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The paper presents the results of a study of parameters used for determining the amount of organic pollutants in wastewater flowing into a collective wastewater treatment plant with a population equivalent of about 120 000 PE. The plant constituted part of a sewage system. Assays were performed for biochemical oxygen demand (BOD5), chemical oxygen demand (CODCr), permanganete index (CODMn) and total organic carbon (TOC). In addition, the pH of wastewater and its alkalinity were determined. Sampling of incoming wastewater and measurement of physicochemical parameters were performed once a week, in the spring. A total of 44 samples of wastewater were collected. The correlations between the individual parameters and the correlations between the ratios of these parameters were determined. It was found that it was possible to estimate, with suffi cient accuracy, the values of selected parameters for determining the concentrations of organic compounds in municipal wastewater, knowing the values of other parameters in this group. Moreover, it was shown that, knowing the relationship between the analyzed parameters, one can find multiple regression equations for a given type of wastewater, which allow one to calculate the remaining relationships with a good fit, i.e. a determination factor R2greater than 0.6

Everyday, tons of food wastes are thrown to the environment constantly by people in the cities, which contain many beneficial substances. One of these beneficial wastes of unknown value is eggshells. Although the eggshells are considered negligible and not valuable material, however, the many researches that dealt with this substance proved its high potential to turn into many useful substances such as a catalyst in biodiesel preparation or as a media for adsorption of heavy metals and dyes or soil fertilizer and others. The current study highlighted the benefit possibility of white chicken eggshells (WCES) from another side, considering it as a raw material for preparing acetone one of the most important industrial solvents. The results obtained showed that this worthless raw material has a high effectiveness for acetone production by containing 94.519% by weight of calcium carbonate. The process of converting WCES to acetone was carried out in two steps, the first one were producing calcium acetate by treating it with glacial acetic acid in a batch mode reaction unit at room temperature and agitation speed, acid % excess and contact time of 200 rpm, 20% and 2 h respectively. Whereas, the next step involved conducting the process of thermal decomposition of the prepared calcium acetate at 380 °C to produce acetone in a stainless steel reactor. The results of testing the samples by the gas chromatography (GC) showed that the maximum yield of acetone is 96.395%. With these experimental results, this study presents a new idea in food waste management as one of the solid waste types by utilizing it in a useful manner to produce important materials in an economical, beneficial and environmentally friendly way, reaching to the concept of zero residue level (ZRL).

We show that a A-linear map of Hilbert A-modules is induced by a unitary Hilbert module operator if and only if it extends to an ordinary unitary on appropriately defined enveloping Hilbert spaces. Applications to the theory of multiplicative unitaries let us to compute the equivalence classes of Hilbert modules over a class of C*-algebraic quantum groups. We, thus, develop a theory that, for example, could be used to show non-existence of certain co-actions. In particular, we show that the Cuntz semigroup functor takes a co-action to a multiplicative action.

As an aromatic polymer in nature, lignin has recently attracted gross attention because of its advantages of high carbon content, low cost and bio-renewability. However, most lignin is directly burnt for power generation to satisfy the energy demand of the pulp mills. As a result, only a handful of isolated lignin is used as a raw material. Thus, increasing value addition on lignin to expand its scope of applications is currently a challenge demanding immediate attention. Many efforts have been made in the valorization of lignin, including the preparation of precursors for carbon fibers. However, its complex structure and diversity significantly restrict the spinnability of lignin. In this review, we provide elaborate knowledge on the preparation of lignin-based carbon fibers ranging from the relationships among chemical structures, formation conditions and properties of fibers, to their potential applications. Specifically, control procedures for different spinning methods of lignin, including melt spinning, solution spinning and electrospinning, together with stabilization and carbonization are deeply discussed to provide an overall understanding towards the formation of lignin-based carbon fibers. We also offer perspectives on the challenges and new directions for future development of lignin-based carbon fibers.

Graphical abstract, Introduction Face masks are regarded as effective Personal Protective Equipment (PPE) during the COVID-19 pandemic. However, the dominant polypropylene (PP)-based masks are devoid of antiviral/antibacterial activities and create enormous environmental burdens after disposal. Objectives Here we report a facile and potentially scalable method to fabricate biodegradable, breathable, and biocidal cellulose nonwovens (BCNWs) to address both environmental and hygienic problems of commercially available face masks. Methods TEMPO-oxidized cellulose nonwovens are rendered antiviral/antibacterial via covalent bonding with disinfecting polyhexamethylene guanidine or neomycin sulfate through carbodiimide coupling chemistry. Results The obtained results showed that the BCNWs have virucidal rate of >99.14%, bactericidal efficiency of >99.51%, no leaching-out effect, and excellent air permeability of >1111.5 mm s−1. More importantly, the as-prepared BCNWs can inactivate SARS-CoV-2 instantly. Conclusions This strategy provides a new platform for the green fabrication of multifunctional cellulose nonwovens as scalable bio-protective layers with superior performance for various PPE in fighting COVID-19 or future pandemics. Additionally, replacing the non-biodegradable non-antimicrobial PP-based masks with the cellulose-based masks can reduce the plastic wastes and lower the greenhouse gas production from the incineration of disposed masks.

Biorefinery is pivotal to the sustainability of modern chemical industry. However, since biomass is oxygen-enriched, new and green chemical strategies are required for expanding the biomass derived chemical space. In this work, synthesis of natural products dihydrocapsaicin and dihydrocapsiate was achieved exclusively from lignocellulosic platform chemicals. Natural products dihydrocapsaicin and dihydrocapsiate were synthesized exclusively from lignocellulosic platform chemicals, using furfural (from hemicellulose) and methyl isopropyl ketone (from cellulose) through aldol condensation-hydrolysis-hydrodeoxygenation to synthesize 8-methylnonanoic acid and then combined with vanillin derivates (from lignin). This synthesis demonstrates the feasibility of constructing natural products entirely from renewable biomass platform through green processes. The utilization of inherent functional groups of biomass demonstrates their potential to open up chemical space.

