Level Of Complexity Research Articles

Simvastatin dengan Senyawa Turunan Β-Siklodextrin

This study aims to determine the profile of the inclusion complex formed between simvastatin and β-cyclodextrin and its derivatives. To determine the level of stability and determine the solubility level of the inclusion complex formed between simvastatin and β-cyclodextrin and its derivatives (2-hydroxypropyl-β-cyclodextrin and 2,6-di-O-methyl-β-cyclodextrin) in water solvent.The method used is qualitative descriptive using the molecular docking method, modeling and anchoring the inclusion complex model.The research results show that the simvastatin/HP-β-CD inclusion complex has a higher level of solubility in water solvents compared to the simvastatin/β-CD and simvastatin/DM-β-CD inclusion complexes, with a ΔGsolvation value of -1032.34 kJ/mol.Conclusion: Simvastatin is able to bind to the cavity of 2-hydroxypropyl-β-cyclodextrin, 2,6-di-O-methyl-β-cyclodextrin, and simvastatin/β-cyclodextrin with the following order of bond strength: simvastatin/2-hydroxypropyl-β -cyclodextrin > simvastatin/2,6-di-O-methyl-β-cyclodextrin > simvastatin/β-cyclodextrin. Keywords: Inclusion Complex, Molecular Docking, Molecular Dynamics Simulation, Β-Cyclodextrin, Simvastatin.

Factors Influencing Willingness to Participate in Clinical Studies in Pediatric Anesthesia (FILIPPA): A vignette-based, structured interview study.

Informed consent is a relevant backdrop for conducting clinical trials, particularly those involving children. While several factors are known to influence the willingness to consent to pediatric anesthesia studies, the influence of study design on consenting behavior is unknown. To quantify the impact of study complexity on willingness to consent to pediatric anesthesia studies. We conducted a vignette-based interview study by presenting three hypothetical studies to 106 parents or legal guardians whose children were scheduled to undergo anesthesia. These studies differed in level of complexity and included an example of a prospective observational study, a randomized controlled trial, and a phase-II-pharmacological study. Primary outcome was the willingness to consent, using a 5-point Likert scale ranging from "absolutely consent" to "absolutely decline". Secondary outcomes were the effects of child-related (such as sex, age, previous anesthesia, research exposure) and proxy-related factors. Response probabilities for "absolute consent" were 90.9% [95% CI 85.3-96.5] for the observational study, 48.6% [95% CI 38.3-58.9] for the randomized controlled trial, and 32.7% [95% CI 23.9-41.6] for the phase-II-pharmacological study. Response probabilities for "absolutely decline" were 1.6% [95% CI 0.3-2.8], 14.4% [95% CI 8.3-20.5], and 24.7% [95% CI 16.6-32.7], respectively. Significant effects were found for previous research exposure (OR = 0.486 [95% CI 0.256-0.923], p = .027), older age (OR = 0.963 [95% CI 0.927-0.999], p = .045) and the gender of the parent or legal guardian, as mothers were less willing to consent (OR = 0.234 [95% CI 0.107-0.512], p < .001). Willingness to consent decreased with increasing level of study complexity. When conducting more complex studies, greater efforts need to be made to increase the enrollment of pediatric patients.

Surgical Extraction of Mandibular Third Molars: Risk Assessment and Predictable Complexity

Objective. The study aims to develop and validate new practical and simple but robust index to determine the complexity level of surgical extraction of mandibular third molars (MTM). Methods. A cross-sectional study with sample of 100 patients referred for management of MTM. Complexity index scores recorded twice at 6 weeks intervals preoperatively by oral and maxillofacial specialist. Prediction of the surgical difficulty and the degree of agreement of different variable indexes were calculated. Results. Out of 100, 78 patients with 139 impacted MTM, 26 were males and 52 were female, with a mean age of 26.40±6.39 years (range, 18-45 years). The prediction of operative difficulty was significant as measured by impaction angulation, distance to ramus, and impaction depth. Most cases were classified as complexity level II (63 [80%]). Although only 3 out of 100 cases (4%) were classified as complexity level III. There was no significant difference in age based on the gender (male: 27.54±7.35 years, female: 25.83±5.86 years; P=0.268). Conclusion. Using MTM complexity index as a preoperative tool to evaluate the surgical difficulty, including different patient-specific variables could facilitate treatment planning, help prevent complications and assess the possibility of referral.

