Financial Losses
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2021 ◽  
Vol 5 (OOPSLA) ◽  
pp. 1-30
Christian Bräm ◽  
Marco Eilers ◽  
Peter Müller ◽  
Robin Sierra ◽  
Alexander J. Summers

Smart contracts are programs that execute in blockchains such as Ethereum to manipulate digital assets. Since bugs in smart contracts may lead to substantial financial losses, there is considerable interest in formally proving their correctness. However, the specification and verification of smart contracts faces challenges that rarely arise in other application domains. Smart contracts frequently interact with unverified, potentially adversarial outside code, which substantially weakens the assumptions that formal analyses can (soundly) make. Moreover, the core functionality of smart contracts is to manipulate and transfer resources; describing this functionality concisely requires dedicated specification support. Current reasoning techniques do not fully address these challenges, being restricted in their scope or expressiveness (in particular, in the presence of re-entrant calls), and offering limited means of expressing the resource transfers a contract performs. In this paper, we present a novel specification methodology tailored to the domain of smart contracts. Our specifications and associated reasoning technique are the first to enable: (1) sound and precise reasoning in the presence of unverified code and arbitrary re-entrancy, (2) modular reasoning about collaborating smart contracts, and (3) domain-specific specifications for resources and resource transfers, expressing a contract's behaviour in intuitive and concise ways and excluding typical errors by default. We have implemented our approach in 2vyper, an SMT-based automated verification tool for Ethereum smart contracts written in Vyper, and demonstrated its effectiveness for verifying strong correctness guarantees for real-world contracts.

2021 ◽  
Vol 1 (2) ◽  
pp. 093-099
Nermeen Abdel-Fattah Shehab ◽  
Ahmed Atef Faggal ◽  
Ashraf Ali Nessim

The idea of searching: This study tends to assess the impact of implementing evidence-based infection prevention in healthcare facilities in Egypt, with the aim of improving surveillance systems and altering the facility designs according to the data acquired on HAIs patterns. Background: Hospital acquired infections (HAIs) are becoming one of the major concerns for the patients and healthcare providers leading to significant increase in mortality rates, morbidity rates and financial losses for healthcare organizations. The incidence rate of HAI in Egypt was as recorded as 3.7% recently. Certain environmental interventions, implemented during construction of the healthcare facility could lead to enhanced prevention against the transmission and spread of the HAIs. Studies revealed that the integration of Surveillance programs could provide evidence for the designers to alter the healthcare facility design with the aim of infection prevention. Therefore, EBD approach is used to potentially measure psychological and physical effects of the environment design of a health facility on the patients and hospital staff. Methodology: Previous scientific literature is assessed to collect the relevant data which is then organized and analyzed in this study. A systematic review is generated based on the analytical outcomes of the selected data. Conclusion: EBD approach has the potential to prominently decrease HAIs burden in Egyptian healthcare facilities as it provides a diverse insight into the layout, equipment, and materials that contribute in the transmission of pathogens due to faulty design. Findings and recommendations: In order to improve the poor indoor quality by MEP (mechanical, electrical, and plumbing), previous studies have also indicated certain solutions including advancements in private room, improved surface selections, isolation, integration of touchless systems, and enhanced ventilation systems that must be applied in the healthcare facilities in Egypt for infection prevention.

2021 ◽  
Vol 53 (3) ◽  
Lindita Ibishi ◽  
Arben Musliu ◽  
Blerta Mehmedi ◽  
Agim Rexhepi ◽  
Curtic R. Youngs ◽  

The health of dairy cows is an important factor affecting the profitability of dairy farms worldwide, and lameness is regarded as one of the most costly dairy cattle diseases. The aim of this study was to estimate the economic cost of cow lameness among Kosovo dairy farms. Data collected from 56 dairy farms were analysed with a farm-level stochastic (Monte Carlo) simulation model to estimate the cost of lameness. Lameness-associated sources of economic loss examined within the model included: reduced milk production, treatment cost, discarded milk, reduced cow body weight, and premature culling. Results showed that prevalence of lameness among cows on Kosovo dairy farms ranged from 17% to 39%. The average annual cost of lameness was estimated at €338.57 per farm (or €46.25 per cow). Reduced milk production was the largest financial contribution to the cost of lameness (45% of total economic loss) followed by premature culling (31% of total economic loss). Discarded milk, reduced cow body weight, and cost of treatment each contributed approximately 8% to the total economic loss. These findings indicate that dairy farmers need to be more cognizant of the financial losses associated with lameness and should be encouraged to implement management strategies to reduce lameness as a means of enhancing farm profitability.

