Henning Schulte, Christian Ammon, Frauke Hagenkamp-Korth
et al.
Previous studies have shown that ammonia emissions can be continuously reduced through the application of a urease inhibitor (UI) in cattle and pig farming. However, there is no information on whether the use of these inhibitors also has an effect on other emissions, and whether it leads to an increase or decrease in these emissions.
In this study, carbon dioxide, ammonia, methane, nitrous oxide and odour emissions were measured in three mechanically ventilated, fully slatted pig fattening houses in Germany during 2019–2020. The UI was applied daily to compartments, and effects on emission values were comparatively analysed using four different calculation approaches: linear mixed model, direct case-control, case-control in time and a novel ratio-difference approach.
As expected, a significant reduction in ammonia emissions of 22–24 % was observed across all four calculation approaches and all three farms, confirming the effectiveness of the UI; no decisive effects on carbon dioxide, methane or odour emissions were found. Effects on nitrous oxide emissions could not be reliably analysed due to low concentrations which were below the Fourier-transform infrared spectroscopy (FTIR) quantification limit.
It is recommended to calculate the reduction effect using a combined approach so that over- and underestimation of the effect can be avoided. Two approaches are available for this purpose: the ratio-difference and linear mixed model. The ratio-difference approach has a simplicity of calculation and the ability to achieve results very similar to those of the linear mixed model.
We conduct a pre-registered randomized controlled trial to test for income targeting in labor supply decisions among sellers of a Swedish street paper. These workers face liquidity constraints, high income volatility, and discretion over hours. Treated individuals received a 25 percent bonus per copy sold for the duration of an issue, simulating an increase in earnings potential. Treated sellers sold more papers, worked longer hours, and took fewer days off. These findings contrast with studies on intertemporal labor supply that find small substitution effects. Notably, when we apply strategies similar to observational studies, we recover patterns consistent with income targeting.
This paper presents a quantitative framework for optimizing human AI workforce allocation in software development, translatable to other labor categories. I formalize baseline and AI-collapsed labor models, derive tipping point equations for safe headcount reduction, and embed them in a multi objective evolutionary optimization setup. NSGAII experiments reveal reproducible, phase specific automation strategies that reduce cost while maintaining quality and stable workloads.
Over the past three decades, extreme climate events have caused losses of worth USD 4.5 trillion. Using a panel of 151 countries (1995-2019), I examine how extreme climate conditions shape gender gap in labor force participation. Key results show that the gender gap in paid labor exhibits a U-shaped relationship with droughts and an inverted U-shaped relationship with extreme wet conditions. The drought pattern is primarily driven by gender gap in employment while wetness affects gender gap in participation through unemployment. These relationships vary with country characteristics. Countries with high disaster-displacement risk exhibit declining gender gaps in participation during excess wetness while moderate-risk economies experience expanded gaps during droughts. Furthermore, the drought U-shape is most pronounced in countries with low to moderate empowerment while the nonlinear wet responses is concentrated only in moderately empowered countries. Lastly, both droughts and excess wetness expands gender gap in countries with weak net resilience to climate shocks.
Anna Grace Ulses, Joshua Krissansen-Totton, Tyler D. Robinson
et al.
The search for life outside our solar system is at the forefront of modern astronomy, and telescopes such as the Habitable Worlds Observatory (HWO) are being designed to identify biosignatures. Molecular oxygen, O$_2$, is considered a promising indication of life, yet substantial abiotic O$_2$ may accumulate from H$_2$O photolysis and hydrogen escape on a lifeless, fully (100%) ocean-covered terrestrial planet when surface O$_2$ sinks are suppressed. This so-called waterworld false positive scenario could be ruled out with land detection because exposed land precludes extremely deep oceans (~50 Earth oceans) given topographic limits set by the crushing strength of rocks. Land detection is possible because plausible geologic surfaces exhibit increasing reflectance with wavelength in the visible, whereas liquid water and ice/snow have flat or decreasing reflectance, respectively. Here, we present reflected light retrievals to demonstrate that HWO could detect land on an exo-Earth in the disk-averaged spectrum. Given a signal-to-noise ratio of 20 spectrum, Earth-like land fractions can be confidently detected with 0.3-1.1 $μ$m spectral coverage (resolution R~140 in the visible, R~7 in the UV, with Earth-like atmosphere and clouds). We emphasize the need for UV spectroscopy down to at least 0.3 $μ$m to break an O$_3$-land degeneracy. We find that the SNR and resolution requirements in the visible/UV imply that a larger aperture (~8 m) will be necessary to ensure the observing times required for land detection are feasible for most HWO terrestrial habitable zone targets. These results strongly inform the HWO minimum requirements to corroborate possible oxygen biosignatures.