Novel rapid screening assay to incorporate complexity and increase throughput in early-stage plant biological testing

The search for new biological products with a positive impact on crop performance is typically initiated by laboratory based in vitro assays. However, live plants and their associated microbes are often removed from in vitro testing assays as a way to reduce biological complexity (variation) and facilitate molecular techniques in the pursuit of uncovering mode-of-action (MoA) mechanisms. Nevertheless, when studying biological candidates intended for use in agriculture, it is essential to incorporate this complexity and validate mechanisms under conditions as close to in situ as possible in order to understand the capacities and MoA of the biologicals in the intended application environments. To address this paradox, we have developed a high-capacity early-stage plant assay that incorporates a live non-sterile plant while also enabling molecular MoA investigations, and that can be conducted in laboratories without greenhouse facilities. The high-capacity design features plants grown in 8-chamber transparent boxes to allow for multiplex imaging and increased biological replicates for greater statistical power. The transparent box design allows the visualization of shoots, roots, tagged-microbes, or visible substrates, and further non-destructive access to shoots or roots for sampling. The boxes are held in racks that hold eight plant boxes during growth in a 19 by 17 cm space, further increasing the throughput to >670 plants per m2 and easing the logistical challenges of plant assays. Furthermore, the box can support various levels of microbial complexity with the option to select the plant growth medium that meets experimental objectives, as well as using sterile or non-sterile seeds. A script-based post-imaging quantification was developed to automate image processing and allow for individual plant readings, further enabling increased statistical confidence. As proof of concept, we use the high-capacity plant system to evaluate the biocontrol potential of Pseudomonas protegens and the biostimulation potential of Pseudomonas koreensis, and are in both cases able to show statistically significant differing plant biomass between treatments under these closer-to-nature conditions. We further demonstrate that the high-capacity plant system is suitable for paired molecular investigations by performing metabolomics and qPCR DNA quantification directly from the plant box to explore in situ chemical MoA, as well as confirm the survival of the P. protegens strains to validate their role in the improved plant phenotype. In conclusion, the study presents a modular high-capacity plant assay system that enables increased throughput functional testing of microbial biocontrol and biostimulant candidates in planta. This novel assaying system saves time, reduces human error, provides quantitative and non-destructive in planta data, and can be used in laboratories without greenhouse facilities. We therefore believe it provides a potent early-stage testing option that bridges in vitro and greenhouse testing, and will expedite the discovery of superior next-generation biological products in agriculture.

Acupuncture modulates the AMPK/PGC-1 signaling pathway to facilitate mitochondrial biogenesis and neural recovery in ischemic stroke rats.

