State-based Approach Research Articles

An Update on Australian Policy around Lamb Marking with Examination of Potential Drivers. Comment on Johnston et al. How Well Does Australian Animal Welfare Policy Reflect Scientific Evidence: A Case Study Approach Based on Lamb Marking. Animals 2023, 13, 1358.

This commentary provides an update and clarification on the legislative landscape surrounding mulesing in Australia since the publication of the 2023 study, "How Well Does Australian Animal Welfare Policy Reflect Scientific Evidence: A Case Study Approach Based on Lamb Marking". The article explores legislative changes mandating the use of pain relief for mulesing in various states, emphasising Victoria's original role, and highlighting the fragmented state-based legislative approach to animal welfare. It discusses the impact of these legislative changes on industry practices and animal welfare outcomes. The commentary highlights the complexities of policy development in this area, due in part to the diverse and often conflicting interests of stakeholders and the public. It underscores the importance of transparency, stakeholder collaboration, and scientifically informed policymaking to effectively enhance animal welfare standards.

Intermediate-based virtual screening of c-Kit kinase inhibitors as potential anti-tumor agents via ab inito folding, molecular dynamics simulation, and molecular docking

Uncontrolled activation of c-Kit is closely related to the pathogenesis and progression of leukemia, gastrointestinal cancer, and other malignant diseases. Although there are several inhibitors available, due to the limitation of selectivity and the unfavorable side effects, designing and discovering highly selective inhibitors targeting c-Kit kinase, especially the gain of function mutation (for example c-Kit D816V), is still necessary. To identify novel c-Kit inhibitors, a metastable state-based virtual screening approach, which was successfully implemented in other kinase inhibitors, was employed in the current study. The results from our current study demonstrated the residues adjacent to the DFG motif within the activation loop could fold into short α-helices aside from the random coil, which was commonly found in the crystal structure. By expanding the conformation pool of the activation loop via PyRosetta-based ab initio folding protocol, we constructed a series of structural models of the c-Kit kinase intermediate between the inactive and active states. After evaluation of the thermal stability of the metastable state with molecular dynamics simulation, one structural model showed higher stability of α-helix, and the activation loop was retained. Considering the wild-type and D816V mutated KIT kinase shared similar metastable states during the kinase activation process, we developed a hypothesis that the identified intermediate might hold the potential to identify inhibitors targeting D816V mutations from the compound database. As expected, the intermediate structure showed higher selectivity to KIT D816V selective inhibitors, such as bezuclastinib, avapritinib, BLU-263, and elenestinib, than imatinib or masitinib. The virtual screening of the available KIT kinase inhibitor database further identified vorolanib, semaxanib, henatinib, and pexmetinib may possess potential inhibitory effects against wild type, as well as the mutated c-Kit kinase. The results from our current study not only proposed a novel structural model that could be used for the identification of selective c-Kit D816V inhibitors but also identified several potential inhibitors from available kinase inhibitors, which might shed new light on the design of new therapeutic approaches for c-Kit mutation-driven malignant diseases.

Analyzing Data Streams from Cyber-Physical-Systems: A Case Study

We show that conducting a process-mining-centric analysis concerning cyber-physical systems provides insights into usage behavior. To show that, we perform our analysis on connected-vehicle data. We transform connected-vehicle data into an event log. We analyze the resulting event log using various process-mining techniques. In particular, we apply basic statistical analysis as well as process-discovery and conformance-checking techniques to receive a well-representative process model. We apply various process-enhancement techniques to get deeper insights. Finally, we capture a multi-perspective view using a state-based approach. We show deviations between a de-jure model and our picked process model, leading to better knowledge concerning real user behavior. We observed that the predefined escalation of warning states does not happen. Additionally, we verified system requirements. Furthermore, we show that the reasons for drivers’ behavior are not related to system issues. Applying process-mining techniques to data concerning cyber-physical systems provides valuable insights into their functionality in a real-world setting. By utilizing process-mining techniques, we can extract insights to a human-understandable level and provide a well-studied access point.

