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An intrabody area nanonetwork (intra-BANN) is a set of nanoscale devices, which have outstanding cellular level precision and accuracy for enabling noninvasive healthcare monitoring and disease diagnosis. In this article, we design a novel feedforward neural networks (FFNNs) based data aggregation scheme that integrates the attributes of artificial intelligence to boost the computational intelligence of intra-BANNs for prolonged network lifetime. In the proposed scheme, data division and labeling are performed to transmit detected information using two different types of packets with different sizes to save energy resources and to avoid redundant data transmission. FFNN-based periodic data transmission exploits the fitness function approximation characteristics of FFNN to increase the transmission probability of critical information with minimum energy consumption and delay, whereas our proposed event-driven data transmission also ensures the transmission of high priority data with minimal delay and storage overhead. The detailed evaluation and comparison of our proposed framework with three existing schemes conducted using the Nano-Sim tool highlight that our proposed scheme performs 50%–60% better than state-of-the-art schemes in terms of residual energy, delay, and packet loss.

The solubilization behavior of hexanuclear copper complex, [Cu6(η1:μ2-C4H10NO)4(η1:η1:μ2-C5H9O2)4(η1:μ1-C5H9O2)2(μ3-OH)2] (1) (where, C4H10NO = N,N-dimethylethanolamine and C5H9O2 = pivalic acid) i...

Desublimation-based Carbon dioxide (CO2) removal from the natural gas (NG) in a cryogenic packed bed becomes challenging due to the dry ice formation inside the packed bed. Therefore, the cryogenic packed bed has been used for NG purification only in batch processes. However, to fulfill the energy requirements, it is crucial to develop a continuous process for NG purification, which is difficult for a single cryogenic packed bed. In the current research, a novel cryogenic packed bed system is proposed for continuous capture of desublimation based CO2 capture from natural gas. The process feasibility of the switching concept was proved through dynamic simulation and experimental study on CO2 separation from multi-component NG. It was observed that increasing CO2 content decreases the switching and saturation time of the cryogenic packed bed. The total energy required per packed bed per cycle in the switching system is 133.35. The saturation time for pure CO2 feed, NG sample-1, and NG sample-2 were 300, 500, and 600 s, respectively. An excellent agreement was observed between the results obtained from the experimental results and that obtained through dynamic simulation. A switching time of 200 s was found for a CO2 recovery of more than 98 %. This research work offers scientific data and theoretical support for the industrial application of switched cryogenic packed bed setup in CO2 capture from natural gas in the future. Moreover, in the current research work, scale-up study, and automatic control system for the switched packed bed are recommended for the future.

In this paper, a two-stage approach is proposed on a joint dispatch of thermal power generation and variable resources including a storage system. Although, the dispatch of alternate energy along with conventional resources has become increasingly important in the new utility environment. However, recent studies based on the uncertainty and worst-case scenario-oriented robust optimization methodology reveal the perplexities associated with renewable energy sources (RES). First, the load demand is predicted through a convolutional neural network (CNN) by taking the ISO-NECA hourly real-time data. Then, the joint dispatch of energy and spinning reserve capacity is performed with the integration of RES and battery storage system (BSS) to satisfy the predicted load demand. In addition, the generation system is penalized with a cost factor against load not served for the amount of energy demand which is not fulfilled due to generation constraints. Meanwhile, due to ramping of thermal units, the available surplus power will be stored in the backup energy storage system considering the state of charge of the storage system. The proposed method is applied on the IEEE-standard 6-Bus system and particle swarm optimization (PSO) algorithm is used to solve the cost minimization objective function. Finally, the proposed system performance has been verified along with the reliability during two worst-case scenarios, i.e., sudden drop in power demand and a short-fall at the generation end.

There are surplus applications in modern smart cities where localization of indoor environments is critical ranging from surveillance and trailing in smart structures to the localized wireless distribution of advertising content in shopping malls. These applications are only successful if a robust and cost-effective real-time system is developed for precise localization. Another aspect considered for indoor localization is power consumption. Recent wireless standards such as Bluetooth Low Energy (BLE) and LoRa consume less power which makes them a perfect candidate for indoor localization. This work aims to carry out an experimental evaluation which would help to decide which wireless standard i.e., Wi-Fi, Bluetooth Low Energy (BLE), and LoRa are most suitable for indoor localization. Experiments are carried out using trilateration in three multiple environments. RSSI is used to calculate the coordinates of a sensor node. Results obtained from the experiment show that Wi-Fi is most accurate with an average error of 0.54 m. LoRa is second most accurate with an average error of 0.62 m and BLE is the least accurate with an average error of 0.82 m. These results can be used to decide which wireless standard is best suited for indoor localization.

Traditionally in supply chain management suppliers and customers are evaluated independently. This is in spite of the fact that supplier performance has direct impact on the performance of the customer. This research develops a mechanism for simultaneous evaluation and prioritization of supplier and customer using the sustainability metrics comprising economic, environment and social measures. In order to quantify the qualitative factors, priority index is introduced to measure the prioritization of customers and suppliers simultaneously. To get the priority index for each customer and supplier, a novel analytical hierarchical process-based fuzzy inference decision support system (AHP-FIDSS) has been introduced. An AHP-FIDSS involves the factor screening, hierarchical structure modeling, quantification of qualitative factors, and their conversion to quantitative values. AHP-FIDSS is knowledge-based system involving expert’s decision alternatives or logical rules. The number of input variables and their levels are proportional to the number of logical rules and rule size. In order to reduce the numbers of rules, a Taguchi orthogonal array is used that reduces the numbers of rules, thereby substantially simplifying the evaluation process A case study of a supply chain of surgical instruments has been presented for the real-time application of the proposed model. The results exhibited the simultaneous classification of customers and suppliers with respect to their priority index. Higher value of priority index indicates higher importance and vice versa. This research is useful for the supply chain mangers in procurement, sales and production to develop an importance classification for customers and suppliers in a supply chain.

