​Duration of Composting and Changes in Temperature, pH and C/N Ratio during Composting: A Review

Composting is the most viable treatment for biodegradable solid waste. Numerous techniques have been developed by different agencies to carry out composting. The most common method is aerobic bin method carried out on small scale. Compost piles and windrow methods needs larger land area and are mainly carried out by industries. The total time for completion of composting depend upon the type of substrate and the methods employed. Moisture content, temperature, pH and C:N ratio are among the most important factors for carrying out composting. The present review emphasised on the estimation of time taken by different types of substrates under different methods of composting and the changes in temperature, pH and C:N ratio occurring therein.

Bias, Information, Noise: The BIN Model of Forecasting

A four-year series of subjective probability forecasting tournaments sponsored by the U.S. intelligence community revealed a host of replicable drivers of predictive accuracy, including experimental interventions such as training in probabilistic reasoning, anti‐groupthink teaming, and tracking of talent. Drawing on these data, we propose a Bayesian BIN model (Bias, Information, Noise) for disentangling the underlying processes that enable forecasters and forecasting methods to improve—either by tamping down bias and noise in judgment or by ramping up the efficient extraction of valid information from the environment. The BIN model reveals that noise reduction plays a surprisingly consistent role across all three methods of enhancing performance. We see the BIN method as useful in focusing managerial interventions on what works when and why in a wide range of domains. An R-package called BINtools implements our method and is available on the first author’s personal website. This paper was accepted by Manel Baucells, decision analysis.

Pengolahan Limbah Organik Pasar Menggunakan Reaktor Cacing Tanah (Lumbricus rubellus) dengan Metode Continuous Flow Bin

Market waste is the second largest waste after household waste. Vegetable and fruit waste is the largest organic waste that comes from market waste. Various waste management efforts have been carried out by the government and the community, but have not completely resolved the waste problem. The purpose of this study was to determine the effect of market organic waste processing using an earthworm reactor (Lumbricus rubellus) with a modified Continuous Flow Bin method on cocoon production, biomass, and exmecat. This study used an experimental method with a completely randomized design (CRD), with three treatments of a continuous flow bin modified three types of mixed media materials to increase cocoon production, biomass, and exmecat quality. The results showed that the type of reactor had a significant effect (P ≤ 0.05) on cocoon production, the amount of biomass, biomass weight, broodstock mortality, exmecat production, media temperature, and media humidity, but had no effect on media pH. The use of reactor 2 (R2) with a mixture of 50% horse feces + 50% rice straw + feed 50 grams / day / nest box for market organic waste is the best reactor compared to reactor 1 (R1) and reactor 3 (R3). The amount of organic waste that can be processed by earthworms (Lumbricus rubellus) either as a medium or as feed is an average of 4.35 kg / nest box for 40 days of cultivation with a stocking density of 25 grams of earthworms / nest box. Based on the results of this study, it can be concluded that the ability of earthworms (Lumbricus rubellus) to process market organic waste using the modified Continuous Flow Bin method can reach 4.35 times their body weight / day. Thus, this waste processing method can be a complete solution to solving market organic waste management problems.

BLINK: A Package for Next Level of Genome Wide Association Studies with Both Individuals and Markers in Millions

Big data, accumulated from biomedical and agronomic studies, provides the potential to identify genes controlling complex human diseases and agriculturally important traits through genome-wide association studies (GWAS). However, big data also leads to extreme computational challenges, especially when sophisticated statistical models are employed to simultaneously reduce false positives and false negatives. The newly developed Fixed and random model Circulating Probability Unification (FarmCPU) method uses a bin method under the assumption that Quantitative Trait Nucleotides (QTNs) are evenly distributed throughout the genome. The estimated QTNs are used to separate a mixed linear model into a computationally efficient fixed effect model (FEM) and a computationally expensive random effect model (REM), which are then used iteratively. To completely eliminate the computationally expensive REM, we replaced REM with FEM by using Bayesian information criteria. To eliminate the requirement that QTNs be evenly distributed throughout the genome, we replaced the bin method with linkage disequilibrium information. The new method is called Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK). Both real and simulated data analyses demonstrated that BLINK improves statistical power compared to FarmCPU, in addition to a remarkable improvement in computing time. Now, a dataset with half million markers and one million individuals can be analyzed within five hours, compared with one week using FarmCPU.