Harnessing photosynthetic C18O16O discrimination dynamics under leaf water nonsteady state to estimate mesophyll conductance: a new, regression-based method.

Mesophyll conductance (gm) is a crucial plant trait that can significantly limit photosynthesis. Measurement of photosynthetic C18O16O discrimination (Δ18O) has proved to be the only viable means of resolving gm in both C3 and C4 plants. However, the currently available methods to exploit Δ18O for gm estimation are error prone due to their inadequacy in constraining the degree of oxygen isotope exchange (θ) during mesophyll CO2 hydration. Here, we capitalized on experimental manipulation of leaf water isotopic dynamics to establish a novel, nonsteady state, regression-based approach for simultaneous determination of gm and θ from online Δ18O measurements. We demonstrated the methodological and theoretical robustness of this new Δ18O-gm estimation approach and showed through measurements on several C3 and C4 species that thisapproach can serve as a benchmark method against which to identify previously-unrecognized biases of the existing Δ18O-gm methods. Our results highlight the unique value of this nonsteady state-based approach for contributing to ongoing efforts toward quantitative understanding of mesophyll conductance for crop yield improvement and carbon cycle modeling.

Optimized Meta-path extracted graph neural network for embedded computer performance evaluation model

The process of evaluating the effectiveness, speed, and general capabilities of a computer system that is embedded inside a larger device or system and created for a particular purpose is known as embedded computer performance evaluation. Specialized computing systems called embedded systems are built into a wide range of gadgets, including medical, industrial, , automotive and consumer electronics. In this manuscript, Optimized Meta-path extracted graph neural network for embedded computer performance evaluation model(MEGNN-EC-PEM) is proposed. Initially, the input data is obtained from real-time sensor measurements and system metrics for model training and testing. The input image is pre-processed using Orthogonal Master-slave Adaptive Notch Filter (OMANF) and it removes the noise from the collected data. Then, the pre- processed data are fed to embedded computer performance using Meta-path extracted graph neural network (MEGNN). In general, MEGNN does not express adapting optimization techniques to determine optimal parameters to assure precise embedded computer performance evaluation model. Hence, the Hunter–prey optimization algorithm (HPOA) is used to optimize Meta-path extracted graph neural network which accurately categorized embedded computer performance. Then, the proposed MEGNN-EC-PEM is implemented and the performance metrics like Modelling Accuracy, latency, Throughput, memory cost and Energy Consumption are analyzed. The performance of the MEGNN-EC-PEM approach attains 19.41%, 20.08% and 32.57% higher modelling accuracy, 22.41%, 23.08% and 24.57% lower latency and 23.01%, 23.08% and 24.07% higher throughput when analyzed through existing techniques like a state-based modelling approach for effective performance evaluation of embedded system architectures at transaction level (SMA-EPE-ESAT), Evaluating the performance of pre-trained convolutional neural network for audio classification on embedded schemes for anomaly detection in smart cities (PCNN-ACES-ADSC) and Animal behaviour classification via deep learning on embedded systems (ABC-VDL-ES) methods respectively.

A state-based inverse reinforcement learning approach to model activity-travel choices behavior with reward function recovery

Behaviorally oriented activity-travel choices (ATC) modeling is a principal part of travel demand analysis. Traditional econometric and rule-based methods require explicit model structures and complex domain knowledge. While several recent studies used machine learning models, especially adversarial inverse reinforcement learning (IRL) models, to learn potential ATC patterns with less expert-designed settings, they lack a clear representation of rational ATC behavior. In this study, we propose a data-driven IRL framework based on the maximum causal approach to minimize f-divergences between expert and agent state marginal distributions, which provides a more sample-efficient measurement. In addition, we specify a separate state-only reward function and derive an analytical gradient of the f-divergence objective with respect to reward parameters to ensure good convergences. The method can recover a stationary reward function, which assures the agent to get close to the expert behavior when training from scratch. We validate the proposed model using cellular signaling data from Chongqing, China by comparing with baseline models (behavior cloning, policy-based, and reward-based models) in aspects of policy performance comparison, reward recovery, and reward transfer tasks. The experiment results indicate that the proposed model outperforms existing methods and is relatively less sensitive to the number of expert demonstrations. Qualitative analyses are provided on the fundamental ATC preferences on different features given the reward function recovered from the observed mobility trajectories, and on the learning behaviors under different choices of f-divergence.