Viswa Narayanan Sankaranarayanan, Akshit Saradagi, Sumeet Satpute
et al.
In this article, we present a centralized approach for the control of multiple unmanned aerial vehicles (UAVs) for landing on moving unmanned ground vehicles (UGVs) using control barrier functions (CBFs). The proposed control framework employs two kinds of CBFs to impose safety constraints on the UAVs' motion. The first class of CBFs (LCBF) is a three-dimensional exponentially decaying function centered above the landing platform, designed to safely and precisely land UAVs on the UGVs. The second set is a spherical CBF (SCBF), defined between every pair of UAVs, which avoids collisions between them. The LCBF is time-varying and adapts to the motions of the UGVs. In the proposed CBF approach, the control input from the UAV's nominal tracking controller designed to reach the landing platform is filtered to choose a minimally-deviating control input that ensures safety (as defined by the CBFs). As the control inputs of every UAV are shared in establishing multiple CBF constraints, we prove that the control inputs are shared without conflict in rendering the safe sets forward invariant. The performance of the control framework is validated through a simulated scenario involving three UAVs landing on three moving targets.
The Unigram tokenization algorithm offers a probabilistic alternative to the greedy heuristics of Byte-Pair Encoding. Despite its theoretical elegance, its implementation in practice is complex, limiting its adoption to the SentencePiece package and adapters thereof. We bridge this gap between theory and practice by providing a clear guide to implementation and parameter choices. We also identify a simpler algorithm that accepts slightly higher training loss in exchange for improved compression.
deepTerra is a comprehensive platform designed to facilitate the classification of land surface features using machine learning and satellite imagery. The platform includes modules for data collection, image augmentation, training, testing, and prediction, streamlining the entire workflow for image classification tasks. This paper presents a detailed overview of the capabilities of deepTerra, shows how it has been applied to various research areas, and discusses the future directions it might take.
We study the loop and suspension functors on the category of augmented $\mathbb{E}_n$-algebras. One application is to the formality of the cochain algebra of the $n$-sphere. We show that it is formal as an $\mathbb{E}_n$-algebra, also with coefficients in general commutative ring spectra, but rarely $\mathbb{E}_{n+1}$-formal unless the coefficients are rational. Along the way we show that the free functor from operads in spectra to monads in spectra is fully faithful on a nice subcategory of operads which in particular contains the stable $\mathbb{E}_n$-operads for finite $n$. We use this to interpret our results on loop and suspension functors of augmented algebras in operadic terms.
Kamil Bader El Dine, Noujoud Nader, Mohamad Khalil
et al.
Preterm labor (PL) has globally become the leading cause of death in children under the age of 5 years. To address this problem, this paper will provide a new approach by analyzing the EHG signals, which are recorded on the abdomen of the mother during labor and pregnancy. The EHG signal reflects the electrical activity that induces the mechanical contraction of the myometrium. Because EHGs are known to be non-stationary signals, and because we anticipate connectivity to alter during contraction, we applied the windowing approach on real signals to help us identify the best windows and the best nodes with the most significant data to be used for classification. The suggested pipeline includes i) divide the 16 EHG signals that are recorded from the abdomen of pregnant women in N windows; ii) apply the connectivity matrices on each window; iii) apply the Graph theory-based measures on the connectivity matrices on each window; iv) apply the consensus Matrix on each window in order to retrieve the best windows and the best nodes. Following that, several neural network and machine learning methods are applied to the best windows and best nodes to categorize pregnancy and labor contractions, based on the different input parameters (connectivity method alone, connectivity method plus graph parameters, best nodes, all nodes, best windows, all windows). Results showed that the best nodes are nodes 8, 9, 10, 11, and 12; while the best windows are 2, 4, and 5. The classification results obtained by using only these best nodes are better than when using the whole nodes. The results are always better when using the full burst, whatever the chosen nodes. Thus, the windowing approach proved to be an innovative technique that can improve the differentiation between labor and pregnancy EHG signals.
