Observation of Scaling Violations in Scaled Momentum Distributions at HERA

Charged particle production has been measured in deep inelastic scattering (DIS) events over a large range of $x$ and $Q^2$ using the ZEUS detector. The evolution of the scaled momentum, $x_p$, with $Q^2,$ in the range 10 to 1280 $GeV^2$, has been investigated in the current fragmentation region of the Breit frame. The results show clear evidence, in a single experiment, for scaling violations in scaled momenta as a function of $Q^2$.Comment: 21 pages including 4 figures, to be published in Physics Letters B. Two references adde

A fuzzy Bayesian network approach for risk analysis in process industries

YesFault tree analysis is a widely used method of risk assessment in process industries. However, the classical fault tree approach has its own limitations such as the inability to deal with uncertain failure data and to consider statistical dependence among the failure events. In this paper, we propose a comprehensive framework for the risk assessment in process industries under the conditions of uncertainty and statistical dependency of events. The proposed approach makes the use of expert knowledge and fuzzy set theory for handling the uncertainty in the failure data and employs the Bayesian network modeling for capturing dependency among the events and for a robust probabilistic reasoning in the conditions of uncertainty. The effectiveness of the approach was demonstrated by performing risk assessment in an ethylene transportation line unit in an ethylene oxide (EO) production plant

An Approach to Update the Failure Rates of Safety Barriers Based on Operating Experience

Hazardous events in process plants like the leakage of dangerous substances can result in severe damage, and such an event is often defined as the TOP event of a fault tree analysis (FTA) in a quantitative risk analysis. The TOP event probability can then be calculated if the basic events probabilities are provided. These probabilities are often determined based on generic reliability data which do not necessarily reflect the operational and environmental characteristics of a plant of interest. This paper presents an approach based on Bayesian network (BN) analysis to explicitly include experience data collected during the plant operation to make the generic probabilities more plant specific. The approach is illustrated via a pressure vessel containing a toxic substance in an Ammonia production plant. In this case study, the failure rate distribution in the BN is updated as the new information becomes available during plant operation. The results show that the suggested approach effectively reflects the operating experience of a specific plant.publishedVersio

The role of fishing material culture in communities’ sense of place as an added-value in management of coastal areas

Fishing communities in many places around the world are facing significant challenges due to new policies and environmental developments. While it is imperative to ensure sustainability of natural resources, many policies may overlook the contribution of fisheries to the sociocultural well-being of coastal communities. Authors address the problem of valuing the sociocultural benefits of fishing by exploring the role of fishing landscapes and traditional working waterfronts in maintaining sense of place in fishing communities. The paper explores how sense of place contributes to understanding the relationship between fishing and cultural-ecosystem services, drawing on case studies from four U.S. fishing communities in Brunswick County, North Carolina. Through semi-structured and in-depth interviews with fishing communities members, resident photography and sites visits, this paper outlines how fishing contributes to sense of place in terms of placeattachment and cultural-social memory. By understanding the relationship between fishers’ sense of place, and the physical environment in fishing communities in Brunswick County, the authors identify the complexity and interrelated elements that shape the relationship between fishermen and their cultural landscape. The paper suggests that realizing the value of fishing cultural landscape can encourage policies that promote preservation of fishing cultural heritage for the sociocultural benefit of communitie

Evaluating the Effectiveness of GPT-4 Turbo in Creating Defeaters for Assurance Cases

Assurance cases (ACs) are structured arguments that support the verification of the correct implementation of systems' non-functional requirements, such as safety and security, thereby preventing system failures which could lead to catastrophic outcomes, including loss of lives. ACs facilitate the certification of systems in accordance with industrial standards, for example, DO-178C and ISO 26262. Identifying defeaters arguments that refute these ACs is essential for improving the robustness and confidence in ACs. To automate this task, we introduce a novel method that leverages the capabilities of GPT-4 Turbo, an advanced Large Language Model (LLM) developed by OpenAI, to identify defeaters within ACs formalized using the Eliminative Argumentation (EA) notation. Our initial evaluation gauges the model's proficiency in understanding and generating arguments within this framework. The findings indicate that GPT-4 Turbo excels in EA notation and is capable of generating various types of defeaters

An alternative approach to assessing feasibility of flushing sediment from reservoirs

Effective parameters on feasibility of sediment flushing through reservoirs include hydrological, hydraulic, and topographic properties of the reservoirs. In this study, the performances of the Decision tree forest (DTF) and Group method of data handling (GMDH) for assessing feasibility of flushing sediment from reservoirs, were investigated. In this way, Decision tree Forest, that combines multiple Decision tree, used to evaluate the relative importance of factors affecting flushing sediment. At the second step, GMDH deployed to predict the feasibility of flushing sediment from reservoirs. Results indicate that these models, as an efficient novel approach with an acceptable range of error, can be used successfully for assessing feasibility of flushing sediment from reservoirs

Is Serum TGF-β1 and TGF-β2 levels Correlated to Children with Autism Intensity?

Objective Transforming growth factor-beta (TGF-β), a group of multifunctional growth factors, plays an important role in the neuron survival and neurodevelopmental functions. Some studies have evaluated the correlation between TGF-β1 and TGF-β2 abnormalities and autism spectrum disorders. In this study, we compared the TGF-β1 andTGF-β2 levels between autistic and intellectually normal individuals. Materials & Methods The study population consisted of 39 autistic and 30 age-matched intellectually normal individuals (control group). Blood samples were taken from all individuals, and all patients were divided into 2 groups (mild-to-moderate and severe) according to the childhood autism rating scale. The cytokines levels were measured by Enzyme Linked Immunosorbent Assay (ELISA). Results The mean concentration of TGF-β1 was significantly lower (P < 0.0001) in children with autism compared to the control group (25.3 ± 6.5 versus 35.1 ± 9.4 ng/mL, respectively). Also, the mean concentration of TGF-β2 in children with autism (32.35± 7.75 ng/mL) was higher compared to those in the control group (30.47± 4.36 ng/mL); however, this difference did not reach statistical significance (P = 0.21). A positive correlation was observed between TGF-β1 concentration and autism severity (r = 0.41; P = 0.02), whereas a negative correlation was found between TGF-β2 concentration andautism severity (r = -0.41; P = 0.02). Conclusion The results of the present investigation suggest that there is a decrease in the levels of TGF-β1 in the serum of patients with autism and this cytokine may be effective in the treatment of the pathophysiological aspects of autism