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In modeling a discrete event driven system, Petri net recommender systems can play a very important role in describing the structural and behavioral properties of complex and tricky networks. The finite and infinite perfect binary tree forms a predictive model which can map the input information to output information based on the inputs' attributes. A perfect binary tree can be used for three types of recommender systems such as: collaborative filtering, a content-based approach, and a hybrid approach. In this article, the authors show the existence of a Petri net whose reachability tree is a Perfect Infinite Binary Tree (PIBT).

Breakthrough infections with SARS-CoV-2 Delta variant have been reported in doubly-vaccinated recipients and as re-infections. Studies of viral spread within hospital settings have highlighted the potential for transmission between doubly-vaccinated patients and health care workers and have highlighted the benefits of high-grade respiratory protection for health care workers. However the extent to which vaccination is preventative of viral spread in health care settings is less well studied. Here, we analysed data from 118 vaccinated health care workers (HCW) across two hospitals in India, constructing two probable transmission networks involving six HCWs in Hospital A and eight HCWs in Hospital B from epidemiological and virus genome sequence data, using a suite of computational approaches. A maximum likelihood reconstruction of transmission involving known cases of infection suggests a high probability that doubly vaccinated HCWs transmitted SARS-CoV-2 between each other and highlights potential cases of virus transmission between individuals who had received two doses of vaccine. Our findings show firstly that vaccination may reduce rates of transmission, supporting the need for ongoing infection control measures even in highly vaccinated populations, and secondly we have described a novel approach to identifying transmissions that is scalable and rapid, without the need for an infection control infrastructure.

Boolean Petri net (BPN) and Crisp Boolean Petri net (CBPN) is a well-studied graph model since 2010 which has several applications in mathematical modeling of complex or tricky networks. Modeling any network with Petri net which can generate binary numbers as marking vectors in its reachability tree is still has much uses. In CBPN with a minimum number of transition and minimum number of steps of reachability tree, minimal execution time to run the machine has not been noted till date, thus it’s necessary to sort out this problem. Possibly it may occur due to some forbidden structure which hinders any 1-safe Petri net to be a CBPN. In this paper, we present some forbidden digraphs whose presence interrupts the generation of binary n-vectors exactly once. Any 1-safe Petri net is not a CBPN if it contains any of the subnet induced to the four forbidden structures discussed in this paper.

Hybrid treatment technology in recent time is seen as novel treatment technology for treatment of various types of wastewaters. Over the past few decades, electrochemical treatment coupled with adsorption for wastewater treatment has gained much attention. Raw hospital wastewater (HWW) was treated coupling continuous flow electrochemical coagulation (ECC) and adsorption for removal of pollutants/contaminants chemical oxygen demand (COD), colour, and total dissolved solids (TDS). The electrochemical treatments in continuous mode were carried out for applied voltage of 12 and 18 V using Al and SS electrodes for flow rates 6 Lph, 4 Lph, 2 Lph, and 1 Lph coupled with adsorption. COD removal of 79.4% and 75.26% were obtained at 90 min ET for 12 and 18 V using Al electrodes with corresponding colour removal of 77.15% and 74.49% and TDS residual concentration of 452 mg/L (12 V) (45.14% removal) and 431 mg/L (18 V) (47.69% removal) from its initial concentration Co of 824 mg/L respectively. Similarly, continuous ECC studies using SS electrodes showed maximum COD removal of 75.26% and 73.85 for 12 and 18 V at 90 min ET for flow rate 1 Lph. Colour removal of 77.15% and 74.46% was observed at the end of 330 min ET with simultaneous TDS concentration reduction of 423 mg/L (48.66% removal) and 408 mg/L (50.48% removal) respectively. The initial COD and colour concentration before ECC were 768 mg/L and 0.404 (absorbance). The breakthrough capacity for Al electrodes was comparable with SS electrodes ranging from 73 to 107.78 g/L. Adsorption kinetics showed that the Adam Bhorat model constants increased with increase in flow rates with pollutant removal controlled by external mass transfer. The ECC + adsorption coupled hybrid treatment process showed disinfection potential by destruction of the microbial mass by both Al and SS electrodes. At the end of the electrochemical treatment, the agar plate count was < 10 CFU/mL for Al and < 5 CFU/mL for SS electrodes. The operating cost of the hybrid process is 41.65 and 50.95 INR/m3 of HWW treated for the desired 75–80% COD removal using Al and SS electrodes. The ECC coupled adsorption process showed its effectiveness and may be successfully applied for the treatment.

