Sustainability Performance of Certified and Non-certified Food

The dataset Sustainability performance of certified and non-certified food (https://www.doi.org/10.15454/OP51SJ) contains 25 indicators of economic, environmental, and social performance, estimated for 27 certified food value chains and their 27 conventional reference products. The indicators are estimated at different levels of the value chain: farm level, processing level, and retail level. It also contains the raw data based on which the indicators are estimated, its source, and the completed spreadsheet calculators for the following indicators: carbon footprint and food miles. This article describes the common method and indicators used to collect data for the twenty-seven certified products and their conventional counterparts. It presents the assumptions and choices, the process of data collection, and the indicator estimation methods designed to assess the three sustainability dimensions within a reasonable time constraint. That is: three person-months for each food quality scheme and its non-certified reference product. Several prioritisations were set regarding data collection (indicator, variable, value chain level) together with a level of representativeness specific to each variable and product type (country and sector). Technical details on how relatively common variables (e.g., number of animals per hectare) are combined into indicators (e.g., carbon footprint) are provided in the full documentation of the dataset.

Stochastic variational variable selection for high-dimensional microbiome data

BackgroundThe rapid and accurate identification of a minimal-size core set of representative microbial species plays an important role in the clustering of microbial community data and interpretation of the clustering results. However, the huge dimensionality of microbial metagenomics data sets is a major challenge for the existing methods such as Dirichlet multinomial mixture (DMM) models. In the framework of the existing methods, computational burdens for identifying a small number of representative species from a huge number of observed species remain a challenge.ResultsWe proposed a novel framework to improve the performance of the widely used DMM approach by combining three ideas: (i) We extended the finite DMM model to the infinite case, via the consideration of Dirichlet process mixtures and estimate the number of clusters as a random variables. (ii) We proposed an indicator variable to identify representative operational taxonomic units that substantially contribute to the differentiation among clusters. (iii) To address the computational burdens of the high-dimensional microbiome data, we proposed are a stochastic variational inference, which approximates the posterior distribution using a controllable distribution called variational distribution, and stochastic optimization algorithms for fast computation. With the proposed method named stochastic variational variable selection (SVVS), we analyzed the root microbiome data collected in our soybean field experiment and the human gut microbiome data from three published data sets of large-scale case-control studies.ConclusionsSVVS demonstrated a better performance and significantly faster computation than existing methods in all cases of testing data sets. In particular, SVVS is the only method that can analyze the massive high-dimensional microbial data with above 50,000 microbial species and 1,000 samples. Furthermore, it was suggested that microbial species selected as a core set played important roles in the recent microbiome studies.

Risk Model of German Corona Warning App – Reloaded

Aim. In this paper we discuss the risk model of the German Corona Warning App in two versions. Both are based on a general semi-quantitative risk approach that is not state of the art anymore and for some application domains even deprecated. The main problem is that parameter estimates are often only ordinal scale or rank numbers for which such operations as multiplication or division are not clearly specified. Therefore, it may results in underestimation or overestimation of the associated risk. Methods. The risk models that are used in the apps are analyzed. Comparison of the nomenclature of model parameters, their influence on the result, approaches to the generation of a combined risk assessment is carried out. The effectiveness of the models is analyzed. Results. It is shown that most of the parameters in the model are used only as binary indicator variable. It has been found that the Corona Warning App uses a much more limited model that does not even assess risk, but relies on one parameter which is weighted exposure time. It has been shown that the application underestimates this parameter and therefore may erroneously reassure users. Thus, it may be concluded that the basic risk model implemented before version 1.7.1., is rather a dosimetric model that depends on the calculated virus concentration and does not depend on exposure and other parameters (excluding some threshold values). It is not even a risk model as defined by many standards. Changes of the risk model in the later version are not fundamental. In particular the later model also assesses not individual risk, but individual exposure according to the results. In addition, the model greatly underestimates the duration of exposure. Although it is reported that about 60% of the app’s users have shared positive test results, the absolute number of published results is less than 10% of all positive test results. Therefore, from an individual point of view, the application is effective only in 10% of cases, or even less. Conclusions. As the Corona Warning App also has other systematic limitations and shortcomings it is advised not to rely on its results but rather on Covid testing or vaccination. In addition, if there are enough virus tests available in the near future, the application will even become outdated. It will be better to develop an application that can assess risks a priori, as a kind of decision support for its users based on their individual risk profile.

