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The depth and width of drip infiltration play a critical role in designing effective irrigation strategies. However, existing models primarily focus on continuous irrigation and fail to predict wetting patterns under intermittent drip irrigation. This study developed an infiltration model to estimate soil moisture depth and width under intermittent drip irrigation and identified strategies that enhance effective water storage. Indoor soil box simulations were conducted, with continuous drip irrigation as the control. Results showed that intermittent irrigation increased infiltration width and reduced depth, maximizing water storage efficiency. We recommend adopting an intermittent irrigation system with 1.5 h of irrigation followed by a 0.5 h interval, repeated four times. This system increased effective water storage by up to 16.23% compared to continuous irrigation. The proposed method is suitable for sandy loam farmland in southern Xinjiang and can significantly improve water use efficiency in arid regions. [ABSTRACT FROM AUTHOR]

In the past two decades, there have been three coronavirus outbreaks that have caused significant economic and health crises. Biologists predict that more coronaviruses may emerge in the near future. Therefore, it is crucial to develop preventive vaccines that can effectively combat multiple coronaviruses. In this study, we employed computational approaches to analyze genetically related coronaviruses, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants, focusing on the spike glycoprotein as a potential vaccine candidate. By predicting common epitopes, we identified the top epitopes and combined them to create a multi-epitope candidate vaccine. The overall quality of the candidate vaccine was validated through in silico analyses, confirming its antigenicity, immunogenicity, and stability. In silico docking and simulation studies suggested a stable interaction between the multi-epitope candidate vaccine and human toll-like receptor 2 (TLR2). In silico codon optimization and cloning were used to further explore the successful expression of the designed candidate vaccine in a prokaryotic expression system. Based on computational analysis, the designed candidate vaccine was found to be stable and non-allergenic in the human body. The efficiency of the multi-epitope vaccine in triggering effective cellular and humoral immune responses was assessed through immune stimulation, demonstrating that the designed candidate vaccine can elicit specific immune responses against multiple coronaviruses. Therefore, it holds promise as a potential candidate vaccine against existing and future coronaviruses. [ABSTRACT FROM AUTHOR]

Simple Summary: In this study, we designed a multi-epitope vaccine candidate for M. hyorhinis. This is the first study using conserved regions of seven members of Vlps and has shown an effective approach toward vaccine development. The proposed vaccine candidate is highly stable and soluble. It elicits an impressive immune response and can be regarded as a hypothetical vaccine candidate in the future. Further laboratory tests and animal inoculation are required to validate vaccine efficacy and safety to combat swine infections. Mycoplasma hyorhinis (M. hyorhinis) is responsible for infections in the swine population. Such infections are usually cured by using antimicrobials and lead to develop resistance. Until now, there has been no effective vaccine to eradicate the disease. This study used conserved domains found in seven members of the variable lipoprotein (VlpA-G) family in order to design a multi-epitope candidate vaccine (MEV) against M. hyorhinis. The immunoinformatics approach was followed to predict epitopes, and a vaccine construct consisting of an adjuvant, two B cell epitopes, two HTL epitopes, and one CTL epitope was designed. The suitability of the vaccine construct was identified by its non-allergen, non-toxic, and antigenic nature. A molecular dynamic simulation was executed to assess the stability of the TLR2 docked structure. An immune simulation showed a high immune response toward the antigen. The protein sequence was reverse-translated, and codons were optimized to gain a high expression level in E. coli. The proposed vaccine construct may be a candidate for a multi-epitope vaccine. Experimental validation is required in future to test the safety and efficacy of the hypothetical candidate vaccine. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has affected billions of lives and is expected to impose a significant burden on the economy worldwide. Vaccination is the only way to prevent the infection. However, convincing people to get themselves vaccinated is challenging in developing countries such as Pakistan. Therefore, a cross-sectional questionnaire-based study was conducted (n = 982 participants) all over Pakistan to evaluate the perception, knowledge, attitude, and acceptance of the general public towards the SARS-CoV-2 vaccine, in general, and a booster dose of SARS-CoV-2, in particular. The highest number of participants were from the province of Punjab (84.5%), followed by Islamabad (3.8%), Sindh (3.7%), Khyber Pakhtunkhwa (2.7%), Baluchistan (2.6%), Gilgit Baltistan (1.4%), and Azad Jammu and Kashmir (1.4%). A total of 915 participants were vaccinated against COVID-19, out of which 62.2% received one booster dose, followed by double booster doses (25.5%) and single vaccine shots (12.3%). The highest number of vaccinated participants were from Punjab (85.8%), followed by Islamabad (3.9%), Sindh (2.8%); Khyber Pakhtunkhwa (2.6%); Baluchistan (2.3%); Gilgit-Baltistan (1.3%); and Azad, Jammu, and Kashmir (1.2%). Among the vaccinated individuals, 71.4% were unemployed, 27.4% were employed (653), and 1.2% were retired from service. However, no significant association was observed among genders and educational levels in regard to acceptance of the booster vaccine. The outcomes of the study revealed that the increased acceptance of booster doses of the SARS-CoV-2 vaccines among the public was associated with the intent of personal and family protection. Moreover, individuals with low socioeconomic status and pregnant females showed the least acceptance towards the vaccine inoculation. The study also revealed a decline trend of accepting SARS-CoV-2 vaccine among children. [ABSTRACT FROM AUTHOR]