Now-a-days, in the manufacturing, sustainability has become a necessity partly due to the threats created by traditional manufacturing practices, and due to regulations imposed by stakeholders. Sustainable manufacturing implies the creation of products that utilize minimum resources, has minimum negative impacts on environment and are safe for society at large at an affordable cost. This study proposes a fuzzy inference system-based model for the evaluation of manufacturing sustainability of small and medium enterprises (SMEs). In order to assess the manufacturing SMEs, decision makers’ opinion of the importance of sustainability measures and indicators and also the performance of enterprise with respect to indicators are gathered using linguistic variables. An illustrative list of sustainability indicators for manufacturing SMEs is identified considering the characteristics of SMEs. The implementation of our model for a manufacturing SME identified weak areas of performance which require appropriate strategy to enhance the overall sustainability. Based on the output of this assessment model and further deliberations with decision makers, case company is in process of selecting an appropriate strategy to reduce the environmental impacts. This model serves as a tool to assists the decision makers in assessing various dimensions of sustainability within their manufacturing SMEs.

Sustainability has become a necessity, partly due to the threats created by traditional manufacturing practices, and due to regulations imposed by stakeholders. Performance evaluation is an important component of sustainability initiatives in manufacturing organizations. This study proposes a sustainability evaluation method for manufacturing SMEs using integrated fuzzy analytical hierarchal process (FAHP) and fuzzy inference system (FIS) approach. The performance indicators are identified from literature considering the characteristics of SMEs. Balanced scorecard framework is used to categorize the indicators among its four aspects. The linguistic variables are used to collect the opinions of decision makers about the performance ratings and importance of the aspects and corresponding indicators. The FAHP method is applied to determine the relative weights of measures and indicators. The performance ratings of the organization with respect to indicators and relative weights of indicators are combined to obtain the weighted performance ratings. The weighted performance ratings are considered as inputs to FIS. The hierarchal FIS is applied to derive the overall sustainability performance. Using a case study of manufacturing SME, the sustainability score of the organization was elicited in accordance with this procedure. Consequently, a sensitivity analysis of the proposed method reveals the most important basic indicators affecting overall sustainability, identifying areas which decision makers should place special attention. This method can also assist managers of larger enterprises to assess the effectiveness of their sustainability strategies, especially when dealing with suppliers from the SMEs.

Three-dimensional (3D) batch ECC of raw health care facility wastewater (HCFWW) was adopted using stainless steel (SS) and aluminum (Al) scrap metal particle electrodes. ECC treatment was focused on priority quality parameters viz., chemical oxygen demand (COD), color, and other important water quality parameters. Sludge settling and filterability for post-ECC slurry were investigated after ECC. COD removals of 87.56 and 87.2% were achieved for current densities (CD) 83.33 and 125 A/m 2 using SS-3D electrodes, and similarly, 86.99 and 86.23% COD removal for Al-3D electrodes. Simultaneously, color removals were 88.50 and 87.60% for CD 166.66 A/m 2 (4A) using SS and Al-3D electrodes. Water quality parameters viz., nitrate, phosphates, and sulfate were also removed by 93.18%, 96.83%, and 41.07% for SS-3D electrodes, while Al-3D electrodes showed 93.15%, 96.72%, and 25.94% removal. Post-ECC slurry settling was good for all the applied CD using SS-3D electrodes generating dense and sturdy flocs. Al-3D electrodes showed excellent floc settling properties. SS-3D electrode flocs displayed good filterability at 1A with α : 2.497 × 10 11  m kg −1 and R m 1.946 × 10 10   m −1 . Post-ECC slurry using Al-3D electrodes were viscous causing delayed filterability giving α : 1.1760 × 10 11  m kg −1 and R m  1.504 × 10 9  m −1 for 3A. E. coli was destroyed by 97 and 98% for 2A and 3A respectively. Clear water reclamation of 85–90% and pollutants/contaminants removed within a short HRT of 75 min proved the effectiveness of adopting 3D-ECC for treating raw HCFWW.