The Short-Term Load Forecasting for Special Days Based on Bagged Regression Trees in Qingdao, China

There are many factors that affect short-term load forecasting performance, such as weather and holidays. However, most of the existing load forecasting models lack more detailed considerations for some special days. In this paper, the applicability of the bagged regression trees (BRT) model combined with eight variables is investigated to forecast short-term load in Qingdao. The comparative experiments show that the accuracy and speed of forecasting have some improvements using the BRT than the artificial neural network (ANN). Then, an indicator variable is newly proposed to capture the abnormal information during special days, which include national statutory holidays, bridging days, and proximity days. The BRT model combined with this indicator variable is tested on the load series measured in 2018. Experiments demonstrate that the improved model generates more accurate predictive results than BRT model combined with previously variables on special days.

Preoperative Metastatic Brain Tumor-Associated Intracerebral Hemorrhage Is Associated With Dismal Prognosis

ObjectIntra-tumoral hemorrhage is considered an imaging characteristic of advanced cancer disease. However, data on the influence of intra-tumoral hemorrhage in patients with brain metastases (BM) remains scarce. We aimed at investigating patients with BM who underwent neurosurgical resection of the metastatic lesion for a potential impact of preoperative hemorrhagic transformation on overall survival (OS).MethodsBetween 2013 and 2018, 357 patients with BM were surgically treated at the authors’ neuro-oncological center. Preoperative magnetic resonance imaging (MRI) examinations were assessed for the occurrence of malignant hemorrhagic transformation.Results122 of 375 patients (34%) with BM revealed preoperative intra-tumoral hemorrhage. Patients with hemorrhagic transformed BM exhibited a median OS of 5 months compared to 12 months for patients without intra-tumoral hemorrhage. Multivariate analysis revealed preoperative hemorrhagic transformation as an independent and significant predictor for worsened OS.ConclusionsThe present study identifies preoperative intra-tumoral hemorrhage as an indicator variable for poor prognosis in patients with BM undergoing neurosurgical treatment.

How Vulnerable Are American States to Wildfires? A Livelihood Vulnerability Assessment

Quantifying livelihood vulnerability to wildland fires in the United States is challenging because of the need to systematically integrate multidimensional variables into its analysis. We aim to measure wildfire threats amongst humans and their physical and social environment by developing a framework to calculate the livelihood vulnerability index (LVI) for the top 14 American states most recently exposed to wildfires. The LVI is computed by assessing each state’s contributing factors (exposure, sensitivity, and adaptive capacity) to wildfire events. These contributing factors are determined through a set of indicator variables that are categorized into corresponding groups to produce an LVI framework. The framework is validated by performing a principal component analysis (PCA), ensuring that each selected indicator variable corresponds to the correct contributing factor. Our results indicate that Arizona and New Mexico experience the greatest livelihood vulnerability. In contrast, California, Florida, and Texas experience the least livelihood vulnerability. While California has one of the highest exposures and sensitivity to wildfires, results indicate that it has a relatively high adaptive capacity, in comparison to the other states, suggesting it has measures in place to withstand these vulnerabilities. These results are critical to wildfire managers, government, policymakers, and research scientists for identifying and providing better resiliency and adaptation measures to support states that are most vulnerable to wildfires.

Prediction of Changes in Seafloor Depths Based on Time Series of Bathymetry Observations: Dutch North Sea Case

Guaranteeing safety of navigation within the Netherlands Continental Shelf (NCS), while efficiently using its ocean mapping resources, is a key task of Netherlands Hydrographic Service (NLHS) and Rijkswaterstaat (RWS). Resurvey frequencies depend on seafloor dynamics and the aim of this research is to model the seafloor dynamics to predict changes in seafloor depth that would require resurveying. Characterisation of the seafloor dynamics is based on available time series of bathymetry data obtained from the acoustic remote sensing method of both single-beam echosounding (SBES) and multibeam echosounding (MBES). This time series is used to define a library of mathematical models describing the seafloor dynamics in relation to spatial and temporal changes in depth. An adaptive, functional model selection procedure is developed using a nodal analysis (0D) approach, based on statistical hypothesis testing using a combination of the Overall Model Test (OMT) statistic and Generalised Likelihood Ratio Test (GLRT). This approach ensures that each model has an equal chance of being selected, when more than one hypothesis is plausible for areas that exhibit varying seafloor dynamics. This ensures a more flexible and rigorous decision on the choice of the nominal model assumption. The addition of piecewise linear models to the library offers another characterisation of the trends in the nodal time series. This has led to an optimised model selection procedure and parameterisation of each nodal time series, which is used for the spatial and temporal predictions of the changes in the depths and associated uncertainties. The model selection results show that the models can detect the changes in the seafloor depths with spatial consistency and similarity, particularly in the shoaling areas where tidal sandwaves are present. The predicted changes in depths and uncertainties are translated into a probability risk-alert map by evaluating the probabilities of an indicator variable exceeding a certain decision threshold. This research can further support the decision-making process when optimising resurvey frequencies.

