This paper investigates the issue of predicting the financial performance of firms in registered manufacturing in developing countries using machine learning methods along with economic theory to explain the findings. While literature suggests that predictability of top line measures related to sales is lower compared to bottom line measures such as net profits for small informal establishments in developing countries, we find the opposite holds true for firms in registered manufacturing in the food processing industry in India based on the results from machine learning techniques of Bayesian additive regression trees (BART), boosted trees, bootstrap forests, and regression tree algorithms. BART models in validation outperformed the other algorithms in predictability of the dependent variables. Across ten validation studies, BART had an average R2 ranging from 0.922 to 0.934 and boosted tree models had an average R2 ranging from 0.873 to 0.905 for predicting sales. A key significant independent variable for predicting sales across all categories and algorithms was real raw material expenses explaining approximately 83% to 88% of the total sums of squares in all validations. This is in line with the realities of the food processing industry, which is intensive in its raw material usage. The dependent variable ‘profits’ (as measured by real profits before depreciation, interest, taxes and amortization, ‘real_pbdita’) was more difficult to predict relative to sales. BART models again outperformed the other algorithms in validation with an average R2 ranging from 0.745 to 0.818. Key significant variables in the models were more diverse where raw material expenses, compensation to employees, and net- or total-fixed assets explained the largest proportions of the total sums of squares. The results from a machine learning approach with a Bayesian perspective can enhance the understanding of the mechanisms that translate sales into profits for registered manufacturing, thereby aiding policy-making for small businesses in the formal sector in developing countries.

Microplastics (MPs) and nanoplastics (NPs) from mulch films and other plastic materials employed in vegetable and small fruit production pose a major threat to agricultural ecosystems. For conducting controlled studies on MPs' and NPs' (MNPs') ecotoxicity to soil organisms and plants and fate and transport in soil, surrogate MNPs are required that mimic MNPs that form in agricultural fields. We have developed a procedure to prepare MPs from plastic films or pellets using mechanical milling and sieving, and conversion of the resultant MPs into NPs through wet grinding, both steps of which mimic the degradation and fragmentation of plastics in nature. The major goal of this study was to determine if cryogenic exposure of two biodegradable mulch films effectively mimics the embrittlement caused by environmental weathering in terms of the dimensional, thermal, chemical, and biodegradability properties of the formed MNPs. We found differences in size, surface charge, thermal and chemical properties, and biodegradability in soil between MNPs prepared from cryogenically treated vs. environmentally weathered films, related to the photochemical reactions occurring in the environment that were not mimicked by cryogenic treatment, such as depolymerization and cross-link formation. We also investigated the size reduction process for NPs and found that the size distribution was bimodal, with populations centered at 50 nm and 150-300 nm, and as the size reduction process progressed, the former subpopulation's proportion increased. The biodegradability of MPs in soil was greater than for NPs, a counter-intuitive trend since greater surface area exposure for NPs would increase biodegradability. The result isassociated with differences in surface and chemical properties and to minor components that are readily leached out during the formation of NPs. In summary, the use of weathered plastics as feedstock would likely produce MNPs that are more realistic than cryogenically-treated unweathered films for use in experimental studies.

The natural variation of the data signatures of airborne aerosols from calibrated cigarette particles were quantified using enhanced Bonferroni methods. The significance of the problem of improving analytical methods for understanding the natural variation of airborne particles cannot be understated given the positive impact for mitigating harmful airborne particles. The data presented in this paper were obtained using experiments to examine the effect of a carbon-brush-based bipolar ionization on filtration efficiency of a MERV 10 filter in a recirculating HVAC system. Ionization technology is deployed throughout the world as a multilayered approach with filtration for improving indoor air quality. Despite its wide use, ionization is still considered an emerging technology due to a dearth of peer-reviewed literature. Poorly designed test protocols and a lack of robust statistical methods for analyzing experimental data are the primary reasons. Presented herein is a statistical groundwork for analyzing ionization-efficacy data from highly controlled and properly designed particulate-matter test trials. Results are presented for three experimental groups where bipolar ionization was used to study the behaviors of data signatures from cigarette-smoke aerosol particles ranging in size from 49.6 to 201.7 nm. Statistical control bands of the data from these experimental groups revealed that bipolar ionization had significant changes to the pdfs and reductions in the natural variation of the data signatures for the particle count (number of particles) across all particle sizes. Statistical control bands may provide enhanced quantitative knowledge of variation and provide expanded inference that goes beyond examination of percentiles only. The implications from this research are profound, as it lays the groundwork for the development of highly effective ionization-filtration layered strategies to mitigate the hazards of airborne particulates and is the first step towards creating robust efficacy test standards for the industry.