In order to investigate the effect of washing water pH on textile damage for delicate garments, the study was conducted such that a customer survey was first applied to randomly selected users to analyze domestic washing machine using habits of Turkish consumers. Based on the findings of the survey, the experimental study was designed in two successive phases to investigate the dimensional and mechanical behavior of certain types of delicate textiles against varying washing conditions by taking in particular, water properties, namely pH, hardness, and temperature into account, and accordingly to determine the optimized washing conditions for such textiles. Firstly, tergotometer was employed as a washing machine simulator. Within the light of the results obtained, the experimental work of the second phase of the study was conducted, which involved a domestic washing machine as a real-life scenario. All of the results, including the correlation between the data sets obtained from the tergotometer and domestic washing machine trials, were statistically analyzed using Minitab 17. The study produced some important findings regarding the effect of washing water pH on delicate textiles, in addition to an algorithm for improving the present washing program, minimizing textile damage for mainly wool and silk garments.

COVID-19 has forced government and health agencies to take measures to mitigate the spread of the disease and thus safeguard as many lives as possible. These measures have initially impacted the economy of many countries, and therefore they have been forced to gradually return to a new normalcy, in what they have called reopening. For reopening policies to be effective, it is necessary that the people in charge of drawing up these policies know the local behavior of the propagation of COVID-19, and beyond this they can understand that between the cases of COVID-19 and the socioeconomic conditions of their population there is a relationship. For this reason, in this article a case study is presented, which allowed to evaluate the relationship between positive cases of COVID-19 and the multidimensional poverty index (MPI) in the city of Manizales, Colombia. The results of an exploratory analysis, obtained with the use of remote sensing data, are presented, which allowed to confirm the relationship in mention, and it is hoped that this can serve the municipal administration in its decision making.

Serial femtosecond crystallography (SFX) is a powerful technique that exploits X-ray free-electron lasers to determine the structure of macromolecules at room temperature. Despite the impressive exposition of structural details with this novel crystallographic approach, the methods currently available to introduce crystals into the path of the X-ray beam sometimes exhibit serious drawbacks. Samples requiring liquid injection of crystal slurries consume large quantities of crystals (at times up to a gram of protein per data set), may not be compatible with vacuum configurations on beamlines or provide a high background due to additional sheathing liquids present during the injection. Proposed and characterized here is the use of an immiscible inert oil phase to supplement the flow of sample in a hybrid microfluidic 3D-printed co-flow device. Co-flow generation is reported with sample and oil phases flowing in parallel, resulting in stable injection conditions for two different resin materials experimentally. A numerical model is presented that adequately predicts these flow-rate conditions. The co-flow generating devices reduce crystal clogging effects, have the potential to conserve protein crystal samples up to 95% and will allow degradation-free light-induced time-resolved SFX.

The efficiency of hot water extraction (HWE) is dependent on the size of treated wood. While previous research regarding this size-effect has focused on HWE treating sawdust and wood chips, this study investigated its effect on wood blocks with precise dimensions and a broad range of treatment conditions. Scots pine (Pinus sylvestris L.) sapwood samples with dimensions of 10 × 10 × 20 mm3 and 25 × 25 × 50 mm3 (R × T × L) were HWE treated at 130–170 °C for 40–200 min using liquid-to-solid ratios of 4–20. Our results showed that wood mass loss, which was primarily caused by the decomposition of hemicelluloses, was larger when using small samples. This was mainly assigned to a higher quantity of acetone-soluble decomposition products that remained within the large samples, due to longer distances for diffusion and mass transfer from the wood blocks to the extraction liquid. In line with wood mass loss differences, the amount of dissolved compounds (i.e., carbohydrates) in the extraction liquid at different treatment severities was dependent on the wood size, while the liquid-to-solid ratio had only modest effects. However, composition changes of the extraction liquid, in particular changes in the proportion of poly- and monocarbohydrates with increasing treatment severity, were similar for both sample sizes.

The aim of the work was to assess the efficiency of ozonation and ozonation in combination with H$_2$O$_2$ in jet loop reactor to increase biotreatability of persistent veterinary antibiotic Tiamulin. The efficiency of oxidative processes was monitored by combined approach based on determination of efficiency of wastewater treatment and impact to waste sludge stabilization. Degradation of Tiamulin in model wastewater (100 mg L$^{−1}$) during oxidation was followed by COD and DOC measurements while changes in biodegradability were determined by respirometric measurements. Biogas production potential was also determined to identify problems related to anaerobic digestion of waste sludge resulted in treatment of Tiamulin-contaminated wastewater. At ozone dose of 69 g$_{ozone}$ g$_{COD}^{−1}$ and 220 g$_{ozone}$ g$_{DOC}^{−1}$removal for COD and DOC was 26% and 17%, respectively. Better biotreatability was confirmed by respirometric testing. H$_2$O$_2$ addition did not improve removal efficiency (11–13%). The second stage of nitrification was suppressed by the addition of Tiamulin and ozonation again recovered N-NO$^−_3$ formation. O$_3$/H$_2$O$_2$ treated sample reduced the nitrification, especially formation of N-NO$^−_2$ in the first phase of the process. Simultaneously, quadratic model was developed to describe the relationship between oxygen uptake rate and changes in ammonium nitrogen concentration due to the oxidative treatment. The positive impact of ozone was also confirmed by ozonation of Tiamulin-contaminated (400 mg L$^{−1}$) waste sludge where biogas production potential was increased for 6-times. Combination of approaches confirmed, that O$_3$ effectively increase the treatability of Tiamulin in wastewater and sludge while addition of the hydrogen peroxide generally did not improve the performance of the processes.

In the 21st century the academic field of Child–Computer Interaction (CCI) arose alongside burgeoning interactive technology and digital media industries that targeted children. We believe that the field of CCIU is at an important point in its development, analogous to when a child becomes a teen. Over the last few years we have each had many informal conversations with other CCI researchers in which we discuss issues such as, what is our responsibility as researchers beyond academe? What values underlie our conceptions of a ”good” childhood and the role of interactive technology in it? And, how do we ensure that our field continues to grow and evolve in ways that are consistent with our responsibilities and values? To address these and other complex questions that have been drawing our attention we came together to reflect, discuss and create a position paper for our community, in which we outline some of the issues we see facing our community at this time. To inform our deliberations with opinions beyond our own we conducted an informal consultation with 25 members of the CCI community. Our responders spanned junior to senior researchers, represented diverse geographies and included industry practitioners. These diverse responses provided further content for our reflections, and helped us see perspectives beyond our own. The result of this informal process is this speculative paper in which we propose a series of seven provocations that aim to disrupt some of the normative assumptions held in our field. Our goal in doing this is to open up dialogue in our community about these issues and promote consideration of the alternative visions we present for where we might focus our attention and efforts. We see our contribution not as truth or a definitive statement of a vision for the field, but rather as our opinion about some of the complex issues we face and that we think should be considered through dialogue as we move into the next phase of our development as an academic and scholarly community. We believe that it is urgent and critical for our field that we take up these questions, explore diverse perspectives, and critically work towards decisions and actions that will define our identity and the value of our contributions as we move forward into the next 20 years of research in CCI.