IntroductionHarsh ecological conditions, including water scarcity, have limited vegetation life in desert areas. Consequently, the cultivation of drought-resistant plants compatible with desert areas and their expansion, while creating suitable vegetation, increases biodiversity, controls desertification and is oriented towards the sustainability of desert ecosystems. Cannabis is a drought-tolerant plant which, because of its great genetic diversity, has the ability to grow in different climates, particularly in semi-desert areas. Appropriate agricultural management enhances the vegetation, production and productivity of agricultural products. In this context, it is important to study planting date and plant density as factors impacting production. Planting dates must be chosen to allow sufficient time for each stage of growth and development. The use of optimal plant density may improve plant growth and increase plant yield by reducing intra-plant competition. Results of search on two densities of cannabis plants of 8 and 16 plants per m-2 in Birjand, the highest seed yield was obtained from a density of 16 plants per m-2. Finding on densities of 50, 150, and 250 plants per m-2 in Mashhad, and 30, 90, and 150 plants per m-2 in Shirvan reported that as the density of cannabis plants increased, the flowering date decreased in both regions. Given the arid and semi-arid climate of South Khorasan, planting plants compatible with the climate of the region, such as cannabis, can increase vegetation cover while producing an acceptable yield. The objectives of the current research are to study the effect of agricultural management on the growth characteristics of the forgotten cannabis plant in semi-arid climate of Birjand. Material and MethodsThe current research was carried out in Center of Agriculture and Natural Resources Research if South Khorasan, located at 59′ 13° east longitude and 53° 32′north latitude, and 1491m above sea level. South Khorasan province has a desert and semi-desert climate. Before preparing the soil to determine the required amount of chemical and organic fertilizers, the soil in the field was analyzed. Data on temperature changes and the total number of sunny hours of various months during the cannabis growing period were received from the Birjand weather station. The experiment was conducted as a split plot based on a randomized complete block design with three replications. Treatments investigated included planting date on three levels of May 12, May 27 and June 11 as the main plot and plant density at three levels of 22.2, 11.1 and 7.4 plants per m-2 as the sub plot. In this research, the phenological characteristics including the number of days to emergency, days to flowering, days to seed filling, days to physiological maturity, length of vegetative period, length of reproductive period, length of flowering period, and morphological characteristics including plant height, number of main stem branches, stem diameter and seed yield were investigated. Statistical analysis of the data was done using SAS software and the comparison of averages was done based on Duncan's 5% multiple range test. Results and DiscussionThe results showed that the impact of planting date on all morphophenological traits was significant, with the exception of stem diameter. The delay in planting between May 12 and June 11 significantly reduced the length of phenological stages, and vegetative growth of cannabis, and ultimately caused a 48% decrease in seed yield. Late cultivation, due to the increase in temperature, the plant completes its vegetative growth faster. The delay in planting by shortening the period of effective growth, reducing the photosynthetic potential of the plant, and coinciding with the period of seed filling with low temperatures and shortening of the day has led to a decrease in the quantity and the filling speed of the seeds, and subsequently the yield of the seeds decreases. It has been reported that a 20-day delay in seeding from 10 May led to a 46% decrease in seed yield under climatic conditions in Azerbaijan. The effect of plant density on morphological traits, number of days until flowering of female plants, days until seed set, days until physiological maturity, length of vegetative period, length of flowering period and seed yield were significant. The increase in density from 7.4 to 22.2 plants per m-2, while delaying flowering, increased seed yield by 15.4%. Increased plant density due to higher plant height and increased number of plants per unit area increased seed yield. Results of search on two densities of cannabis plants of 8 and 16 plants per m-2 in Birjand, the highest seed yield was obtained from a density of 16 plants per m-2. To achieve proper yield performance, and develop cannabis cultivation- as a plant compatible with the semi-desert region- the planting date of May 12 and the density of 22.2 plants per m-2 can be used.