The main objective of this study was to investigate the role and mechanism of acupuncture on anti-nerve injury in the acute phase by regulating mitochondrial energy metabolism via monophosphate-activated protein kinase (AMPK)/peroxisome proliferator-activated receptor-γ coactivator 1α (PGC-1α) axis in rat ischemic stroke. Middle cerebral artery occlusion (MCAO) was established by middle cerebral artery occlusion/reperfusion. One-week of acupuncture was performed during the acute phase of ischemic stroke. The neurological function and brain tissue integrity were evaluated. Mitochondrial function (intracellular ATP level and the activity of mitochondrial respiratory chain complex I) and the level of NADH oxidase (NOX) were detected by enzymatic chemistry. Next, the potential molecular mechanisms were explored by western blotting, fluorescence quantitative PCR and immunohistochemistry method. (1) Acupuncture treatment for MCAO/R rats showed a significant improvement in the infarcted tissue accompanied by functional recovery in Zea-Longa score and balance beam score outcomes, motor function performances. (2) Acupuncture increased the levels of ATP and mitochondrial respiratory chain complex I, decreased the NOX levels in cerebral ischemia established by suture-occluded method. (3) Acupuncture reduced the necrosis dissolution of neuronal cells and meningeal edema, while promoting angiogenesis. (4) Quantitative immunohistochemical staining results showed acupuncture can increase the expression of AMPK, p-AMPK and the mitochondrial transcription factor PGC-1α, NRF2, TFAM and uncoupling protein 2 (UCP2). Meanwhile, acupuncture treatment up-regulated the expression of the corresponding protein. (5) Subsequently, acupuncture enhanced AMPK phosphorylation as well as the expression of PGC-1α, NRF2, TFAM and UCP2, implicated in mitochondrial synthesis and cellular apoptosis. (6) Finally, injections of AMPK antagonists and activators confirmed AMPK as a therapeutic target for the anti-nerve damage effects of acupuncture. Acupuncture intervention relieved ischemic stroke progression in MCAO rats by promoting energy metabolism and mitochondrial biogenesis in the brain and alleviating neuronal apoptosis, which was mediated by eliciting AMPK/PGC-1α axis, among them AMPK is a therapeutic target.

Establishing Standardization Guidelines For Finite-Element Optomechanical Simulations of Refractive Laser Surgeries: An Application to Photorefractive Keratectomy.

Computational models can help clinicians plan surgeries by accounting for factors such as mechanical imbalances or testing different surgical techniques beforehand. Different levels of modeling complexity are found in the literature, and it is still not clear what aspects should be included to obtain accurate results in finite-element (FE) corneal models. This work presents a methodology to narrow down minimal requirements of modeling features to report clinical data for a refractive intervention such as PRK. A pipeline to create FE models of a refractive surgery is presented: It tests different geometries, boundary conditions, loading, and mesh size on the optomechanical simulation output. The mechanical model for the corneal tissue accounts for the collagen fiber distribution in human corneas. Both mechanical and optical outcome are analyzed for the different models. Finally, the methodology is applied to five patient-specific models to ensure accuracy. To simulate the postsurgical corneal optomechanics, our results suggest that the most precise outcome is obtained with patient-specific models with a 100 µm mesh size, sliding boundary condition at the limbus, and intraocular pressure enforced as a distributed load. A methodology for laser surgery simulation has been developed that is able to reproduce the optical target of the laser intervention while also analyzing the mechanical outcome. The lack of standardization in modeling refractive interventions leads to different simulation strategies, making difficult to compare them against other publications. This work establishes the standardization guidelines to be followed when performing optomechanical simulations of refractive interventions.