Microplastics (MPs) and nanoplastics (NPs) dispersed in agricultural ecosystems can pose a severe threat to biota in soil and nearby waterways. In addition, chemicals such as pesticides adsorbed by NPs can harm soil organisms and potentially enter the food chain. In this context, agriculturally utilized plastics such as plastic mulch films contribute significantly to plastic pollution in agricultural ecosystems. However, most fundamental studies of fate and ecotoxicity employ idealized and poorly representative MP materials, such as polystyrene microspheres. Therefore, as described herein, we developed a lab-scale multi-step procedure to mechanically form representative MPs and NPs for such studies. The plastic material was prepared from commercially available plastic mulch films of polybutyrate adipate-co-terephthalate (PBAT) that were embrittled through either cryogenic treatment (CRYO) or environmental weathering (W), and from untreated PBAT pellets. The plastic materials were then treated by mechanical milling to form MPs with a size of 46-840 µm, mimicking the abrasion of plastic fragments by wind and mechanical machinery. The MPs were then sieved into several size fractions to enable further analysis. Finally, the 106 µm sieve fraction was subjected to wet grinding to generate NPs of 20-900 nm, a process that mimics the slow size reduction process for terrestrial MPs. The dimensions and the shape for MPs were determined through image analysis of stereomicrographs, and dynamic light scattering (DLS) was employed to assess particle size for NPs. MPs and NPs formed through this process possessed irregular shapes, which is in line with the geometric properties of MPs recovered from agricultural fields. Overall, this size reduction method proved efficient for forming MPs and NPs composed of biodegradable plastics such as polybutylene adipate-co-terephthalate (PBAT), representing mulch materials used for agricultural specialty crop production.

Manufacturing for a multitude of continuous processing applications in the era of automation and ‘Industry 4.0’ is focused on rapid throughput while producing products of acceptable quality that meet customer specifications. Monitoring the stability or statistical control of key process parameters using data acquired from online sensors is fundamental to successful automation in manufacturing applications. This study addresses the significant problem of positive autocorrelation in data collected from online sensors, which may impair assessment of statistical control. Sensor data collected at short time intervals typically have significant autocorrelation, and traditional statistical process control (SPC) techniques cannot be deployed. There is a plethora of literature on techniques for SPC in the presence of positive autocorrelation. This paper contributes to this area of study by investigating the performance of ‘Copula’ based control charts by assessing the average run length (ARL) when the subsequent observations are correlated and follow the AR(1) model. The conditional distribution of yt given yt−1 is used in deriving the control chart limits for three different categories of Copulas: Gaussian, Clayton, and Farlie-Gumbel-Morgenstern Copulas. Preliminary results suggest that the overall performance of the Clayton Copula and Farlie-Gumbel-Morgenstern Copula is better compared to other Archimedean Copulas. The Clayton Copula is the more robust with respect to changes in the process standard deviation as the correlation coefficient increases.

A Poly(vinyl alcohol) (PVA)/cellulose nanocrystals (CNCs) composite nanofibrous air filter was fabricated via electrospinning. CNCs were added to improve the overall filtration performance for particulate matter (PM) removal. The integral effect of different properties of electrospinning suspension on the PM2.5 removal efficiency and pressure drops were studied. To optimize the fabrication parameters, variables such as suspension concertation and CNCs percentage at different levels were systematically investigated using response surface methodology. The feasible operating space, where the suspension concentration and CNCs percentage were varied from 6 to 8% and 5 to 20%, respectively, was evaluated with reduced experimental runs using a face-centered central composite design. Our results indicate the quadratic models developed for predicting responses were adequate. The optimum filtration performance for PM2.5 was achieved with 7.34% of suspension concentration and 20% of CNCs percentage, where the removal efficiency was 94% and the pressure drop was only 34.9 Pa.

