predict the time and number of people reported to be infected with the COVID-19 Omicron variant. Two out of these three models, rational and birational, provide accurate predictions for countries that practice strict mitigation measures, but fail to provide accurate predictions for countries that practice partial mitigation measures. Therefore, we introduce a time-series model based on neural networks to predict the time and number of people reported to be infected with the COVID-19 Omicron variant in a given country that practices both partial and strict mitigation measures. A logistics-informed neural network algorithm was also introduced. This algorithm takes as input the daily and cumulative number of people who are reported to be infected with the COVID-19 Omicron variant in the given country. The algorithm helps determine the analytical solution involving several constant parameters for each model from the available data. The accuracy of these models is demonstrated using error metrics on Omicron variant data for Portugal, Italy, and China. Our findings demonstrate that the constant model could not accurately predict the daily or cumulative infections of the COVID-19 Omicron variant in the observed country because of the long series of existing data of the epidemics. However, the rational and birational models accurately predicted cumulative infections in countries adopting strict mitigation measures, but they fell short in predicting the daily infections. Furthermore, both models performed poorly in countries with partial mitigation measures. Notably, the time-series model stood out for its versatility, effectively predicting both daily and cumulative infections in countries irrespective of the stringency of their mitigation measures.

Bridging Development and Disruption: Comprehensive Insights into Focal Cortical Dysplasia and Epileptic Management.

Focal cortical dysplasia (FCD) is a prominent neurological disorder characterized by disruptions in localized brain cell organization and development. This narrative review delineates the multi-faceted nature of FCD, emphasizing its correlation with drug-resistant epilepsy, predominantly in children and young adults. We explore the historical context of FCD, highlighting its indispensable role in shaping our comprehension of epilepsy and cortical anomalies. The clinical spectrum of FCD is broad, encompassing diverse seizure patterns, cognitive impairments, and associated neuropsychiatric disorders. We underscore the importance of differential diagnosis, with techniques ranging from electroencephalogram (EEG) interpretations to microscopic evaluations, and discuss advanced diagnostic modalities, such as the 3T magnetic resonance imaging (MRI) epilepsy protocols. Therapeutically, while anti-seizure medications are often first-line interventions, surgically refractory cases necessitate more invasive procedures, underscoring the importance of individualized treatment. Furthermore, the review touches upon the prognostic aspects of FCD, highlighting the importance of personalized care regimens, and provides insights into emerging therapeutic avenues, including the potential of the mammalian target of rapamycin (mTOR) pathway. Conclusively, this review accentuates the complex relationship between brain development and epileptogenicity inherent to FCD and underscores the promise of future research in enhancing patient outcomes.

Epigenetics in Neurological and Psychiatric Disorders: A Comprehensive Review of Current Understanding and Future Perspectives.

The burgeoning field of epigenetics offers transformative insights into the complex landscape of neurological and psychiatric disorders. By unraveling the intricate interplay between genetic, epigenetic, environmental, and lifestyle factors, this comprehensive review highlights the multifaceted nature of mental health. The exploration reveals the potential of epigenetic modifications to revolutionize our understanding, diagnosis, treatment, and prevention of these disorders. Emphasizing the importance of multidisciplinary collaborations, large-scale studies, technological advancements, and ethical considerations, the review asserts the promise of epigenetics as a vital tool for personalized medicine, early intervention, and public health strategies. While acknowledging the challenges in a still-emerging field, the review paints an optimistic picture of epigenetics as a groundbreaking approach that can reshape mental healthcare, offering hope for those affected by neurological and psychiatric conditions. The future trajectory of the field relies on interdisciplinary efforts, ethical diligence, innovative technologies, and translating scientific insights into real-world applications, thereby unlocking the vast potential of epigenetics in mental health.

Asymmetrical analysis of economic complexity and economic freedom on environment in South Asia: A NARDL approach.