Attenuation Correction for Ka-band Cloud Radar Using X-band Weather Radar Data

In order to correct attenuated millimeter-wavelength (Ka-band) radar data and address the problem of instability, an attenuation correction methodology (attenuation correction with variation trend constraint; VTC) was developed. Using synchronous observation conditions and multi-band radars, the VTC method adopts the variation trends of reflectivity in X-band radar data captured with wavelet transform as a constraint to adjust reflectivity factors of millimeter-wavelength radar. The correction was evaluated by comparing reflectivities obtained by millimeter-wavelength cloud radar and X-band weather radar. Experiments showed that attenuation was a major contributory factor in the different reflectivities of the two radars when relatively intense echoes exist, and the attenuation correction developed in this study significantly improved data quality for millimeter-wavelength radar. Reflectivity differences between the two radars were reduced and reflectivity correlations were enhanced. Errors caused by attenuation were eliminated, while variation details in the reflectivity factors were retained. The VTC method is superior to the bin-by-bin method in terms of correction amplitude and can be used for attenuation correction of shorter wavelength radar assisted by longer wavelength radar data.

EVALUATION OF A HEATING SYSTEM WITH A HEAT PUMP ACCORDING TO THE MODE OF INPUT DATA

The paper is focused on evaluating a heating system with an air source heat pump using the bin method. The main goal of the paper is to find the difference between three modes of input outside air temperature data in the calculation. Outside air temperatures are used in three modes, an hour based calculation, monthly frequencies and annual frequencies based calculations.

Numerical Experiments to Analyze Cloud Microphysical Processes Depicted in Vertical Profiles of Radar Reflectivity of Warm Clouds

Information about microphysical processes in warm clouds embedded in satellite measurements must be untangled to be used to improve the parameterization in global models. In this paper, the relationship between vertical profiles of horizontally averaged radar reflectivity Zm and cloud optical depth from cloud top τd was investigated using a hybrid cloud microphysical model and a forward simulator of satellite measurements. The particle size distributions were explicitly simulated using a bin method in a kinematic framework. In contrast to previous interpretations of satellite-observed data, three patterns of the Zm–τd relationship related to microphysical processes were identified. The first is related to the autoconversion process, which causes Zm to increase upward with decreasing τd. Before the initiation of surface precipitation, Zm increases downward with τd in the upper part of the cloud, which is considered to be a second characteristic pattern and is caused by the accretion process. The appearance of this pattern corresponds to the initiation of efficient production of raindrops in the cloud. The third is related to the sedimentation and evaporation of raindrops causing Zm to decrease downward with τd in the lower part of the Zm–τd relationship. It was also found that the bulk collection efficiency has a partially positive correlation with the slope factor of Zm with regard to τd and that the slope factor could be a gross measure of the collection efficiency in partial cases. This study also shows that differences in the aerosol concentration modulate the duration of these three patterns and change the slope factor of Zm.

Inverting proteomics analysis provides powerful insight into the peptide/protein conundrum

In proteomics, a large proportion of mass spectrometry (MS) data is ignored due to the lack of, or insufficient statistical evidence for mappable peptides. In reality, only a small fraction of features are expected to be differentially relevant anyway. Mapping spectra to peptides and subsequently, proteins, produces uncertainty at several levels. We propose it is better to analyze proteomic profiling data directly at MS level, and then relate these features to peptides/proteins. In a renal cancer data comprising 12 normal and 12 cancer subjects, we demonstrate that a simple rule-based binning approach can give rise to informative features. We note that the peptides associated with significant spectral bins gave rise to better class separation than the corresponding proteins, suggesting a loss of signal in the peptide-to-protein transition. Additionally, the binning approach sharpens focus on relevant protein splice forms rather than just canonical sequences. Taken together, the inverted raw spectra analysis paradigm, which is realised by the MZ-Bin method described in this article, provides new possibilities and insights, in how MS-data can be interpreted.