Nanobiotechnology is an emerging field among researchers. Currently the development of plant-mediated biogenic nanoparticles is gaining much attention. It is notable that the biogenic nanoparticles particularly silver nanoparticles are therapeutically more effective than its chemically synthesized form. Thus, here in, the development of silver nanoparticles (Fj-AgNPs) using aqueous extract of ripe fruit of Flacourtia jangomas as a reducing agent is investigated. The developed Fj-AgNPs were additionally compared with ammine modified silver nanoparticles (Am-AgNPs), developed through chemical route using the reported approach and confirm the phyto-reduction of Ag+ to Ag0.UV–visible spectrum has exhibited a peak at 418 nm and FTIR peak profile (at 1587.6, 1386.4, and 1076 cm−1 with corresponding compounds) in addition to the diffraction peak at 38°, 44°, 64°,78° in PXRD spectrum confirmed the synthesis of Fj-AgNPs with 8.29 nm average crystallite size. Enhanced anti-bacterial effect is observed in the case of Fj-AgNPs as compared to Am-AgNps. These results refer to the idea that the use of the aqueous extract of ripe fruit of Flacourtia jangomas could be a good replacement of EDTA in the synthesis of silver nanoparticles.

In recent past, to meet the growing energy demand of electricity, integration of renewable energy resources (RESs) in an electrical network is a center of attention. Furthermore, optimal integration of these RESs make this task more challenging because of their intermittent nature. Therefore, in the present study power flow problem is treated as a multi-constraint, multi-objective optimal power flow (MOOPF) problem along with optimal integration of RESs. Whereas, the objectives of MOOPF are threefold: overall generation cost, real power loss of system and carbon emission reduction of thermal sources. In this work, a computationally efficient technique is presented to find the most feasible values of different control variables of the power system having distributed RESs. Whereas, the constraint satisfaction is achieved by using penalty function approach (PFA) and to further develop true Pareto front (PF), Pareto dominance method is used to categorize Pareto dominate solution. Moreover, to deal with intermittent nature of RES, probability density function (PDF) and stochastic power models of RES are used to calculate available power from RESs. Since, objectives of the MOOPF problem are conflicting in nature, after having the set of non-dominating solutions fuzzy membership function (FMF) approach has been used to extract the best compromise solution (BCS). To test the validity of developed technique, the IEEE-30 bus system has been modified with integration of RESs and final optimization problem is solved by using particle swarm optimization (PSO) algorithm. Simulation results show the achievement of proposed technique managing fuel cost value long with the optimal values of other objectives.

Despite the universal importance of price based demand response (DR) for managing electric vehicle (EV) charging load, the academic literature has explored various mechanisms to its implementation. The prequel to this work has demonstrated that implementation of load management schemes on the basis of price based DR programs leads to costlier scheduling for low or constant energy consumers. In this regard, the proposed work has considered and expanded the same idea from analytical as well as implementation point of view to multiple EV charging regions and respective loads. We present a novel mechanism to calculate EV charging prices using individualized energy consumption patterns of EVs in each region. In this regard, all EV regions/stations receive a dynamic price signal which is non-discriminatory in nature. The dynamic price signals are specifically designed to mitigate the impact of discriminatory prices on end user's cost. Furthermore, the other objectives of these non-discriminatory prices are to lower energy cost and rebound peaks without affecting utility objective (i.e., net revenue). Initially, a new mathematical model is presented to calculate charging prices based on real time load demand and market dynamics. Then relatively a well behaved functional form of the optimization problem is formulated and the cost minimization objective function is solved by using genetic algorithm (GA). The optimization program successfully converges to give global optimum solution validating the effectiveness of proposed mechanism. Finally, the analytical and simulation results are conducted to show the achievements of our proposed work in terms of fair cost distribution with high user satisfaction. It is also proved that in both mechanisms, the utility's revenue remains unaffected.

An Intrabody Nanonetwork (IBNN) is constituted by nanoscale devices that are implanted inside the human body for monitoring of physiological parameters for disease diagnosis and treatment purposes. The extraordinary accuracy and precision of these nanoscale devices in cellular level disease diagnosis and drug delivery are envisioned to advance the traditional healthcare system. However, the feature constraints of these nanoscale devices, such as inadequate energy resources, topology-unawareness, and limited computational power, challenges the development of energy-efficient routing protocol for IBNNs. The presented work concentrates on the primary limitations and responsibilities of IBNNs and designs a routing protocol that incorporates characteristics of Exponential Weighted Moving Average (EWMA) Based Opportunistic Data Transmission (EWMA-ODT) and Artificial Colony Algorithm Based Query Response Transmission (ABC-QRT) approaches for efficiently handling the routing challenges of IBNNs. In EWMA-ODT, the moving Nano Biosensors (NBSs) employ the EWMA method attributes to aggregate detected data by assigning high weightage to the recent detected information. Later, the aggregated data is transmitted to the Nano Router (NR) when the direct data transmission opportunity is available, the reception of aggregated briefs NR about the condition of the network after the last successful interaction with minimum energy consumption. Whereas, the ABC-QRT approach introduces the ABC algorithm for the selection of those optimal NBSs that have maximum fitness value for satisfying the data transmission demand of the external healthcare system with minimal traffic overhead. The simulation results validate that the joint contribution of these approaches enhances IBNNs lifetime and reduces end-to-end delay as compared to the flooding scheme.

In process industry, failure or rupture of pressurized vessel is very dangerous especially when there is an escape of flammable gaseous mixture that can cause potential fire or explosion. One of the scenarios that causes such accidents is the blowdown process. Therefore, it becomes crucial to control blowdown process to prevent such accidents. It is important to design optimally to make sure that blowdown valve is according to the requirements. For the safe use of a pressure relief system, some of the parameters are critical, for example, selection of construction material, sizing of relief valves, temperature, and pressure, etc. There is no literature currently available that discusses all the mathematical models or simulation tools for optimum design of the blowdown process. This subject matters because the available models or tools cover different aspects of blowdown process. A meticulous review is required to present the applications of these models and tools based on the accidental scenarios. Therefore, this paper critically reviews the models and tools that are developed purposely to calculate optimum blowdown parameters based on fluid and vessel conditions. Recommendations are given for the development of new simulation tool to simulate phase change conditions especially when solid formation is involved.

