Estimation Of Value Research Articles

Application of Global Burden of Animal Diseases methods at country level: experiences of the Ethiopia case study.

Animals play a central role in human livelihoods and welfare. Animal diseases have a great impact on the benefits humans derive from animals and can also pose a risk to human health. Better control of animal diseases generates wider societal benefits, including reducing the climate and ecological impacts of livestock and improving animal welfare. To better understand the scale of investment justified for the control and prevention of animal disease, the wide-ranging impacts of disease on animal production and health must be measured. The Global Burden of Animal Diseases (GBADs) programme is quantifying animal disease burden from the local to global levels. The GBADs programme includes country case studies for national- and local-level analysis. Ethiopia is the first case study country in which GBADs methods have been applied. GBADs'Ethiopia case study consists of three activity areas: i) stakeholder engagement; ii) livestock disease burden estimation, including data collection, analytics, evidence generation and communication; and iii) capacity building in animal health economics. At the start of the case study, various stakeholder communication platforms were used to familiarise stakeholders with GBADs and engage their support in various ways, including data access, and, through this engagement, to ensure the programme tools and outputs were relevant and useful to their needs. Existing data were retrieved from multiple sources and used to estimate disease burden. This process involved multiple steps, including estimation of biomass and economic value, the Animal Health Loss Envelope (farm-level disease burden), wider economic impacts and attribution of the disease burden to different levels of causes. This was carried out for major livestock species (cattle, sheep, goats and poultry) in Ethiopia. Capacity building on animal health economics was carried out for GBADs end users to increase competence in utilising animal health economic evidence, including GBADs outputs. This article documents experiences of the implementation of these activities in the GBADs Ethiopia case study.

Association between cardiovascular inflammation and alterations in immune system induced by HIV infection detected on [18F]FDG PET/MRI

ObjectiveTo assess by [18F]FDG PET/MR the biomarkers of HIV-induced inflammation at baseline and 1 year post-antiretroviral therapy (ART). MethodsProspective study, 14 patients, newly diagnosed HIV-positive, asymptomatic.[18F]FDG PET/MRI (PET/MR-3.0T, Signa.GE) whole body and heart was performed, baseline and 1 year post-ART.Qualitative vascular assessment (hepatic reference). Quantitative assessment (SUVmax) of the whole body. T1 and T2 value estimation in 16 myocardial segments. ResultsBaseline CMR showed in 3 (21.4%) a decreased LVEF, normalising post-TAR. Fibrosis was ruled out (T1), with no signs of myocardial oedema (T2) at baseline or post-TAR.Four (28.6%) showed baseline vascular [18F]FDG uptake, two in ascending thoracic aorta and two in ascending and descending thoracic aorta, normalising post-TAR.All (100%) showed basal lymph-nodes activity; supra (n:14) and infradiaphragmatic (n:13), laterocervical (n:14) and inguinal (n:13), with variable number of territories (9 patients >6;64.3%). Post-ART, 7 patients (50%) showed resolution and the other 7 reduction in extension (0 patients >5): 7 supra (100%) and 2 infradiaphragmatic (28.6%), 5 in the axilla and 2 in the groin.All (100%) had persistent basal adenoid uptake post-ART, 9 (64.3%) splenic all resolved post-ART and 7 (50.5%) gastric, persistent 3 post-ART. ConclusionsCardiovascular biomarkers by [18F]FDG PET/MR have shown baseline 28.6% of patients with large vessel activity and 21.4% with low LVEF, normalising post-ART.Inflammatory/immune biomarkers showed baseline activity in 100% of lymph-nodes, 100% adenoids, 64.3% splenic and 50.5% gastric. Post-TAR the reduction was 50% lymph-nodes, 0% adenoid, 100% splenic and 57.1% gastric.

Intelligent trapezoid and variable weight combination-based reconstructed GM model

The GM(1,1) model's prediction accuracy is significantly influenced by the accuracy of background value estimation. The traditional trapezoidal background value can only be applied to a specific data sequence. Therefore, this study proposes a GM(1,1) model background value reconstruction approach based on the combination of intelligent trapezoidal and variable weights in order to increase the model’s application as well as its prediction accuracy. The trapezoidal background value function with slope and point position parameters is called model I. Then, a set of point position parameter sequences, with a new background value function is constructed, called model II. A genetic algorithm is utilized to seek for the values of the parameters to be determined in both models I and II. The results showed that for the exponential growth data series, model I and II have higher prediction accuracy compared to traditional models. For data sequences, taking the traffic volume series of a road from 2014 to 2023, the prediction accuracy of this paper's model I method can be improved by 0.3643 % and 0.2725 % compared with Deng's and Wang's models. The prediction accuracy of this paper's model II method has been further improved by 0.1075 % compared with that of model I.