ENVIRONMENTAL ASSESSMENT OF SOIL STATE IN THE IMPACT ZONE OF THE SREDNEURALSKY COPPER SMELTER

As part of the study of the ecological state of soils and vegetation in the zone of impact of the Sredneuralsky Copper Smelter (SUMZ), the following tasks were solved: the content of heavy metals in the soils of the test sites laid within the zone of impact of the SUMZ was determined; macrokinetic patterns of seasonal dynamics of the normalized relative vegetation index (NDVI) at the test sites in the zone of impact of the SUMZ were revealed; macrokinetic patterns of vegetation response in the form of NDVI, calculated according to MODIS and Landsat 8 data, to soil pollution by a complex of heavy metals in the zone of impact of the SUMZ were revealed; soil quality was ranked according to the patterns of vegetation response in the form of NDVI to soil pollution by a complex of heavy metals within the boundaries of the natural protection zone (NZZ) of the SUMZ according to 2023. The intra-seasonal dynamics of photosynthetically active biomass in the form of NDVI was modeled using a theoretical growth equation based on MODIS satellite data. The seasonal maximum of the vegetation index at the trial sites in 2012 fell on the 25-28 week. In 2023, it occurred on the 27-33 week, depending on the location of the sampling point. The rate of maximum achievement is characterized by weak intra-seasonal and interannual variation. The patterns of changes in the concentration of photosynthetically active biomass in the form of maximum NDVI for the 2023 season in response to the gross content of a complex of heavy metals (Cu, Pb, Cd and Zn) in the soil of the test sites were modeled using the theoretical equation of dose dependence. The maximum permissible level of the heavy metal complex in the soil in the form of a geometric mean, which does not cause a decrease in the values of the vegetation index calculated according to the data of the MODIS and Landsat 8 satellites, was 101 and 106 mg·kg-1, respectively. The analysis of histograms of the distribution of NDVI values within the NZZ was carried out, which showed how the true frequencies of occurrence of the values of the vegetation index correspond to the empirical boundary of the NZZ associated with the maximum dose dependence point identified by the study of the 2012. The nature of the distribution of true NDVI values for the NZZ exclusively fully supports the used zoning theory based on the analysis of dose dependence. The modern boundaries of the SUMZ natural protection zone are 4-7 km away from the center of the sanitary protection zone.

Efficient river hydrodynamics modelling in realistic river systems using a Fourier neural operator-based network

Effective river management relies heavily on the accurate simulation of river flow dynamics to develop scenario-based strategies and inform decision making. Deep learning (DL) models, as alternatives to conventional simulation techniques, offer a compelling blend of precision and efficiency. This study introduces FNOCL (Fourier Neural Operator with ConvLSTM units), a novel DL neural network for modelling unsteady natural river flows. The network is adept at handling the complexities of spatial and temporal variations. FNOCL builds upon the Fourier neural operator (FNO) by incorporating convolutional long short-term memory (ConvLSTM) layers, combining robust performance with high proficiency in terms of capturing spatiotemporal patterns. Notably, FNOCL accommodates complex river cross-section profiles, thus enabling the accurate modelling of flow dynamics within intricate river morphologies. Our study applies FNOCL to two distinct river networks with varying morphology complexity levels. With reference to the HEC-RAS model, FNOCL showcases its ability to attain high accuracy in model prediction tasks and superior generalization with regard to addressing the uncertainty associated with inflow variations and cross-sectional changes. In comparative analyses with benchmark models (i.e., FNO and ConvLSTM), FNOCL performs most efficiently during the training process and obtains the most accurate water level and river flow predictions in the spatiotemporal domain. Furthermore, FNOCL exhibits a substantial speedup over HEC-RAS, notably reaching up to 50 times in a complex river network. These results highlight FNOCL as an efficient surrogate for conventional simulators, facilitating precise analyses across various scenarios. It provides efficient computations for system responses within complex river flow networks, thereby enhancing applications in river management, such as flood risk assessment, flood control, and the planning and design of hydraulic control structures.

On the optimality of stepwise policies for managing capacity, inventory and backorders

We consider the problem of simultaneously managing capacity, inventory and backorders in a multi-mode production environment modelled via Brownian motion. The presence of more than two production modes adds an additional level of complexity: not just when to change modes, but also which mode to change to. We show that, under the two assumptions that demand is the overriding source of variability in the process and that the cost to change from one production mode to another is proportional to the difference in the production capacities, a policy that moves stepwise among the modes minimizes the long-run average cost. Examples demonstrate that if either assumption is violated no policy that moves stepwise among the modes may be optimal. To focus on the complexity of identifying when to change modes and which mode to change to, we restrict our model to simple convex holding and backorder costs and linear processing costs and costs for rejecting demand and idling capacity. We adopt the economic average cost model that allows the manager to reject demand or idle capacity at any time. We demonstrate that under our two assumptions we may impose an ordering assumption on the relative value functions that dramatically simplifies the classic Hamilton-Jacobi-Bellman equations. We show that under the economic average cost model a production mode that is initially unattractive may later become attractive as new modes are added. Our arguments are essentially constructive and lead to a practical algorithm for finding an optimal policy.

