Reasonable Complexity Research Articles

Understanding safety engineering practice: Comparing safety engineering practice as desired, as required, and as observed

Good safety management means that continuous attempts are made to improve safety engineering practice. These improvements are often through creating interventions to perceived problems. Many of these interventions seem to have been largely ineffective, suggesting that they may not be addressing the real impediments to good safety engineering practice. We do not argue that existing tools for improving safety engineering practice (such as checklists) are necessarily deficient, rather we challenge whether they are being employed to correct the causes of impediments to better practice. Safety practice ‘As Observed’ (the actual safety engineering activities performed) is informed by defined processes (safety practice ‘As Required’). These processes aim to ensure practice achieves the best safety outcomes (safety practice ‘As Desired’). For many different and complex reasons ‘As Observed’ safety practice may not be equivalent to the safety practice ‘As Required’. Similarly safety practice ‘As Required’ may not be equivalent to safety practice ‘As Desired’. All of these discrepancies could play a significant role in poor safety engineering practice. By exploring these discrepancies it becomes possible to understand the causes of deficiencies in practice, and to start to propose effective interventions. In this paper we introduce and discuss a process for understanding safety engineering practice based around modelling safety practice ‘As Desired’, ‘As Required’, and ‘As Observed’, and the interactions between these elements. We describe how this process can be used to evaluate safety engineering practice and inform the design of effective improvements. We present an example of how the process may be applied to understand safety practice for software safety assurance in the military domain.

Input-oriented demonstration learning for hybrid evidence fact verification

Hybrid evidence fact verification aims to verify claims against retrieved text and table evidence. Existing methods primarily focus on evidence format conversion and unification strategies to reformat the input to align with the corpus of the pre-trained textual or tabular models. However, these models fail to extract task-specific knowledge from the language model (LM) to learn the correlation between input claim-evidence pairs and output verdicts. Therefore, we propose an input-oriented demonstration learning approach (IODL) for hybrid evidence fact verification. IODL activates the LM’s comprehension capability by incorporating a few examples (task demonstrations) into the input context. Our approach comprises a novel retrieval strategy for selecting examples with similar reasoning patterns to the inputs and a demonstration fusion network that mitigates the LM’s input length limitation, allowing it to leverage more examples. Consequently, the quality and quantity of task demonstrations can be simultaneously enhanced, enabling the demonstration learning paradigm to be well-adapted to our supervised fact verification task. Extensive experiments on the large-scale benchmark FEVEROUS dataset demonstrate the superiority of our proposed method over state-of-the-art baselines. IODL consistently outperforms conventional demonstration-based methods in low-resource and complex reasoning challenge scenarios.

Water Inquiry in Geography Education: A Comparative Analysis of Analytical Thinking Enhancement in Hydrosphere Dynamics among Indonesian High School Students

Analytical thinking skills, characterised by complex reasoning and the creation of meaningful learning processes, are crucial for students to master in the evolving educational landscape. Despite the widespread application of inquiry-based learning methods, there needs to be more research exploring the specific effectiveness of the Water Inquiry Learning Model within the context of geography education, particularly in the domain of hydrosphere dynamics. This study aimed to ascertain the influence of the Water Inquiry Learning Model on the analytical thinking abilities of 10th-grade students at SMA Negeri 2 Batu, Malang, Indonesia. Utilising a proper experimental design with a pretest-posttest control group setup, two classes were observed: X-7 (experimental) and X-8 (control). Findings revealed a superior gain score of 72% for class X-7 compared to 45% for class X-8, signifying a significant enhancement in the experimental group's analytical thinking capabilities. These results underscore the powerful impact of the Water Inquiry Learning Model on students' analytical thinking skills. Nonetheless, a deeper understanding and adaptation of the model by students are imperative. Future implementations should provide detailed instructions regarding using biomonitoring cards, a component that may further amplify learning outcomes.

Bypassing the Single Junction Limit with Advanced Photovoltaic Architectures.