Research on high-frequency trading strategies optimization based on deep learning

High-frequency trading is derived from programmatic trading and market maker mechanisms, and unlike low-frequency trading, it uses ultra-high-speed computers to acquire and analyze high-frequency trading data in the market, so as to identify price change patterns and quickly execute trades to complete the change of hands. In this work, we focused on optimizing high-frequency trading strategies using deep reinforcement learning. Specifically, we employ a dual deep Q network model, which consists of two deep neural networks that are used to generate action value estimates and provide target action values, respectively. The advantage of this structure is that it reduces bias in the estimation process, thereby improving the stability and efficiency of learning. The input layer of the model receives multi-dimensional features of market data, such as price, volume, order depth, etc., which are normalized and then fed into the network. The hidden layer includes multiple layers of fully connected layers to enhance the nonlinear representation of the model and the ability to handle complex market dynamics. We have also introduced batch normalization and dropout layers in the network to prevent overfitting merges and improve the generalization ability of the model. Eventually, the output layer generates action values corresponding to each possible trading action, deciding to buy, sell, or hold a position. In the experimental analysis, this study verifies the superiority of the proposed model in processing high-frequency trading data and executing trading decisions by comparing it with traditional trading strategies and other machine learning methods.

Phytosanitary Cold Treatment of the Spotted Wing Drosophila (Diptera: Drosophilidae) in Postharvest ‘Red Globe’ Grapes

The spotted wing drosophila, Drosophila suzukii (Matsumura), is currently distributed in the main soft-skinned fruits production areas in China and 59 other countries, presenting a significant threat to importing nations. Optimal phytosanitary treatments, including fumigation, irradiation, and cold treatment, have been developed to prevent the international movement of this invasive fly. To determine the most cold-tolerant stage and facilitate the development of the technical schedules requested by the Technical Panel on Phytosanitary Treatment (TPPT), cold treatments of D. suzukii immature stages in ‘Red Globe’ grapes were conducted. Dose–mortality data at 0 °C and 2 °C from repeated trials were subjected to analysis of covariance, linear regression, and probit analysis. Results identified 3 d old pupae as the most cold-tolerant stage, followed by 1 d old pupae, 4 d old larvae, and 6 h old eggs with similar tolerance. The 2 d old larvae were the most sensitive stage. In subsequent confirmatory tests, 3 d old pupae were subjected to cold treatment at 0 °C for 9 and 10 days, and at 2 °C for 10 and 12 days, based on the probit estimation of the probit-9 value. No adult emergence occurred in the confirmatory tests except for one deformed adult from a 2 °C 10 d treatment. Therefore, the recommended treatment schedule requires fruit temperatures below 0.00 °C (or 1.62 °C) for no less than 10 (or 12) continuous days, with treatment efficacy not less than 99.9960% (or 99.9955%) at a 95% confidence level, respectively. These schedules are intended for submission to TPPT for the development of phytosanitary treatment standards.

Storytelling changes the content and perceived value of event memories

Memories are not only stored for personal recall, but also to communicate knowledge to others in service of adaptive decision-making. Prior research shows that goals to share information can change which content is communicated in memory as well as the linguistic style embedded in this communication. Yet, little is known as to how communication-related alterations in memory narration drive differences of value processing in listeners. Here, we test how memory communication alters multi-featural recall for complex events and the downstream consequence on value estimations in naïve listeners. Participants recalled a memory of playing an exploratory videogame at a 24-h delay under instructions to either share (i.e., social condition) or recall (i.e., control condition) their memory. Sharing goals systematically altered the content and linguistic style of recall, such that narrators from the social condition were biased towards recall of non-episodic details and communicated their memories with more clout, less formality, and less authenticity. Across two independent samples of naïve listeners, these features differentially influenced value estimations of the video game. We found that greater clout was associated with greater enjoyment while listening to memories (hedonic value), and that greater inclusion of non-episodic details resulted in greater willingness to purchase the video game (motivational drive). These findings indicate that sharing an experience as a story can change the content and linguistic tone of memory recall, which in turn shape perceived value in naïve listeners.