The pandemic outbreak of human coronavirus is a global health concern that affects people of all ages and genders, but there is currently still no effective, approved and potential drug against human coronavirus, as many other coronavirus vaccines have serious side effects while the development of small antiviral inhibitors has gained tremendous attention. For this research, HE was used as a therapeutic target, as the spike protein displays a high binding affinity for both host ACE2 and viral HE glycoprotein. Molecular docking, pharmacophore modelling and virtual screening of 38,000 natural compounds were employed to find out the best natural inhibitor against human coronaviruses with more efficiency and fewer side effects and further evaluated via MD simulation, PCA, DCCR and MMGBSA. The lead compound 'Calceolarioside B' was identified on the basis of pharmacophoric features which depict favorable binding (ΔGbind −37.6799 kcal/mol) with the HE(5N11) receptor that describes positive correlation movements in active site residues with better stability, a robust H-bond network, compactness and reliable ADMET properties. The Fraxinus sieboldiana Blume plant containing the Calceolarioside B compound could be used as a potential inhibitor that shows a higher efficacy and potency with fewer side effects. This research work will aid investigators in the testing and identification of chemicals that are effective and useful against human coronavirus. [ABSTRACT FROM AUTHOR]

The live storage of Pakistan's major reservoirs, such as the Tarbela reservoir, has decreased in recent decades due to the sedimentation load from the Upper Indus Basin, located in High Mountain Asia. The government of Khyber Pakhtunkhwa took the initiative in 2014 and introduced the Billion Tree Afforestation Project (BTAP). They planted one billion trees by August 2017, mostly in hilly areas. In 2018, the Government of Pakistan also launched a project of 10 billion trees in five years. We assessed the effect of different land-use and land-cover (LULC) scenarios on the water yield and sediment load in the Tarbela reservoir of Pakistan. The soil and water assessment tool (SWAT) model was used to predict the impacts of the LULC changes on the water yield and sediment load under three distinct scenarios: before plantation (2013), after planting one billion trees (2017), and after planting ten billion trees (2025). The model calibration and validation were performed from 1984 to 2000 and 2001 to 2010, respectively, using the SUFI2 algorithm in SWAT-CUP at the Bisham Qila gauging station. The statistical evaluation parameters showed a strong relationship between observed and simulated streamflows: calibration (R2 = 0.85, PBIAS = 11.2%, NSE = 0.84) and validation (R2 = 0.88, PBIAS = 10.5%, NSE = 0.86). The validation results for the sediment load were satisfactory, indicating reliable model performance and validity accuracy (R2 = 0.88, PBIAS = −19.92%, NSE = 0.86). Under the LULC change scenarios, the water yield's absolute mean annual values decreased from 54 mm to 45 mm for the first and second scenarios, while the third scenario had an estimated 35 mm mean annual water yield in the Tarbela reservoir. The sediment load results for the second scenario (2017) showed a 12% reduction in the sediment flow in the Tarbela reservoir after 1 billion trees were planted. In the third scenario (2025), following the planting of 10 billion trees, among which 3 billion were in the Tarbela basin, the sediment load was predicted to decrease by 22%. The overall results will help to inform the water managers and policymakers ahead of time for the best management and planning for the sustainable use of the water reservoirs and watershed management. [ABSTRACT FROM AUTHOR]

