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.

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.

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.

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.

SEIR model with unreported infected population and dynamic parameters for the spread of COVID-19.

Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that can be transmitted through human interaction. In this paper, we present a Piecewise Susceptible-Exposed-Infectious-Unreported-Removed model for infectious diseases and discuss qualitatively and quantitatively. The parameters are explored by mathematical and statistical methods. Numerical simulations of these models are performed on COVID-19 US data and Python is used in the visualization of results. Outbreak factor is generated by piecewise model to explore the future trend of the US pandemic. Several error metrics are given to discuss the accuracy of the models. The main achievement of this paper is to propose the piecewise model and find the relationship between spread of pandemic and mitigation measures to control it by observing the results of numerical simulations. Performance analysis of piecewise model is presented based on COVID-19 data obtained by 'worldmeter'.

Integrated phosphorus nutrient sources improve wheat yield and phosphorus use efficiency under sub humid conditions.

Majority of Pakistani soils are deficient in phosphorus. Phosphorus is usually applied in form of synthetic fertilizer. However integrated use of P from synthetic and organic sources can be more profitable and beneficial on sustainable basis. Field trials were conducted at research farm University of Poonch, Rawalakot, AJK, Pakistan for two consecutive years to check the comparative effects of synthetic fertilizer and organic manures applied alone and in combinations on the phosphorus use efficiency (PUE), wheat yield and yield components. Shafaq-06 cultivar of wheat was used as test cultivar. Ten treatments were included: (I) Control (P0) without application of fertilizer; (II) SSP @ 60 kg/ha (P60SSP); (III) SSP @ 90 kg/ha (P90SSP); (IV) SSP @ 120 kg/ha (P120SSP); (V) PM @60 kg/ha (P60PM); (VI) PM @90 kg/ha (P90PM); (VII) PM @120 kg/ha (P120PM); (VIII) SSP @30 kg/ha + PM @30 kg/ha (P30SSP+30PM); (IX) SSP @45 kg/ha + PM @45 kg/ha (P45SSP+45PM); (X) SSP @60 kg/ha + PM @60 kg/ha (P60SSP+60PM) which were laid out under the Randomized Complete Block Design. Significantly higher values for yield of grain (2397 kg/ha) was recorded with PM + SSP @ 60 kg P2O5 ha-1 each. Likewise, FPUE, PIR of wheat and AFPUE was quite higher with combined use of PM and SSP i.e. P60SSP+60PM treatment. Additionally, increase in PUE, wheat yield and yield components associated with combined treated plot would help to minimize the use of high cost synthetic mineral fertilizers and represents an environmentally and agronomically sound management strategy.

Sociodemographic and Clinical Profiles of Patients with Irritable Bowel Syndrome: A Cross-sectional, Multi-centric, Epidemiological Study in India.

AIMS: Irritable bowel syndrome (IBS) is a functional gastrointestinal disorder characterized by impaired gut-brain interaction. Considering the paucity of evidence in the Indian setting, the current study was conducted to determine the sociodemographics, clinical profiles, management practices, and patients' perception among newly diagnosed patients with IBS. METHODS: This was a cross-sectional, single-visit, observational, non-interventional, epidemiological study conducted across 12 centres. The primary objective was evaluation of sociodemographic and clinical profiles. The key secondary objective was assessment of gastrointestinal symptom severity including evaluation of anxiety and depression using the hospital anxiety and depression scale (HADS) scores. Knowledge, attitude, and practices (KAP) were evaluated as an exploratory objective. RESULTS: Out of 300 enrolled patients, 120 (40%) were aged 31-45 years (mean age: 38.55±12.45 years), and 204 were men (68%). Overall, 40% of patients belonged to the upper-middle-class, with a Kuppuswamy score of 16-25. Most patients (91%) did not work in night shifts. Only 13% of patients performed more than recommended physical activity. Stress and food were the leading triggers for IBS (29%). Abdominal pain and diarrhoea as cardinal symptoms were reported by 43.3% and 33.0% patients, respectively. Borderline abnormal anxiety and depression were reported by 21.3% and 26.7% of patients, respectively. KAP assessment revealed that 56.0% of patients had poor knowledge, 26.3% had moderate knowledge, and 17.7% had good knowledge about IBS; nevertheless, 43% of patients maintained high levels of precaution towards managing symptoms. CONCLUSION: Given the limited knowledge about IBS in India among newly diagnosed patients, strategies to enhance awareness about the condition are warranted.