Multijunction devices and photon up- and down-conversion are prominent concepts aimed at increasing photovoltaic efficiencies beyond the single junction limit. Integrating these concepts into advanced architectures may address long-standing issues such as processing complexity, microstructure control, and resilience against spectral changes of the incoming radiation. However, so far, no models have been established to predict the performance of such integrated architectures. Here, a simulation environment based on Bayesian optimization is presented, that can predict and virtually optimize the electrical performance of multi-junction architectures, both vertical and lateral, in combination with up- and down-conversion materials. Microstructure effects on performance are explicitly considered using machine-learned predictive models from high throughput experimentation on simpler architectures. Two architectures that would surpass the single junction limit of photovoltaic energy conversion at reasonable complexity are identified: a vertical "staggered half octave system," where selective absorption allows the use of 6 different bandgaps, and the lateral "overlapping rainbow system" where selective irradiation allows the use of a narrowband energy acceptor with reduced voltage losses, according to the energy gap law. Both architectures would be highly resilient against spectral changes, in contrast with two terminal multi-junction architectures which are limited by Kirchhoff's law.

Analysis of organic chemistry students’ developing reasoning elicited by a scaffolded case comparison activity

Recent efforts in organic chemistry education research focus on investigating activities and strategies designed to elicit students’ mechanistic reasoning. This study investigates how a scaffolded case comparison activity implemented in an introductory organic chemistry course elicits and supports students’ mechanistic reasoning in an authentic classroom setting. The activity included an adaptation of a previously reported reasoning scaffold to support small-group student discussions comparing organic reactions. We analyzed students’ written responses to the in-class activity using Hammer's resources framework and Toulmin's argumentation model, interwoven to create an anti-deficit approach to exploring students’ developing reasoning. The analysis of students’ written artifacts sought to identify ways in which a scaffolded case comparison implemented in a collaborative class setting may support students’ engagement in complex reasoning and argumentation development. We found that the in-class activity elicited students’ writing about various aspects of mechanistic reasoning, including identifying explicit and implicit properties, dynamic reasoning, and multivariate reasoning. These findings indicate that the activity can engage students in complex mechanistic reasoning aspects in the classroom setting. Furthermore, this study extends the literature by detailing the nuances of students’ developing causal reasoning with energetic and electrostatic accounts as shown in their writing. The results highlight students’ emerging causal reasoning with varying levels of complexity and conceptual integration. This study provides direct implications for instructors seeking to implement similar classroom activities. The findings indicate directions for future research on the development of instructional activities and tools that further support students’ developing causal reasoning, such as adapting existing scaffolding structures to support argumentation development and the integration of challenging concepts such as energetics.

Context-based Derivation of Holistic Sustainability Requirements in the Early Phase of Product Development

Gaining knowledge about the sustainable context is fundamental for the success of a product development, as a lack of knowledge limits the possibilities of innovation. The contextual boundaries of a system often do not adequately describe the influence on sustainability, so that product developers are unaware of problem shifts within and between the areas of impact on sustainability. For reasons of complexity, a therefore needed holistic captured system context is insufficiently taken into account in existing approaches. Within the scope of this article, a framework for Sustainable Context Engineering is developed, that provides a formalization of a holistic sustainable system context for the analysis and synthesis of relations in product development. Furthermore, a requirements methodology based on SPES 2020 is proposed, which supports the decomposition of requirements by model-based templates via a subtractive structure. This promotes communication and situational awareness among developers in order to influence the sustainability goals in the early phase of the product development process. Further research will be made in order to implement the approach in a model-based manner and to define a finite number of measurement parameters based on interdisciplinary sustainability models.

Reasoning Affordances With Tables and Bar Charts.