An electron microscopy investigation was performed to understand the relationship between the microstructure and properties of carbonized cellulose and lignin (softwood kraft lignin) relative to the structure of the original biomass components. Structure details at micro- and molecular levels were investigated by scanning transmission electron microscopy. Atomic-resolution images revealed the presence of random disordered carbon in carbonized cellulose (C-CNC) and of large domains of well-ordered carbon with graphite sheet structure in carbonized lignin (C-Lignin). These structural differences explain why C-CNC exhibits higher surface area and porosity than C-Lignin. The presence of certain well-ordered carbon in carbonized lignin indicates some of the carbon in lignin are graphitized with heat treatment temperature up to 950 °C. This result is encouraging for future endeavors of attaining acceptable modulus of carbon fiber from lignin given suitable modifications to the chemistry and structure of lignin. The results of this research contribute to an improved understanding of the carbonization mechanism of the key cellulose and lignin components of biomass materials.

Traceability of natural resources, from the cradle to the final product is a crucial issue to secure sustainable material usage as well as to optimize and control processes over the whole supply chain. In the forest products industries the material can be tracked by different technologies, but for the first step of material flow, from the forest to the industry, no systematic and complete technology has been developed. On the way to close this data gap the fingerprint technology for wooden logs looks promising. It uses inherent properties of a wood stem for identification. In this paper hyperspectral cameras are applied to gain images of Norway spruce (Picea abies [L.] Karst.) log end faces in different spectral ranges. The images are converted to a biometric template of feature vectors and a matching algorithm is used to evaluate if the biometric templates are similar or not. Based on this, matching scores specific spectral ranges which contain information to distinguish between different log end faces are identified. The method developed in this paper is a necessary and successful step to define scanning system parameters for fingerprint recognition systems for wood log traceability from the forest.

An approach for implementing statistical process control and other statistical methods as a cost-savings measure in the treated-wood industries is outlined. The purpose of the study is to use industry data to improve understanding of the application of continuous improvement methods. Variation in wood treatment is a cost when higher-than-necessary chemical retention targets are required to meet specifications. The data for this study were obtained in confidence from the American Lumber Standard Committee and were paired, normalized assay retentions for charges inspected by both the treating facility and auditing agencies. Capability analyses were developed from this data for three use categories established by the American Wood Protection Association (AWPA), including UC3B (above ground, exterior), UC4A (ground contact, freshwater, general use), and UC4B (ground contact, freshwater, critical structures, or high decay hazard zones). Agency and industry data indicate that between 4.45 and 9.82 percent of the charges were below the lower confidence limit of the passing standard (LCLAWPA), depending on use category. A Taguchi loss function (TLF), which is quadratic based and decomposes the monetary loss into shift and variation components, was developed to estimate the additional cost due to process variation. For example, if a treatment input cost of $1.00/ft(3) is assumed for UC3B, reducing the variation in total retention allows lowering treatment targets, e.g., 1.45 to 1.38, reducing costs to $0.76/ft(3). The study provides some important continuous improvement tools for this industry such as control charts, C-pk, C-pm capability indices, and the one-sided TLF.

A key challenge in applied science when planning a designed experiment is to determine the aliasing structure of the interaction effects and selecting the appropriate levels for the factors. In this study, kernel tree methods are used as precursors to identify significant interactions and levels of the factors useful for developing a designed experiment. This approach is aligned with integrating data science with the applied sciences to reduce the time from innovation in research and development to the advancement of new products, a very important consideration in today’s world of rapid advancements in industries such as pharmaceutical, medicine, aerospace, etc. Significant interaction effects for six common independent variables using boosted trees and random forests of k = 1000 and k = 10,000 bootstraps were identified from industrial databases. The four common variables were related to speed, pressing time, pressing temperature, and fiber refining. These common variables maximized tensile strength of medium density fiberboard (MDF) and the ultimate static load of oriented strand board (OSB), both widely-used industrial products. Given the results of the kernel tree methods, four possible designs with interaction effects were developed: full factorial, fractional factorial Resolution IV, Box–Behnken, and Central Composite Designs (CCD).

With the extensive use of nitrogen-based fertilizer in agriculture, ammonia emissions, especially from cattle manure, are a serious environmental threat for soil and air. The European community committed to reduce the ammonia emissions by 30% by the year 2030 compared to 2005. After a moderate initial reduction, the last report showed no further improvements in the last four years, keeping the 30% reduction a very challenging target for the next decade. In this study, the mitigation effect of different types of tannin and tannin-based adsorbent on the ammonia emission from manure was investigated. Firstly, we conducted a template study monitoring the ammonia emissions registered by addition of the tannin-based powders to a 0.1% ammonia solution and then we repeated the experiments with ready-to-spread farm-made manure slurry. The results showed that all tannin-based powders induced sensible reduction of pH and ammonia emitted. Reductions higher than 75% and 95% were registered for ammonia solution and cattle slurry, respectively, when using flavonoid-based powders. These findings are very promising considering that tannins and their derivatives will be extensively available due to the increasing interest on their exploitation for the synthesis of new-generation “green” materials.