A key part of interpreting, visualizing, and monitoring the surface conditions of remote-sensing images is enhancing the quality of low-light images. It aims to produce higher contrast, noise-suppressed, and better quality images from the low-light version. Recently, Retinex theory-based enhancement methods have gained a lot of attention because of their robustness. In this study, Retinex-based low-light enhancement methods are compared to other state-of-the-art low-light enhancement methods to determine their generalization ability and computational costs. Different commonly used test datasets covering different content and lighting conditions are used to compare the robustness of Retinex-based methods and other low-light enhancement techniques. Different evaluation metrics are used to compare the results, and an average ranking system is suggested to rank the enhancement methods. [ABSTRACT FROM AUTHOR]

The amount of surface soil moisture (SSM) is a crucial ecohydrological natural resource that regulates important land surface processes. It affects critical land–atmospheric phenomena, including the division of energy and water (infiltration, runoff, and evaporation), that impacts the effectiveness of agricultural output (sensible and latent heat fluxes and surface air temperature). Despite its significance, there are several difficulties in making precise measurements, monitoring, and interpreting SSM at high spatial and temporal resolutions. The current study critically reviews the methods and procedures for calculating SSM and the variables influencing measurement accuracy and applicability under different fields, climates, and operational conditions. For laboratory and field measurements, this study divides SSM estimate strategies into (i) direct and (ii) indirect procedures. The accuracy and applicability of a technique depends on the environment and the resources at hand. Comparative research is geographically restricted, although precise and economical—direct measuring techniques like the gravimetric method are time-consuming and destructive. In contrast, indirect methods are more expensive and do not produce measurements at the spatial scale but produce precise data on a temporal scale. While measuring SSM across more significant regions, ground-penetrating radar and remote sensing methods are susceptible to errors caused by overlapping data and atmospheric factors. On the other hand, soft computing techniques like machine/deep learning are quite handy for estimating SSM without any technical or laborious procedures. We determine that factors, e.g., topography, soil type, vegetation, climate change, groundwater level, depth of soil, etc., primarily influence the SSM measurements. Different techniques have been put into practice for various practical situations, although comparisons between them are not available frequently in publications. Each method offers a unique set of potential advantages and disadvantages. The most accurate way of identifying the best soil moisture technique is the value selection method (VSM). The neutron probe is preferable to the FDR or TDR sensor for measuring soil moisture. Remote sensing techniques have filled the need for large-scale, highly spatiotemporal soil moisture monitoring. Through self-learning capabilities in data-scarce areas, machine/deep learning approaches facilitate soil moisture measurement and prediction. [ABSTRACT FROM AUTHOR]

This paper presents the integration of shunt capacitors in the radial distribution grids (RDG) with constant and time-varying load consideration for the reduction of power losses and total annual cost, which turns to enhance the voltage profile and annual net savings. To gather the stated goals, three objective functions are formulated with system constraints. To solve this identified problem, a novel optimization technique based on the modification of particle swarm optimization is proposed. The solution methodology is divided into two phases. In phase one, potential candidate buses are nominated using the loss sensitivity factor method and in phase two the proposed technique first selects the optimal buses for the capacitor placement among the potential buses then it decides the optimal sizing of the capacitors as well. To demonstrate the performance in terms of efficiency and strength, the proposed technique is tested on IEEE 15, 33, and 69 bus system for the optimal placement and sizing of capacitors (OPSC) problem. The results are achieved in terms of annual net savings for 15 bus (47.66 % c a s e − 1 , 32.76 % c a s e − 2 , 26.46 % c a s e − 3 ) , 33 bus (33.09% c a s e − 1 , 27.06 % c a s e − 2 , 24.15 % c a s e − 3 ), and 69 bus (34.51% c a s e − 1 , 29.43 % c a s e − 2 , 25.83 % c a s e − 3 ) which are comparable to other state of the art methods, and it also indicates the success of the proposed technique. [ABSTRACT FROM AUTHOR]

