value loss
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Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 423
Author(s):  
Juhani Rantaniemi ◽  
Jaakko Jääskeläinen ◽  
Jukka Lassila ◽  
Samuli Honkapuro

This paper presents a methodology for rapid generation of synthetic transmission networks and uses it to investigate how a transmission distance-based value loss affects the overall grid power flow. The networks are created with a graph theory-based method and compared to existing energy systems. The power production is located on these synthetic networks by solving a facility location optimization problem with variable distance-based value losses. Next, AC power flow is computed for a snapshot of each network using the Newton–Raphson method and the transmission grid power flow is analyzed. The presented method enables rapid analysis of several grid topologies and offers a way to compare the effects of production incentives and renewable energy policies in different network conditions.


2021 ◽  
Author(s):  
Abhishek Gupta

In this thesis, we propose an environment perception framework for autonomous driving using deep reinforcement learning (DRL) that exhibits learning in autonomous vehicles under complex interactions with the environment, without being explicitly trained on driving datasets. Unlike existing techniques, our proposed technique takes the learning loss into account under deterministic as well as stochastic policy gradient. We apply DRL to object detection and safe navigation while enhancing a self-driving vehicle’s ability to discern meaningful information from surrounding data. For efficient environmental perception and object detection, various Q-learning based methods have been proposed in the literature. Unlike other works, this thesis proposes a collaborative deterministic as well as stochastic policy gradient based on DRL. Our technique is a combination of variational autoencoder (VAE), deep deterministic policy gradient (DDPG), and soft actor-critic (SAC) that adequately trains a self-driving vehicle. In this work, we focus on uninterrupted and reasonably safe autonomous driving without colliding with an obstacle or steering off the track. We propose a collaborative framework that utilizes best features of VAE, DDPG, and SAC and models autonomous driving as partly stochastic and partly deterministic policy gradient problem in continuous action space, and continuous state space. To ensure that the vehicle traverses the road over a considerable period of time, we employ a reward-penalty based system where a higher negative penalty is associated with an unfavourable action and a comparatively lower positive reward is awarded for favourable actions. We also examine the variations in policy loss, value loss, reward function, and cumulative reward for ‘VAE+DDPG’ and ‘VAE+SAC’ over the learning process.


2021 ◽  
Author(s):  
Abhishek Gupta

In this thesis, we propose an environment perception framework for autonomous driving using deep reinforcement learning (DRL) that exhibits learning in autonomous vehicles under complex interactions with the environment, without being explicitly trained on driving datasets. Unlike existing techniques, our proposed technique takes the learning loss into account under deterministic as well as stochastic policy gradient. We apply DRL to object detection and safe navigation while enhancing a self-driving vehicle’s ability to discern meaningful information from surrounding data. For efficient environmental perception and object detection, various Q-learning based methods have been proposed in the literature. Unlike other works, this thesis proposes a collaborative deterministic as well as stochastic policy gradient based on DRL. Our technique is a combination of variational autoencoder (VAE), deep deterministic policy gradient (DDPG), and soft actor-critic (SAC) that adequately trains a self-driving vehicle. In this work, we focus on uninterrupted and reasonably safe autonomous driving without colliding with an obstacle or steering off the track. We propose a collaborative framework that utilizes best features of VAE, DDPG, and SAC and models autonomous driving as partly stochastic and partly deterministic policy gradient problem in continuous action space, and continuous state space. To ensure that the vehicle traverses the road over a considerable period of time, we employ a reward-penalty based system where a higher negative penalty is associated with an unfavourable action and a comparatively lower positive reward is awarded for favourable actions. We also examine the variations in policy loss, value loss, reward function, and cumulative reward for ‘VAE+DDPG’ and ‘VAE+SAC’ over the learning process.


2021 ◽  
Author(s):  
Messod Daniel Beneish ◽  
Patrick Vorst

We compare seven fraud prediction models with a cost-based measure that nets the benefits of correctly anticipating instances of fraud, against the costs borne by incorrectly flagging non-fraud firms. We find that even the best models trade off false to true positives at rates exceeding 100:1. Indeed, the high number of false positives makes all seven models considered too costly for auditors to implement, even in subsamples where misreporting is more likely. For investors, M-Score and at higher cut-offs the F-Score, are the only models providing a net benefit. For regulators, several models are economically viable as false positive costs are limited by the number of investigations regulators can initiate, and by the relatively low market value loss a "falsely accused" firm would bear in denials of requests under the Freedom of Information Act (FOIA). Our results are similar whether we consider fraud or two alternative restatement samples.


2021 ◽  
Author(s):  
Hon Min Ooi ◽  
Mohamad Huzair Munawer ◽  
Peck Loo Kiew

Abstract With the growing demand for chitosan in a variety of applications and the drawbacks associated with crustacean shell-based chitosan, there is a need to look for alternative sources of chitosan extraction and production. Chitosan was extracted from the scales of red snapper ( Lutjanus johnii ) in this study. It was discovered that the concentration of solvent (HCl and NaOH) and temperature at various stages of the extraction process influenced the yield of extracted fish scale chitosan. The characterization result revealed that the commercial crustacean-based chitosan and the extracted fish scale chitosan had similar Fourier-transform infrared spectroscopy (FTIR) spectra, indicating that the two had similar functional groups. The degree of deacetylation (DDA) of the extracted fish scale chitosan was determined to be 76.9%, with the ash value, loss on drying, and solubility being 1.28%, 3.62%, and 88.8%, respectively. The extracted and commercialised chitosan were found to be similar in all characterization results. The potential of fish scale chitosan as a food preservative and shelf-life enhancer was then investigated in this study. Strawberries coated with chitosan were stored at various temperatures, and their physical appearance and moisture loss were recorded. When used in conjunction with traditional preservation techniques such as storage in a cool environment, fish scale chitosan was found to be capable of preventing up to 50% moisture loss in strawberries.