Air pollution has become a global concern, and numerous studies have focused on developing filters for particulate matter (PM) removal. However, the reusability of air filters, which could lower resources consumption and waste discharge, is also necessary to be investigated. In this study, a water washable electrospun poly (vinyl alcohol) (PVA)/cellulose nanocrystals (CNCs) nanofibrous air filter for PM removal was prepared. The watersoluble polymer composite was converted to be completely water-resistant via a facile heat treatment without adding any crosslinking agent. Our results indicate the increased crystallinity is the key factor to improve the aqueous stability of PVA/CNCs fibers. The CNCs provided additional nucleation sites for PVA crystallization during electrospinning and heating process. By loading 20% CNCs and heating at 140 degrees C for 5 min, the crystallinity degree and crystal size of PVA were increased from 54.7% and 3.3 nm to 85.4% and 6.3 nm, respectively. The reusability of fabricated filters was tested by water washing over 5 cycles. The results show the heavily fouled filters (pressure drop 1000 Pa) were regenerated effectively: the PM2.5 removal efficiency was maintained at above 95%, meanwhile the pressure drop remained less than 100 Pa after repetitive usage (PM2.5 mass concentration > 500 mu g m(-3)). Considering the raw materials are nontoxic and biodegradable, the heating temperature is relatively low, and the process is short, it is a promising green method to manufacture long lifetime air filter.

Release of microplastics (MPs) and nanoplastics (NPs) into agricultural fields is of great concern due to their reported ecotoxicity to organisms that provide beneficial service to the soil such as earthworms, and the potential ability of MPs and NPs to enter the food chain. Most fundamental studies of the fate and transport of plastic particulates in terrestrial environments employ idealized MP materials as models, such as monodisperse polystyrene spheres. In contrast, plastics that reside in agricultural soils consist of polydisperse fragments resulting from degraded films employed in agriculture. There exists a need for more representative materials in fundamental studies of the fate, transport, and ecotoxicity of MPs and NPs in soil ecosystems. The objective of this study was therefore to develop a procedure to produce MPs and NPs from agricultural plastics (a mulch film prepared biodegradable polymer polybutyrate adipate-co-terephthalate (PBAT) and low-density PE [LDPE]), and to characterize the resultant materials. Soaking of PBAT films under cryogenic conditions promoted embrittlement, similar to what occurs through environmental weathering. LDPE and cryogenically-treated PBAT underwent mechanical milling followed by sieve fractionation into MP fractions of 840 μm, 250 μm, 106 μm, and 45 μm. The 106 μm fraction was subjected to wet grinding to produce NPs of average particle size 366.0 nm and 389.4 nm for PBAT and LDPE, respectively. A two-parameter Weibull model described the MPs' particle size distributions, while NPs possessed bimodal distributions. Size reduction did not produce any changes in the chemical properties of the plastics, except for slight depolymerization and an increase of crystallinity resulting from cryogenic treatment. This study suggests that MPs form from cutting and high-impact mechanical degradation as would occur during the tillage into soil, and that NPs form from the MP fragments in regions of relative weakness that possess lower molecular weight polymers and crystallinity.

This research focuses on the statistical evaluation of the feedstock attributes of the biomass supply chain and the estimation of attribute costs as a function of the feedstock variability. Challenges of using cellulosic feedstocks include the variability of feedstock quality (e.g., ash content and moisture content), which impacts the final cost of the manufactured product. Statistical Process Control (SPC), Taguchi Loss Function, and components of variance techniques were illustrated for quantifying cumulative variance in the biomass supply chain. Costs in the presence of cumulative variance were estimated for switchgrass (Panicum virgatum L.) and loblolly pine residues (Pinus taeda L.). Findings of the study indicated that additional costs from ash content variability in switchgrass increased the net cost by $19.15 per dry tonne. Additional costs from densification due to particle size variation increased net cost by $11.59 per dry tonne. Moisture content variation increased costs by $14.86 per dry tonne. This would represent a 50 to 100% increase in costs due to variation based on a $60 to $70 per dry tonne manufactured product cost. This study illustrates that total costs may be considerably underestimated if the influence of variance for key factors in the supply chain and associated costs are not estimated.