Day-ahead electricity pricing is an important strategy for electricity providers to improve grid stability through load scheduling. In this paper, we investigate a general framework for modelling electricity retail pricing based on load demand and market price information. Without any a priori knowledge, we have considered a finite time approach with dynamic system inputs. Our objective is to minimize the average system cost and rebound peaks through energy procurement price, load scheduling and renewable energy source (RES) integration. Initially, the energy consumption cost is calculated based on market clearing price and scheduled load. Then, through reformulation and subsequent modification of optimization problem, we utilize a day-ahead price information to construct individualized price profiles for each user, respectively. To analyse the applicability of proposed pricing policy, analytical solution is obtained which is further validated through comparison with solution obtained from genetic algorithm (GA). From results, it is observed that proposed price policy is non-discriminatory in nature and each user obtained a fair electricity tariff rather than a day-ahead price, which is based on load demand and consumption variation of other users. We also show that optimization problem is sequentially solved with bounded performance guarantee and asymptotic optimality. Finally, simulations are carried in different scenarios; aggregated load and market price, and aggregated load, individualized load, market price and proposed price. Results reveal that our proposed mechanism can charge the price to each user with 23.77% decrease or 5.12% increase based on system requirements.

Nanoparticles of double heterojunction n-CuO-p-NiO-n-ZnO system and its nano-hybrids with reduced graphene oxide (rGO) were synthesized by simple and easily adoptable co-precipitation method. The prepared n-CuO-p-NiO-n-ZnO nanoparticles and their hybrids with rGO were characterized by various characterization techniques. Structural, optical and morphological characterization was done by X-rays diffraction (XRD), Fourier Transform Infrared (FTIR), UV–Visible spectroscopy etc. It was found that the developed hetero-structure is composed by p-NiO as cubic, n-CuO as monoclinic and n-ZnO as hexagonal crystal structure. Formation of heterojunction is confirmed by current-voltage (I-V) measurement. The optical bandgap energy of heterojunction was around 1.6 eV which made this system active in both UV and Visible light region. Photocatalytic degradation efficiency was examined using methylene blue and it showed prominent enhancement as compared to pure metal oxides. This excellent efficiency was attributed due to efficient charge carrier separation which leads to inhibit recombination rate at photocatalyst interface. Moreover, photocatalytic activity was increased meaningfully up to 70% by graphene based n-CuO-p-NiO-n-ZnO. This excellent increment in activity was due to large surface area offered by graphene which caused adsorption of dye molecules and helped in transfer of electrons to metal conduction band.

Purpose In the field of knitwear, dimensional stability is assumed as a critical problem that affects the quality and salability of a product. Although much work has been done in this area with a focus on the factors affecting fabric shrinkage, however, there is a lack of work on knitwears with respect to their dimensional stability. The purpose of this paper is to investigate the impact of stitching parameters and wash types on the dimensional properties of knitwear. Design/methodology/approach The crew-neck t-shirts were prepared by using pique knitted fabrics. Different sewing and finishing parameters were used that include stitch density, stitch type, stitching thread and wash type. The critical measurements of the selected garment are taken as output variables which are body width, sleeve length, body length and across shoulder. After laundering process, shrinkage percentage was calculated by using before-wash and after-wash measurements. Findings This study shows that the stitching parameters affect significantly on knitwear’s shrinkage. Thus, when patterns are being developed for the cutting of fabric, expected shrinkage, known as residual shrinkage, must be considered to avoid unexpected changes in garment shape. Originality/value This research will be useful for knitwear manufacturing industry.

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.

In this paper, Jr.NiFe2O4 nanoparticles (NPs) were synthesized first time using the leaves extract of Juglans regia via a straightforward process. The physio and phytochemical analysis of plant confirm the presence of macromolecules which function as bio-reductant and stabilize the nanoparticles. The Jr.NiFe2O4 NPs were characterized by UV–visible, FTIR spectroscopy, PXRD pattern, SEM and TGA/DTA analysis. The nanoparticles proved to be optically active having a value of indirect bandgap of energy in the range of 1.53 eV. The Jr.NiFe2O4 NPs have the ability in scavenging 2,2-diphenyl-1-picrylhydrazyl hydrate (DPPH) free radicals and showed 58.01% ± 1.2% scavenging activity at 100 µg/mL concentrations. The photocatalytic degradation study of ciprofloxacin (CIP) and Congo red (CR) reveals that the highest degradation rate was acquired for CIP using pH = 3, at 254 nm, while 85% of removal rate was analysed for CR. The kinetic studies in case of CR removal followed pseudo-first-order model with thermodynamic parameters (∆G° = − 5.87 kJ mol−1 K, ΔH° = 1393.50 kJ mol−1 and ΔS° = 22.537 kJ mol−1 K) with error analysis. Overall, these data recommend an innovative inspiring application of a plant-mediated synthesis of Jr.NiFe2O4 NPs.

In this paper we propose random linear network coding (RLNC) as an efficient encoding scheme for unicast flows in single-hop ad hoc network and multi hop wireless mesh networks (WMN). The traditional approach for encoding unicast flows, known as inter-flow network coding (NC), assumes instant decoding and assumption of reliable feedback channel to minimizes the total number of transmissions. But under the practical consideration of the wireless medium access control (MAC), the gain of inter-flow NC technique suffers from higher latency due to the MAC overhead. We validate the result of our work on TelosB motes testbed. Our testbed implementation shows that the proposed approach of using RLNC reduces the transmission completion time by up to 40% compared to the inter-flow NC scheme for single-hop data dissemination and multi-hop wireless routing problems.