Playing to learn: developing self-directed learning skills through serious games

Purpose Higher education is undergoing a significant transformation, demanding a rethinking of instructional methods, the establishment of novel assessment frameworks and the incorporation of state-of-the-art technologies to advance the development of learning competencies. The use of simulations is an alternative that offers satisfactory results and favors learning in new generations. This study aims to present a methodology for assessing the advancement of self-directed learning (SDL) in participants through simulation techniques and show that the incorporation of simulations can improve participants’ SDL skills. Design/methodology/approach This study uses a quantitative approach to evaluate the evolution of various competencies related to SDL among participants. Includes the application of scales specifically designed to measure readiness for SDL, the use of factor analysis to identify and validate essential factors and conducting hypothesis testing to verify shifts in students’ perceptions regarding the significance of SDL-related skills. Findings The findings of this study reveal a positive impact of simulations on the SDL readiness. Factor analysis identified five key factors: self-management skills, openness to learning opportunities, initiative and independence in learning, self-concept as an effective learner and desire for Learning. The use of simulations significantly improved critical thinking, logical reasoning, analysis and decision-making capabilities. These results demonstrate the effectiveness of this technique as a valuable tool for developing SDL skills and highlight the potential of such interventions in complex and uncertain learning environments. Research limitations/implications The study provided valuable insights into the impact of simulations in the logistics field, particularly in the development of SDL-related skills. There is a need for studies in other areas of knowledge that would allow for the generalization of these results. Likewise, it is advisable to use scales that measure the development of other types of skills. Additionally, the study’s focus on short-term effects may not fully capture the long-term development of SDL-related skills. Future research should address these limitations, expand the sample size, conduct longitudinal studies and explore possible contextual and external factors that may influence SDL outcomes in various educational settings, leading to broader and more robust conclusions. Practical implications The practical implications of this study are significant for both higher education institutions and educators in the logistics domain. The positive impact of the serious game on participants’ SDL skills highlights the potential of incorporating interactive and engaging learning tools in the curriculum. Educators can leverage serious games to foster critical thinking, logical reasoning and decision-making abilities in their students. Moreover, the identified key factors influencing SDL development provide valuable guidance for designing targeted interventions to enhance students’ self-management, initiative and love for learning. Social implications The social implications of this study underscore the importance of equipping individuals with SDL skills in a rapidly changing world. By integrating serious games and fostering SDL competencies, higher education institutions can contribute to a more adaptable and resilient workforce. Empowering students with critical thinking, innovative thinking and independent learning, they can enhance their ability to navigate the challenges of the labor market. This, in turn, may lead to a more informed and proactive society capable of addressing complex issues and making well-informed decisions. Originality/value This article arises from empirical exploration with students from two countries in Latin America (Mexico and Colombia), examining the importance of games in developing SDL skills. Furthermore, it promotes the use of a methodology with a low level of complexity, and that can be replicated with different games. This perspective adds valuable insights to the existing literature, offering practical implications for educators and institutions seeking effective strategies to promote SDL skills among students.

From simple to complex: a sequential method for enhancing time series forecasting with deep learning

Abstract Time series forecasting is a well-known deep learning application field in which previous data are used to predict the future behavior of the series. Recently, several deep learning approaches have been proposed in which several nonlinear functions are applied to the input to obtain the output. In this paper, we introduce a novel method to improve the performance of deep learning models in time series forecasting. This method divides the model into hierarchies or levels from simpler to more complex ones. Simpler levels handle smoothed versions of the input, whereas the most complex level processes the original time series. This method follows the human learning process where general/simpler tasks are performed first, and afterward, more precise/harder ones are accomplished. Our proposed methodology has been applied to the LSTM architecture, showing remarkable performance in various time series. In addition, a comparison is reported including a standard LSTM and novel methods such as DeepAR, Temporal Fusion Transformer, NBEATS and Echo State Network.