Rights and wellbeing in sport policy and provision: a New Zealand case-study

ABSTRACT This article reviews ways in which leisure and sport provision have been seen as a social necessity or public good, or an element of a citizen's rights in a single democratic society. We present a case study of the development and implementation of sport policy in Aotearoa New Zealand (NZ). We highlight the specificity of socio-political influences upon the emergence of state support for leisure development, and the creation of forms of access to leisure activity for the wider population. In the development of sport policy in innovative and sustained partnerships NZ has established a state-based approach to sport, with the wellbeing potential of sport integrated into cross-government thinking, planning and policy; this has prioritized the right to access sport for all sectors of the population, with recognition of significant wellbeing and health benefits for all.

Performance models of data parallel DAG workflows for large scale data analytics

Directed Acyclic Graph (DAG) workflows are widely used for large-scale data analytics in cluster-based distributed computing systems. The performance model for a DAG on data-parallel frameworks (e.g., MapReduce) is a research challenge because the allocation of preemptable system resources among parallel jobs may dynamically vary during execution. This resource allocation variation during execution makes it difficult to accurately estimate the execution time. In this paper, we tackle this challenge by proposing a new cost model, called Bottleneck Oriented Estimation (BOE), to estimate the allocation of preemptable resources by identifying the bottleneck to accurately predict task execution time. For a DAG workflow, we propose a state-based approach to iteratively use the resource allocation property among stages to estimate the overall execution plan. Furthermore, to handle the skewness of various jobs, we refine the model with the order statistics theory to improve estimation accuracy. Extensive experiments were performed to validate these cost models with HiBench and TPC-H workloads. The BOE model outperforms the state-of-the-art models by a factor of five for task execution time estimation. For the refined skew-aware model, the average prediction error is under 3% when estimating the execution time of 51 hybrid analytics (HiBench) and query (TPC-H) DAG workflows.

A State-Based Potential Game-Theoretic Approach for Massive Residential Consumers Participating in Demand Response

In order to realize massive residential consumers participating in demand response (DR), load aggregator (LA) can be employed to aggregate flexible loads. In this article, a distributed optimization approach based on state-based potential game (SPG) is proposed to describe the bidding behavior among LAs in day-ahead DR market for load shifting amount. Under the consideration of DR resource's energy consumption satisfaction, profit-oriented bidding decision problem is modeled from the perspective of individual LA and DR market. Basically, the establishment and proof of an SPG model is presented in details. Then, the best-strategy-response iterative algorithm is proposed to search for the Nash equilibrium in a distributed way. Finally, simulation is carried out to verify the efficiency and effectiveness of the proposed approach. Simulation results show that the whole profit of all LAs increases 0.15%, while the running time of the algorithm reduces 59.53% compared with the conventional algorithm in the noncooperative game.

P380: Implementation of an innovative, state-based approach to improving access to genetic services*

P380: Implementation of an innovative, state-based approach to improving access to genetic services*

Bidding and Charging Scheduling Optimization for the Urban Electric Bus Operator

With the development of electric buses (EBs) in urban areas, how to effectively utilize their charging demand flexibility and the considerable energy stored in the on-board batteries becomes an urgent issue for the electric bus operator (EBO) to consider. This paper focuses on the bidding decision in energy and reserve markets for the urban EBO owning multiple electric bus stations with energy storage systems (ESSs), and the charging scheduling of all EBs. A trip-chain based electric vehicle boundary model is proposed firstly to concisely describe the flexibility region for an EB with multiple sequential trips, which can be further aggregated via a state-based approach to adapt to large-scale problems. The uncertain boundaries are also derived taking the uncertainty of trip energy consumption into account. In order to deal with the issue that the boundary aggregation will possibly influence the feasibility of decomposing the total power to each individual EB, the stochastic bidding and EB scheduling optimization problem in a hierarchical optimization framework is proposed. Accelerated Benders decomposition is utilized to efficiently solve the problem. Case studies verify that the proposed model can dramatically reduce the computational burden, effectively consider the uncertainty and take advantage of the flexibility of both EBs and ESSs to achieve better economic performance.