Grace Nkansa Asante, Paul Owusu Takyi, Gideon Mensah
ABSTRACTIt is hypothesized that a well-functioning financial market is necessary but not sufficient condition to achieve the expected economic growth. Therefore, policy instruments of government aimed at streamlining financial sector activity in sub-Saharan Africa (SSA) are imperative. As a result, this paper explores the effect of financial development on economic growth by allowing the link between the two far variables to be mediated by the quality of institutions for the period 2000–2019. Using Twenty-nine (29) countries and the System-Generalized Method of Moments (system-GMM) estimation method, it is found that financial development has a positive and significant effect on economic growth. In addition, it is found that, when rule of law, political stability, and regulatory quality are highly effective, the positive effect of financial development on economic growth is magnified.
Economic growth, development, planning, Human settlements. Communities
Farhan Zeb Khaskhelly, Ali Raza, Hemal Azhar
et al.
Purpose
The purpose of this study is to explore and analyze corporate social responsibility (CSR) as a helpful tool in solving significant societal concerns in countries where there is a greater desire for social and economic growth, such as Pakistan.
Methodology
In order to examine the current issues on supply chain collaboration for sustainability, this paper used a triangulation research method. In order to determine indicators in a CSR-intensive environment, data, and literature, the energy sector publications on EUR-Lex, international and European official papers, and the online site of the European Commission data sources were analyzed in this study. The indicators were divided into groups based on their sources (sets of standards and guidelines, council frameworks, document series, tools, and comprehensive legislation), as well as their intended uses (financial, social, and environmental).
Findings
The findings state that supply chain collaboration completely fulfills CSR for a viable economy. It focuses on three leading fashion brands and assesses their impact using open-source data, past research, and their official websites. It also highlights how, in comparison to global corporations, Pakistani business satisfies their corporate social responsibility.
Conclusion
It is concluded that a supply chain can help companies minimize the environmental impact of their supply chain processes. Further, CSR is a part of the supply chain that helps businesses determine their social and economic responsibilities by focusing on environmental aspects to add to a more sustainable economy.
The experimental study of the heat transfer coefficient of nanofluid plays a significant role in improving the heat transfer rate of the heat exchanger. A natural convection apparatus was used to study heat transfer in the suspension of Al2O3 nanoparticles in a water-ethylene glycol mixture base fluid. The effects of the heat input, the nanoparticle volume fraction, and the base fluid concentration on the heat transfer coefficient were studied using a 23 full factorial design matrix (16 experimental runs) and the MINITAB Design software. The levels for the heat input, nanoparticle volume fraction, and base fluid concentration were 10 and 100 W, 0.1 and 1 vol.%, and 30 and 50 vol.%, respectively. The residual, contour, 3D surface plots, and Pareto chart were drawn from the experimental results. The observed heat transfer coefficient showed the highest enhancement with the high level of the nanoparticle volume fraction and a moderate enhancement with the high level of heat input, and a slight enhancement with the base fluid concentration.
Abstract In modern society, system integration that enables multiple subsystems to function as one is emerging in various fields like industry, commerce, and infrastructure. Although it has been proved that integration value could be tapped to the maximum with controllable cost by optimising the integration schemes in certain fields, there is still a lack of a general method for modelling and analysing the process of system integration. To address this need, this paper proposes an analysis framework of system integration. The concepts of integration object, integration strategy, integration time, integration cost and integration value are introduced to describe the integration process. Further, three optimisation models of the local optimisation (OPT1), phase optimisation (OPT2) and integration optimisation (OPT3) are constructed. The proposed framework can also supervise and compare the performance of intermediate processes of different integration schemes. Two case studies in the commerce and energy fields are analysed to illustrate the function of the proposed framework.
Production of electric energy or power. Powerplants. Central stations, Energy industries. Energy policy. Fuel trade
We introduce OpenEarthMap, a benchmark dataset, for global high-resolution land cover mapping. OpenEarthMap consists of 2.2 million segments of 5000 aerial and satellite images covering 97 regions from 44 countries across 6 continents, with manually annotated 8-class land cover labels at a 0.25--0.5m ground sampling distance. Semantic segmentation models trained on the OpenEarthMap generalize worldwide and can be used as off-the-shelf models in a variety of applications. We evaluate the performance of state-of-the-art methods for unsupervised domain adaptation and present challenging problem settings suitable for further technical development. We also investigate lightweight models using automated neural architecture search for limited computational resources and fast mapping. The dataset is available at https://open-earth-map.org.