Spatial variation of body wave attenuation in Garhwal-Kumaun Himalaya region, India

We have investigated the spatial variation of body wave attenuation in Garhwal-Kumaun Himalaya region from the datasets of 465 well located earthquakes, recorded at 52 broadband stations between 2017 and 2020. The body wave attenuation parameters QP and QS were estimated at each station by applying the extended coda normalization method for five different central frequencies ranging from 1.5 to 18 Hz. Strong frequency-dependent body wave attenuation was observed at each station over the whole region. Also, we found a significant variation of QP and QS from north to south which is consistent with geotectonic diversity beneath the study region. We have also made separate estimations of frequency-dependent relations for Garhwal and Kumaun Himalaya region in order to investigate the lateral variation in attenuation characteristics, and obtained the frequency-dependent relations as follows: QP=30±3f1.05±0.05, QS=143±20f0.88±0.07 for the Garhwal Himalaya region and QP=31±1f1.09±0.02, QS=121±11f1.00±0.04 for the Kuamun Himalaya region. Obtained Q values indicate strong body wave attenuation for both the region with no significant lateral variation. It may suggest the presence of crustal level folding, faulting, aqueous fluids, and metamorphic fluids below both the regions. Also, the ratios of QS/QP are high (>1) for the entire analyzed frequency range, suggesting a significant level of heterogeneity and tectonic complexities in the crust of the study region.

Whistleblowing academic research: historical perspective

Purpose For more than four decades, scholars from diverse disciplines and countries have been interested in the act of whistleblowing. To battle financial fraud, financial regulators have been developing whistleblowing programs to motivate and protect whistleblowers, i.e. those who sound the alarm after witnessing an illegal act in their organization. The purpose of this article is to review five historical phases of whistleblowing research. The authors analyze the themes covered by whistleblowing studies conducted over the past 50 years and draw a snapshot of the evolution of whistleblowing research. Design/methodology/approach The authors examine academic papers published between 1970 and 2022 and inventory the disciplines involved in the literature and changes in the definition of whistleblowing. Findings The findings show the progress made in academic research (especially for the accounting discipline) regarding whistleblowing. The themes covered by academic studies became progressively diverse. However, this broader scope limited the depth of analysis and the level of self-criticism in the academic research. All but a few articles fail to view whistleblowing in light of its actual level of complexity, and the rationale behind limiting the definition of whistleblowing can only increase this myopia. Although most academic studies have adopted Near and Miceli (1985) definition of whistleblowing, the literature has yet to reach a consensus. Indeed, the analysis shows that Near and Miceli’s (1985) definition of whistleblowing is incomplete and narrow by today’s standards, not to mention out of step with regulators’ needs. Originality/value The main contributions are offering a big picture of whistleblowing academic research's evolution and proposing a more complete and updated view of this act.

Inhalation of Microplastics-A Toxicological Complexity.

Humans are chronically exposed to airborne microplastics (MPs) by inhalation. Various types of polymer particles have been detected in lung samples, which could pose a threat to human health. Inhalation toxicological studies are crucial for assessing the effects of airborne MPs and for exposure-reduction measures. This communication paper addresses important health concerns related to MPs, taking into consideration three levels of complexity, i.e., the particles themselves, the additives present in the plastics, and the exogenous substances adsorbed onto them. This approach aims to obtain a comprehensive toxicological profile of deposited MPs in the lungs, encompassing local and systemic effects. The physicochemical characteristics of MPs may play a pivotal role in lung toxicity. Although evidence suggests toxic effects of MPs in animal and cell models, no established causal link with pulmonary or systemic diseases in humans has been established. The transfer of MPs and associated chemicals from the lungs into the bloodstream and/or pulmonary circulation remains to be confirmed in humans. Understanding the toxicity of MPs requires a multidisciplinary investigation using a One Health approach.