Crude oil, which exported to refineries, already contains salt, water, and fouling crude oil received with salt content not less than 50 ppm. Dewania refinery with a capacity of 20,000 BPSD, which serves with two crude distillation units, each unit with a capacity of 10,000 BPSD, which operate without crude desalter. In an aim to reduce the effects of salts, water and, fouling associated with crude oil, two crude distillation units connected with one crude oil desalter with a capacity of 20,000 BPSD (one desalter). crude oil desalter transferred from (Daura Refinery) to Dewania refinery, in aim to reduce salt content from 50ppm to 5 ppm and mitigate water and other fouling. Crude oil desalter installed in the middle distance between two crude distillations units (90 m from each unit isometric piping). Crude oil, which is pumped by a charge pump to preheated in crude oil distillation unit with a train of heat exchangers. When the pipeline size increased from 4″ to 6″, which reduces the pressure dropped from 0.946 to 0.15 bar for each transfer pipeline and in consequence, the total pressure drop reduces from 11.011 to 10.215 bar for the whole unit. In an aim to reduce the heat dissipated from surface of transfer pipeline. Each transfer pipeline insulated with calcium silicate insulator, the thickness of insulator increased from 38mm to 50mm in an aim to reduce heat loss from −101.56 watts/m to −84.282 watts/m, which reduced temperature difference between the surface pipeline and environment from 13 to 10°C.

Enzymatic treatments have gained popularity in the textile industry because of environmental friendly and energy conserving alternatives. Advancement in biotechnology and modification of enzymes has been focused based on various textile process applications. All the manufacturing steps of textile chemical processing, enzymes are using for implementations of the green technology to meet up the challenge of fourth industrial revolution. In this category, amylases, peroxidase used for desizing and bleaching, cellulase activates for bio polishing and denim finishing. This chapter summarizes the current developments of enzyme technology and highlights the environment-friendly and sustainable enzymatic textile processing in the textile industry.

Funder: CSC Cambridge Scholarship, In this study, we evaluate the suitability of using DC-biased aluminium resonators as low-frequency kinetic inductance detectors capable of operating in the frequency range of 50–120 GHz. Our analysis routine for supercurrent-biased resonators is based on the Usadel equations and gives outputs including density of states, complex conductivities, transmission line properties, and quasiparticle lifetimes. Results from our analysis confirm previous experimental observations on resonant frequency tuneability and retention of high quality factor. Crucially, our analysis suggests that DC-biased resonators demonstrate significantly suppressed superconducting density of states gap. Consequently these resonators have lower frequency detection threshold and are suitable materials for low-frequency kinetic inductance detectors.

Intelligent responsive microcarriers have emerged as a promising class of biomaterials for therapeutic delivery and tissue regeneration, since they can respond to external stimuli and release the loaded drugs in an active manner. Among various available stimuli, near‐infrared (NIR) light is particularly attractive because it can penetrate biotic tissues with sufficient intensity and minimal damage. In this work, a kind of photoresponsive delivery microcarriers (PDMs) is developed using microfluidics. The microcarriers consist of NIR‐absorbing graphene oxide, thermosensitive poly(N‐isopropylacrylamide), and biocompatible gelatin methacrylate. Under NIR light, the PDMs exhibit an evident volume shrinkage and effectively trigger the drug release. After the NIR light is switched off, the shrunken microcarriers return to their original size. This reversible process can be stably repeated for many cycles. An in vitro experiment demonstrates that the NIR‐radiated PDMs can actively release vascular endothelial growth factors and improve the tube formation of human umbilical vein endothelial cells. The results from the in vivo experiment also show an obvious photothermal effect and superior therapeutic efficacy of these PDMs in a rat model of tissue defects. These features make the PDMs an excellent drug delivery system and represent a great potential for clinical applications in tissue repair., Photoresponsive delivery microcarriers are developed using microfluidics, which consist of graphene oxide, poly(N‐isopropylacrylamide), and gelatin methacrylate. These microcarriers can actively respond to near‐infrared light and release the encapsulated drugs according to the irradiation frequency. After being implanted into a rat model of abdominal wall defects, they also exhibit potential value in tissue repair.

Organosolv processes provide access to less degraded and higher purity lignins. However, these are rarely applied to softwood species with high lignin contents and complex condensed structures. We are presenting here the results of an optimized organosolv process, allowing for a high extraction yield of organosolv lignin from black spruce (Picea mariana), while preserving a good solid cellulose recovery. Optimized conditions are 70% ethanol liquor with a ratio of 1:10 (wood:liquor) and 5% FeCl3·6H2O as catalyst, treated in a Parr reactor for 90 min at 180 °C. The cellulosic pulp was then extracted with pure ethanol to recover the 2nd lignin. This 2nd lignin was darker in colour and denser than the 1st lignin obtained by precipitation from residual liquor. The other lignin properties were almost identical. The total lignin recovery averaged at 74% (with a purity of 97%), the highest result for lignin recovery from softwoods published so far.

Supercritical water gasification (SCWG) utilizes water as the reaction medium to convert biomass into a mixture of gases (i.e., H2, CO2, CO, CH4, and other short-chain hydrocarbons) at typical operating temperature ≥ 400 °C and pressure ≥ 23 MPa. Understanding kinetics of SCWG processes is critically important for future development and scale-up application of the SCWG technology. Thus, this study aimed to develop a general kinetic model to predict the yields of gases from SCWG of various lignocellulosic feedstocks with varying contents of cellulose, hemicellulose and lignin. The model was established based on the experimental results from K2CO3-catalyzed SCWG of various biomass model compounds including cellulose, xylan and lignin at 450–550 °C for 10–50 min, followed by validation of the model using the experimental data obtained from SCWG of real biomass (i.e., corn stalk and pinewood) under same reaction conditions. The validation results showed that the general kinetic model developed could accurately predict the yields of gases from real biomass SCWG and the quantitative influences of temperature and residence time on the gas yield in biomass SCWG. Additionally, the kinetic model has been validated other SCWG data in the literature obtained with various facilities and different lignocellulosic biomass feedstocks. The development of the general kinetic model provides an applicable way to assess the potential of SCWG of various lignocellulosic biomass feedstocks for producing combustible gases at various conditions.

