Complex Relationship Research Articles

ASO Author Reflections: Loss of Intestinal Integrity During Oncological Surgery and Postoperative Complications: A Complex Relationship.

ASO Author Reflections: Loss of Intestinal Integrity During Oncological Surgery and Postoperative Complications: A Complex Relationship.

Navigating the Landscape of AI in Reading: Unveiling Opportunities and Considerations

The article delves into the complex relationship between reading and artificial intelligence, examining the several ways in which AI has affected our reading behaviours. It explores the benefits that AI offers, from virtual reading tutors to individualized learning platforms. But amid the promises, there are issues like short-form content and distractions. The paper stresses the significance of a careful and balanced approach to incorporating technology in education while offering ideas for using AI to improve students' reading abilities.

Re‐examination of statistical relationships between dietary fats and other risk factors, and cardiovascular disease, based on two crucial datasets

AbstractCardiovascular disease (CVD) is the major cause of death in many regions around the world, and several of its risk factors might be linked to diets. To improve public health and the understanding of this topic, we look at the recent Minnesota Coronary Experiment (MCE) analysis that used t‐test and Cox model to evaluate CVD risks. However, these parametric methods might suffer from three problems: small sample size, right‐censored bias, and lack of long‐term evidence. To overcome the first of these challenges, we utilize a nonparametric permutation test to examine the relationship between dietary fats and serum total cholesterol. To address the second problem, we use a resampling‐based rank test to examine whether the serum total cholesterol level affects CVD deaths. For the third issue, we use some extra‐Framingham Heart Study (FHS) data with an A/B test to look for meta‐relationship between diets, risk factors, and CVD risks. We show that, firstly, the link between low saturated fat diets and reduction in serum total cholesterol is strong. Secondly, reducing serum total cholesterol does not robustly have an impact on CVD hazards in the diet group. Lastly, the A/B test result suggests a more complicated relationship regarding abnormal diastolic blood pressure ranges caused by diets and how these might affect the associative link between the cholesterol level and heart disease risks. This study not only helps us to deeply analyze the MCE data but also, in combination with the long‐term FHS data, reveals possible complex relationships behind diets, risk factors, and heart disease.

Preventing Machines From Lying: Why Interdisciplinary Collaboration is Essential for Understanding Artefactual or Artefactually Dependent Expert Evidence

This article demonstrates a significantly different approach to managing probative risks arising from the complex and fast changing relationship between law and computer science. Law's historical problem in adapting to scientific and technologically dependent evidence production is seen less as a socio-techno issue than an ethical failure within criminal justice. This often arises because of an acceptance of epistemological incomprehension between lawyers and scientists. Something compounded by the political economy of criminal justice and safeguard evasion within state institutions. What is required is an exceptionally broad interdisciplinary collaboration to enable criminal justice decision-makers to understand and manage the risk of further ethical failure. If academic studies of law and technology are to address practitioner concerns, it is often necessary, however, to step down the doctrinal analysis to a specific jurisdictional level.

The Influence of Japanese Nuclear Sewage Pollution on China's Import and Export Trade

The Fukushima Daiichi disaster occurred in 2011, leading to ongoing concerns about contaminated water. China and Japan have a complex trade relationship, which makes any disruption important. This background sets the stage for exploring potential trade implications. The research will discuss how Japan's nuclear and water pollution affect China's import and export trade. First, we will cover the background of the accident at the Fukushima nuclear power plant in Japan and the atomic sewage treatment plan. Next, we will analyze the potential impact of this incident on China's import and export trade, covering trade relations, seafood, and other aspects. Finally, we will discuss China's measures and proposals to address this impact. In short, it is concluded that Sino-Japanese trade will be subject to inevitable fluctuations in the short and medium term, but in the long run, its transaction will still show a steady upward trend.

Female sex steroids and epilepsy: Part 2. A practical and human focus on catamenial epilepsy.