It’s not distance but similarity of distance: changing stimulus relations affect the control of action sequences

Interacting with our environment happens on different levels of complexity: While there are individual and simple actions like an isolated button press, most actions are more complex and involve sequences of simpler actions. The degree to which multiple simple actions are represented as one action sequence can be measured via so-called response-response binding effects. When two or more responses are executed consecutively, they are integrated into one representation so that repetition of one response can start retrieval of the other. Executing such an action sequence typically involves responding to multiple objects or stimuli. Here, we investigated whether the spatial relation of these stimuli affects action sequence execution. To that end, we varied the distance between stimuli in a response-response binding task. Stimulus distance might affect response-response binding effects in one of two ways: It might directly affect the representation of the response sequence, making integration and retrieval between responses more likely if the responses relate to close stimuli. Alternatively, the similarity of stimulus distribution during integration and retrieval might be decisive, leading to larger binding effects if stimulus distance is identical during integration and retrieval. We found stronger binding effects with constant than with changing stimulus distance, indicating that action integration and retrieval can easily affect performance also if responses refer to separated objects. However, this effect on performance is diminished by changing spatial distribution of stimuli at the times of integration and retrieval.

CIME4R: Exploring iterative, AI-guided chemical reaction optimization campaigns in their parameter space

Chemical reaction optimization (RO) is an iterative process that results in large, high-dimensional datasets. Current tools allow for only limited analysis and understanding of parameter spaces, making it hard for scientists to review or follow changes throughout the process. With the recent emergence of using artificial intelligence (AI) models to aid RO, another level of complexity has been added. Helping to assess the quality of a model’s prediction and understand its decision is critical to supporting human-AI collaboration and trust calibration. To address this, we propose CIME4R—an open-source interactive web application for analyzing RO data and AI predictions. CIME4R supports users in (i) comprehending a reaction parameter space, (ii) investigating how an RO process developed over iterations, (iii) identifying critical factors of a reaction, and (iv) understanding model predictions. This facilitates making informed decisions during the RO process and helps users to review a completed RO process, especially in AI-guided RO. CIME4R aids decision-making through the interaction between humans and AI by combining the strengths of expert experience and high computational precision. We developed and tested CIME4R with domain experts and verified its usefulness in three case studies. Using CIME4R the experts were able to produce valuable insights from past RO campaigns and to make informed decisions on which experiments to perform next. We believe that CIME4R is the beginning of an open-source community project with the potential to improve the workflow of scientists working in the reaction optimization domain.Scientific contributionTo the best of our knowledge, CIME4R is the first open-source interactive web application tailored to the peculiar analysis requirements of reaction optimization (RO) campaigns. Due to the growing use of AI in RO, we developed CIME4R with a special focus on facilitating human-AI collaboration and understanding of AI models. We developed and evaluated CIME4R in collaboration with domain experts to verify its practical usefulness.

The relationship between knowledge accumulation and gender norms

Gender norms, encompassing social and cultural attitudes that dictate suitable behaviors for women and men, are pivotal in shaping societal dynamics. This paper explores the intricate relationship between knowledge accumulation, as reflected in a nation’s economic complexity, and its gender norms. Drawing on the revealed preferences of a substantial cohort, totaling 64,954 individuals from up to 49 countries, and their respective countries’ economic complexity indices, we uncover a non-trivial relationship. At lower levels of economic complexity, there exists an inverse correlation between less rigid gender norms and technological sophistication. This suggests that the initial stages of knowledge accumulation coincide with inflexible gender norms, particularly regarding women’s roles within households. However, as economic complexity surpasses a certain threshold, this correlation reverses. Further knowledge accumulation aligns with more adaptable gender norms, indicating a shift towards less rigidity, especially concerning women’s roles in the public sphere. These findings underline the connection between economic development and shifting gender norms. Notably, while economic complexity catalyzes a transition towards less rigid norms regarding women’s household roles at lower stages, it is only at a certain threshold that significant changes in norms pertaining to women’s public roles become evident. This study contributes to understanding the evolution of gender norms in the context of economic development. By shedding light on the intricate dynamics between knowledge accumulation and gender norms, it emphasizes the significance of considering socioeconomic factors in addressing gender norms. Findings have implications for policymakers and stakeholders offering valuable insights into the mechanisms driving societal change.