The expansion of investment strategies and capital markets is altering the significance and empirical rationality of the Efficient Market Hypothesis. The vitality of capital markets is essential for efficiency research. The authors explore here the development and contemporary status of the efficient market hypothesis by emphasizing anomaly/excess returns. Investors often fail to get excess returns; however, thus far, market anomalies have been witnessed and stock prices have diverged from their intrinsic value. This paper presents an analysis of anomaly returns in the presence of the theory of the efficient market. Moreover, the market efficiency progression is reviewed and its present status is explored. Finally, the authors provide enough evidence of a data snooping issue, which violates and challenges the existing proof and creates room for replication studies in modern finance. [ABSTRACT FROM AUTHOR]

In this paper, we present an energy optimization technique to schedule three types of household appliances (user dependent, interactive schedulable and unschedulable) in response to the dynamic behaviours of customers, electricity prices and weather conditions. Our optimization technique schedules household appliances in real time to optimally control their energy consumption, such that the electricity bills of end users are reduced while not compromising on user comfort. More specifically, we use the binary multiple knapsack problem formulation technique to design an objective function, which is solved via the constraint optimization technique. Simulation results show that average aggregated energy savings with and without considering the human presence control system are 11.77% and 5.91%, respectively. [ABSTRACT FROM AUTHOR]

This paper presents real time information based energy management algorithms to reduce electricity cost and peak to average ratio (PAR) while preserving user comfort in a smart home. We categorize household appliances into thermostatically controlled (tc), user aware (ua), elastic (el), inelastic (iel) and regular (r) appliances/loads. An optimization problem is formulated to reduce electricity cost by determining the optimal use of household appliances. The operational schedules of these appliances are optimized in response to the electricity price signals and customer preferences to maximize electricity cost saving and user comfort while minimizing curtailed energy. Mathematical optimization models of tc appliances, i.e., air-conditioner and refrigerator, are proposed which are solved by using intelligent programmable communication thermostat ( iPCT). We add extra intelligence to conventional programmable communication thermostat (CPCT) by using genetic algorithm (GA) to control tc appliances under comfort constraints. The optimization models for ua, el, and iel appliances are solved subject to electricity cost minimization and PAR reduction. Considering user comfort, el appliances are considered where users can adjust appliance waiting time to increase or decrease their comfort level. Furthermore, energy demand of r appliances is fulfilled via local supply where the major objective is to reduce the fuel cost of various generators by proper scheduling. Simulation results show that the proposed algorithms efficiently schedule the energy demand of all types of appliances by considering identified constraints (i.e., PAR, variable prices, temperature, capacity limit and waiting time). [ABSTRACT FROM AUTHOR]

In this paper, we propose mathematical optimization models of household energy units to optimally control the major residential energy loads while preserving the user preferences. User comfort is modelled in a simple way, which considers appliance class, user preferences and weather conditions. The wind-driven optimization (WDO) algorithm with the objective function of comfort maximization along with minimum electricity cost is defined and implemented. On the other hand, for maximum electricity bill and peak reduction, min-max regret-based knapsack problem (K-WDO) algorithm is used. To validate the effectiveness of the proposed algorithms, extensive simulations are conducted for several scenarios. The simulations show that the proposed algorithms provide with the best optimal results with a fast convergence rate, as compared to the existing techniques. [ABSTRACT FROM AUTHOR]

Price based demand response is an important strategy to facilitate energy retailers and end-users to maintain a balance between demand and supply while providing the opportunity to end users to get monetary incentives. In this work, we consider real-time electricity pricing policy to further calculate the incentives in terms of reduced electricity price and cost. Initially, a mathematical model based on the backtracking technique is developed to calculate the load shifted and consumed in any time slot. Then, based on this, the electricity price is calculated for all types of users to estimate the incentives through load shifting profiles. To keep the load under the upper limit, the load is shifted in other time slots in such a way to facilitate end-users regarding social welfare. The user who is not interested in participating load shifting program will not get any benefit. Then the well behaved functional form optimization problem is solved by using a heuristic-based genetic algorithm (GA), wwhich converged within an insignificant amount of time with the best optimal results. Simulation results reflect that the users can obtain some real incentives by participating in the load scheduling process. [ABSTRACT FROM AUTHOR]