The environment has become a growing concern for many countries, as pollution and other environmental degradation can harm human health, economic growth, and overall well-being. This paper probes into the asymmetrical implications of economic complexity and freedom on ecological quality in four South Asian countries from 1995 to 2019. Using Nonlinear Autoregressive Distributed Lag methodology approach, our findings indicate that carbon dioxide (CO2) emissions are intensified by economic freedom both in the long and short term, while negative and positive shocks to economic complexity increase CO2 emissions in the long term. However, a negative economic complexity shock increases CO2 emissions, whereas a positive shock has the opposite effect in the short run. Moreover, our results confirm the validity of the environmental Kuznets curve (EKC) hypothesis in the long run. Furthermore, we find that renewable energy usage and the interaction of FDI and renewable energy usage can help reduce environmental damage in both the short and long run. The findings suggest that countries should focus on attracting foreign direct investment that promotes the use of renewable energy. Additionally, policies aimed at encouraging renewable energy use should be implemented. It is important to note that as economic freedom and complexity increase, there is a corresponding increase in CO2 emissions. Therefore, South Asian policy makers are advised to prioritize the reduction in fossil fuels, the promotion of energy-saving technologies and efficient production, and trade that encourages the transition of renewable energy sources to reduce CO2 emissions.

A Novel Variant in VPS13B Underlying Cohen Syndrome.

Pathogenic variants in vacuolar protein sorting 13 homolog B (VPS13B) cause Cohen syndrome (CS), a clinically diverse neurodevelopmental disorder. We used whole exome and Sanger sequencing to identify disease-causing variants in a Pakistani family with intellectual disability, microcephaly, facial dysmorphism, neutropenia, truncal obesity, speech delay, motor delay, and insomnia. We identified a novel homozygous nonsense variant c.8841G > A: p.(W2947∗) in VPS13B (NM_017890.5) which segregated with the disease. Sleep disturbances are commonly seen in neurodevelopmental disorders and can exacerbate medical issues if left untreated. We demonstrate that individuals with Cohen syndrome may also be affected by sleep disturbances. In conclusion, we expand the genetic and phenotypic features of Cohen syndrome in the Pakistani population.

Co-Doped CeO2/Activated C Nanocomposite Functionalized with Ionic Liquid for Colorimetric Biosensing of H2O2 via Peroxidase Mimicking.

Hydrogen peroxide acts as a byproduct of oxidative metabolism, and oxidative stress caused by its excess amount, causes different types of cancer. Thus, fast and cost-friendly analytical methods need to be developed for H2O2. Ionic liquid (IL)-coated cobalt (Co)-doped cerium oxide (CeO2)/activated carbon (C) nanocomposite has been used to assess the peroxidase-like activity for the colorimetric detection of H2O2. Both activated C and IL have a synergistic effect on the electrical conductivity of the nanocomposites to catalyze the oxidation of 3,3',5,5'-tetramethylbenzidine (TMB). The Co-doped CeO2/activated C nanocomposite has been synthesized by the co-precipitation method and characterized by UV-Vis spectrophotometry, FTIR, SEM, EDX, Raman spectroscopy, and XRD. The prepared nanocomposite was functionalized with IL to avoid agglomeration. H2O2 concentration, incubation time, pH, TMB concentration, and quantity of the capped nanocomposite were tuned. The proposed sensing probe gave a limit of detection of 1.3 × 10-8 M, a limit of quantification of 1.4 × 10-8 M, and an R2 of 0.999. The sensor gave a colorimetric response within 2 min at pH 6 at room temperature. The co-existing species did not show any interference during the sensing probe. The proposed sensor showed high sensitivity and selectivity and was used to detect H2O2 in cancer patients' urine samples.

Managing straw and nitrogen fertilizer based on nitrate threshold for balancing nitrogen requirement of maize and nitrate residue.

Optimized straw and nitrogen (N) fertilizer management instrumental in realizing synchronized soil N supply and crop N requirement (Nr), reducing nitrate-N leaching and achieving efficient and cleaner agricultural production systems, especially in the areas with poor soil fertility retention. A three-year field trial during 2019-2021 was conducted in northwest China with different straw incorporation methods (SM) (without straw or biochar (NI), straw incorporation (SI) and straw-derived biochar incorporation (BI)) combined with four N application rates (NR) (0, 225, 300, and 375 kg ha-1). The grain yield, Nr and the critical nitrate threshold in the root zone (0-100 cm soil layer; NAc) after maize harvest were determined to optimize straw and N inputs for maize yield enhancement and nitrate residue control. Then the prediction methods of optimal N rate determined with NAc (TONR) and soil testing were modified for straw or straw-derived biochar incorporated spring maize production in the future. The results showed that grain yield and nitrate residue in the deep soil (100-200 cm soil; NA100-200) after maize harvest increased by N application, grain yield further increased but NA100-200 decreased when combined with SI and BI (P < 0.05). In particular, a significant increase in grain yield, Nr and N recovery efficiency (NRE) under BI was attributed to an increase in soil N supply and N assimilation after the tassel stage (VT) of maize as compared with SI (P < 0.05). The NAc values were determined as 49, 104 and 67 kg ha-1 under NI, SI and BI, respectively for maintaining N supply and preventing leaching into 100-200 cm soil. Compared with the economically optimal N rate (EONR), BI combined with TONR (268 kg N ha-1) reduced the N rate by 22 kg ha-1 per year and NA100-200 by 5.3% and increased NRE by 5.7% to achieve 99.7% maximum yield (14.448 Mg ha-1), which was a sustainable management method of straw and N rate for enhancing spring maize production and controlling soil nitrate leaching.