Catamenial epilepsy is the best described and most researched sex steroid-specific seizure exacerbation. Yet despite this there are no current evidence-based treatments, nor an accepted diagnostic tool. The best tool we currently have is tracking seizures over menstrual cycles; however, the reality of tracking seizures and menstrual cycles is fraught with challenges. In Part 1 of this two-part review, we outlined the often complex and reciprocal relationship between seizures and sex steroids. An adaptable means of tracking is required. In this review, we outline the extent and limitations of current knowledge on catamenial epilepsy. We use sample data to show how seizure exacerbations can be tracked in short/long and even irregular menstrual cycles. We describe how seizure severity, an often overlooked and underresearched form of catamenial seizure exacerbation, can also be tracked. Finally, given the lack of treatment options for females profoundly affected by catamenial epilepsy, Section3 focuses on current methods and models for researching sex steroids and seizures as well as limitations and future directions. To permit more informative, mechanism-focused research in humans, the need for both a consistent classification of catamenial epilepsy and an objective biomarker is highlighted.

The Impact of Public Debt on Economic Growth

One of the main areas of inquiry nowadays is how government debt influences the growth of the economy. Researchers have come to different conclusions regarding the manner in which public debt consequences growth in the economy. As a result, this essay examines how public debt affects an economy or a group of economies' ability to grow. The paper examines the effect of the federal debt on commercial growth by compiling and evaluating relevant research from 2018 to 2023. Threshold is a crucial piece of reference information for examining the process by which public debt has influenced expansion in the economy. The objective is to evaluate the consequence of debt incurred by governments on the development of the economy, determining whether it is favorable or adverse. If the ratio of debt to GDP falls below a certain threshold, it indicates that the public economy is positively contributing to economic growth. A higher threshold value also suggests that the economy can handle additional public responsibilities, increasing the likelihood of popular support for economic expansion. This implies that there exists a complex and nonlinear relationship.

StructSim: Meta-Structure-Based Similarity Measure in Heterogeneous Information Networks

Similarity measures in heterogeneous information networks (HINs) have become increasingly important in recent years. Most measures in such networks are based on the meta path, a relation sequence connecting object types. However, in real-world scenarios, there exist many complex semantic relationships, which cannot be captured by the meta path. Therefore, a meta structure is proposed, which is a directed acyclic graph of object and relation types. In this paper, we explore the complex semantic meanings in HINs and propose a meta-structure-based similarity measure called StructSim. StructSim models the probability of subgraph expansion with bias from source node to target node. Different from existing methods, StructSim claims that the subgraph expansion is biased, i.e., the probability may be different when expanding from the same node to different nodes with the same type based on the meta structure. Moreover, StructSim defines the expansion bias by considering two types of node information, including out-neighbors of current expanded nodes and in-neighbors of next hop nodes to be expanded. To facilitate the implementation of StructSim, we further designed the node composition operator and expansion probability matrix with bias. Extensive experiments on DBLP and YAGO datasets demonstrate that StructSim is more effective than the state-of-the-art approaches.

Democracy and planetary politics: Achille Mbembe and futures of digital citizenship education for life

ABSTRACT Building on critical, performative, and emancipatory visions for digital citizenship education, this paper analyzes social media within systems of global capitalism in a time of climate crisis, which not only introduce opaquely complex agencies but shape relations on a planetary scale. To reconceptualize digital citizenship education in a computational age, with consideration for just relations and the future health of the planet, this paper draws on the work of African philosopher Achille Mbembe, who articulates how plasticity among human and non-human agencies is being used within capitalist systems to take power over the living – a power that is shaped by colonial hierarchies. In place of individualism, Mbembe’s philosophy emphasizes the need for collective action to repair harms and work across differences towards protection and sharing of what we have in-common. Connecting his thinking to digital citizenship, there is potential for education to support decolonial disenclosure of social media’s capitalist capture and engage in deep historical learning towards the replacement of exclusionary, necropolitical forces with care and communing in support of life. This paper offers possibilities and provocations for digital citizenship education, inviting readers to respond in their own contexts in ways that consider our planetary relations.

Biolinguistic graph fusion model for circRNA-miRNA association prediction.