A viewer's existing beliefs can prevent accurate reasoning with data visualizations. In particular, confirmation bias can cause people to overweigh information that confirms their beliefs, and dismiss information that disconfirms them. We tested whether confirmation bias exists when people reason with visualized data and whether certain visualization designs can elicit less biased reasoning strategies. We asked crowdworkers to solve reasoning problems that had the potential to evoke both poor reasoning strategies and confirmation bias. We created two scenarios, one in which we primed people with a belief before asking them to make a decision, and another in which people held pre-existing beliefs. The data was presented as either a table, a bar table, or a bar chart. To correctly solve the problem, participants should use a complex reasoning strategy to compare two ratios, each between two pairs of values. But participants could also be tempted to use simpler, superficial heuristics, shortcuts, or biased strategies to reason about the problem. Presenting the data in a table format helped participants reason with the correct ratio strategy while showing the data as a bar table or a bar chart led participants towards incorrect heuristics. Confirmation bias was not significantly present when beliefs were primed, but it was present when beliefs were pre-existing. Additionally, the table presentation format was more likely to afford the ratio reasoning strategy, and the use of ratio strategy was more likely to lead to the correct answer. These findings suggest that data presentation formats can affect affordances for reasoning.

Unifying Structure Reasoning and Language Pre-Training for Complex Reasoning Tasks

Unifying Structure Reasoning and Language Pre-Training for Complex Reasoning Tasks

Smartboard: Visual Exploration of Team Tactics with LLM Agent.

Tactics play an important role in team sports by guiding how players interact on the field. Both sports fans and experts have a demand for analyzing sports tactics. Existing approaches allow users to visually perceive the multivariate tactical effects. However, these approaches require users to experience a complex reasoning process to connect the multiple interactions within each tactic to the final tactical effect. In this work, we collaborate with basketball experts and propose a progressive approach to help users gain a deeper understanding of how each tactic works and customize tactics on demand. Users can progressively sketch on a tactic board, and a coach agent will simulate the possible actions in each step and present the simulation to users with facet visualizations. We develop an extensible framework that integrates large language models (LLMs) and visualizations to help users communicate with the coach agent with multimodal inputs. Based on the framework, we design and develop Smartboard, an agent-based interactive visualization system for fine-grained tactical analysis, especially for play design. Smartboard provides users with a structured process of setup, simulation, and evolution, allowing for iterative exploration of tactics based on specific personalized scenarios. We conduct case studies based on real-world basketball datasets to demonstrate the effectiveness and usefulness of our system.

Surface kinetics and pressure dependence of propane oxidation over platinum

The catalytic total oxidation of propane over platinum was investigated experimentally and numerically at pressures of 1–7 bar and catalyst temperatures up to 700 K. A wire microcalorimeter was employed to determine the global rate parameters of the catalytic reaction within the kinetically controlled regime. For 1 bar pressure, the dissociative adsorption of C3H8 on Pt and its subsequent decomposition were modeled as two lumped steps based on global reaction parameters. A detailed and thermodynamically consistent catalytic mechanism was constructed by incorporating these lumped steps with an existing atmospheric-pressure H-C2 elementary reaction model. Two-dimensional CFD simulations using the developed global and detailed reaction mechanisms closely reproduced the measured heat release rates. The intricate dependence of catalytic ignition and reactivity on pressure was further elucidated. Ignition temperatures were found to be linearly correlated to pressures, due to the weaker net adsorption of oxygen compared to that of propane, which progressively aggravated at higher pressures and in turn hindered ignition. More importantly, a non-monotonic pressure dependence of the C3H8 catalytic reactivity on Pt, which gradually diminishes with increasing temperatures, is reported for the first time. The temperature range of this non-monotonic behavior (< 650 K) is of special importance for part-load and idling operations of gas turbines using hybrid hetero-/homogeneous combustion approaches and for normal operations of recuperative microreactors. Thus, this work provides key information for the design and optimization of such devices utilizing Pt as catalyst.