(1) Background: Enterococcus faecium DO is an environmental microbe, which is a mesophilic, facultative, Gram-positive, and multiple habitat microorganism. Enterococcus faecium DO is responsible for many diseases in human. The fight against infectious diseases is confronted by the development of multiple drug resistance in E. faecium. The focus of this research work is to identify a novel compound against this pathogen by using bioinformatics tools and technology. (2) Methods: We screened the proteome (accession No. PRJNA55353) information from the genome database of the National Centre for Biotechnology Information (NCBI) and suggested a potential drug target. I-TASSER was used to predict the three-dimensional structure of the protein, and the structure was optimized and minimized by different tools. PubChem and ChEBI were used to retrieve the inhibitors. Pharmacophore modeling and virtual screening were performed to identify novel compounds. Binding interactions of compounds with target protein were checked using LigPlot. pkCSM, SwissADME, and ProTox-II were used for adsorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. (3) Results: Novel selected compounds have improved absorption and have better ADMET properties. Based on our results, the chemically identified inhibitor ZINC48942 targeted the receptor that can inhibit the activity of infection in E. faecium. This research work will be beneficial for the scientific community and could aid in the design of a new drug against E. faecium infections. (4) Conclusions: It was observed that novel compounds are potential inhibitors with more efficacy and fewer side effects. This research work will help researchers in testing and identification of these chemicals useful against E. faecium. [ABSTRACT FROM AUTHOR]

Simple Summary: Spodoptera litura (Fabricius) (Lepidoptera: Noctuidae) is a notorious and polyphagous pest of several economically important agricultural crops. It is worldwide in distribution and primarily managed through typical dependence on insecticides, which resulted in health and the environmental challenges and selected for resistance development in S. litura field populations. Resistance caused chemical control failures and S. litura outbreaks around the world. This necessitated development of eco-friendly alternative approaches such as biological control. With this view, current study investigated the functional response of Harmonia axyridis (Pallas) (Coleoptera: Coccinellidae) at various growth stages (i.e., 1st, 2nd, 3rd and 4th instars, and male and female stages) and temperatures (i.e., 15, 20, 25, 30 and 35 °C) against S. litura eggs to enable the recognition of efficient biocontrol stages that could be utilized to suppress S. litura populations. In our findings, egg consumption depended on the growth stage of the predator as well as temperature. All stages consumed S. litura eggs, but more promising stages with active egg consumption were the 4th instar and adults (male and female) typically at higher temperatures (25–35 °C). We conclude that these stages may be exploited to suppress S. litura populations in fields and greenhouses. Spodoptera litura (Fabricius) (Lepidoptera: Noctuidae) is a major pest of several economically important crops with worldwide distribution. Use of insecticides is the principal strategy for its management, which has subsequently led to insecticide resistance and control failures. Functional response of Harmonia axyridis (Pallas) (Coleoptera: Coccinellidae) at larval and adult stages was evaluated in this study, using S. litura eggs as the prey at various temperatures varying between 15 and 35 °C. Based on logistic model findings, linear parameters of various predatory stages of H. axyridis at various temperatures were significantly negative, which indicate a type II functional response. The theoretical maximum number (T/Th) of eggs consumed increased with increasing temperature across all predatory stages. According to the random predator equation, the coefficients of attack rate increased and that of handling time decreased as the temperature increased. The 4th instar and adult stages were superior candidates for biocontrol of the target prey, typically at higher temperatures. The maximum attack rate (0.546 ± 0.058 h−1) and lowest handling time (0.189 ± 0.004 h) were exhibited by the females at 30 and 35 °C, respectively, whereas these parameters were inferior for early instars. These findings clearly depict that the 4th instar and adult predators are efficient egg consumers and can serve as potential suppressors of S. litura field populations. The limitations of the predictions formulated by functional response trials are also discussed. [ABSTRACT FROM AUTHOR]