Genetic Variants at Exon 3 in AAT Gene in Relation to Milk Production Traits in Sahiwal and Karan Fries Cattle

Background: The present study pertained to records on milk production and milk constituents of 100 Sahiwal cattle and 115 Karan Fries cattle, the data collected over a period of 2004 to 2016 from Animal Genetics and Breeding division from ICAR-National Dairy Research Institute, Karnal Haryana. Methods: Good quality genomic DNA was used for amplification of exon 3 of AAT gene (474bp) by polymerase chain reaction under optimized conditions and Sequenced data analysis for SNPs detection. The analysis was carried out with appropriate Statistical method using software’s in the computer centre of the institute under the Restricted Maximum Likelihood Method (REML), Estimation of breeding value, Association Estimation, Effect of genotypes on Breeding Value. Result: In Sahiwal SNP at position G6997C was highly associated for FL305DMY, FLTMY, and FL305DSNFY. The mean ± SE of GG genotype for FL305DMY, FLTMY, FL305DFY, FL305DSNFY and FL305DPYwere found to be 1748.45±6.47, 1962.30±8.47, 100.33±0.44, 154.41±0.09 and 43.99±0.10 respectively and for GC genotype 1860.17±5.86, 2050.44±7.66, 100.35±0.39, 155.28±0.08 and 43.82±0.10 respectively. GG genotype was superior for FL305DPYand Heterozygous GC genotype was superior for rest of other traits. In Karan Fries cattle SNP at position G6997C was highly associated for FL305DMY, FLTMY, FL305DSNFY and FL305DPY. The mean ± SE of GG genotype for FL305DMY, FLTMY, FL305DFY, FL305DSNFY, FL305DPY were found to be 3564.74±6.0, 4584.50±6.00, 135.05±4.76, 278.53±0.06 and 113.80±0.06 respectively and for GC genotypeswere 3459.23±7.48, 4478.76±7.48, 136.19±5.96, 277.50±0.07, 112.75±0.08 respectively. Heterozygous (GC) genotype was superior for FL305DFY and GG genotype was superior for FL305DMY, FLTDMY, FL305DSNFY and FL305DPY.

Implications of treatment duration and frequency for value and cost-effective price of Alzheimer treatments.

Disease-modifying Alzheimer treatments are becoming available. The value of the treatments will be attenuated by their complexity of delivery and monitoring, creating additional medical cost and caregiver burden. To estimate net treatment value using different assumptions for treatment duration and intensity. We estimated the lifetime value of hypothetical treatments that reduce disease progression by 30% from a payer perspective, which considers cost offsets, i.e., reduced medical and formal social care costs, and quality-adjusted life-year gains, and a societal perspective, which adds reduction in caregiver burden. Estimates for gross value of the treatment were based on a prior publication, medical cost on Medicare payment rates, and caregiver time use on a survey of 21 clinics. We analyzed 5 hypothetical treatment scenarios: treatment until progression to moderate dementia with (1) biweekly and (2) 4-weekly infusions, and time-limited infusions every 4 weeks for (3) 72, (4) 52, and (5) 24 weeks. Treatment until progression to moderate dementia would take 5.7 years and generate gross value of $20,734 in direct cost offsets, $83,761 from a payer and $87,749 from a societal perspective, respectively. Added medical cost and caregiver burden for the 5 scenarios would be $44,179, $24,875, $21,632, $20,416, and $14,350, respectively. The maximum value-based price per year would be $7,687, $11,088, $47,708, $67,273, and $158,954. Assuming identical efficacy and safety, the net value generation of time-limited treatment is projected to be larger than that of chronic treatment. Such determination of net lifetime value can be useful to determine value-based prices for different treatment types.