The Obligation of EIA in the International Jurisprudence and Its Impact on the BBNJ Negotiations

Environmental impact assessment (EIA) has been widely recognized as a crucial instrument for sound decision-making that will promote environmental conservation and sustainable development. International jurisprudence has clarified the obligation of EIA under customary international law, especially concerning its legal status, threshold standards, and procedural requirements, as well as the issue of State responsibility. As suggested by this article, international tribunals tend to adopt a procedural-oriented and State-based approach regarding the obligation of EIA, leaving more discretion to the States pursuing the proposed activities. This approach is insufficient to realize the role of EIA in fostering sound decision-making. As for the BBNJ negotiations, in light of the legal status of the Area in international law and the established rules governing the issues of EIA, especially the three Regulations and Recommendations adopted by ISA, the future implementation agreement should reinforce international regulations regarding the substance and implementation of EIA obligations.

Understanding Subordinate Animal Welfare Legislation in Australia: Assembling the Regulations and Codes of Practice.

Simple SummaryIn Australia, animal welfare protection is a state and territory responsibility. Having eight separate state and territories results in eight separate animal welfare legal frameworks, all which contain a primary piece of legislation and secondary or subordinate forms of legislation. These subordinate forms are known as regulations and codes of practice and are lower-ranking laws that are given powers under the primary legislation. Subordinate laws contain crucial details that govern everyday human–animal interactions and industry practices, for example companion animals used for breeding. There has been no review of the animal welfare-focused subordinate laws in Australia. This study has assembled each animal welfare regulation and code of practice in force in Australia as a reference for practitioners working in specific animal-related areas to easily identify relevant documents pertaining to welfare and understand the nature of their responsibilities in terms of compliance with these documents. A total of 201 pieces of subordinate legislation were identified and presented through the following utility categories of animals: companion, production, wild/exotic, entertainment. The benefits of housing the information identified from this study on an online database for animal industries to use are discussed.The state-based approach to regulating animal welfare in Australia is thought to create national dis-uniformity in that each state and territory legislates and operates inconsistently. The animal welfare legal framework in each of the eight Australian jurisdictions is made up of a primary statute and subordinate legislation, where subordinate animal welfare legislation, in the forms of regulations and codes of practices, are lower-ranking laws that are given power under the jurisdiction’s specific animal welfare statute. Since a review of animal welfare statutes identified broad patterns between the jurisdictions, this study is intended to be complementary by collating the subordinate legislation to provide a more comprehensive understanding of animal welfare laws in Australia. Using targeted search strategies stemming from the eight enabling animal welfare statutes, this study identified 201 pieces of subordinate legislation in force between 28 March 2022 and 5 April 2022. The scope of subordinate legislation is depicted through the following utility categories of animals: companion, production, wild/exotic, entertainment. Whilst subordinate legislation differed between the jurisdictions, it was common for similar welfare concerns or topic areas to be protected in higher-order legislation (statutes or regulations). Additionally, many jurisdictions were found to have similar shortcomings, all which likely could be managed through a mechanism of national data collection.

Cosmopolitan state citizenship: realistic utopias and their limits

ABSTRACT In this Comment, I focus on the state-based approach to citizenship and global justice adopted by Christine Hobden in Citizenship in a Globalised World. I briefly reconstruct the methodological justification Hobden provides before identifying what I take to be innovative in this approach, and the contribution Hobden makes to extant debates between nationalist and cosmopolitan conceptions of justice and responsibility. I then consider the limitations of a state-based approach, in particular, questioning the extent to which the public authority necessary for the protection of equal moral freedom should be conflated with sovereign states. I conclude by gesturing towards the ways that Hobden’s account can provide guidance on global justice beyond and without the state.