There is a dire need to cut post-harvest food loss to improve global food security, public health, and sustainable natural resource use. Fruits and vegetables constitute one of the most perishable food items and typically require cold storage systems, adequate packaging, and efficient transport and road systems to reduce their loss. This research showed that by stabilizing cornstarch and beeswax emulsion with cellulose nanocrystals, a sustainable and edible coating that substantially reduces the spoilage of fruits and other produce could be obtained. Native and aliphatic succinic anhydride-modified CNCs were employed as Pickering emulsifiers. The effect of emulsion stabilizers concentrations on the enhancement of physical properties, reduction of water vapor loss, and oxygen permeability was investigated, and 2 wt.% modified CNC was selected as the optimal Pickering emulsion stabilizer. Fresh bananas, strawberries, and fresh-cut apples that were dip-coated with the formulated edible coating displayed magnificent color and freshness preservation by reducing oxygen activities and preventing moisture losses. Overall, the studied emulsion is edible and inexpensive, making it an exciting material that can be used to combat fresh produce waste caused by oxidative and enzymatic over-ripening.

Biofiltration is a widely used process in drinking water treatment plants to remove natural organic matter (NOM). A novel biofiltration process using ion exchange resins as supporting media (i.e., biological ion exchange or BIEX) has been demonstrated to provide a superior performance compared to conventional biological activated carbon (BAC). In order to optimize the performance of BIEX filters, the impact of temperature and empty bed contact time (EBCT) on NOM removal was systematically studied. In the present study, bench-scale BIEX filters were set up in parallel with BAC filters and operated at different temperatures (i.e., 4 °C, 10 °C and 20 °C) and EBCTs (i.e., 7.5 min, 15 min and 30 min). Higher average dissolved organic carbon (DOC) removal was achieved in BIEX filters (73 ± 6%) than BAC filters (22 ± 9%) at the steady state with an EBCT of 30 min. Higher temperatures improved NOM removal in both BAC and BIEX filters, with the impact being greater at lower EBCTs (i.e., 7.5 min and 15 min). Higher EBCTs could also improve NOM removal, with the impact being greater at lower temperatures (i.e., 4 °C and 10 °C). DOC removal for BIEX and BAC filters can be modeled with a first-order kinetic model (R2 = 0.93–0.99). BAC had a higher temperature activity coefficient than BIEX (1.0675 vs. 1.0429), indicating that temperature has a greater impact on BAC filtration than BIEX filtration. Overall, temperature and EBCT must be considered simultaneously for biofilters to efficiently remove NOM.

Maximizing biomass production is a key factor in developing large-scale microalgae cultivation. One major aspect to address is the cultivation medium composition in regards to the process objective. In this study, the effect of eight important nutrients/factors (glucose, nitrate, phosphate, sulfate, magnesium, iron, manganese, and calcium) and their interactions were investigated on the biomass production of the microalgae Tetradesmus obliquus under mixotrophic conditions. The Taguchi method, a robust tool for experimental design optimization, was used to find the optimum media composition for biomass production, with only a few well-defined experimental sets. Using Taguchi's L18 orthogonal array resulted to a 4-fold enhancement in biomass yields, with a maximum of about 7.98 g/L. Nitrate exhibited the most significant effect. Iron and phosphate showed a strong influence on short-term cultivations (6 to 9 days), while glucose and calcium had more impact on long-term cultivations (15 days). The overall optimum experimental conditions were provided.

Membrane fouling in a membrane bioreactor (MBR) is highly influenced by the characteristics of the influent, the mixed liquor microbial community and the operational parameters, all of which are environment specific. Therefore, we studied the dynamics of microbial community during the treatment of real municipal wastewater in a pilotscale anoxic-oxic (A/O) MBR equipped with a gravity-driven membrane filtration system. The MBR was operated at three different solid retention times (SRTs): 25, 40 and 10 days for a total period of 180 days in Nordic environmental conditions. Analysis of microbial community dynamics revealed a high diversity of microbial species at SRT of 40 days, whereas SRT of 25 days was superior with microbial richness. Production of soluble microbial products (SMP) and extracellular polymeric substances (EPS) was found to be intensely connected with the SRT and food to microorganism (F/M) ratio. Relatively longer operational period with the lowest rate of membrane fouling was observed at SRT of 25 days, which was resulted from the superior microbial community, lowest production of SMP and loosely bound EPS as well as the lower filtration resistance of larger sludge flocs. Abundance of quorum quenching (QQ) bacteria and granular floc forming bacterial genera at SRT of 25 days provided relatively lower membrane fouling tendency and larger floc formation, respectively. On the other hand, substantial amount of various surface colonizing and EPS producing bacteria was found at SRT of 10 days, which promoted more rapid membrane fouling compared with the fouling rate seen at other tested SRTs. To sum up, this research provides a realistic insight into the impact of SRT on microbial community dynamics and resulting characteristics of mixed liquor, floc size distribution and membrane fouling for improved MBR operation. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

The interest in the A-stage of the adsorption/bio-oxidation (A/B) process has considerably increased due to its capacity of carbon redirection to the solids stream. Induced by its flexible and compact design, the Alternating Activated Adsorption (AAA) was recently implemented in full-scale as an alternative A-stage system. However, the literature on such a system is scarce. In this article for the first time, the performance of the novel AAA system is evaluated. Two lab-scale AAA systems were operated as a primary settler replacement (AAA-1) or to complement the primary settler (AAA-2). Systems were assessed in terms of process control, effluent quality and carbon diversion. As settling and aeration are performed in the same reactor, AAA maintained high MLSS (2121 ± 293 mg/L for AAA-1 and 806 ± 116 mg/L for AAA-2) compared to the literature at such a very low aerobic SRT (