Robust Estimation Method against Poisoning Attacks for Key-Value Data with Local Differential Privacy

Local differential privacy (LDP) protects user information from potential threats by randomizing data on individual devices before transmission to untrusted collectors. This method enables collectors to derive user statistics by analyzing randomized data, thereby presenting a promising avenue for privacy-preserving data collection. In the context of key–value data, in which discrete and continuous values coexist, PrivKV has been introduced as an LDP protocol to ensure secure collection. However, this framework is susceptible to poisoning attacks. To address this vulnerability, we propose an expectation maximization (EM)-based algorithm combined with a cryptographic protocol to facilitate secure random sampling. Our LDP protocol, known as emPrivKV, exhibits two key advantages: it improves the accuracy of statistical information estimation from randomized data, and enhances resilience against the manipulation of statistics, that is, poisoning attacks. These attacks involve malicious users manipulating the analysis results without detection. This study presents the empirical results of applying the emPrivKV protocol to both synthetic and open datasets, highlighting a notable improvement in the precision of statistical value estimation and robustness against poisoning attacks. As a result, emPrivKV improved the frequency and the mean gains by 17.1% and 25.9%, respectively, compared to PrivKV, with the number of fake users being 0.1 of the genuine users. Our findings contribute to the ongoing discourse on refining LDP protocols for key–value data in scenarios involving privacy-sensitive information.

Characterization of surface asperities to understand its effect on fatigue life of electron beam powder bed fusion manufactured Ti-6Al-4 V

Surface asperities play a leading role in determining the fatigue life of as-built Ti-6Al-4 V components manufactured by electron beam powder bed fusion (PBF-EB). Several roughness parameters are available to characterize the surface asperities This study focuses on identifying the surface roughness parameter that correlates best with fatigue life. To this end, several fatigue test specimens were manufactured using the PBF-EB process and utilizing different contour melting strategies, thus producing as-built surfaces with varying roughness. The focus variation microscopy technique was employed to obtain surface roughness parameters for the as-built surfaces. Selected specimens were characterized using x-ray computed tomography (XCT). Tomography can detect surface-connected features obscured by other parts of the surface that are not visible through optical microscopy. The fatigue life of all specimens was determined using four-point bend testing. Through regression model analysis, maximum pit height (Sv) was identified as the statistically significant roughness parameter with the best fit affecting fatigue life. The fracture zone was closely inspected based on the data collected through XCT prior to fatigue tests. This led to another estimate of the worst-case value for the statistically significant roughness parameter Sv. The Sv parameter values obtained from optical microscopy and XCT were used as the initial crack size in a crack growth model to predict fatigue life. It is observed that life estimates based solely on optical measurements of Sv can be overly optimistic, a situation that must be avoided in predictive design calculations.

A welfare analysis of Medicaid and recidivism.

We present conservative estimates for the marginal value of public funds (MVPF) associated with providing Medicaid to inmates exiting prison. The MVPF measures the ratio between a policy's social benefits and its governmental costs. Our MVPF estimates suggest that every additional $1 the government spends on providing inmates exiting prison with Medicaid coverage can result in social benefits ranging between $3.45 and $10.62. A large proportion of the benefits we consider stems from the reduced future criminal involvement among former inmates who receive Medicaid. Employing a difference-in-differences approach, we find that Medicaid expansions reduce the average number of times a released inmate is reimprisoned within 1year by approximately 11.5%. By combining this estimate with key values reported elsewhere (e.g., victimization costs, data on victimization and incarceration), we quantify specific benefits arising from the policy. These encompass diminished criminal harm due to lower reoffense rates, direct benefits to former inmates through Medicaid coverage, increased employment opportunities, and reduced loss of liberty resulting from fewer future reimprisonments. Net-costs consist of the cost of providing Medicaid net of changes in the governmental cost of imprisonment, changes in the tax revenue due to increased employment, and changes in spending on other public assistance programs. We interpret our estimates as conservative since we deliberately err on the side of under-estimating benefits and over-estimating costs when data on specific items are imprecise or incomplete. Our findings align closely with others in the sparse literature investigating the crime-related welfare impacts of Medicaid access, underscoring the substantial indirect benefits public health insurance programs can offer through crime reduction, in addition to their direct health-related advantages.