2021 ◽  
Maxim Viktorovich Miklyaev ◽  
Ivan Vyacheslavovich Denisov ◽  
Ivan Mikhailovich Gavrilin

Abstract Well construction in the Volga-Ural Region faces different sorts of complications, the most common ones being the loss of drilling fluids and rockslides. Such complications may cause considerable financial losses due to non-productive time (NPT) and longer well construction periods. Moreover, there are complications, which might occur both during well construction and during its exploitation. The commonest complications are sustained casing pressure (SCP) and annular flow. The complications, which occur when operating a well, also have a negative effect on the economic efficiency of well operation and call for additional actions, for example, repair and insulation works, which require well shutdown and killing, though a desired outcome still cannot be guaranteed; moreover, it is possible that several different operations may have to be carried out. In addition, the occurrence of SCP during well life is one of the most crucial problems that may cause well abandonment due to high risks posed by its operation. It is known that the main reasons for SCP are as follows: Channels in cement stone Casing leaks Leaks in wellhead connections To resolve the problem of cement stone channeling, several measures were taken, such as revising cement slurry designs, cutting time for setting strings on slips, applying two-stage cementing, etc. These measures were not successful, besides, they caused additional expenses for extra equipment (for example, a cementer). In order to reduce the risk of cement stone channeling, a cementing method is required that will allow to apply excess pressure on cement slurry during the period of transition and early strength development. To achieve this goal, a well-known method of controlled pressure cementing may be applied. Its main drawback, however, is that it requires much extra equipment, thus increasing operation expenses. In addition, the abovementioned method allows affecting the cement stone only during the operation process and / or during the waiting on cement (WOC) time. Upon receiving the results of the implemented measures and considering the existing technologies and evaluating the economic efficiency, the need was flagged for developing a combined cementing method. The goal of this method is to modify the production string cementing method with a view to applying excess pressure on cement stone during strength development and throughout the well lifecycle. The introduction of this lining method does not lead to an increase in well construction costs and considerably reduces the risks of losing a well from the production well stock.

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 264-265
Duy Ngoc Do ◽  
Guoyu Hu ◽  
Younes Miar

Abstract American mink (Neovison vison) is the major source of fur for the fur industries worldwide and Aleutian disease (AD) is causing severe financial losses to the mink industry. Different methods have been used to diagnose the AD in mink, but the combination of several methods can be the most appropriate approach for the selection of AD resilient mink. Iodine agglutination test (IAT) and counterimmunoelectrophoresis (CIEP) methods are commonly employed in test-and-remove strategy; meanwhile, enzyme-linked immunosorbent assay (ELISA) and packed-cell volume (PCV) methods are complementary. However, using multiple methods are expensive; and therefore, hindering the corrected use of AD tests in selection. This research presented the assessments of the AD classification based on machine learning algorithms. The Aleutian disease was tested on 1,830 individuals using these tests in an AD positive mink farm (Canadian Centre for Fur Animal Research, NS, Canada). The accuracy of classification for CIEP was evaluated based on the sex information, and IAT, ELISA and PCV test results implemented in seven machine learning classification algorithms (Random Forest, Artificial Neural Networks, C50Tree, Naive Bayes, Generalized Linear Models, Boost, and Linear Discriminant Analysis) using the Caret package in R. The accuracy of prediction varied among the methods. Overall, the Random Forest was the best-performing algorithm for the current dataset with an accuracy of 0.89 in the training data and 0.94 in the testing data. Our work demonstrated the utility and relative ease of using machine learning algorithms to assess the CIEP information, and consequently reducing the cost of AD tests. However, further works require the inclusion of production and reproduction information in the models and extension of phenotypic collection to increase the accuracy of current methods.

Coatings ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1223
Nelly Boshkova ◽  
Kamelia Kamburova ◽  
Tsetska Radeva ◽  
Nikolai Boshkov

Localized corrosion and biofouling cause very serious problems in the marine industries, often related to financial losses and environmental accidents. Aiming to minimize the abovementioned, two types of hybrid Zn-based protective coatings have been composed. They consist of a very thin underlayer of polymer-modified ZnO or CuO nanoparticles and toplayer of galvanic zinc with a thickness of ~14 µm. In order to stabilize the suspensions of CuO or ZnO, respectively, a cationic polyelectrolyte polyethylenimine (PEI) is used. The polymer-modified nanoparticles are electrodeposited on the steel (cathode) surface at very low cathodic current density and following pH values: 1/CuO at pH 9.0, aiming to minimize the effect of aggregation in the suspension and dissolution of the CuO nanoparticles; 2/ZnO at pH 7.5 due to the dissolution of ZnO. Thereafter, ordinary zinc coating is electrodeposited on the CuO or ZnO coated low-carbon steel substrate from a zinc electrolyte at pH 4.5–5.0. The two-step approach described herein can be used for the preparation of hybrid coatings where preservation of particles functionality is required. The distribution of the nanoparticles on the steel surface and morphology of the hybrid coatings are studied by scanning electron microscopy. The thickness of the coatings is evaluated by a straight optical microscope and cross-sections. The protective properties of both systems are investigated in a model corrosive medium of 5% NaCl solution by application of potentiodynamic polarization (PDP) curves, open circuit potential (OCP), cyclic voltammetry (CVA), and polarization resistance (Rp) measurements. The results obtained allow us to conclude that both hybrid coatings with embedded polymer-modified CuO or ZnO nanoparticles ensure enhanced corrosion resistance and protective ability compared to the ordinary zinc.