Mitigation of Salinity Stress in Maize Seedlings by the Application of Vermicompost and Sorghum Water Extracts.

Abiotic stresses are important constraints limiting crop productivity worldwide. Salinity is one of the most devastating environmental factors restraining the production of crops. It is urgently needed to search for environmentally safe and sustainable approaches to mitigate the harmful effects of salinity on plants. Hence, applying vermicompost and low-dose aqueous extract of sorghum delivers a pragmatic solution to ameliorate the detrimental outcomes of salinity on maize seedlings (Zea mays L.). The experiment consisted of three factors, each at different levels, i.e., salinity (control, 6, and 12 dS m-1), vermicompost (control, 5, and 10%), and sorghum water extract (control, 1, and 2%). Higher salt stress negatively influenced the morpho-physiological traits of maize. Nonetheless, applying vermicompost and sorghum water extract at 10% and 2%, respectively, increased tolerance against salinity. The application of 2% sorghum water extract and 10% vermicompost significantly improved morphological characteristics, chlorophyll contents, activities of antioxidant enzymes, leaf and root K+/Na+ ratio, and K+ contents. It decreased Na+ concentration, H2O2, and malondialdehyde contents at higher salinity levels. It can be concluded that soil-applied vermicompost and foliar-applied sorghum water extract mitigates the adverse impacts of salinity by activating the antioxidant defense system, improving chlorophyll contents, and reducing the accumulation of Na+ under salinity.

Data driven time-varying SEIR-LSTM/GRU algorithms to track the spread of COVID-19.

COVID-19 is an infectious disease caused by a newly discovered coronavirus, which has become a worldwide pandemic greatly impacting our daily life and work. A large number of mathematical models, including the susceptible-exposed-infected-removed (SEIR) model and deep learning methods, such as long-short-term-memory (LSTM) and gated recurrent units (GRU)-based methods, have been employed for the analysis and prediction of the COVID-19 outbreak. This paper describes a SEIR-LSTM/GRU algorithm with time-varying parameters that can predict the number of active cases and removed cases in the US. Time-varying reproductive numbers that can illustrate the progress of the epidemic are also produced via this process. The investigation is based on the active cases and total cases data for the USA, as collected from the website "Worldometer". The root mean square error, mean absolute percentage error and r2 score were utilized to assess the model's accuracy.

Synthesis and Characterization of Nanostructured Multi-Layer Cr/SnO2/NiO/Cr Coatings Prepared via E-Beam Evaporation Technique for Metal-Insulator-Insulator-Metal Diodes.

Enhanced non-linearity and asymmetric behavior of the Cr/metal oxide diode is reported, with the addition of two insulator layers of SnO2 and NiO to form the metal-insulator-insulator-metal (MIIM) configuration. Such an MIIM diode shows potential for various applications (rectifiers and electronic equipment) which enable the femtosecond fast intoxication in MIIM diodes. In this work, nanostructured multi-layer Cr/SnO2/NiO/Cr coatings were fabricated via e-beam evaporation with the following thicknesses: 150 nm/20 nm/10 nm/150 nm. Coatings were characterized via Rutherford backscattering (RBS), scanning electron microscopy (SEM), and two-probe conductivity testing. RBS confirmed the layered structure and optimal stoichiometry of the coatings. A non-linear and asymmetric behavior at <1.5 V applied bias with the non-linearity maximum of 2.6 V−1 and the maximum sensitivity of 9.0 V−1 at the DC bias point was observed. The promising performance of the coating is due to two insulating layers which enables resonant tunneling and/or step-tunneling. Based on the properties, the present multi-layer coatings can be employed for MIIM application.