Modeling the effect of shoreline change on real estate value

While significant global research has been dedicated to value estimation and its influencing factors, which are crucial for the real estate sector, a discernible gap exists in studies examining the long-term impact of shoreline modifications, especially those induced by upcoming coastal constructions, on coastal real estate values.The study was conducted in a coastal region of the Karasu district, Sakarya province. The COPREM-2D numerical model was used to predict shoreline change. A hedonic value model, rooted in semi-logarithmic regression, was developed, yielding value equations for residences, plots, and lands. By creating an integrated model in which the shoreline change numerical model and hedonic value numerical model work together, a value estimation application was made with the integrated model for a selected coastal real estate property. Findings indicate that a property valued at TL 155,994.57 in 2021 is projected to depreciate to TL 35,878.75 b y 2028 due to anticipated submersion from shoreline changes. The study underscores the negative value implications of properties nearing the shoreline and the positive value implications of those distancing from it.

Fair value estimates for illiquid cryptocurrency

To address the need for reporting and disclosure of cryptocurrency holdings in compliance with the FASB guidance for the use of fair value measurements for cryptocurrency (FASB, 2023), this paper develops a modeling process for reporting entities to measure the market value of cryptocurrencies with limited or no observable transactions. In this valuation model, we consider the last observable market information with time decay, its comparable assets market index, and dynamic real-time market participants’ sentiment and attention. Notably, the application of exogenous variables allows us to maximize the observable inputs in measuring fair value, such as asset classification based on economic traits and market participants’ attention and sentiment measurement with online media textual analytics. We propose a valuation framework and construct a prediction model that can achieve a prediction accuracy of 87 % on target asset resurging prices.

Considering the land-cover elasticity of ecosystem service value coefficients improves assessments of large land-use changes

Economic development often impacts on ecosystem services. Previous studies have raised public and political awareness of the costs associated with such impacts and the benefits of ecosystem services. In cases where empirical information on the value of ecosystem services is lacking, benefit transfer (BT) approaches that use value estimates from a previously studied site to estimate the economic values of a new target area have been established. One of the most popular BT approaches is unit value transfer, where constant ecosystem service value coefficients are used to assess a given land-use/land-cover (LULC) change. In several case studies assessing LULC changes, such unit value transfers with constant value coefficients are biased when nonmarginal changes are involved. Theoretical considerations suggest that large changes in land allocation should alter the opportunity costs of gaining or losing natural capital because the marginal costs of additional losses increase as some LULC types become scarcer (e.g. natural ecosystems). In contrast, marginal benefits shrink as other LULC types become more abundant (e.g. agricultural replacement systems).Here, we propose an improved method for assessing larger scale (i.e., at national levels and beyond) LULC changes using endogenous value coefficients that account for the size of the land cover allocated to each LULC type and derive an equation for calculating these coefficients. The extent to which the value coefficient changes with variations in the land cover area depends on the land-cover elasticity of the value coefficient. Using a hypothetical numerical example of an area of tropical forest converted into grassland, we show that the bias caused by neglecting this land-cover elasticity can be considerable. We also demonstrate how the elasticity needed to correct the value coefficient can be estimated empirically. Finally, we suggest some modifications for future studies assessing large LULC changes.

NEW APPROACH TOWARDS THE PROBLEM OF SOMATIC CRITERIA OF SECULAR TRENDS. SEXUAL SOMATIC DIMORPHISM

Introduction. The goal of study is description of secular dynamics of sex dimorphism (SD) of height, weight, chest girth of children aged 9 years and teenagers 13 years old from Russia and neighboring countries through 1920–2010. Materials and methods. The main source of data — materials of books on physical development of children and adolescents from cities and rural regions of Russia and USSR, collected and processed according to the universal methodical standards of Research Institute of Hygiene of children and adolescents. The quantitative estimation of SD value was held using Kullback divergence. One-year age groups, contrastive in biological essence, are under discussion. The estimation of the vector and level of associations of SD of anthropometric traits and secular factor was held using correlation analysis. The analysis of local patterns of secular SD dynamics of separate traits was carried out. Results. Secular variability of height and weight SD of 9-year-old children is not significant. While secular dynamics of three body dimensions of 13-year-old teenagers is significant, the highest determination coefficient is fixed for weight. For Moscow samples the decrease of height and weight SD is fixed through the historic interval 1960s – 70s with the following recovery up to the level of 1920s in 2000s. Temporal dynamics of SD of 9-year-old children of St. Petersburg through the interval 1960s – 2000s consists of macrosomization of girls; the tendency for 13-year-old teenagers is opposite. Temporal dynamics of somatic SD of 9-year-old Russian children of Kazan reveals stability through the interval 1960th-1990th; for 13-year-old teenagers’ parameters of transversal body development demonstrate secular stability, as to height – boys compensate their lag compared to girls. For Kirovsk city (Murmansk region) through the interval 1950s – 1970s temporal fluctuations of height and weight SD of 9-year-old children are less than 0,2 standard deviations; height and weight SD of 13-year-old teenagers reveal temporal increase (0,4–0,6 standard deviations). Conclusion. The results confirm the importance of somatic SD as the criterion of secular dynamics for 13-year-old teenagers and witness, as a whole, to the temporal stability of SD during second childhood (9 years). Local differences do not contribute significantly to the intensity of secular dynamics of SD. The process of secular macrosomization of one sex as compared to the other is not the exclusive prerogative of boys and male teenagers.