Abundant in municipal, agricultural, and industrial wastewaters, ammonia is a versatile chemical, which can be used as fertilizer or as a hydrogen carrier for the hydrogen economy. Membrane contactors (MC) are an effective membrane process for selectively recovering ammonia from a wastewater stream, but the system’s continuous acid consumption and the residual acidity in the produced stream remain a major hurdle. In this study, we propose a chemical-free, electrified process that combines MC with bipolar membrane electrodialysis (BMED) to produce ammonia as a gas from wastewater. We devise three different BMED configurations, and identify the most suitable configuration by examining the impact of different operating conditions including pH, initial ammonia concentration, and relative volume ratios on the ammonia recovery and BMED energy consumption. We demonstrate up to ∼68% recovery of theoretically recoverable ammonia, and attribute the unrecovered fraction primarily to the diffusion of neutral ammonia through membranes. While the system is sub-optimal, the relative energy consumption of the system is comparable to the Haber-Bosch process for conventional ammonia production, with potentially lower energy consumption and higher ammonia recovery through higher initial ammonia concentrations, higher relative volume ratios or alternate BMED configurations.

Today there is a rapid development taking place in phenotyping of plants using non-destructive image based machine vision techniques. Machine vision based plant phenotyping ranges from single plant trait estimation to broad assessment of crop canopy for thousands of plants in the field. Plant phenotyping systems either use single imaging method or integrative approach signifying simultaneous use of some of the imaging techniques like visible red, green and blue (RGB) imaging, thermal imaging, chlorophyll fluorescence imaging (CFIM), hyperspectral imaging, 3-dimensional (3-D) imaging or high resolution volumetric imaging. This paper provides an overview of imaging techniques and their applications in the field of plant phenotyping. This paper presents a comprehensive survey on recent machine vision methods for plant trait estimation and classification. In this paper, information about publicly available datasets is provided for uniform comparison among the state-of-the-art phenotyping methods. This paper also presents future research directions related to the use of deep learning based machine vision algorithms for structural (2-D and 3-D), physiological and temporal trait estimation, and classification studies in plants.

In this paper, we propose cryptanalytic results on two lightweight stream ciphers: Fountain v1 and Lizard. The main results of this paper are the followings: \begin{document}$ - $\end{document} We propose a zero-sum distinguisher on reduced round Fountain v1. In this context, we study the non-randomness of the cipher with a careful selection of cube variables. Our obtained cube provides a zero-sum on Fountain v1 till \begin{document}$ 188 $\end{document} initialization rounds and significant non-randomness till \begin{document}$ 189 $\end{document} rounds. This results in a distinguishing attack on Fountain v1 with \begin{document}$ 189 $\end{document} initialization rounds. \begin{document}$ - $\end{document} Further, we find that the same cipher has a weakness against conditional Time-Memory-Data-Tradeoff (TMDTO). We show that TMDTO attack using sampling resistance has online complexity \begin{document}$ 2^{110} $\end{document} and offline complexity \begin{document}$ 2^{146} $\end{document} . \begin{document}$ - $\end{document} Finally, we revisit the Time-Memory-Data-Tradeoff attack on Lizard by Maitra et al. (IEEE Transactions on Computers, 2018) and provide our observations on their work. We show that instead of choosing any random string, some particular strings would provide better results in their proposed attack technique.

At present, the economic benefits of double inlet and double outlet ball mills, which are commonly used in coal-fired power plants, need to be improved. Based on this, this paper explores the factors influencing the output of double inlet and double outlet ball mill by establishing the output system of double inlet and double outlet ball mill. By analyzing the influence factors of the output of double inlet and double outlet ball mill, and combining with system dynamics, the dynamic model of double inlet and double outlet ball mill output system is established, and SPSS is used to fit the model data with the real data. The fitting degree is above 0.8, and the authenticity is high. The simulation results show that the impact energy of barrel grinding, the grinding powder fineness, and the pressure ratio in the ball mill affect the output of double inlet and double outlet ball mill according to certain relations. Finally, the research in this paper provides theoretical and technical support for improving the economic benefits of double inlet and double outlet ball mills.

This paper addresses the problem of anomaly detection for high-dimensional sensing data. The one-class support vector machine (OCSVM) is one of the most popular unsupervised methods for anomaly detection. When data are high dimensional and large scale, however, the efficiency of OCSVM-based methods in anomaly detection suffers. Although dimensionality-reduction tools, such as deep belief networks, can be applied to compress the high-dimensional data to alleviate the problem, the accuracy and timely detection are still hard to improve due to the inherent features of OCSVM. In this paper, we propose a new form of OCSVM model based on the structure of the compressed data and the characteristics of OCSVM. Based on the new model, we design both optimal and approximate methods for model training and testing. We evaluate the performance of our methods with extensive experiments on four real-world datasets. The experimental results demonstrate that our new methods, both optimal and approximate ones, not only significantly outperform the state-of-the-art in accuracy and efficiency, but also achieve the good performance without the need of manual parameter tuning. In addition, our approximate training and testing mechanism can reduce the computing time by three orders of magnitude with a negligible loss in accuracy.

A tweakable enciphering scheme (TES) is a length preserving (tweakable) encryption scheme that provides (tweakable) strong pseudorandom permutation security on arbitrarily long messages. TES is traditionally built using block ciphers and the security of the mode depends on the strong pseudorandom permutation security of the underlying block cipher. In this paper, we construct TESs using public random permutations. Public random permutations are being considered as a replacement of block cipher in several cryptographic schemes including AEs, MACs, etc. However, to our knowledge, a systematic study of constructing TES using public random permutations is missing. In this paper, we give a generic construction of a TES which uses a public random permutation, a length expanding public permutation based PRF and a hash function which is both almost xor universal and almost regular. Further, we propose a concrete length expanding public permutation based PRF construction. We also propose a single keyed TES using a public random permutation and an AXU and almost regular hash function.

Subfield codes of linear codes over finite fields have recently received a lot of attention, as some of these codes are optimal and have applications in secrete sharing, authentication codes and association schemes. In this paper, a class of binary subfield codes is constructed from a special family of MDS codes, and their parameters are explicitly determined. The parameters of their dual codes are also studied. Some of the codes presented in this paper are optimal or almost optimal.