ANALYSIS OF GENERAL AVIATION FIXED-WING AIRCRAFT ACCIDENTS INVOLVING INFLIGHT LOSS OF CONTROL USING A STATE-BASED APPROACH

Inflight loss of control (LOC-I) is a significant cause of General Aviation (GA) fixed-wing aircraft accidents. The United States National Transportation Safety Board’s database provides a rich source of accident data, but conventional analyses of the database yield limited insights to LOC-I. We investigate the causes of 5,726 LOC-I fixed‑wing GA aircraft accidents in the United States in 1999–2008 and 2009–2017 using a state-based modeling approach. The multi-year analysis helps discern changes in causation trends over the last two decades. Our analysis highlights LOC-I causes such as pilot actions and mechanical issues that were not discernible in previous research efforts. The logic rules in the state-based approach help infer missing information from the National Transportation Safety Board (NTSB) accident reports. We inferred that 4.84% (1999–2008) and 7.46% (2009–2017) of LOC-I accidents involved a preflight hazardous aircraft condition. We also inferred that 20.11% (1999–2008) and 19.59% (2009–2017) of LOC-I accidents happened because the aircraft hit an object or terrain. By removing redundant coding and identifying when codes are missing, the state-based approach potentially provides a more consistent way of coding accidents compared to the current coding system.

Optical networks management and control: A review and recent challenges

In the last twenty years, optical networks have witnessed recurrent changes in their management and control architecture. In this paper, we present a historical timeline and a future perspective of the evolution of optical network management and control deployed for Wavelength Switched Optical Networks (WSON), Elastic Optical Networks (EON) and (multilayer) Data Center Networks.Early implementations of WSON envisaged a static and centralized provisioning approach supported by the Management Plane only. Gradually, the requirement of accommodating more network dynamicity in WSON, and later in EON, pushed the adoption of a distributed control, mostly supported by vendor-dependent implementations of the Generalized MultiProtocol Label Switching (GMPLS) protocol suite. The drawbacks of the fully distributed GMPLS-based control, such as resource contention, suboptimal resource usage, and complex computations (e.g., to account for physical layer constraints) showed the necessity to bring back some of the routing/provisioning functions to a centralized Path Computation Element (PCE) capable of accounting for e.g. physical impairments and interworking with GMPLS.The centralized control then gained its momentum and brought a radical change in network control, through the separation of data and control plane introduced by the paradigm of Software Defined Networking (SDN). Such an approach has been gradually extended to optical network control.The paper, eventually, presents the most advanced control techniques, namely the intent-based networking, the observe/decide/act state-based approach providing for autonomic optical network and the (closed-loop) zero-touch service management approach. Advanced traffic conditioning techniques are also detailed, namely the in-band telemetry and the exploitation of Programming Protocol-Independent Packet Processors (P4) language capabilities as well as solutions tailored for data center networks: all of them are still in a research stage and to be integrated within future optical network architectures.

Combining state- and event-based semantics to verify highly available applications

Replicated databases are attractive for managing the data of distributed applications. To achieve high availability, low latency, and high throughput for such applications, consistency has to be weakened. This comes at a price: it is harder to prove application correctness. In this paper, we describe a new and powerful specification and verification technique for highly available applications together with the tool Repliss that partially automates its use. The verification technique is based on existing event-based semantics for database replication and embeds the semantics into a classical state-based verification approach for application programs. As a central contribution, we carefully designed the specification and programming framework in such a way, that the complex concurrent semantics are reduced to sequential verification, which is much easier to handle and provide a better basis for automated tool support. We prove the soundness of this reduction and a restricted form of completeness. All these proofs and the underlying semantics of the system have been formalized in Isabelle/HOL. • Composable proof technique for working with weak consistency. • Expressible specifications with history invariants. • Semantics, technique, and soundness proof verified in Isabelle/HOL. • Implementation of a prototype verification tool to support the technique.

A Pediatric Knee Exoskeleton With Real-Time Adaptive Control for Overground Walking in Ambulatory Individuals With Cerebral Palsy.