Over the past two decades, the United States government conducted detailed analyses of the potential of a biobased national energy strategy that produced four unified studies, namely the 2005–2016 US Billion-Ton Study and updates. With each effort, better perspective was gained on the biophysical potential of biomass and the economic availability of these resources on a national scale. It was also apparent that many questions remained, including crop yields, logistical operations, and systems integration across production and harvest. These reports accentuated the need for improving geospatial performance metrics for biomass supply chains. This study begins to address these problems by developing spatially specific data layers that incorporate data on soils, climatology, growth, and economics for short-rotation woody biomass plantations. Methods were developed to spatially assess the potential productivity and profitability of four candidate species Pinus taeda L., Populus deltoides W. Bartram ex Marshall and Populus hybrids, Eucalyptus grandis Hill ex Maiden, and Eucalyptus benthamii Maiden et Cambage for biomass plantations in the eastern United States. Productivity was estimated using the process-based growth model 3PG (Physiological Processes Predicting Growth) parameterized at the resolution of the United States 5-digit zip code tabulation area (ZCTA). Each ZCTA is unique in terms of species suitability, cost, and productive potential. These data layers make available dedicated energy crop analyses for practitioners interested in facility siting scenarios in conjunction with a species growth potential at a particular location. Production systems for SRWC are extremely regionalized given key biophysical and economic factors that determine the potential for acceptable growth and profitability. This analysis points to the return on invested capital being dependent on the site location of a species within its operable range. Large-scale biomass plantation systems are feasible in regions with higher potential internal rate of return. The higher the potential return, the more desirable it is to plant the specific species on the site. Increasing the available feedstock by lowering cost, increasing productivity, and stabilizing logistics would have a similar effect as higher feedstock prices. The modeled growth can be used for further economic evaluation, carbon sequestration studies, and sustainability research.

The ability to detect the special-cause variation of incoming feedstocks from advanced sensor technology is invaluable to manufacturers. Many on-line sensors produce data signatures that require further off-line statistical processing for interpretation by operational personnel. However, early detection of changes in variation in incoming feedstocks may be imperative to promote early-stage preventive measures. A method is proposed in this applied study for developing control bands to quantify the variation of data signatures in the context of statistical process control (SPC). Control bands based on pointwise prediction intervals constructed from the Bonferroni Inequality and Bayesian smoothing splines are developed. Applications using the control band method for data signatures from near-infrared (NIR) spectroscopy scans of industrial fibers of Switchgrass (Panicum virgatum) used for biofuels production, Loblolly Pine (Pinus taeda) fibers for medium density fiberboard production, and formaldehyde (HCHO) emissions from particleboard were used. Simulations curves (k) of k = 100, k = 1000, and k = 10,000 indicate that the Bonferroni method for detecting special-cause variation is closely aligned with the Shewhart definition of control limits when the pdfs are Gaussian or lognormal.

Particulate matter (PM) air pollution poses a risk to public health, especially in rapidly industrializing countries. One major way to protect individuals from PM exposure is to use fiber-based fil...

Potential risk for disruption to supply of raw materials for bio-based industrial sites from natural disasters has received little attention in site evaluations even though these risks may be significant. Biomass supply in the form of roundwood, forest and agricultural residues, or dedicated short rotation plantations are especially subject to disruptions from meteorological disturbances such as floods, wind and ice storms. The objective of this study was to account for risk from natural disasters in assessing the economic supply of biomass for the Eastern United States in a geospatial context, at the sub-county level (5-digit ZIP Code Tabulation Area). The Presidential Disaster Declaration database was used to identify risk zones where natural disasters frequently exceeded the response capability of State and local governments. Risk levels were estimated from exposure (the combination of the extent of natural hazards and the biomass assets at risk) and adaptive capacity (comprised of economic, social, and environmental factors). Of the 25,044 geospatial polygons in the 33-state study region, 43.8% were located in low risk regions, 26.8% were considered at moderate risk for disruption, and 29.4% were considered at high risk for disruption. The lowest risk locations were in southern Georgia, South Carolina, and Texas.