A Boolean function in \begin{document}$ n $\end{document} variables is 2-rotation symmetric if it is invariant under even powers of \begin{document}$ \rho(x_1, \ldots, x_n) = (x_2, \ldots, x_n, x_1) $\end{document} , but not under the first power (ordinary rotation symmetry); we call such a function a 2-function. A 2-function is called monomial rotation symmetric (MRS) if it is generated by applying powers of \begin{document}$ \rho^2 $\end{document} to a single monomial. If the quartic MRS 2-function in \begin{document}$ 2n $\end{document} variables has a monomial \begin{document}$ x_1 x_q x_r x_s $\end{document} , then we use the notation \begin{document}$ {2-}(1,q,r,s)_{2n} $\end{document} for the function. A detailed theory of equivalence of quartic MRS 2-functions in \begin{document}$ 2n $\end{document} variables was given in a \begin{document}$ 2020 $\end{document} paper by Cusick, Cheon and Dougan. This theory divides naturally into two classes, called \begin{document}$ mf1 $\end{document} and \begin{document}$ mf2 $\end{document} in the paper. After describing the equivalence classes, the second major problem is giving details of the linear recursions that the Hamming weights for any sequence of functions \begin{document}$ {2-}(1,q,r,s)_{2n} $\end{document} (with \begin{document}$ q say), \begin{document}$ n = s, s+1, \ldots $\end{document} can be shown to satisfy. This problem was solved for the \begin{document}$ mf1 $\end{document} case only in the \begin{document}$ 2020 $\end{document} paper. Using new ideas about "short" functions, Cusick and Cheon found formulas for the \begin{document}$ mf2 $\end{document} weights in a \begin{document}$ 2021 $\end{document} sequel to the \begin{document}$ 2020 $\end{document} paper. In this paper the actual recursions for the weights in the \begin{document}$ mf2 $\end{document} case are determined.

In this paper, we first introduce a class of quartic Boolean functions. And then, the construction of weightwise perfectly balanced Boolean functions on \begin{document}$ 2^m $\end{document} variables are given by modifying the support of the quartic functions, where \begin{document}$ m $\end{document} is a positive integer. The algebraic degree, the weightwise nonlinearity, and the algebraic immunity of the newly constructed weightwise perfectly balanced functions are discussed at the end of this paper.

To identify the abnormal characteristics of the oplegnathus punctatus is great importance to the detection of iridovirus disease in the breeding environment. In this paper, an advanced neural network model to identify the characteristics of the oplegnathus punctatus and predict its different periods of suffering from iridovirus disease is proposed based on the establishment of a data set. First of all, a standard format data set of oplegnathus punctatus and an abnormal format date set are established in order to verify the effectiveness of the method in this paper. And then, the feature extraction fusion method is used for preprocessing in terms of the abnormal format data set, which combines the edge features extracted by the improved multi-template Sobel operator and the color features extracted by the HSV model. Finally, an improved VGG-GoogleNet network recognition model comes into being through the fusion and improvement of the VGG and GoogleNet neural network structure. The experiments results show that the prediction accuracy rate for oplegnathus punctatus suffering from iridovirus disease in the the abnormal format data set and the standard format data set are improved, which reach 98.55% and 69.18%.

So-called spatial web queries retrieve web content representing points of interest, such that the points of interest have descriptions that are relevant to query keywords and are located close to a query location. Two broad categories of such queries exist. The first encompasses queries that retrieve single spatial web objects that each satisfy the query arguments. Most proposals belong to this category. The second category, to which this paper's proposal belongs, encompasses queries that support exploratory user behavior and retrieve sets of objects that represent regions of space that may be of interest to the user. Specifically, the paper proposes a new type of query, the top-k spatial textual cluster retrieval ( $k$ -STC) query that returns the top-k clusters that (i) are located close to a query location, (ii) contain objects that are relevant with regard to given query keywords, and (iii) have an object density that exceeds a given threshold. To compute this query, we propose a DBSCAN-based approach and an OPTICS-based approach that rely on on-line density-based clustering and that exploit early stop conditions. Empirical studies on real data sets offer evidence that the paper's proposals can find good quality clusters and are capable of excellent performance.

This paper presents and evaluates a dual rotor axial flux permanent magnet motor for electric aircraft applications. Several features, including grain oriented electrical steel (GOES), magnet segmentation, and wires with rectangular cross-sections, are used to improve torque density and efficiency. Rather than simply optimizing the motor by itself, this paper evaluates the tradeoffs between motor performance and its interfaces with the drive, thermal management system (TMS), and mechanical structure. This information can be used along with similar analyses of the drive, TMS, and structure to select a design that achieves the system-level optimal performance. The paper uses finite element simulations to characterize tradeoffs between active mass, efficiency, fundamental frequency, power factor, axial forces on the rotors, and cooling surface area. Several designs exceed 95% efficiency at takeoff with less than 8 kg of active mass. While high pole counts, a large outer radius, and short stator teeth tend to optimize the magnetic performance at takeoff, this can reduce cruise efficiency, reduce the surface area through which the TMS can extract heat, increase the fundamental frequency the drive must supply, and increase the structural mass required to support the rotors. Additionally, designs with 20 °C cooler magnets were simulated to evaluate the impact of a more effective TMS, but the improvements in magnetic performance were relatively small.

An {\em orientation} of an undirected graph $G$ is an assignment of exactly one direction to each edge of $G$. Converting two-way traffic networks to one-way traffic networks and bidirectional communication networks to unidirectional communication networks are practical instances of graph orientations. In these contexts minimising the diameter of the resulting oriented graph is of prime interest. The $n$-star network topology was proposed as an alternative to the hypercube network topology for multiprocessor systems by Akers and Krishnamurthy [IEEE Trans. on Computers (1989)]. The $n$-star graph $S_n$ consists of $n!$ vertices, each labelled with a distinct permutation of $[n]$. Two vertices are adjacent if their labels differ exactly in the first and one other position. $S_n$ is an $(n-1)$-regular, vertex-transitive graph with diameter $\lfloor 3(n-1)/2 \rfloor$. Orientations of $S_n$, called unidirectional star graphs and distributed routing protocols over them were studied by Day and Tripathi [Information Processing Letters (1993)] and Fujita [The First International Symposium on Computing and Networking (CANDAR 2013)]. Fujita showed that the (directed) diameter of this unidirectional star graph $\overrightarrow{S_n}$ is at most $\lceil{5n/2}\rceil + 2$. In this paper, we propose a new distributed routing algorithm for the same $\overrightarrow{S_n}$ analysed by Fujita, which routes a packet from any node $s$ to any node $t$ at an undirected distance $d$ from $s$ using at most $\min\{4d+4, 2n+4\}$ hops. This shows that the (directed) diameter of $\overrightarrow{S_n}$ is at most $2n+4$. We also show that the diameter of $\overrightarrow{S_n}$ is at least $2n$ when $n \geq 7$, thereby showing that our upper bound is tight up to an additive factor., Full version of the paper to be presented in CALDAM 2020