Upward Bullying as Experienced by Chinese Nurse Managers: A Qualitative Study

Aim. To understand the current situation of upward bullying in the Chinese nursing field and explore the manifestations, reasons, and outcomes of upward bullying experienced by Chinese nurse managers. Background. Workplace bullying, a serious social problem, is characterised by recurring incidents of intimidating, aggressive, and hostile behaviour. Bullying in the nursing profession exhibits all or some of the above traits. The evidence of upward bullying by subordinate nurses against nurses in positions of authority or power is limited in China. Methods. This qualitative study was conducted with semistructured, in‐depth interviews involving 12 hospital nurse managers in Wuhan, Hubei Province, between June and August 2023. The data were analysed using the Colaizzi seven‐step analysis method with Nvivo 12.0 software as a support. Results. We grouped our findings into three main categories: manifestations of upward bullying; reasons for upward bullying; and outcomes of upward bullying. Conclusions. Nurse managers in China are exposed to upward bullying in many forms and for complex reasons. More emphasis needs to be given to creating a positive work environment for them to facilitate their managerial role. Implications for Nursing Management. This study probes the realities of upward bullying against Chinese nurse managers and highlights the need for managers to develop the skills needed to identify, manage, and prevent bullying from subordinates. By contributing to the development of interventions and strategies that address workplace bullying, this study shows promise for enhancing managerial effectiveness and improving the nursing practice environment.

Analysis of the opening of zones with abnormally high reservoir pressures in the oil and gas fields of the Western part of Turkmenistan

The article provides information about the change in the gradient of abnormally high reservoir pressure (AHRP) with increasing depth in the oil and gas fields of Turkmenistan. The analysis of the origin of AHRP is given, which subsequently leads to severe complications due to the manifestations of reservoir fluids, and the hydrodynamic causes of complications are considered in depth. Hypotheses and endogenous opinions are analyzed, as well as the interaction of a complex of reasons for the origin of abnormally high reservoir pressures associated with geological, geochemical and mechanical processes. This work can be used to fulfill the tasks set when drilling ultra-deep wells with abnormally high reservoir pressures, as well as to predict the causes of complications and possible prevention of these complications by regulating the density of drilling mud. Keywords: oil and gas occurrence; pressure; gradient; washing liquid; migration; degassing.

SOME ASPECTS OF IMPLEMENTATION OF INTERNATIONAL HUMANITARIAN LAW STANDARDS IN THE FIELD OF CAPTIVITY OF WAR IN RUSSIAN LEGISLATION AT THE PRESENT STAGE

Issues of legal regulation of military captivity are very relevant. At different times, humanity has experienced many wars and military conflicts, with which came experience and the realization that even during armed confrontation it is necessary to respect the law, show mercy and humanity towards an enemy who is helpless or does not want to fight. The emergence of norms in the field of military captivity in international humanitarian law dictated the necessary desire of countries to implement these norms into national legislation. The rapid development of modern law and legal policy determines the increase in the culture of society, and at the same time the preservation of humanity and respect for the human dignity of prisoners of war. Our country, being a highly developed and rule-of-law state, has always strived to implement the norms of international law in the field of military captivity, both in Soviet and Russian legislation. However, in the history of domestic law to this day, organizational, political and legal problems arise in the implementation of the norms of international humanitarian law relating to military captivity into national legislation. The above problems are a consequence of a complex of social reasons, both objective and private. Continuing research into the dynamics of the process of implementing the norms of international law in the field of military captivity into Russian legislation, a comprehensive study of the issue of legal implementation of the implemented norms will allow us to find ways to solve problems in this area, as well as draw a conclusion regarding the further development of legal norms relating to military captivity in our country.

Эволюция отношения ведущих британских политических партий к расширению состава ЕС (1997–2010 гг.)

In the presented article, the author consistently analyzed the approaches of the key British political parties (Conservative and Labor) in relation to the initiative to expand the EU during the period of the Labor Party’s rule (1997–2010). The stages of transformation of British politicians’ approaches to this issue were highlighted, the reasons for the change in their position were analyzed, and the consequences that demonstrated the diver-gence of the viewpoint of some political forces with the opinion of the electorate were revealed. Conclusion dwells upon the fact that the change in the political assessment of the EU enlargement by the key British parties became a significant component in the complex of reasons for the defeat of the Labor Party in the 2010 par-liamentary elections, and also contributed to the development and consistent steps of the Conservative Party in the direction of protecting the rights of British citizens as part of European cooperation.

Novel Results on SNR Estimation for Bandlimited Optical Intensity Channels.