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.

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.

Today’s power systems are subject to the high penetration of dynamic load. Volatility and intermittency of the dynamic load demand need to be compensated through optimization and scheduling without compromising user comfort. This paper proposes a multi-agent-based multi-layered hierarchical control system for residential load management under real-time pricing environment. The major objectives are to reduce peak load demand, electricity cost, and user discomfort. In doing so, different types of agents, i.e., price agent $p_{a}$ , sensor agent $s_{a}$ , decision agent $d_{a}$ , load agent $l_{a}$ , and action agent $a_{a}$ , are developed to control residential loads, such as normal load ( nl ) and heavy load ( hl ). To handle price uncertainty, dynamically, optimal stopping rule (OSR) theory has been used. Two variants of OSR are proposed: 1) priority inversion logic-based OSR to subsidize the responsive consumers and 2) maximum energy consumption limit $Q$ -based OSR-Q to maximize the profit of energy retailers. Finally, the proposed mechanism is validated on a set of loads to show the applicability and proficiency under a dynamic environment.

Standard processing timings play an important role in efficient production management and are measured using work measurement techniques. Time study is a widely used work measurement technique in repetitive manufacturing processes. In present study, time study technique was used to enhance productivity of a garment production line in a work wear manufacturing factory. The factory consisted of 350 sewing machines and was producing work wears including trousers, vests, jackets and coveralls. Stop watch time study – analysis of existing set up – explored the existing time losses in production line caused by unevenness of work, excessive material transportation and higher work-in-process. Improvements were made through: (i) sequence of activities, (ii) tasks reassignment to the work stations and (iii) line balancing at predetermined cycle time. Hourly production charts were maintained at each work station to monitor outputs. The results of machine productivity were compared before and after employing changes. From results, 36% increase in average machine productivity was observed. Hence, it can be concluded that time study is an effective tool for increasing productivity in apparel manufacturing. The added advantages including optimization of material flow, balanced production line and reduced work in process were also achieved. The results of this study are useful for sewing factories making other woven, knitted and home textile products. In addition, football, shoe and gloves manufacturing industry may reef benefits from findings of this study.

In this work, a pot experiment was conducted to evaluate the effect of organic amendments in alleviation of toxicity of untreated domestic wastewater to okra (Abelmoschus esculentus) and purslane (Portulaca oleracea). Three organic amendments including farm yard manure (FYM), poultry manure (PM) and bagasse ash (BGA) at the rate of 1% and 3% were applied to the soil to evaluate their positive impact on vegetable plants. Results showed that untreated wastewater impeded the growth of both vegetables and increased uptake of heavy metals (HMs) (i.e. Cu, Ni, Pb and Zn) by plants whereas organic amendments significantly improved the growth of plants and decreased the uptake of HMs. Shoot and root lengths and dry weights of plants were boosted by the FYM and PM, while chlorophyll a and b and carotenoids were enhanced by PM and BGA amendments. Interestingly, reduced HMs uptake was found where 3% of BGA or FYM were applied. Overall the results of this study showed that all three organic amendments were useful for improving growth of vegetables and alleviating the phytotoxicity of untreated wastewater and uptake of HMs by plants. Our findings suggest that these organic materials could be useful for improving agricultural productivity in untreated wastewater irrigated areas.

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.

The composition of natural gas strongly depends on the geological conditions and depth of the gas well. Presently, CO2 content in the discovered natural gas reservoirs is up to 90%. Prior to customer use, the amount of CO2 needs to be reduced. Cryogenic CO2 capture is one of the emerging technology for CO2 capture from natural gas. Comprehensive knowledge of vapor-liquid, vapor-solid, and solid-liquid equilibria for the gaseous mixture is required for designing cryogenic separation processes for CO2 capture from its gaseous mixtures. No thermodynamic phase data is available for the highest CO2 content N.G mixture. The present work is an effort to generate the thermodynamic data for CO2 capture at low temperatures. CO2 solid phase identification and quantification need to be studied for the CO2-CH4 binary mixture. In this work, four samples of natural gas having CO2 content 40%, 60%, 75% and 90% were selected for simulation through Aspen HYSYS. Pressure-temperature data and temperature-composition data for the binary mixtures were predicted using PR EoS for CO2 phase identification and quantification respectively. Simulation for temperature- composition phase envelope was carried out at different pressures i.e. 10, 20, 30, and 40 bar. The operating region selected from the P-T phase envelop was from 1 to 40 bars. From the temperature composition graphs, it was concluded that temperature for the maximum CO2 capture in S-V region at 10, 20, 30, and 40 bar is -95, -90, -75, and -70 °C respectively.

Sewing thread is one of the most important components of a sewn product that contributes significantly in the useful life of a product. Stitch class 504 is the one which is used in all types of sew...

The interactional study of two anionic dyes viz. Reactive Blue-250 and Reactive Red-198 in the presence of single cationic surfactant and mixed-surfactant media is reported in this manuscript. To unveil the dye-surfactant interactions, a cationic surfactant Cetyltrimethylammonium bromide (CTAB) and its mixture with a nonionic surfactant Triton X-100 (TX), were used. The interactions were investigated using UV–Visible spectroscopy in sub-micellar, micellar, and post-micellar concentrations. The extent of solubilization in terms of partition constant (Kc), partition coefficient (Kx), binding constant (Kb), Gibbs free energies (ΔGp and ΔGb), and the approximate location of dye molecules in the micelles was assessed from spectroscopic measurements. The mixing of nonionic/cationic surfactants in appropriate amounts enhanced the solubilization of both reactive dyes. The experimental results revealed the spontaneous nature of solubilization and the dye-surfactant aggregates in single and mixed-surfactant media were stable. The synergism of cationic surfactant with nonionic counterpart is favorable to attain deep entrapment in the micelles than the single cationic surfactant. This method may provide inexpensive, highly efficient, and time-saving procedures in the removal of dyes from water resources.