Stink bug Agonoscelis spp. (Heteroptera: Pentatomidae) - An emerging threat for seed production in alfalfa crop (Medicago sativa L.) and their successful management.

Fodder crops play an important role in sustainable agriculture as they provide feed for animals, which is ultimately converted to human food. Alfalfa is one of the most important fodder crops having high nutritive value for livestock. However, seed production of alfalfa crop is seriously affected by several factors and the highest reduction in seed yield is caused by stink bug infestation. The current study evaluated different insecticides to control stink bugs during 2016-17. The efficacy of ten insecticides, i.e., acephate, dimethoate, malathion, chlorpyriphos, bifenthrin, lambdacyhalothrin, deltamethrin, acetamiprid, imidacloprid and carbosulfan was tested on Agonoscelis spp. (Heteroptera Pentatomidae). The mortality of stink bug was recorded at one, three, five, seven, ten and fifteen days after insecticide application. Similarly, the population of pollinators was recorded before and one, three and five days after the application of insecticides. It was observed that acetamiprid (81.14%) and acephate (80.65%) caused the highest mortality of stink bug and proved most effective. Insecticides application decreased the pollinators' population one day after spray; however, it was rehabilitated three days after insecticide application. Insecticide application increased seed yield from 28.05 kg/acre (during last four year without chemical control) to 116 kg/acre in 2016-17 (with chemical control). It is concluded that acetamiprid and acephate can be successfully used in integrated management program of increasing alfalfa seed production.

Interference of horse purslane (Trianthema portulacastrum L.) and other weeds affect yield of autumn planted maize (Zea mays L.).

To assess comparative losses of Trianthema portulacastrum (HP) relative to other weeds, the experiment was set during consecutive summer seasons 2018 and 2019 at the Research Farm MNS-University of Agriculture, Multan, Pakistan. Experiment consisted three replications which were laid out under randomized complete block design. Experiment consisted of ten treatments viz: weeds free (whole season), HP free till 20 Days after emergence (DAE), HP free till 40 DAE, HP free till 60 DAE, all weeds free 20 DAE, all weeds free 40 DAE, all weeds free 60 DAE, weedy check (all weeds), weedy check except HP and weedy check containing only HP. During 2018 in all weeds weedy check, maximum HP relative density (33.33%) was observed while in 2019, plot where weeds were controlled from growing till 20 DAE showed (80%) relative density at 30 DAE. HP maximum frequency (66.67%, 77.78%) and relative frequency (66%, 100%) was recorded at 45 DAE in plots where HP was kept controlled till 20 DAE and all weeds kept controlled till 20 DAE, respectively. Maximum number of grains per cob (738, 700.68), 1000 grain weight (306.66, 271.51 g) and grain yield (6150, 8015 kg hec-1) were recorded in plots which were kept all weed free till 60 DAE. As the competition period of weeds increased over 40 DAE, it substantially reduced yield of maize. Keeping the plots HP free till 40 DAE in the maize fields with HP as the major dominating weed, likely increase in maize grain yield is up to 30% compared to the fields where HP left un attended throughout the growing season. However, if maize field is infested with a mix of weeds with more than one dominating weeds including HP, compared to weedy situation the whole season, 30% higher grain yield can be obtained if all weeds are kept controlled till 40 DAE. Hence it can be concluded that whether the farmers face heavy HP infestation only or the mix of weeds as dominating weeds, in either case farmer should control weeds within first 40 days in maize field for better grain yield.