In a previous work of the author about non-data-aided estimation of the signal-to-noise ratio (SNR) for bandlimited optical intensity channels, a couple of limitations have been identified in terms of error performance and computational complexity. In the current paper, these deficiencies are avoided by the introduction of a second receiver filter with specific properties that is operated in parallel to the receiver filter normally used in this respect. Although not initially intended, the concept is also applied to data-aided SNR estimation by deriving a maximum likelihood algorithm and the Cramer-Rao lower bound (CRLB) as the theoretical limit of the error performance. In the next step, the dual-filter framework is used in the context of SNR estimation without knowledge about data symbols. The most significant benefit of this method is that the number of payload data employed for the estimation procedure might be selected arbitrarily long without impacting the spectral efficiency of the link. Since the computation of the true CRLB was out of scope due to complexity reasons, an asymptotic variant for very low SNR values is analyzed, which ends up in a closed-form solution. Furthermore, an algorithm based on first- and second-order moments of the samples at the dual-filter output is investigated, which turned out to be very attractive in terms of error performance and computational complexity.

Generation of Training Examples for Tabular Natural Language Inference

Tabular data is becoming increasingly important in Natural Language Processing (NLP) tasks, such as Tabular Natural Language Inference (TNLI). Given a table and a hypothesis expressed in NL text, the goal is to assess if the former structured data supports or refutes the latter. In this work, we focus on the role played by the annotated data in training the inference model. We introduce a system, Tenet, for the automatic augmentation and generation of training examples for TNLI. Given the tables, existing approaches are either based on human annotators, and thus expensive, or on methods that produce simple examples that lack data variety and complex reasoning. Instead, our approach is built around the intuition that SQL queries are the right tool to achieve variety in the generated examples, both in terms of data variety and reasoning complexity. The first is achieved by evidence-queries that identify cell values over tables according to different data patterns. Once the data for the example is identified, semantic-queries describe the different ways such data can be identified with standard SQL clauses. These rich descriptions are then verbalized as text to create the annotated examples for the TNLI task. The same approach is also extended to create counterfactual examples, i.e., examples where the hypothesis is false, with a method based on injecting errors in the original (clean) table. For all steps, we introduce generic generation algorithms that take as input only the tables. For our experimental study, we use three datasets from the TNLI literature and two crafted by us on more complex tables. Tenet generates human-like examples, which lead to the effective training of several inference models with results comparable to those obtained by training the same models with manually-written examples.

P133: Impairment of Executive Functions in Dialysis Patients. Necessity of Evaluation Cognitive State in Advanced Chronic Kidney Disease

Introduction:Cognitive impairment is common and underdiagnosed in patients in hemodialysis (HD) can be moderate and severe. About 38% of patients in Dialysis has serious deficits in executive function and this affects patients of all ages.Material and methods:Based on the case of an 82-year-old patient in a HD program who enters from Nephrology to the Convalescence Unit for miscontrol of pain due to ganglion and liver recidivism after years of cystectomy by bladder neoplasia and having acute confusional syndrome (ACS) subsequently presented after treatmet with morphine MST and Tramadol. We performed an integral geriatric evaluation that showed a deterioration of the executive functions. Through medical literature we analyze this find of atypical pattern and its possible relationship to renal disease.Results:BI 100/100. No known history of cognitive impairment. Despite optimal pain control with fentanyl patch, pregabalin, paracetamol, metamizol and dexamethasone, without new episodes of ACS, we saw high difficulties in comprehension time schedule, spatial location, bad understanding of medical treatment, etc. Neuropsychological study showed MMSE 24/30; SKT subtest of immediate memory 1/12, null recognition without understanding the task. Memory Imparily Screen 5/8, Test clock 3/7. In summary there were deficits in executive functions such as complex attention, mental control, fluency and reasoning. Fluctuating memory abilities. Mild multiple cognitive impairment executive domain. Atypical Profile.Conclusions:There is a strong correlation between the decrease in GF and the degree of cognitive impairment. This worsens with the Dialysis due to hemodynamic changes. It is essential to evaluate the cognitive situation in all patients with CKD. Being the most affected cognitive domain, the function executive,it can avoid in these patients the correct assesment of established medications and dietary restrictions that are so important in the control of potassium, phosphorus and liquids that cause greater complications and even more mortality.