The development of environmental friendly and benign strategies for the purification of dye polluted water is a growing challenge. To this end, Solanum americanum functionalized copper oxide-graphene oxide (CuOSA/GO) nanohybrid were prepared by an efficient, eco-friendly, and economical green procedure by using Solanum americanum leaf extract as a reducing agent (i.e., for the reduction of metal salt) as well as the stabilizing agent. The formation of CuOSA/GO nanohybrid was confirmed by using various spectroscopic and microscopic techniques. Microsphere shaped clusters of CuOSA were shown to be coated over GO surface confirmed via SEM while the crystallite size of 5.48 nm was calculated from powder x-ray diffraction data., i.e., Methyl blue (MB) (98% ± 6.9 removals in 60 min), Methyl green (MG) (87% ± 6.62 removals in 60 min), Methyl orange (MO) (69% ± 7.02 removal in 60 min), Methyl violet (MV) (63% ± 6.97 removals in 60 min) and Rhodamine B (RB) (36% ± 6.98 removals in 60 min) with apparent rate constants of 0.038, 0.034, 0.019, 0.016 and 0.007 min−1 respectively. The enhanced rate was obtained due to the large surface area provided by the GO and the synergetic effect. The green synthesis approach and photocatalytic efficiency make the CuOSA/GO nanohybrid a promising candidate for the purification of dye polluted water via photocatalytic oxidation method.

Quality control at every stage of manufacturing is a key aspect of the quality management system of any organization. Inspection at different stages of manufacturing is essential to achieve required quality of the product. This knowledge area has been studied extensively in the past with respect to inspection strategies, inspection location, and inspection intervals to minimize inspection cost. However, there is a lack of literature that examines the relationship between inspection performance and factors related to human labor and inspection time of different products. Here, offline inspection is investigated to achieve the process target values by determining the optimal number of inspectors for different products. Three skill levels for inspectors are selected on the basis of their inspection errors, inspection quantities, and inspection cost. The purpose of this study is to achieve the optimum results of objective functions that consist of inspection cost, outgoing quality, and inspection quantity by determining the optimal value of decision variables, i.e., the number of inspectors with respect to their skill. A multi-objective optimization model is developed using a stochastic approach to determine the optimal results of the objective functions and decision variables. Firstly, goal programming is employed to verify the optimization model by using numerical examples. Secondly, sensitivity analysis is considered to illustrate the effect of incoming quantity on inspection performance and optimal combination of decision variables.

The strategic importance of transactive energy (TE) in the world is steadily increasing, and therefore transactive control and coordination mechanism require effective strategy to achieve sustainable competitive advantages. Strengths, weaknesses, opportunities and threats (SWOT) analysis is one of the most effective approaches used for analysing strategic design of a system. In this study, structured SWOT analysis has been performed focusing on three advancements, namely PowerMatcher, intelligator and energy flexibility platform and interface, made in the area of transactive control and coordination mechanism. As a conclusion, applied the proposed methodology to carry out effective combinatorial strategy planning for the TE-based coordination mechanism. © 2017 The Institution of Engineering and Technology. All rights reserved. e-distribuzione; Enedis; People Make Glasgow; SP Energy Networks; WSP

Recently, Home Energy Management (HEM) controllers have been widely used for residential load management in a smart grid. Generally, residential load management aims to reduce the electricity bills and also curtail the Peak-to-Average Ratio (PAR). In this paper, we design a HEM controller on the basis of four heuristic algorithms: Bacterial Foraging Optimization Algorithm (BFOA), Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO), and Wind Driven Optimization (WDO). Moreover, we proposed a hybrid algorithm which is Genetic BPSO (GBPSO). All the selected algorithms are tested with the consideration of essential home appliances in Real Time Pricing (RTP) environment. Simulation results show that each algorithm in the HEM controller reduces the electricity cost and curtails the PAR. GA based HEM controller performs relatively better in term of PAR reduction; it curtails approximately 34% PAR. Similarly, BPSO based HEM controller performs relatively better in term of cost reduction, as it reduces approximately 36% cost. Moreover, GBPSO based HEM controller performs better than the other algorithms based HEM controllers in terms of both cost reduction and PAR curtailment.

In developed countries, the application of the quality management system (QMS) is widely proven. However, in developing countries, like Pakistan, industries are not mature enough to understand and implement the system. The aim of this study is to examine the perception of quality, implementation of QMS and cost of quality (COQ) program in value-added garment sector, which is one of the major subsectors of textile industry in Pakistan. The methodology of the study is a questionnaire survey. The findings suggest that the majority of the respondent firms have well implemented quality management (QM) practices. Trend of results also depicts that most of the organizations wish to acquire ISO certification, which shows that the industrial processes are customer-centered thus striving to fulfil customer requirements. It can also be concluded that most of the organizations lack the COQ concept, its understanding and appropriate implementation.

Increasing penetration of distributed renewable energy sources in LV grid leads to a number of power quality challenges. Meanwhile, changes in recent renewable energy-related grid codes foresee the massive introduction of remotely controllable inverters in the LV grid. In this paper, the future grid scenario is taken under consideration, thus proposing a solution for overvoltage problem while taking advantage of such controllable inverters. The paper discusses a novel approach to mitigate overvoltage problem by integrating the concept of P–V droop control into a cyber-physical paradigm. The proposed approach introduces a parameter, referred as virtual maximum power point that enables the proposed solution to mitigate overvoltage problem only by using local measurements. Above all, the proposed approach prevents any new hardware-related changes in the inverters. This paper simulates the proposed approach using a co-simulation model. However, to speed up the field implementation of the proposed approach, laboratory experiments were conducted and the solutions for the challenges in real-life implementation, including ICT-related challenges, are developed. Lastly, this paper discusses about the outcomes of the proposed approach in the light of both the theory and practical. In addition to the proposed overvoltage mitigation solution, the possibilities of using the controllable inverters for additional network services are also discussed.