2021 ◽  
Vol 9 (11) ◽  
pp. 2729-2735
Author(s):  
Yaramala Chetana ◽  
Sridurga Ch.

Analytical study of Ayurvedic preparations is the need of the present scientific era. Though the Ayurvedic drugs are time tested and have been used successfully in the management of various ailments it is now necessary to prove their quality, efficacy and safety to the scientific world through various modern analytical parameters. The Sneha Kalpa is par excellent to other dosage forms due to their wider advantages like increased absorption and extraction of fat-soluble active principles Sneha Kalpa is the only dosage form that can be administered conveniently both internally as well as externally. Malatyadi Taila is an important herbal formulation mentioned in Chakradutta for the management of the disease Darunaka. Dandruff is an irritative disease of the scalp in which shedding of dead tissue from the scalp with itching sensation is the cardinal feature. It can be correlated to Darunaka the cardinal symptoms of the disease Darunaka are Kandu (itching), Kesha Chyuti (falling of hair), Swapa (abnormalities of touch sensation), Rukshata (roughness or dryness of the skin) and Twak Sputana (breaking or cracking of the skin). Chakradutta has mentioned the application of Malatyadi Taila in the treatment of Darunaka. An attempt has been made in the present study to prepare Malatyadi Taila and standardise it through analytical parameters like organoleptic properties, refractive index, acid value, saponification value, iodine value, loss on drying for developing standards. All the parameters were found to be good and within the standards. Keywords: analytical standardization, Malatyadi Taila, saponification value, HPTLC.


Animals ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 3116
Author(s):  
Ram Pratim Deka ◽  
Ulf Magnusson ◽  
Delia Grace ◽  
Thomas F. Randolph ◽  
Rajeswari Shome ◽  
...  

Reproductive problems in dairy animals reduce fertility, prevent conception, create problems in the delivery of healthy calves, lead to postpartum complications, increase inter-calving periods, reduce milk yield, and lower overall lifetime productivity. This study aimed at understanding the incidence of reproductive problems and the cost caused by these. The study covered 954 dairy animals in Bihar and 1348 dairy animals in Assam that were selected using a multi-stage random sampling method. The costs were calculated as the sum of income losses and expenditures incurred. The major cost incurred resulted from extended calving intervals (46.1% of the total cost), followed by loss through salvage selling (38.1%), expenditure for treatment of repeat breeders (5.9%), loss of milk production (5.3%) and expenditure for extra inseminations (2.0%). About one fifth of the selected reproductive problems were left untreated. The estimated cost of reproductive problems was Indian Rupees (INR) 2424.9 (USD 36.1) per dairy animal per year (of the total dairy animal population) which represented approximately 4.1% of the mean value loss of dairy animals (INR 58,966/USD 877) per year. Reproductive problems were significantly (p < 0.001) higher among improved (exotic breed or cross-bred) dairy animals than indigenous (native breed or nondescript indigenous) dairy animals. The study suggests that with the increase of improved dairy animal population, the loss may further increase. The study concludes that any economic estimation of reproduction problems based on aetiology without confirmatory diagnoses could be highly misleading because of the complex nature of the problems.


2021 ◽  
Vol 28 (1) ◽  
Author(s):  
C.I. Ejiofor ◽  
L.C. Ochei

Spam mail has indeed become a global dilemma due to its coevolutionary nature. It has resulted in the loss of organizational resources, possibly financial cost incurred as well as time spent in addressing spam related issues. This has pushed organizations and researchers to the pinnacle of research with the aim of identifying needed solutions. This research paper explores the rich capabilities of Convolutional Neural Network (CNN) for predicting spam mail taking cognizant natural language capabilities. Spam mail prediction was simulated using a simulator built utilizing python programming language to capture the fundamentals of CNN. The CNN training was actualized using 10 epochs. The 1st epoch offers a training time of 4mins, 39s with a loss of 1.7578, accuracy of 0.3508, value loss of 1.2130 and value accuracy 0f 0.5719 while the 10th epoch presents a training time of 4mins, 6s with a loss of 0.5896, accuracy of 0.7936, value loss of 0.8941 and value accuracy of 0.6986.


2021 ◽  
Vol 118 (30) ◽  
pp. e2022650118
Author(s):  
Alexandre Pastor-Bernier ◽  
Arkadiusz Stasiak ◽  
Wolfram Schultz

Sensitivity to satiety constitutes a basic requirement for neuronal coding of subjective reward value. Satiety from natural ongoing consumption affects reward functions in learning and approach behavior. More specifically, satiety reduces the subjective economic value of individual rewards during choice between options that typically contain multiple reward components. The unconfounded assessment of economic reward value requires tests at choice indifference between two options, which is difficult to achieve with sated rewards. By conceptualizing choices between options with multiple reward components (“bundles”), Revealed Preference Theory may offer a solution. Despite satiety, choices against an unaltered reference bundle may remain indifferent when the reduced value of a sated bundle reward is compensated by larger amounts of an unsated reward of the same bundle, and then the value loss of the sated reward is indicated by the amount of the added unsated reward. Here, we show psychophysically titrated choice indifference in monkeys between bundles of differently sated rewards. Neuronal chosen value signals in the orbitofrontal cortex (OFC) followed closely the subjective value change within recording periods of individual neurons. A neuronal classifier distinguishing the bundles and predicting choice substantiated the subjective value change. The choice between conventional single rewards confirmed the neuronal changes seen with two-reward bundles. Thus, reward-specific satiety reduces subjective reward value signals in OFC. With satiety being an important factor of subjective reward value, these results extend the notion of subjective economic reward value coding in OFC neurons.


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