Democracy and Cultural Factors: The Case of Albania

In the context of democracy, the world is divided between nations who are thought to be democratic par excellence, while others embark on a challenging path to democracy. Albanians have sought for democracy since the fall of dictatorial regime in 1990. The pursuit of this ideal has been impaired by substantial governmental challenges in key areas of the country. Nowadays, concerns have arisen even in the most democratic countries, because of the discontent that exists among their citizens. In regions like the Balkans, a new term has emerged: “stabilitocracy”. This concept seeks to examine the complex reasons behind countries that appear to be democratic but struggle to fully embrace democratic principles. In these nations, the three fundamental pillars of democracy as the rule of law, human rights, free and fair elections, and fair representation by state authorities, remain vulnerable. However, in the Albanian context, these elements have remained unstable since the aftermath of the dictatorial regime. Scholars analyze the fact that democracy in a country is influenced by their culture and economic development. When either of those components fails to align with the democratic ideal, democracy will be at risk, or it will lead the ground for the creation of an alternative political regime. This study searches into Albania’s path towards democracy, with a focused exploration of the factors shaping this unique form of “democracy” that is emerging in the current context.&#x0D; &#x0D; Received: 23 September 2023 / Accepted: 30 October 2023 / Published: 23 November 2023

Beyond empathy: Cognitive capabilities increase or curb altruism in middle childhood

Altruistic behavior, which intentionally benefits a recipient without expectation of a reward or at a cost to the actor, is observed throughout the lifespan from everyday interactions to emergency situations. Empathy has long been considered a major driver of altruistic action, but the social information processing model supports the idea that other cognitive processes may also play a role in altruistic intention and behavior. Our aim was to investigate how visual analysis, attention, inhibitory control, and theory of mind capabilities uniquely contribute to predicting altruistic intention and behavior in a sample of 67 French children (35 girls and 32 boys; Mage = 9.92 ± 0.99 years) from Paris and neighboring suburbs. Using a Bayesian analysis framework, we showed that in younger grade levels visual analysis and selective attention are strong predictors of altruistic intention and that inhibitory control strongly predicts altruistic behavior in a dictator game. Processes underlying theory of mind, however, negatively predict altruistic behavior in the youngest grade. In higher grade levels, we found that stronger attention and inhibitory control predicts lower altruistic intention and behavior. Empathy was not found to predict altruistic intention or behavior. These results suggest that different cognitive capabilities are involved in altruistic intention and behavior and that their contribution changes throughout middle childhood as social constraints deepen and altruism calls on more complex reasoning.

Performance analysis of large language models in the domain of legal argument mining.

Generative pre-trained transformers (GPT) have recently demonstrated excellent performance in various natural language tasks. The development of ChatGPT and the recently released GPT-4 model has shown competence in solving complex and higher-order reasoning tasks without further training or fine-tuning. However, the applicability and strength of these models in classifying legal texts in the context of argument mining are yet to be realized and have not been tested thoroughly. In this study, we investigate the effectiveness of GPT-like models, specifically GPT-3.5 and GPT-4, for argument mining via prompting. We closely study the model's performance considering diverse prompt formulation and example selection in the prompt via semantic search using state-of-the-art embedding models from OpenAI and sentence transformers. We primarily concentrate on the argument component classification task on the legal corpus from the European Court of Human Rights. To address these models' inherent non-deterministic nature and make our result statistically sound, we conducted 5-fold cross-validation on the test set. Our experiments demonstrate, quite surprisingly, that relatively small domain-specific models outperform GPT 3.5 and GPT-4 in the F1-score for premise and conclusion classes, with 1.9% and 12% improvements, respectively. We hypothesize that the performance drop indirectly reflects the complexity of the structure in the dataset, which we verify through prompt and data analysis. Nevertheless, our results demonstrate a noteworthy variation in the performance of GPT models based on prompt formulation. We observe comparable performance between the two embedding models, with a slight improvement in the local model's ability for prompt selection. This suggests that local models are as semantically rich as the embeddings from the OpenAI model. Our results indicate that the structure of prompts significantly impacts the performance of GPT models and should be considered when designing them.