Gait training via a wearable device in children with cerebral palsy (CP) offers the potential to increase therapy dosage and intensity compared to current approaches. Here, we report the design and characterization of a pediatric knee exoskeleton (P.REX) with a microcontroller based multi-layered closed loop control system to provide individualized control capability. Exoskeleton performance was evaluated through benchtop and human subject testing. Step response tests show the averaged 90% rise was 26 ± 0.2 ms for 5 Nm, 22 ± 0.2 ms for 10 Nm, 32 ± 0.4 ms for 15 Nm. Torque bandwidth of P.REX was 12 Hz and output impedance was less than 1.8 Nm with control on (Zero mode). Three different control strategies can be deployed to apply assistance to knee extension: state-based assistance, impedance-based trajectory tracking, and real-time adaptive control. One participant with typical development (TD) and one participant with crouch gait from CP were recruited to evaluate P.REX in overground walking tests. Data from the participant with TD were used to validate control system performance. Kinematic and kinetic data were collected by motion capture and compared to exoskeleton on-board sensors to evaluate control system performance with results demonstrating that the control system functioned as intended. The data from the participant with CP are part of a larger ongoing study. Results for this participant compare walking with P.REX in two control modes: a state-based approach that provided constant knee extension assistance during early stance, mid-stance and late swing (Est+Mst+Lsw mode) and an Adaptive mode providing knee extension assistance proportional to estimated knee moment during stance. Both were well tolerated and significantly improved knee extension compared to walking without extension assistance (Zero mode). There was less reduction in gait speed during use of the adaptive controller, suggesting that it may be more intuitive than state-based constant assistance for this individual. Future work will investigate the effects of exoskeleton assistance during overground gait training in children with neurological disorders and will aim to identify the optimal individualized control strategy for exoskeleton prescription.

Peridynamic modelling of blasting induced rock fractures

This paper presents a peridynamics-based computational approach for modelling blasting induced rock fractures. A new non-ordinary state-based peridynamics approach is proposed in conjunction with a Johnson-Holmquist (JH2) constitutive model to consider the pressure dependency, strain rate effect, and viscoplasticity of rocks under blasting loads. The fracturing process in a rock is assessed based on both the JH2 damage model and a tension failure model. The former evaluates the material response pertaining to excessive plastic strain and the latter is used to gauge failure based on tensile stress in consideration of strain rate effect. Detonation in the explosives is simulated using updated Lagrangian peridynamics in conjunction with Jones-Wilkins-Lee (JWL) equation of state. Simulations of single-hole blasting in granite rock are presented and compared with experimental records. The proposed approach is shown to capture reasonably well the plastic material failure surrounding the borehole as well as the tensile cracks on both radial and circumferential directions. Further sensitivity studies indicate that the intact strength parameters in the JH2 model and the tensile strength of material play a vital role in producing the obtained fracture patterns and should hereby be selected with care. The presented computational approach offers a rigorous basis for future development of versatile, multi-physics-integrated computational framework on rock blasting simulations.

A state-based approach to genomics for rare disease and population screening

The Alabama Genomic Health Initiative (AGHI) is a state-funded effort to provide genomic testing. AGHI engages two distinct cohorts across the state of Alabama. One cohort includes children and adults with undiagnosed rare disease; a second includes an unselected adult population. Here we describe findings from the first 176 rare disease and 5369 population cohort AGHI participants. AGHI participants enroll in one of two arms of a research protocol that provides access to genomic testing results and biobank participation. Rare disease cohort participants receive genome sequencing to identify primary and secondary findings. Population cohort participants receive genotyping to identify pathogenic and likely pathogenic variants for actionable conditions. Within the rare disease cohort, genome sequencing identified likely pathogenic or pathogenic variation in 20% of affected individuals. Within the population cohort, 1.5% of individuals received a positive genotyping result. The rate of genotyping results corroborated by reported personal or family history varied by gene. AGHI demonstrates the ability to provide useful health information in two contexts: rare undiagnosed disease and population screening. This utility should motivate continued exploration of ways in which emerging genomic technologies might benefit broad populations.