The number of devices equipped with GPS sensors has increased enormously, which generates a massive amount of data. To analyse this huge data for various applications is still challenging. One such application is to predict the future location of an ambulance in the healthcare system based on its previous locations. For example, many smart city applications rely on user movement and location prediction like SnapTrends and Geofeedia. There are many models and algorithms which help predict the future location with high probabilities. However, in terms of efficiency and accuracy, the existing algorithms are still improving. In this study, a novel algorithm, NextSTMove, is proposed according to the available dataset which results in lower latency and higher probability. Apache Spark, a big data platform, was used for reducing the processing time and efficiently managing computing resources. The algorithm achieved 75% to 85% accuracy and in some cases 100% accuracy, where the users do not change their daily routine frequently. After comparing the prediction results of our algorithm, it was experimentally found that it predicts processes up to 300% faster than traditional algorithms. NextSTMove is therefore compared with and without Apache Spark and can help in finding useful knowledge for healthcare medical information systems and other data analytics related solutions especially healthcare engineering.

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%.

In this paper we propose an simple digital learning platform for flexible energy detection using data with fine granularity. The platform is empowered with artificially intelligent methods aiming to quantify the uncertainty of building energy consumption at building level, as well as at the aggregated level. Two major learning tasks are perform in this context: prediction and classification. Firstly, the building energy prediction with various time steps resolution are perform using methods such as Fully Connected Neural Networks (FCNN), Long short-term memory (LSTM), and Decision Trees (DT). Secondly, a Support Vector Machine (SVM) method is used to unlock the building energy flexibility by performing classification assuming three different levels of flexibility. Further on, a collaborative task is integrate within the platform to improve the multi-class classification accuracy. Through the end, we argue that this approach can be considered a solid integrated and automated basic block able to incorporate future AI models in (near) real-time to explore the benefits at the synergy between built environment and emerging smart grid technologies and applications.

Now the design of energy system requires needowners to play an active role by using advanced technical solutions to improve their responsiveness, process adaptability and empower end-users. Although need-owners via Demand Response (DR) has gain sufficient strategic improvement in energy management recently, there are still some fundamental impediments to achieve a trade-off between demand flexibility scheduling and dispatch. To find a solution to the challenge, the paper introduces the steps of an agile development process for energy systems, refers to a co-creation of solutions based on co-existing technologies and iterative development, where needowners requirements and solutions evolve through collaboration between cross-functional intelligent agents. The proof-of-the-concept is investigated by agent-oriented simulation for a generic low-voltage network of the Netherlands, which encounters transformer congestion. Simulation results reveal a significant reduction in congestion over a year while confirming expected levels of performance.

Although congestion management via Demand Response (DR) has gain sufficient popularity recently, there are still some fundamental impediments to achieve a trade-off between demand flexibility scheduling and demand flexibility dispatch for congestion management. To find a solution to the challenge, the paper introduces the concept and design of an Agile Net, which is an agile control strategy for congestion management. The model of Agile Net has triple cores. First, it percepts the network environment by using the concept of demand elasticity. Second, it possesses an online model-free learning technique for the management of network externality, such as congestion. Third, it enables distributed system scalability. The efficiency of the proposed Agile Net is investigated by extending the simulation tool for DR paradigm for a generic low-voltage network of the Netherlands. Simulation results reveal a significant reduction in congestion over a year while confirming expected levels of performance.

Boreal river systems globally provide specialized habitats and conduits for the transfer of nutrients, iron and trace elements (TEs) from wetlands and groundwaters to lakes and oceans. Many large boreal rivers are also the site of controversy due to their dual purpose providing ecosystem services for Indigenous peoples and supporting industrial activity; however, water quality monitoring in these systems is more complex than realized. This work demonstrates the impacts of delayed tributary mixing in a typical large boreal river on the properties and transport of Fe-bearing colloids, and water quality monitoring, in two subsequent autumns. The forms and concentrations of Fe colloids were conserved through the mixing zone for more than 100 km, despite salinity changes. Delayed mixing for ca. 60–80 and more than 100 km led to TE concentration differences of more than five-fold across the river. Using traditional samples from the middle of the river, the coincidence of mixing zones with areas of potential industrial impact obscured the potential contributions of some TEs, and caused the erroneous attribution of other TEs to industry at concentrations that were double upstream values. Adequate water quality monitoring and source attribution in large boreal rivers thus requires high-resolution spatial sampling, both across the river and upstream–downstream of potential natural and industrial sources. Since mixing behavior and water quality in large boreal rivers are highly sensitive to hydrologic changes such as seasonal fluctuations, drought/floods, and climate change, fluctuations in water quality and associated mixing behaviours render traditional monitoring approaches unsuitable.

The effect of mixed-micellization of anionic/non-ionic surfactants on the solubilization of a cationic dye (Methylene blue) is presented in this manuscript. Interactions of dye with the single anionic surfactant sodium dodecyl sulfate (SDS), and its mixture with the non-ionic surfactant (Triton X-100), were investigated. The non-ionic surfactant was initially screened, and then its concentration was kept constant at its critical micellar concentration. The critical micellar concentration, entropy, enthalpy, and free energy of micelle formation (ΔGmic°) were determined by electrical conductivity measurements. The solubilization parameters in terms of the partition coefficient (Kx) and binding constant (Kbind) were assessed by UV–Visible spectroscopy. The role of non-ionic surfactants on the electrostatic interactions of cationic dye and surfactant was also elaborated. Mixed micellization significantly enhanced the solubility of dye molecules as shown by conductivity and absorbance analysis. The disruption of structured water in the vicinal environment of hydrophobic groups of surfactants was responsible for the increase in critical micelle concentration (CMC). The negative values of energies of binding, partitioning, and micellization indicated the spontaneity and feasibility of micellar systems. The experimental outcomes supported the superiority of anionic/non-ionic mixed micellar solubilization over a single anionic surfactant. Thus, the mixed micellar system (SDS + TX-100) assured a higher degree of solubilization than the corresponding single system (SDS).