Except of pest control, insecticides have shown adverse effects on natural enemies as well. Thus, risk assessment of pesticides for biological control agents is critical for effective use in integrated pest management (IPM) schemes. In the present study, the lethal and sublethal effects of chlorpyrifos, a commonly used insecticide that may negatively affect biological control agents, were evaluated on a non-target predator, the Asian ladybeetle Harmonia axyridis. Previous studies have reported on lethal concentrations, but the effects of sublethal concentrations remain unclear. Lethal and sublethal concentrations of chlorpyrifos were applied to third instar larvae of H. axyridis, and different growth and developmental parameters were measured. Treatment with LC10 (4.62 mg a.i. L−1) significantly shortened the developmental period of third instar larvae, whereas it significantly prolonged those of fourth instar larvae and pupa. Treatment with LC30 (9.59 mg a.i. L−1) significantly increased the larval and pupal developmental period compared with that of the control, whereas feeding potential, female fecundity, and adult longevity significantly decreased after LC10 and LC30 treatment. The pre-oviposition period significantly increased compared with that of the control. Population growth parameters, the finite (λ) and intrinsic rate of increase (r) and the net reproductive rate (R0), decreased following exposure to sublethal concentrations of chlorpyrifos. According to the results, the use of chlorpyrifos in IPM schemes requires further research because even sublethal concentrations of this insecticide were harmful to H. axyridis population growth. [ABSTRACT FROM AUTHOR]

This paper is focused on the channel modeling techniques for implementation of narrowband power line communication (NB-PLC) over medium voltage (MV) network for the purpose of advanced metering infrastructure (AMI). Three different types of models, based on deterministic method, statistical method, and network parameters based method are investigated in detail. Transmission line (TL) theory model is used to express the MV network as a two-port network to examine characteristics of sending and receiving NB-PLC signals. Multipath signal propagation model is used to incorporate the effect of multipath signals to determine the NB-PLC transfer function. A Simulink model is proposed which considers the values of MV network to examine the characteristics of NB-PLC signals. Frequency selectivity is also introduced in the impedances to compare variations and characteristics with constant impedances based MV network. A state-of-the-art mechanism for the modeling of capacitive coupling device, and impedances of low voltage (LV) and MV networks is developed. Moreover, a comparative analysis of TL theory and multipath signal propagation models with the proposed Simulink model is presented to validate the performance and accuracy of proposed model. This research work will pave the way to improve the efficiency of next-generation NB-PLC technologies. [ABSTRACT FROM AUTHOR]

With the negative climate impact of fossil fuel power generation and the requirement of global policy to shift towards a green mix of energy production, the investment in renewable energy is an opportunity in developing countries. However, poor economy associated with limited income, funds availability, and regulations governing project funding and development are key factors that challenge investors in the energy sector. Given the various power generation resources, including renewables, it is necessary to evaluate the possible power generation investment options from an economic perspective. To realize this objective, solar PV, wind and diesel power generations are economically compared, considering the incremental rate of return and incremental benefit to cost ratio techniques. The alternative investment options of distributed generation technologies are evaluated for Maharashtra, India under different depreciation methods, and the effect of the latter on selecting the best investment candidate is investigated. The paper also conducts sensitivity analysis to examine the impact of capital cost, operation and maintenance cost, and fuel cost variations on the selection decision considering a comparison of the different general projects' cash flow structures discussed in the literature. The economic aspects of selecting a project among possible alternatives for an investment in the power sector are analyzed, and the presented review provides comprehensive comparisons with respect to the literature approaches. The results reveal that, in the benchmark case study, the PV project is rejected and disregarded from further comparisons with other candidate projects since its equity internal rate of return (10.25%) is less than the minimum accepted rate of return, leaving the selection between wind and diesel energy projects. The study reveals that the incremental rates of return under such a comparison are 37.88%, 45.94% and 37.50% when MACRS, declining balance and straight line depreciations techniques are applied, respectively. Thus, the wind energy project is the favored option in this case. For the economic assessment of other case studies, the application of both sensitivity analysis on the capital cost and operation and maintenance cost and literature approaches to structure the projects reveal that wind energy for Maharashtra, India is a more attractive and feasible option compared to other distribution generation projects, while diesel is only considered to be a good option when its fuel cost is reduced by 5%. Finally, the paper highlights policy implications that can influence the decision to move towards investment in distributed generation technologies as a future research direction. [ABSTRACT FROM AUTHOR]