This paper is the second part of a two-part survey series on large-scale global optimization. The first part covered two major algorithmic approaches to large-scale optimization, namely decomposition methods and hybridization methods such as memetic algorithms and local search. In this part we focus on sampling and variation operators, approximation and surrogate modeling, initialization methods, and parallelization. We also cover a range of problem areas in relation to large-scale global optimization, such as multi-objective optimization, constraint handling, overlapping components, the component imbalance issue, and benchmarks, and applications. The paper also includes a discussion on pitfalls and challenges of current research and identifies several potential areas of future research.

Today, the rapid growth of the internet and advancements in mobile technology and increased internet connectivity have brought us to a data-driven economy where an enormous amount of data is being used to train machine learning models to make strategic decisions. However, in the aftermath of a data breach by Facebook in 2018, there are some serious concerns over user data privacy and security being used to train the Machine Learning models. In this context, a new approach, Federated Machine Learning is now one of the most talked-about and recent approaches. Current research primarily focuses on Federated Learning's advantages over the traditional methods and/or its classification. However, being in a nascent stage of development as a method, certain challenges need to be addressed. This paper intends to address the totality of federated learning with a complete vulnerability assessment. During the study of the literature, it is found that security being promised as one of the key advantages of federated learning can still not be guaranteed because of some issues inherently present, and this can lead to poisoning, inference attacks and insertion of backdoors, etc. This paper intends to provide a complete picture by giving an in-depth and comprehensive analysis of Federated Learning and its taxonomy. It also provides a detailed vulnerability assessment and highlights the challenges faced in the current setting and future research directions to make federated learning a more functional, robust and secure method to train machine learning models.

On-board embedded software developed for spaceflight systems (space software) must adhere to stringent software quality assurance procedures. For example, verification and validation activities are typically performed and assessed by third party organizations. To further minimize the risk of human mistakes, space agencies, such as the European Space Agency (ESA), are looking for automated solutions for the assessment of software testing activities, which play a crucial role in this context. Over the years, mutation analysis has shown to be a promising solution for the automated assessment of test suites; it consists of measuring the quality of a test suite in terms of the percentage of injected faults leading to a test failure. A number of optimization techniques, addressing scalability and accuracy problems, have been proposed to facilitate the industrial adoption of mutation analysis. However, to date, two major problems prevent space agencies from enforcing mutation analysis in space software development. In this paper, we enhance mutation analysis optimization techniques to enable their applicability to embedded software and propose a pipeline that successfully integrates them to address scalability and accuracy issues in this context, as described above. Further, we report on the largest study involving embedded software systems in the mutation analysis literature. Our research is part of a research project funded by ESA ESTEC involving private companies (GomSpace Luxembourg and LuxSpace) in the space sector. These industry partners provided the case studies reported in this paper; they include an on-board software system managing a microsatellite currently on-orbit, a set of libraries used in deployed cubesats, and a mathematical library certified by ESA., Accepted for publication on IEEE TRANSACTIONS ON SOFTWARE ENGINEERING

Localization forms the heart of various autonomous mobile robots. For efficient navigation, these robots need to adopt effective localization strategy. This paper, presents a comprehensive review on localization system, problems, principle and approaches for mobile robots. First, we classify the localization problems in to three categories based on the information of initial position of the robot. Next, we discuss on robot position update principles. Then, we discuss key techniques to localize the mobile robot such as: probabilistic approach, autonomous map building and radio frequency identification (RFID) based scheme. In the probabilistic localization section, we discuss the Markov localization and Kalman filter along with its extended versions. Autonomous map building focuses on the widely used simultaneous localization and mapping (SLAM) approach. This section also discusses on applying SLAM to localize brain-controlled mobile robots. Next, we discuss on applying evolutionary approaches to estimate optimal position. The RFID scheme addresses on effective utilization of RFID tags to track objects and position the robot. We then analyze on position and orientation errors occurred by different localization strategies. We conclude this paper by highlighting future research possibilities.

This paper presents a cooperative game theory approach to design an optimal video steganography framework. An ideal and perfect video steganography system should provide enough space to embed the secret data without degrading the visual quality. However, in a steganographic system, the payload capacity and visual quality are always inversely proportional to each other. This paper proposes a novel game-theoretic approach using strategy adaption to address this issue. This approach utilizes the Iterative Elimination of Strictly Dominant Strategies (IESDS) method to acquire the optimal solution. The obtained optimal solution comes up with the best trade-off between capacity and visual quality. Also, experimental results infer that the optimal solution outperforms the contemporary methods by attaining significant outcomes.

The evolution of wireless body area network (WBAN) has changed the human life for its applications in the field of healthcare, fitness, entertainment and sports etc. However, two of the major challenges in the design of WBAN are energy efficiency and guaranteeing QoS. The load balance of different packet queues in a WBAN plays important role in design of energy efficient and reliable system. In this paper, we propose a Traffic Prioritized Load Balanced Scheduling (TPLBS) algorithm for load balancing in different priority queues in wireless body area networks based on IEEE 802.15.6 model. The main objective of this paper is to minimize packet drop in the queues so that throughput of WBAN can be improved. In this case we consider both priority and location from which the packet received, so that no packet should wait for a long time in the assigned queue to be transmitted to Access Point. The proposed method is simulated using Castalia to study and compare its performance with its counterparts. The simulation results reveal that the proposed protocol works better than the existing methods in terms of delay, throughput and energy efficiency.