The construction sector exerts an exceptional impact on economic development all over the world. Adequate buildings and infrastructures made by the construction sector ensure that a country reaches certain targets like social development, industrialization, freight transportation, sustainable development, and urbanization. This study aims to determine the construction sector’s connectivity with other sectors through complex linkages that contribute immensely to the economy and gross domestic product (GDP). The data were collected from the Department of Statistics Malaysia and the World Bank from the year 1970 to 2019, and the Pearson correlation test, the cointegration test, and the Granger causality test were conducted. The vector error correction model (VECM) was created for short-term and long-term equilibrium analysis and impulse response function (IRF) was performed to study construction industry behavior. Afterwards, the forecasting was done for the year 2020 to 2050 of the Malaysian economy and GDP for the required sectors. It was revealed that some sectors, such as agriculture and services, have forward linkages while other sectors, such as manufacturing and mining, are independent of construction sector causality, which signifies the behavior of the contributing sectors when a recession occurs, hence generating significant revenue. The Malaysian economy is moving towards sustainable production with more emphasis on the construction sector. The outcome can be used as a benchmark by other countries to achieve sustainable development. The significance of this study is its usefulness for experts all over the world in terms of allocating resources to make the construction sector a sustainable sector after receiving a shock. A sustainable conceptual framework has been suggested for global application that shows the factors involved in the growth of the construction industry to ensure its sustainable development with time.

Vehicular edge computing (VEC) is a potential field that distributes computational tasks between VEC servers and local vehicular terminals, hence improve vehicular services. At present, vehicles’ intelligence and capabilities are rapidly improving, which will likely support many new and exciting applications. The network resources are well-utilized by exploiting neighboring vehicles’ available resources while mitigating the VEC server’s heavy burden. However, due to the vehicles’ mobility, network topology, and the available computing resources change rapidly, which are difficult to predict. To tackle this problem, we investigate the task offloading schemes by utilizing vehicle to vehicle and vehicle to infrastructure communication modes and exploiting the vehicle’s under-utilized computation and communication resources, and taking the cost and time consumption into account. We present a promising relay task-offloading scheme in vehicular edge computing (RVEC). According to this scheme, the tasks are offloaded in a vehicle to vehicle relay for computation while being transmitted to VEC servers. Numerical results illustrate that the RVEC scheme substantially enhances the network’s overall offloading cost.

The adsorption mechanism of carbon dioxide (CO2) in the coal matrix is significant in practical stability and migration process of CO2 into a coalbed seam. This study presents the kinetic investigation and the main controlling step of CO2 adsorption capacity onto Malaysian coals. The experimental data of CO2 adsorption were determined using a volumetric technique at 273, 298, 308, and 318 K and pressures up to 99.3 kPa. The experimental data of CO2 adsorption was studied using kinetic based thermodynamic models. Fourier Transform Infrared Spectroscopy and X−Ray Diffraction analyses were performed for the coal samples characterization. The major functional group in all coal samples is hydroxyl (−OH) functional group. X−Ray Diffraction analysis has shown that the coal samples possessed one major peak assigned to quartz (d = 3.348 A). The experimental results were correlated using kinetic models, which include pseudo−first−order, pseudo−second−order, Avrami, and Intra-particle diffusion models. The Intra−particle diffusion model was found in the best compliance with the experimental data. Therefore, the pore−diffusion is considered to be the primary limiting step for CO2 interaction with the coal matrix. This indicated that the molecules of CO2 transferred rapidly from the bulk to the surface of coal matrix and slowly diffused into pores of the coal matrix. The obtained results demonstrated that the overall CO2 interaction with the coal matrix is influenced by the diffusion limiting step. The value of activation energies for all studied coal samples is lower than 8 kJ/mol. This showed that CO2 adsorption onto all investigated coal samples is driven by a physical adsorption process.

The present study aims at the assessment of six satellite rainfall estimates (SREs) in Pakistan. For each assessed products, both real-time (RT) and post adjusted (Adj) versions are considered to highlight their potential benefits in the rainfall estimation at annual, monthly, and daily temporal scales. Three geomorphological climatic zones, i.e., plain, mountainous, and glacial are taken under considerations for the determination of relative potentials of these SREs over Pakistan at global and regional scales. All SREs, in general, have well captured the annual north-south rainfall decreasing patterns and rainfall amounts over the typical arid regions of the country. Regarding the zonal approach, the performance of all SREs has remained good over mountainous region comparative to arid regions. This poor performance in accurate rainfall estimation of all the six SREs over arid regions has made their use questionable in these regions. Over glacier region, all SREs have highly overestimated the rainfall. One possible cause of this overestimation may be due to the low surface temperature and radiation absorption over snow and ice cover, resulting in their misidentification with rainy clouds as daily false alarm ratio has increased from mountainous to glacial regions. Among RT products, CMORPH-RT is the most biased product. The Bias was almost removed on CMORPH-Adj thanks to the gauge adjustment. On a general way, all Adj versions outperformed their respective RT versions at all considered temporal scales and have confirmed the positive effects of gauge adjustment. CMORPH-Adj and TMPA-Adj have shown the best agreement with in situ data in terms of Bias, RMSE, and CC over the entire study area.

With the introduction of smart meters, dynamic pricing and home energy management systems, residential customers are able to react to changes in electricity prices. In an unbundled market, the energy supplier and the network operator may have conflicting interests with respect to demand response (DR) programs. As the customer participation is essential to a well-functioning DR program, it is needed to assess which DR programs offers the most benefits to customers. Two DR program options are analysed for low-voltage feeders: a program from the energy supplier based on the electricity price, and a demand response program from the network operator based on the loading of the network. Depending on the grid topology the benefits can change significantly between the two DR programs. DR from an energy supplier point of view might induce under-voltages which lead to grid reinforcements, while load shifting from a network point of view can generate higher electricity cost.