Earnings management when firms face mandatory contributions

PurposeIn this paper, we aim to answer two questions. First, whether firms manipulate reported earnings via pension assumptions when facing mandatory contributions. Second, whether firms alter their earnings management behavior when the Financial Accounting Standard Board (FASB) mandates disclosure of pension asset composition and a description of investment strategy under SFAS 132R.Design/methodology/approachOur basic approach is to run linear regressions of firm-year assumed returns on the log of pension sensitivity measures, controlling for current and lagged actual returns from pension assets, fiscal year dummies and industry dummies. The larger the pension sensitivity ratios, the stronger the effects from inflated ERRs on reported earnings. We confirm the early results that the regression slopes are positive and highly significant. We construct an indicator variable DMC to capture the mandatory contributions firms face and another indicator variable D132R to capture the effect of SFAS 132R. DMC takes the value of one for fiscal years during which an acquisition takes place and zero otherwise. D132R takes the value of one for fiscal years after December 15, 2003 and zero otherwise.FindingsOur sample covers the period from June 1992 to December 2017. Our key results are as follows. The estimated coefficient (t-statistic) on DMC is 0.308 (6.87). Firms facing mandatory contributions tend to set ERRs at an average 0.308% higher. The estimated coefficient (t-statistic) on D132R is −2.190 (−13.70). The new disclosure requirement under SFAS 132R constrains all firms to set ERRs at an average 2.190% lower. The estimate (t-statistic) on the interactive term DMA×D132R is −0.237 (−3.29). When mandatory contributions happen during the post-SFAS 132R period, firms tend to set ERRs at 0.237% lower than they would do otherwise in the pre-SFAS 132R period.Originality/valueWhen firms face mandatory contributions, typically firm experience negative stock market returns. We examine whether managers manage earnings to mitigate such negative impact. We find that firms inflate assumed returns on pension assets to boost their reported earnings when facing mandatory contributions. We also find that managers alter earnings management behavior, in the case of mandatory contributions, following the introduction of new pension disclosure standards under SFAS 132R that become effective on December 15, 2003. Under the new SFAS 132R requirement, firms need to disclose asset allocation and describe investment strategies. This imposes restrictions on managers' discretion in making ERR assumptions, since now the composition of pension assets is a key determinant of the assumed expected rate of return on pension assets. Firms need to justify their ERRs with their asset allocations.

Perancangan Tempat Pengolahan Sampah (TPS) 3R di Kelurahan Sungai Jawi Dalam Kota Pontianak