Biodiesel is a renewable fuel usually produced from vegetable oils and animal fats. This study investigates the extraction of oil and its conversion into biodiesel by base-catalyzed transesterification. Firstly, the effect of various solvents (methanol, n-hexane, chloroform, di-ethyl ether) on extraction of oil from non-edible crops, such as R. communis and M. azedarach, were examined. It was observed that a higher concentration of oil was obtained from R. communis (43.6%) as compared to M. azedarach (35.6%) by using methanol and n-hexane, respectively. The extracted oils were subjected to NaOH (1%) catalyzed transesterification by analyzing the effect of oil/methanol molar ratio (1:4, 1:6, 1:8 and 1:10) and varying temperature (20, 40, 60 and 80 °C) for 2.5 h of reaction time. M. azedarach yielded 88% and R. communis yielded 93% biodiesel in 1:6 and 1:8 molar concentrations at ambient temperature whereas, 60 °C was selected as an optimum temperature, giving 90% (M. azedarach) and 94% (R. communis) biodiesel. The extracted oil and biodiesel were characterized for various parameters and most of the properties fulfilled the American Society for Testing and Materials (ASTM) standard biodiesel. The further characterization of fatty acids was done by Gas Chromatography/Mass Spectrometer (GC/MS) and oleic acid was found to be dominant in M. azedarach (61.5%) and R. communis contained ricinoleic acid (75.53%). Furthermore, the functional groups were analyzed by Fourier Transform Infrared Spectroscopy. The results suggested that both of the oils are easily available and can be used for commercial biodiesel production at a cost-effective scale. [ABSTRACT FROM AUTHOR]

In most demand response (DR) based residential load management systems, shifting a considerable amount of load in low price intervals reduces end user cost, however, it may create rebound peaks and user dissatisfaction. To overcome these problems, this work presents a novel approach to optimizing load demand and storage management in response to dynamic pricing using machine learning and optimization algorithms. Unlike traditional load scheduling mechanisms, the proposed algorithm is based on finding suggested low tariff area using artificial neural network (ANN). Where the historical load demand individualized power consumption profiles of all users and real time pricing (RTP) signal are used as input parameters for a forecasting module for training and validating the network. In a response, the ANN module provides a suggested low tariff area to all users such that the electricity tariff below the low tariff area is market based. While the users are charged high prices on the basis of a proposed load based pricing policy (LBPP) if they violate low tariff area, which is based on RTP and inclining block rate (IBR). However, we first developed the mathematical models of load, pricing and energy storage systems (ESS), which are an integral part of the optimization problem. Then, based on suggested low tariff area, the problem is formulated as a linear programming (LP) optimization problem and is solved by using both deterministic and heuristic algorithms. The proposed mechanism is validated via extensive simulations and results show the effectiveness in terms of minimizing the electricity bill as well as intercepting the creation of minimal-price peaks. Therefore, the proposed energy management scheme is beneficial to both end user and utility company. [ABSTRACT FROM AUTHOR]

The smart grid (SG) has emerged as a key enabling technology facilitating the integration of variable energy resources with the objective of load management and reduced carbon-dioxide (CO 2 ) emissions. However, dynamic load consumption trends and inherent intermittent nature of renewable generations may cause uncertainty in active resource management. Eventually, these uncertainties pose serious challenges to the energy management system. To address these challenges, this work establishes an efficient load scheduling scheme by jointly considering an on-site photo-voltaic (PV) system and an energy storage system (ESS). An optimum PV-site matching technique was used to optimally select the highest capacity and lowest cost PV module. Furthermore, the best-fit of PV array in regard with load is anticipated using least square method (LSM). Initially, the mathematical models of PV energy generation, consumption and ESS are presented along with load categorization through Zero and Finite shift methods. Then, the final problem is formulated as a multiobjective optimization problem which is solved by using the proposed Dijkstra algorithm (DA). The proposed algorithm quantifies day-ahead electricity market consumption cost, used energy mixes, curtailed load, and grid imbalances. However, to further analyse and compare the performance of proposed model, the results of the proposed algorithm are compared with the genetic algorithm (GA), binary particle swarm optimization (BPSO), and optimal pattern recognition algorithm (OPRA), respectively. Simulation results show that DA achieved 51.72% cost reduction when grid and renewable sources are used. Similarly, DA outperforms other algorithms in terms of maximum peak to average ratio (PAR) reduction, which is 10.22%. [ABSTRACT FROM AUTHOR]