Intrinsic Properties of Multi-Layer TiO2/V2O5/TiO2 Coatings Prepared via E-Beam Evaporation.

Nanocomposite multi-layer TiO2/V2O5/TiO2 thin films were prepared via electron-beam evaporation using high-purity targets (TiO2 and V2O5 purity > 99.9%) at substrate temperatures of 270 °C (TiO2) and 25 °C (V2O5) under a partial pressure of oxygen of 2 × 10−4 mbar to maintain the stoichiometry. Rutherford backscattering spectrometry was used to confirm the layer structure and the optimal stoichiometry of the thin films, with a particle size of 20 to 40 nm. The thin films showed an optical transmittance of ~78% in the visible region and a reflectance of ~90% in the infrared. A decrease in transmittance was observed due to the greater cumulative thickness of the three layers and multiple reflections at the interface of the layers. The optical bandgap of the TiO2 mono-layer was ~3.49 eV, whereas that of the multi-layer TiO2/V2O5/TiO2 reached ~3.51 eV. The increase in the optical bandgap was due to the inter-diffusion of the layers at an elevated substrate temperature during the deposition. The intrinsic, structural, and morphological features of the TiO2/V2O5/TiO2 thin films suggest their efficient use as a solar water heater system.

Performance comparison of structured [Formula: see text] based looptune and LQR for a 4-DOF robotic manipulator.

We explore looptune, a MATLAB-based structured [Formula: see text] synthesis technique in the context of robotics. Position control of a 4 Degree of Freedom (DOF) serial robotic manipulator developed using Simulink is the problem under consideration. Three full state feedback control systems were developed, analyzed and compared for both steady-state and transient performance using the Linear Quadratic Regulator (LQR) and looptune. Initially, a single gain feedback controller was synthesized using LQR. This system was then modified by augmenting the state feedback controller with Proportional Integral (PI) and Integral regulators, thereby creating a second and third control system respectively. In both the second and third control systems, the LQR synthesized gain and additional gains were further tuned using looptune to achieve improvement in performance. The second and third systems were also compared in terms of tracking a time-dependent trajectory. Finally, the LQR and looptune synthesized controllers were tested for robustness by simultaneously increasing the mass of each manipulator link. In comparison to LQR, the second system consisting of Single Input Single Output (SISO) PI controllers and the state feedback matrix succeeded in meeting the control objectives in terms of performance, optimality, trajectory tracking, and robustness. The third system did not improve performance in contrast to LQR, but still showed robustness under mass variation. In conclusion, our results have shown looptune to have a comparatively better performance over LQR thereby highlighting its promising potential for future emerging control system applications.

Thin layer drying kinetics and quality dynamics of persimmon (Diospyros kaki) treated with preservatives and solar dried under different temperatures.

Poor postharvest handling, microbial infestation, and high respiration rate are some the factors are responsible for poor storage life of perishable commodities. Therefore, effective preservation of these commodities is needed to lower the damages and extend shelf life. Preservation is regarded as the action taken to maintain desired properties of a perishable commodity as long as possible. Persimmon (Diospyros kaki) is perishable fruit with high nutritive value; however, has very short shelf-life. Therefore, effective preservation and drying is needed to extend its storage life. Drying temperature and preservatives significantly influence the quality of perishable vegetables and fruits during drying. The current study investigated the effect of different temperatures and preservatives on drying kinetics and organoleptic quality attributes of persimmon. Persimmon fruits were treated with preservatives (25% honey, 25% aloe vera, 2% sodium benzoate, 1% potassium metabisulfite, and 2% citric acid solutions) under different drying temperatures (40, 45, and 50°C). All observed parameters were significantly affected by individual effects of temperatures and preservatives, except ash contents. Similarly, interactive effects were significant for all parameters except total soluble sugars, ash contents, and vitamin C. Generally, fruits treated with citric acid and dried under 50°C had 8.2% moisture loss hour-1, 14.9 drying hours, 0.030 g H2O g-1 hr-1, 1.23° Brix of total soluble solids, 6.71 pH, 1.35% acidity, and 6.3 mg vitamin C. These values were better than the rest of the preservatives and drying temperatures used in the study. Therefore, treating fruits with citric acid and drying at 50°C was found a promising technique to extend storage life of persimmon fruits. It is recommended that persimmon fruits dried at 50°C and preserved in citric acid can be used for longer storage period.