Selection of an appropriate sustainability strategy is a multi-criteria decision making (MCDM) problem for manufacturing organizations due to incommensurate and conflicting evaluation criteria. In addition, incomplete information and different opinions of decision makers lead to uncertainties such as interval data and fuzziness. This study proposes a hierarchal MCDM method by combining Analytical Hierarchal Process (AHP) and VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) methods under interval-valued fuzzy environment to deal with ranking of sustainable manufacturing strategies. Linguistic variables were used to assess the ratings of strategies and weights for selection criteria. These linguistic variables were expressed in the triangular interval-valued fuzzy sets. Using a case study of manufacturing small and medium enterprise, the final ranking of the strategies was elicited in accordance with this procedure. Subsequently, a sensitivity analysis was performed to validate the stability of the proposed final ranking. This method can be used as a decision making tool for alternative or strategy selection in other areas where uncertainties are inherent.

Light‐absorbing carbonaceous aerosols that dominate atmospheric aerosol warming over India remain poorly characterized. Here, we delve into UV‐visible‐IR spectral aerosol absorption properties at nine PAN‐India COALESCE network sites (Venkataraman et al., 2020, https://doi.org/10.1175/bams‐d‐19‐0030.1). Absorption properties were estimated from aerosol‐laden polytetrafluoroethylene filters using a well‐constrained technique incorporating filter‐to‐particle correction factors. The measurements revealed spatiotemporal heterogeneity in spectral intrinsic and extrinsic absorption properties. Absorption analysis at near‐UV wavelengths from carbonaceous aerosols at these regional sites revealed large near‐ultraviolet brown carbon absorption contributions from 21% to 68%—emphasizing the need to include these particles in climate models. Further, satellite‐retrieved column‐integrated absorption was dominated by surface absorption, which opens possibilities of using satellite measurements to model surface‐layer optical properties (limited to specific sites) at a higher spatial resolution. Both the satellite‐modeled and direct in‐situ absorption measurements can aid in validating and constraining climate modeling efforts that suffer from absorption underestimations and high uncertainties in radiative forcing estimates. Plain Language Summary Particulate pollution in the atmosphere scatter and absorb incoming solar energy, thus cooling or warming Earth's atmosphere. In developing countries and especially in India, one of the most polluted regions of the world, the extent to which particles can absorb solar energy and warm the atmosphere is not well understood. Here, for the first time, we measure particle absorption simultaneously at nine ground sites across India, in diverse geographical regions with different levels and types of particulate pollution. We find that organic carbon particles exert large absorption at near‐ultraviolet wavelengths, which contain significant solar energy. These light absorbing organic carbon particles, called brown carbon, are emitted in large quantities from biomass burning (e.g., burning crop residue and cooking on wood‐fired stoves). Comparing ground measurements of absorption with satellite‐retrieved measurements that are representative of the entire atmospheric column, we find that near‐surface atmospheric particles can exert significant warming. This study highlights the need to improve climate model simulations of particulate pollution's impact on the climate by incorporating spatiotemporal surface‐level absorption measurements, including absorption by brown carbon particles. Key Points Measurements at nine regional PAN‐India sites reveal several regions with large aerosol absorption strength Brown carbon contributes significantly (21%–68%) to near‐ultraviolet absorption, indicating its importance in shortwave light absorption Strong correlations observed between satellite data and surface absorption indicate future potential in modeling surface absorption

Field investigations were conducted at research farm JNKVV Jabalpur (Madhya Pradesh) during kharif season of 2010-11 and 2011-12 to study to the growth,development and production efficiency in rice by adopting suitable planting geometry, varieties and planting depth. The study revealed that the 30 cm × 30 cm planting geometry had superiority in parameters viz., plant height, and functional leaves/ hill the 30 cm × 30 cm planting geometry had superiority in various parameters were significantly in plant geometry. Rice variety MR-219 with shallow depth of planting (2.5 cm) recorded better growth parameters viz., plant height, Number of tillers/m^sup 2^ and functional leaves/hill were markedly superior in growth parameters. Grain and straw yields were superior with the MR-219 variety and 25 cm × 25 cm planting geometry with shallow depth of planting.