Biodiversity value of tree vegetation in Rainbow Forest Biosite, Ijen Geopark, East Java, Indonesia

Abstract. Sulistiyowati H, Hasanah EA, Siddiq AM, Ratnasari T, Dewi N, Kurnianto AS. 2024. Biodiversity value of tree vegetation in Rainbow Forest Biosite, Ijen Geopark, East Java, Indonesia. Biodiversitas 25: 2670-2678. Biodiversity value representing the monetary estimation of the ecological value (ecoval) of species composing a forest is often applied for ecosystem change assessment. Tree community in Rainbow Forest Biosite (RFB) or Biosite Hutan Pelangi, Ijen Geopark, East Java, Indonesia was planted in 1937 and has experienced significant regrowth and changes over time by replacing one another until a mature forest becomes established. Yet, no empirical work has been done to assess the biodiversity value in RFB. Therefore, this study aims to provide a comprehensive estimation of the ecological value of tree vegetation in the RFB at Ijen Geopark. As many as 100 plots with size of 10×10 m each were established to acquire structural data of tree vegetation. Semi-destructive method was used to collect functional data on tree carbon. The result shows that RFB comprises 21 families, 34 genera, and 43 species. This forest has high diversity with dominant tree species are Trevesia sundaica Miq. and Dendrocnide stimulans (L.fil.) Chew due to the large number of individual and area coverage. Tree vegetation in RFB has a total basal area of 330 m2/ha and a total volume of 4,171.01 m3/ha, equivalent to an ecoval 182,439,286,595 IDR/ha in monetary terms. Three species including Agathis dammara (Lamb.) Rich. & A.Rich. and Cedrela odorata L., have unique existence, while others are quite unique (88.37%). Tree growth in RFB sequesters approximately 9,773.30 Mg CO2eq/ha while storing 873.17 Mg C/ha and producing 7,105.19 Mg O2eq/ha, equivalent to an ecoval of 11,401,417,827 IDR/ha. In total, the combined structural and functional biodiversity value of the explored vegetation is estimated to be 193,840,704,423 IDR/ha. By assigning this biodiversity value to RFB, authorities should focus on designing and implementing policies to maintain the existing tree species composition naturally and prevent deforestation.

Impact of truncating historical data on prediction ability of dairy sheep selection candidates

Along the last decades, the genetic evaluation methodology has evolved, improving breeding value estimates. Many breeding programmes have historical phenotypic records and large number of generations, but to make use of them could result in more inconveniences than benefits. In this study, the prediction ability of genotyped young animals was assessed by simultaneously evaluating the removal of historical data, two pedigree deepness and two methodologies (traditional BLUP and single−step genomic BLUP or ssGBLUP), using milk yield records of 40 years of three Latxa dairy sheep populations. The linear regression method was used to compare predictions of young rams before and after progeny testing, with six cut-off points, by intervals of 4 years (from 1992 to 2012), and statistics of ratio of accuracies, bias, and dispersion were calculated. The prediction accuracy of selection candidates, when genomic information was included, was the highest in all Latxa populations (between 0.54 and 0.69 with full data set). Nevertheless, the deletion of historical phenotypic data resulted on moderate accuracy gain in the bigger data size populations (mean gain 2.5%), and the smaller population took advantage of a moderate data deletion (2.7% gain by removing data until 2004), reducing accuracy when more records were removed. The bias of validation individuals was lower when the breeding value was predicted based on genomic information (between 2.1 and 13.9), being lower when the biggest amount of data was deleted in the bigger data size populations (5.2% reduction), and the smaller population was benefited from data deletion between 1996 and 2008 (3.8% bias reduction). Meanwhile, the slope of estimated genetic trend was lower when less data were included, and an overestimation of the unknown parent group estimates was observed. The results indicated that ssGBLUP evaluations were outstanding, compared with traditional BLUP evaluations, while the depth of pedigree had a very small influence, and deletion of historical phenotypic data was beneficial. Thus, Latxa routine genetic evaluations would benefit from truncating phenotypic records between 2000 and 2004, the use of two pedigree generations and the implementation of ssGBLUP methodology.