Different roads, same destination: The shared future of plant ecophysiology and ecohydrology.

Terrestrial water fluxes are substantially mediated by vegetation, while the distribution, growth, health, and mortality of plants are strongly influenced by the availability of water. These interactions, playing out across multiple spatial and temporal scales, link the disciplines of plant ecophysiology and ecohydrology. Despite this connection, the disciplines have provided complementary, but largely independent, perspectives on the soil-plant-atmosphere continuum since their crystallization as modern scientific disciplines in the late 20th century. This review traces the development of the two disciplines, from their respective origins in engineering and ecology, their largely independent growth and maturation, and the eventual development of common conceptual and quantitative frameworks. This common ground has allowed explicit coupling of the disciplines to better understand plant function. Case studies both illuminate the limitations of the disciplines working in isolation, and reveal the exciting possibilities created by consilience between the disciplines. The histories of the two disciplines suggest opportunities for new advances will arise from sharing methodologies, working across multiple levels of complexity, and leveraging new observational technologies. Practically, these exchanges can be supported by creating shared scientific spaces. This review argues that consilience and collaboration are essential for robust and evidence-based predictions and policy responses under global change.

Economic complexity and income inequality in Russian regions

Although economic growth leads to a reduction in poverty, the growing income inequality and its possible consequences are of some concern. The level of economic complexity has recently been increasingly viewed as an important predictor of income inequality. In this paper, using pool and models with time fixed effects, the impact of the economic complexity index on income inequality in Russian regions for 2013 and 2015 was estimated. The results of the evaluation of econometric models show that regions with more complex economies showed a higher level of income inequality compared to others. The explanations for the effect obtained include an increase in demand for highly qualified labor as a result of the development of high-tech industries and an increase in wage differentiation between high-skilled and low-skilled workers, as well as the migration of high-skilled workers to economically more complex regions. The main conclusion is that economic complexity is a significant factor of the variation in income inequality and should also be taken into account while conducting economic and social policy in Russian regions.

An Investigation into the Utility of Large Language Models in Geotechnical Education and Problem Solving

The study explores the capabilities of large language models (LLMs), particularly GPT-4, in understanding and solving geotechnical problems, a specialised area that has not been extensively examined in previous research. Employing a question bank obtained from a commonly used textbook in geotechnical engineering, the research assesses GPT-4’s performance across various topics and cognitive complexity levels, utilising different prompting strategies like zero-shot learning, chain-of-thought (CoT) prompting, and custom instructional prompting. The study reveals that while GPT-4 demonstrates significant potential in addressing fundamental geotechnical concepts and problems, its effectiveness varies with specific topics, the complexity of the task, and the prompting strategies employed. The paper categorises errors encountered by GPT-4 into conceptual, grounding, calculation, and model inherent deficiencies related to the interpretation of visual information. Custom instructional prompts, specifically tailored to address GPT-4’s shortcomings, significantly enhance its performance. The study reveals that GPT-4 achieved an overall problem-solving accuracy of 67% with custom instructional prompting, significantly higher than the 28.9% with zero-shot learning and 34% with CoT. However, the study underscores the importance of human oversight in interpreting and verifying GPT-4’s outputs, especially in complex, higher-order cognitive tasks. The findings contribute to understanding the potential and limitations of current LLMs in specialised educational fields, providing insights for educators and researchers in integrating AI tools like GPT-4 into their teaching and problem-solving approaches. The study advocates for a balanced integration of AI in education to enrich educational delivery and experience while emphasising the indispensable role of human expertise alongside technological advancements.