After the acceptance of blockchain technology, there have been applications which aim to use blockchain in their fields. Various approaches have been proposed in past to build a secure Identity Management (IdM) System. This is a novel systematic literature mapping of IdM in blockchain. This paper provides an extensive review on IdM with emphasis on how the emergence of blockchain has addressed the IdM challenges faced over the years. A thorough study has been done on the existing literature. The primary and secondary “search string” were identified and search was conducted on five databases; and after screening the analysis was done. Out of the total studied literature, 30 primary studies published from 2009 to 2020 were selected. Through this paper, the researchers will be able to: 1) find out the research trends in IdM using blockchain, 2) understand the challenges in IdM and report whether blockchain can solve the IdM challenges, 3) scrutinize and understand how the different frameworks of IdM would deal with security, integrity and privacy problems, 4) know about initiatives taken for IdM using blockchain, 5) which consensus algorithms are popular among blockchains, 6) know about the research projects going on in the field of IdM using blockchain.

Word Sense Disambiguation (WSD) is significant for improving the accuracy of the interpretation of a Natural language text. Various supervised learning-based models and knowledge-based models have been developed in the literature for WSD of the language text. However, these models do not provide good results for low-resource languages, due to the lack of labelled and tagged data. Therefore, in this work, we have examined different word embedding techniques for word sense disambiguation of the Hindi language texts. Several studies in the literature show that these embeddings have been utilized for different foreign languages in the field of word sense disambiguation. However, to the best of our knowledge, no such work exists for the Hindi language. Therefore, in this paper, we utilize various existing word embeddings for WSD of Hindi text. Moreover, we have created Hindi word embeddings on articles taken from Wikipedia and test the quality of the created word embeddings using Pearson correlation. In this direction, we perform different experiments and observe that Word2Vec model gives best performance among all the considered embeddings on the used Hindi dataset. In our method, the proposed model directly takes input that is trained with word embedding methods and helps to develop a sense inventory using clustering that has been employed for performing disambiguation. Experimental observations indicate that the performance of the proposed approach is moderate and competent in terms of accuracy. The paper, thus, presents how WSD can leverage these representations to encode rich semantic information.

In this paper, we propose an improvement in the preprocessing stage of the reversible data hiding method based on contrast enhancement applied to grayscale digital images. In conventional proposals, the preprocessing stage plays a fundamental role in the performance of the data hiding algorithm in terms of capacity and visual quality. Thus, this work presents two improvements to the preprocessing stage based on the analysis, merging, and shifting the image histogram, which, in conjunction with the data hiding stage, maximize the embedding capacity while keeping the visual distortion at a minimum. The proposed algorithm was implemented on two image datasets and compared with several related works previously reported in the scientific literature. The experimental results show that the visual quality can be preserved after data have been concealed when employing different embedding rates. Either of the preprocessing techniques presented in this paper can be adopted considering the application scenario and the priority of the requirements of the capacity and visual quality needed.

In spite of the insight commonality may have concerning agrarian practice, fact is that nowadays agricultural science diligence is accurate, precise, data-driven, and vigorous than ever. The emanation of the technologies based on Internet of Things (IoT) has reformed nearly each industry like smart city, smart health, smart grid, smart home, including “smart agriculture or precision agriculture”. Applying machine learning using the IoT data analytics in agricultural sector will rise new benefits to increase the quantity and quality of production from the crop fields to meet the increasing food demand. Such world-shattering advancements are rocking the current agrarian approaches and generating novel and best chances besides a number of limitations. This paper climaxes the power and capability of computing techniques including internet of things, wireless sensor networks, data analytics and machine learning in agriculture. The paper proposed the prediction model of Apple disease in the apple orchards of Kashmir valley using data analytics and Machine learning in IoT system. Furthermore, a local survey was conducted to know from the farmers about the trending technologies and their effect in precision agriculture. Finally, the paper discusses the challenges faced when incorporating these technologies in the traditional farming approaches.

This paper models and investigates risk and security perception of metro travelers, in cases of malevolent, man-made incidents. Data is collected through an extensive survey in Athens, Greece; the survey includes an image-based stated-preference experiment, depicting different risk and security conditions. Econometric models are developed for estimating willingness to pay for mitigating perceived risk and for improving security in the Athens metro system. Results highlight the value that travelers attribute to improving security and avoiding threatening conditions in the Athens metro system, which is estimated to a 25%–30% of the system’s fare level. The paper provides useful insights on acceptable fare changes for improving security and risk perception in the Athens metro system.

During recent years, many researchers and professionals have revealed the endangerment of wireless communication technologies and systems from various cyberattacks, these attacks cause detriment and harm not only to private enterprises but to the government organizations as well. The attackers endeavor new techniques to challenge the security frameworks, use powerful tools and tricks to break any sized keys, security of private and sensitive data is in the stale mark. There are many advancements are being developed to mitigate these attacks. In this conjunction, this paper gives a complete account of survey and review of the various exiting advanced cyber security standards along with challenges faced by the cyber security domain. The new generation attacks are discussed and documented in detail, the advanced key management schemes are also depicted. The quantum cryptography is discussed with its merits and future scope of the same. Overall, the paper would be a kind of technical report to the new researchers to get acquainted with the recent advancements in Cyber security domain.

This paper presents a new contribution to the recommendation system following E-commerce reviews. Here, it implements a dictionary-based sentiment analysis based on four main steps (i) Pre-processing (ii) Semantic word extraction (iii) Feature Extraction (iv) Classification. Initially, the tweets or reviews extracted from the database are subjected to pre-processing, which involves three processes: stop word removal, stemming, and blank space removal. In the semantic word extraction process, the semantic words from the dictionary will be extracted by matching with the extracted keywords. The next process is the feature extraction, which extracts joint holoentropy and cross holoentropy of all keywords, and further proceeds with conditional holoentropy-based feature selection process. Finally, the selected features are subjected to a classification process using Deep Belief Network (DBN), which is a well-performing deep learning algorithm. Moreover, the main contribution of the paper relies on the improvement of DBN architecture, where the number of hidden neurons and activation function is optimally tuned by a hybrid optimization algorithm termed as Grey linked Chick Update-based Chicken Swarm Optimization (GCU-CSO). At last, the comparative analysis of proposed and conventional algorithms in terms of various performance metrics proves the efficacy of the proposed E-commerce recommendation system.