Chronic pain is a devastating condition affecting the physical, psychological, and socioeconomic status of the patient. Inflammation and immunometabolism play roles in the pathophysiology of chronic pain disorders. Electrical neuromodulation approaches have shown a meaningful success in otherwise drug-resistant chronic pain conditions, including failed back surgery, neuropathic pain, and migraine. A literature review (PubMed, MEDLINE/OVID, SCOPUS, and manual searches of the bibliographies of known primary and review articles) was performed using the following search terms: chronic pain disorders, systemic inflammation, immunometabolism, prediction, biomarkers, metabolic disorders, and neuromodulation for chronic pain. Experimental studies indicate a relationship between the development and maintenance of chronic pain conditions and a deteriorated immunometabolic state mediated by circulating cytokines, chemokines, and cellular components. A few uncontrolled in-human studies found increased levels of pro-inflammatory cytokines known to drive metabolic disorders in chronic pain patients undergoing neurostimulation therapies. In this narrative review, we summarize the current knowledge and possible relationships of available neurostimulation therapies for chronic pain with mediators of central and peripheral neuroinflammation and immunometabolism on a molecular level. However, to address the needs for predictive factors and biomarkers, large-scale databank driven clinical trials are needed to determine the clinical value of molecular profiling. [ABSTRACT FROM AUTHOR]

In order to ensure optimal and secure functionality of Micro Grid (MG), energy management system plays vital role in managing multiple electrical load and distributed energy technologies. With the evolution of Smart Grids (SG), energy generation system that includes renewable resources is introduced in MG. This work focuses on coordinated energy management of traditional and renewable resources. Users and MG with storage capacity is taken into account to perform energy management efficiently. First of all, two stage Stackelberg game is formulated. Every player in game theory tries to increase its payoff and also ensures user comfort and system reliability. In the next step, two forecasting techniques are proposed in order to forecast Photo Voltaic Cell (PVC) generation for announcing optimal prices. Furthermore, existence and uniqueness of Nash Equilibrium (NE) of energy management algorithm are also proved. In simulation, results clearly show that proposed game theoretic approach along with storage capacity optimization and forecasting techniques give benefit to both players, i.e., users and MG. The proposed technique Gray wolf optimized Auto Regressive Integrated Moving Average (GARIMA) gives 40% better result and Cuckoo Search Auto Regressive Integrated Moving Average (CARIMA) gives 30% better results as compared to existing techniques. [ABSTRACT FROM AUTHOR]

Smart grid (SG) vision has come to incorporate various communication technologies, which facilitate residential users to adopt different scheduling schemes in order to manage energy usage with reduced carbon emission. In this work, we have proposed a residential load management mechanism with the incorporation of energy resources (RESs) i.e., solar energy. For this purpose, a real-time electricity price (RTP), energy demand, user preferences and renewable energy parameters are taken as an inputs and genetic algorithm (GA) has been used to manage and schedule residential load with the objective of cost, user discomfort, and peak-to-average ratio (PAR) reduction. Initially, RTP is used to reduce the energy consumption cost. However, to minimize the cost along with reducing the peaks, a combined pricing model, i.e., RTP with inclining block rate (IBR) has been used which incorporates user preferences and RES to optimally schedule load demand. User comfort and cost reduction are contradictory objectives, and difficult to maximize, simultaneously. Considering this trade-off, a combined pricing scheme is modelled in such a way that users are given priority to achieve their objective as per their requirements. To validate and analyze the performance of the proposed algorithm, we first propose mathematical models of all utilized loads, and then multi-objective optimization problem has been formulated. Furthermore, analytical results regarding the objective function and the associated constraints have also been provided to validate simulation results. Simulation results demonstrate a significant reduction in the energy cost along with the achievement of both grid stability in terms of reduced peak and high comfort. [ABSTRACT FROM AUTHOR]

This paper proposes a system model for optimal dispatch of the energy and reserve capacity considering uncertain load demand and unsteady power generation. This implicates uncertainty in managing the power demand along with the consideration of utility, user and environmental objectives. The model takes into consideration a day-ahead electricity market that involves the varying power demand bids and generates a required amount of energy in addition with reserve capacity. The lost opportunity cost is also considered and incorporated within the context of expected load not served. Then, the effects of combined and separate dispatching the energy and reserve are investigated. The nonlinear cost curves have been addressed by optimizing the objective function using robust optimization technique. Finally, various cases in accordance with underlying parameters have been considered in order to conduct and evaluate numerical results. Simulation results show the effectiveness of proposed scheduling model in terms of reduced cost and system stability. [ABSTRACT FROM AUTHOR]