The distributed resources – distributed generations, storage, electric vehicles and smart appliances – have fueled many disruptions in the conventional grid dynamics. Increasing tough and competitive situations at demand-side have led the stakeholders of power system to reform, because people are more informed and can make decisions that has given rise to turbulent and volatile electricity markets. In such highly diversified and open market environment, the integrated control strategy has expedited the concept of Demand Response. Currently, the important questions in the area of demand response are likely to shift from whether to adopt dynamic pricing to how to achieve fast demand response by the combination of technology and a competitive retail electricity market.This paper assembles the part and parcel in the light of existing technologies for the development of the concept of agility in demand response, that can play a role as a service package which is designed to meet the customer satisfaction efficiently and reliably as well as improve the market responsiveness.For the first time, this paper encompasses the concept of Agile Demand Response that is defined as the ability of power system to survive and prosper in the competitive retail market by reacting to the market needs fast and effectively which are driven by the customers. This paper also presents the key enablers of the agile demand response, which are (1) Retail Market, (2) Virtual Supply-demand matching mechanism, (3) Integrated information system and (4) Stay Committed. The paper concludes that by bringing the enablers together – Agile DR improves the responsiveness to the retail market changes unambiguously.

The minimisation of the continuously enhancing level of the CO2 released to the atmosphere is one of the most significant issues faced by the scientific community. Rigorous research efforts have been carried out for the development of sustainable and cost-effective nitrogen-rich porous adsorbent materials for energy-efficient and enhanced polar gas separation, i.e. pre-combustion and post-combustion CO2 capture. Among different porous adsorbent materials, the covalent triazine frameworks (CTFs) are found to be remarkable candidates for CO2 capturing because of their facile and scalable synthesis, high surface area, permanent porosity, structural tunability, synthetic diversity, low density, high hydrothermal and physicochemical stability. A contextual overview is described on the key challenges in CO2 sequestration, parameters consideration for the design of CO2 selective porous adsorbents, evaluation criteria for the adsorption processes, assessment criteria for the selection of suitable adsorption configuration, and the factors influencing the CO2 adsorption capacity. This review comprises deep critical scrutiny of the current investigation and development on Triazine-, benzimidazole-, and triazole-based COPs with improved CO2 storage capacities. The conversion of CO2 into useful products including the carbon monoxide (CO), methane (CH4), methanol (CH3OH), and other products including the hydrocarbons has been critically reviewed by using the heterogeneous catalysis. Finally, a concise conclusion and recommendation section are presented indicating that the area of Triazine-, benzimidazole-, and triazole-based COPs for CO2 capture needs more attention to synthesise the next-generation materials for real-time applications.

The number of wireless sensors in use&mdash, for example, the global positioning system (GPS) intelligent sensor&mdash, has increased in recent years. These intelligent sensors generate a vast amount of spatiotemporal data, which can assist in finding patterns of movements. These movement patterns can be used to predict the future location of moving objects, for example, the movement of an emergency vehicle can be predicted for health care decision-making. Although there is a body of research work regarding motion trajectory prediction, there are no guidelines for choosing algorithms best suited for individual needs in uncertain and complex situations and as per the application domains. In this paper, we surveyed existing trajectory prediction algorithms. These algorithms are further ranked scientifically in terms of accuracy (performance), ease of use, and best fit as per the available datasets. Our results show three top algorithms, namely NextPlace, the Markov model, and the hidden Markov model. This study can be beneficial for multicriteria decision-making for various disciplines, including health care.

The minimization of escalating atmospheric emission level of CO2 is alarming. Intensive research has been done to study the gas storage properties of the porous materials, especially, covalent organic polymers (COPs) and their functionalization to improve CO2 storage capacities. COPs are excellent for CO2 capture because of ultra-high specific surface area, high porosity, stability, and structural diversity. A background is reported on the CO2 sources, capturing strategies, separation technologies, the thermo-kinetic aspects of CO2 adsorption, and synthesis methods. This review provides an in-depth critical analysis of the current research on non-functionalized COPs and functionalized COPs with improved CO2 storage capacities.

An individual’s privacy protection is the concerning issue in surveillance videos. Existing research work for individual’s identification on the bases of their skin detection is focused either on different human skin detection techniques, or on protection. This research paper considers both lines of research and proposes a hybrid scheme for human skin detection and protection by utilizing color information in dynamically varying illumination and environmental conditions. For the purpose, the dynamic and explicit skin detection approaches are implemented simultaneously considering the multi-color-space i.e. RGB, perceptual (HSV) and orthogonal (YCbCr) color-spaces and then the human skin is detected by the proposed Combined Threshold Rule (CTR) based segmentation by considering the advantages of three multi-color-spaces. The comparative qualitative and quantitative detection results with average 93.73% accuracy imply that the proposed scheme gain considerable accuracy without incurring the training cost. Secondly, once skin detection has been performed, the detected skin pixels (including false positives) are encrypted with state-of-the-art Advance Encryption Standard with Cipher Feedback Mode (AES-CFB) rather than applying selective encryption on complete video. The proposed scheme preserves the behavior of the subjects within the video, hence can be useful for further image processing and behavior analysis and if required can be decrypted by the authorized user. The experimental results show that average encryption time is 8.268 sec and Encryption Space Ratio (ESR) with an average 7.25 % for HD cricket video (1280 x 720 pixels/frame) strongly imply that to protect a person within a video, the method of encrypting skin detection is preferable. Thirdly, performance comparison between the proposed method in term of Correct Detection Rate (CDR) with an average 91.5%, RGB with 85.86%, HSV with 80.93% and YCbCr with an average 84.8% imply that proposed method has high potential to detect the skin accurately. Furthermore, the security analysis performed confirms that proposed scheme could be a suitable choice for real-time surveillance applications working on resource constrained devices. yes