Diverse randomized value functions: A provably pessimistic approach for offline reinforcement learning

Offline Reinforcement Learning (RL) faces challenges such as distributional shift and unreliable value estimation, especially for out-of-distribution (OOD) actions. To address these issues, existing uncertainty-based methods penalize the value function with uncertainty quantification and require numerous ensemble networks, leading to computational challenges and suboptimal outcomes. In this paper, we introduce a novel strategy that employs diverse randomized value functions to estimate the posterior distribution of Q-values. This approach provides robust uncertainty quantification and estimates the lower confidence bounds (LCB) of Q-values. By applying moderate value penalties for OOD actions, our method fosters a provably pessimistic approach. We also emphasize diversity within randomized value functions and enhance efficiency by introducing a diversity regularization method, thereby reducing the requisite number of networks. These modules result in reliable value estimation and efficient policy learning from offline data. Theoretical analysis shows that our method recovers the provably efficient LCB-penalty under linear MDP assumptions. Extensive empirical results demonstrate that our proposed method significantly outperforms baseline methods in terms of performance and parametric efficiency.

Domain independent heuristics for online stochastic contingent planning

AbstractPartially observable Markov decision processes (POMDP) are a useful model for decision-making under partial observability and stochastic actions. Partially Observable Monte-Carlo Planning (POMCP) is an online algorithm for deciding on the next action to perform, using a Monte-Carlo tree search approach, based on the UCT algorithm for fully observable Markov-decision processes. POMCP develops an action-observation tree, and at the leaves, uses a rollout policy to provide a value estimate for the leaf. As such, POMCP is highly dependent on the rollout policy to compute good estimates, and hence identify good actions. Thus, many practitioners who use POMCP are required to create strong, domain-specific heuristics. In this paper, we model POMDPs as stochastic contingent planning problems. This allows us to leverage domain-independent heuristics that were developed in the planning community. We suggest two heuristics, the first is based on the well-known $$h_{add}$$ h add heuristic from classical planning, and the second is computed in belief space, taking the value of information into account.

Alleviating imbalanced problems of reinforcement learning when applying in real-time power network dispatching and control

Real-time power network dispatching and control (PDC) presents unique challenges that traditional methods cannot effectively address due to the consideration of temporal dynamic factors. Reinforcement learning (RL) has been introduced and proven to be effective. However, given the vast space for solutions, there is an urgent need to enhance the performance of RL further. Analyzing the characteristics of the power network to improve the performance of RL holds significant potential. This article presents a comprehensive analysis of power network characteristics, including imbalanced state distribution and the imbalanced action operation frequency, along with their impact on applying RL in real-time PDC to guide the algorithm design. Guided by the analysis, the paper proposes the Balance Deep Q-Network (DQN) algorithm to mitigate the negative impact of the imbalanced problems on algorithm performance. A K-means-based predefined curriculum learning (KPCL) module is proposed to address the state imbalance issue. It allows for differentiated access to operation scenarios based on their hardness, ensuring a balanced exploration of operation scenarios while maintaining diversity during training. An Option DQN module is proposed to tackle the action imbalance problem, utilizing a branching network structure and hierarchical action selection to reduce the coupling between actions with different usage frequencies, thereby enhancing the accuracy of action value estimation. The effectiveness of the Balance DQN algorithm is demonstrated through extensive experiments conducted on the Grid2Op platform with 14-bus and 36-bus cases. The results indicate that Balance DQN can efficiently alleviate the negative impacts of imbalanced problems on RL for real-time PDC agents and achieve good performance in both cases.