In the implementation of the construction of the recycling area, several stages are needed, one of which is the phase of selecting a suitable location for the construction of the recycling area. This study aims to analyze the potential sites for the construction of recycling area using the participatory selection (selotif) method then after determining the appropriate locations, the design of the recycling area includes reception and sorting areas, composting area and other supporting facilities. The method in carrying out research at the initial stage is to identify the location of the study area and perform population projections to determine waste generation for the next 20 years. The research location in Kelurahan Sungai Jawi Dalam then divides 2 zones of prospective locations to be scored using that method. From the results of population projections in 2021, the population of Kelurahan Sungai Jawi Dalam is 35,402 people with a loading rate of 2.68 liters/person/day so that the capacity of the waste generated is 94,877 liters/day. Then from the scoring of the indicator variable tape, zone A obtained a cumulative value of 1190 and zone B of 1030 so that the zone selected as the location for the construction of recycling area was zone A with the availability of land for development of 896 m2. It is planned that the recycling area will serve 400 family members, with the rate of waste generated then compared to the waste generation of Kelurahan Sungai Jawi Dalam in 2021, the presence of the recycling area which is planned to reduce 4.52% of the waste that goes to the landfill. Recycling area requirements are 166.75 m2 consisting of 5.0 m2 organic waste storage, 7.0 m2 sorting area, 36.75 m2 composting area, 24.0 m2 sifting area, and anorganic waste area. 9.0 m2 then supporting facilities such as warehouse 18.0 m2, office 18.0 m2 security, 3.0 m2 residual area 10.0 m2, garage 30 m2 and bathroom 6.0 m2. Keywords: waste, selotif, recycling area Abstrak Dalam pelaksanaan pembangunan TPS 3R dibutuhkan tahapan pemilihan lokasi yang layak untuk pembangunan TPS 3R. Penelitian ini bertujuan untuk menganalisis calon lokasi TPS 3R dengan metode seleksi partisipatif (selotif), setelah ditentukan lokasi kemudian merancang area TPS 3R berupa area penerimaan dan pemilahan, area komposting dan fasilitas pendukung lainnya. Metode pelaksanaan penelitian pada tahap awal mengidentifikasi lokasi wilayah studi dan proyeksi penduduk untuk mengetahui timbulan sampah hingga 20 tahun mendatang kemudian untuk mendapatkan hasil skoring lokasi terpilih berdasarkan variabel dan indikator yang ditentukan dalam metode selotif dilakukan dengan observasi langsung, wawancara dan kuesioner. Lokasi penelitian di Kelurahan Sungai Jawi Dalam kemudian membagi 2 zona calon lokasi yang akan dilakukan skoring dengan metode selotif. Dari hasil proyeksi penduduk pada tahun 2021, jumlah penduduk Kelurahan Sungai Jawi Dalam sebanyak 35.402 jiwa dengan laju timbulan sampah 2,68 liter/org/hari sehingga kapasitas sampah yang dihasilkan adalah 94.877 liter/hari. Kemudian dari skoring variabel indikator selotif zona A memperoleh nilai komulatif sebesar 1190 dan zona B sebesar 1030 sehingga zona yang terpilih sebagai lokasi pembangunan TPS 3R adalah zona A dengan ketersediaan lahan untuk pembangunan sebesar 896 m2. Direncanakan TPS 3R akan melayani 400KK, dengan laju timbulan sampah yang dihasilkan kemudian dibandingkan dengan timbulan sampah Kelurahan Sungai Jawi Dalam pada tahun 2021, kehadiran TPS 3R yang direncanakan dapat mengurangi 4,52% sampah yang masuk ke TPA. Kebutuhan lahan TPS 3R untuk melayani jumlah KK yang direncanakan sebesar 166,75 m2 terdiri dari penampungan sampah organik 5,0 m2, area pencacah 7,0 m2, area pengomposan 36,75 m2, area pengayakan 24,0 m2, dan area sampah anorganik 9,0 m2 kemudian sarana pendukung seperti gudang 18,0 m2, kantor 18,0 m2 pos jaga 3,0 m2 area residu 10,0 m2, garasi gerobak motor 30 m2 dan kamar mandi / WC 6,0 m2. Kata kunci : sampah, selotif, TPS 3R

Changes in heat-attributable deaths in Prague, Czech Republic, over 1982–2019

<p>Studies projecting the impacts of future climate change on temperature-mortality relationships suggest increasing heat-related mortality in most regions of the world. On the contrary, a reduced risk of heat-related mortality has been observed in many countries over the last decades, suggesting a positive effect of technological development and improved health care systems. However, most of the studies show that the decline in vulnerability of populations to heat has abated in the early 2000s and further decreasing trend is unlikely.</p><p>In this study, we analysed temperature-mortality relationships in Prague, Czech Republic during 1982–2019. The study was restricted to five warmest months (May–September). To investigate possible changes in the temperature–mortality relationship, the study period was divided in four decades (1980s to 2010s). Conditional Poisson Regression coupled with the Distributed Lag Non-Linear Model (DLNM) was run separately in each decade, to derive decade-specific temperature–mortality associations. A stratum indicator variable composed of year, month, and day of the week was used to control for long-term, seasonal trends and weekly effects. The DLNM approach was applied in order to analyse delayed effects of temperature on mortality. The attributable number of deaths (AD) and the attributable fraction (AF %) of total May–September deaths on hot days was calculated from the model’s outputs, separately for each decade. Hot days were defined as days with daily mean temperature larger than the 95th percentile of the decade-specific May–September distribution.</p><p>We observed a quadratic trend shape in the number of deaths attributable to heat; maximum in the 2010s and minimum in the 1990s. The total number of heat-attributable deaths increased from ≈500 to almost 900 per decade between the 1980s and the 2010s, which corresponds to the fraction of 0.90 and 1.75 %, respectively, of the total number of deaths in a warm season.</p>