2021 ◽  
pp. 104063872110506
Silvia D. Carli ◽  
Maria E. Dias ◽  
Maria E. R. J. da Silva ◽  
Gabriela M. Breyer ◽  
Franciele M. Siqueira

Poor reproductive performance in beef cattle caused by infectious agents results in major financial losses as a result of reduced pregnancy rates and extended calving intervals. Bulls can be subclinical chronic carriers of bacterial and protozoal agents involved in cow infertility, such as Campylobacter fetus subsp. venerealis, Ureaplasma diversum, Mycoplasma bovigenitalium, Mycoplasma bovis, and Tritrichomonas foetus. Bulls harbor these microorganisms in their preputial crypts and transmit the agents to cows during natural mating. To obtain an overview of the etiologic agents in the preputial mucus of bulls, we aimed to identify, by PCR assay, C. fetus subsp. venerealis, M. bovis, U. diversum, M. bovigenitalium, and T. foetus in Brazilian bulls from farms with high infertility rates. We collected preputial mucus from 210 bulls on 18 beef cattle farms in Brazil between 2019 and 2020. We found at least one of the infectious agents that we were studying in bulls on 16 of the 18 beef cattle farms tested. We detected at least one infectious agent from 159 of 210 (76%) bulls tested, namely C. fetus subsp. venerealis, M. bovis, U. diversum, M. bovigenitalium, and T. foetus in 87 (55%), 84 (53%), 45 (28%), 28 (18%), and 1 (0.6%) animal, respectively. We found 95 bulls (60%) positive for only 1 etiologic agent (single infection) and 64 bulls (40%) carried multiple agents. Our results demonstrate the occurrence of bacterial and protozoal infectious agents that may be related to infertility in Brazilian beef cattle herds.

Swapandeep Kaur ◽  
Sheifali Gupta ◽  
Swati Singh ◽  
Tanvi Arora

A disaster is a devastating incident that causes a serious disruption of the functions of a community. It leads to loss of human life and environmental and financial losses. Natural disasters cause damage and privation that could last for months and even years. Immediate steps need to be taken and social media platforms like Twitter help to provide relief to the affected public. However, it is difficult to analyze high-volume data obtained from social media posts. Therefore, the efficiency and accuracy of useful data extracted from the enormous posts related to disaster are low. Satellite imagery is gaining popularity because of its ability to cover large temporal and spatial areas. But, both the social media and satellite imagery require the use of automated methods to avoid the errors caused by humans. Deep learning and machine learning have become extremely popular for text and image classification tasks. In this paper, a review has been done on natural disaster detection through information obtained from social media and satellite images using deep learning and machine learning.

2021 ◽  
Manuel Adelino ◽  
Katharina Lewellen ◽  
W. Ben McCartney

Financial constraints can cause firms to reduce product quality when quality is difficult to observe. We test this hypothesis in the context of medical choices at hospitals. Using heart attacks and child deliveries, we ask whether hospitals shift toward more profitable treatment options after a financial shock—the 2008 financial crisis. The crisis was followed by an unprecedented drop in hospital investments, yet the aggregate trends show no discrete shifts in treatment intensity post-2008. For cardiac treatment (but not for child deliveries), we find evidence that hospitals with larger financial losses during the financial crisis subsequently increased their use of intensive treatments relative to hospitals with smaller losses, consistent with the effects of financing constraints. This paper was accepted by David Simchi-Levi, finance.

Agriculture ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 967
Aleksejs Zacepins ◽  
Armands Kviesis ◽  
Vitalijs Komasilovs ◽  
Robert Brodschneider

Precision beekeeping, or precision apiculture, focuses on individual beehive remote monitoring using different measurement systems and sensors. Sometimes, there are debates about the necessity for such systems and the real-life benefits of the substitution of bee colony manual inspection by automatic systems. Remote systems offer many advantages, but also have their disadvantages and costs. We evaluated the economic benefits of the remote detection of the bee colonies’ reproductive state of swarming. We propose two economic models for predicting differences in the benefits of catching a swarm depending on its travel distance. Models are tested by comparing the situation in four different countries (Austria, Ethiopia, Indonesia, and Latvia). The economic model is based on financial losses caused by bee colony swarming and considers the effort needed to catch the swarm following a remote swarm detection event. The economic benefit of catching a swarm after a remote precision beekeeping notification is shown to be a function of the distance/time to reach the apiary. The possible technical range is tempting, but we demonstrated that remote sensing is economically limited by the ability to physically reach the apiary and interact in time, or alternatively, inform a person living close by. An advanced economic model additionally includes the swarm catching probability, which decreases based on travel distance/time. Based on exemplary values from the four countries, the economic potential of detecting and informing beekeepers about swarming events is calculated.

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