Emerging clinical evidence suggests that sophisticated associations with circular ribonucleic acids (RNAs) (circRNAs) and microRNAs (miRNAs) are a critical regulatory factor of various pathological processes and play a critical role in most intricate human diseases. Nonetheless, the above correlations via wet experiments are error-prone and labor-intensive, and the underlying novel circRNA-miRNA association (CMA) has been validated by numerous existing computational methods that rely only on single correlation data. Considering the inadequacy of existing machine learning models, we propose a new model named BGF-CMAP, which combines the gradient boosting decision tree with natural language processing and graph embedding methods to infer associations between circRNAs and miRNAs. Specifically, BGF-CMAP extracts sequence attribute features and interaction behavior features by Word2vec and two homogeneous graph embedding algorithms, large-scale information network embedding and graph factorization, respectively. Multitudinous comprehensive experimental analysis revealed that BGF-CMAP successfully predicted the complex relationship between circRNAs and miRNAs with an accuracy of 82.90% and an area under receiver operating characteristic of 0.9075. Furthermore, 23 of the top 30 miRNA-associated circRNAs of the studies on data were confirmed in relevant experiences, showing that the BGF-CMAP model is superior to others. BGF-CMAP can serve as a helpful model to provide a scientific theoretical basis for the study of CMA prediction.

Collaboration in a Partnership for Primary Health Care: A Case Study From Papua New Guinea.

In low- and middle-income countries, public-private partnerships (PPPs) are often used to support the delivery of primary health care (PHC). We explore the processes of collaboration in a corporate social responsibility investment in PHC that was delivered through a PPP model in Western Province, Papua New Guinea, in 2009-2018 to strengthen PHC services. Qualitative interviews were conducted with stakeholders in the PPP (N=20). Key program documents were also reviewed. Data were coded using a general inductive approach, and Actor-Network Theory (ANT) was used to frame the analysis. Four dominant themes emerged: (1) interpersonal relationships accelerate collaboration, (2) collaboration requires time, (3) formal governance structures encourage collaboration, and (4) internal change disrupts collaboration. The research provides insight into the role of collaboration for more efficient, effective, and impactful design and implementation of PPP for PHC. It makes suggestions for how ANT may be used when designing PPPs and for the ongoing management of relationships between partners. We found ANT to be a useful framework to conceptualize the complex dynamics between the "actors" within the PPP and to identify opportunities for improvement where structural changes may be made to circumvent issues that may compromise effective collaboration. Collaboration is key to the success of PPPs for PHC. We found collaboration is not formulaic but is driven by actors, relationships, time, and governance. ANT can assist in designing, understanding, and managing the complex relationships between stakeholders of a PPP, who often come with diverse agendas, experiences, values, and perspectives.

Modeling and Optimization of Triticale Wort Production Using an Artificial Neural Network and a Genetic Algorithm.

Triticale grain, a wheat-rye hybrid, has been reported to comply very well with the requirements for modern brewing adjuncts. In this study, two triticale varieties, in both unmalted and malted forms, were investigated at various ratios in the grist, applying different mashing regimes and concentrations of the commercial enzyme Shearzyme® 500 L with the aim of evaluating their impact on wort production. In order to capture the complex relationships between the input (triticale ratio, enzyme ratio, mashing regime, and triticale variety) and output variables (wort extract content, wort viscosity, and free amino nitrogen (FAN) content in wort), the study aimed to implement the use of artificial neural networks (ANNs) to model the mashing process. Also, a genetic algorithm (GA) was integrated to minimize a specified multi-objective function, optimizing the mashing process represented by the ANN model. Among the solutions on the Pareto front, one notable set of solutions was found with objective function values of 0.0949, 0.0131, and 1.6812 for the three conflicting objectives, respectively. These values represent a trade-off that optimally balances the different aspects of the optimization problem. The optimized input variables had values of 23%, 9%, 1, and 3 for the respective input variables of triticale ratio, enzyme ratio, mashing regime, and triticale variety. The results derived from the ANN model, applying the GA-optimized input values, were 8.65% w/w for wort extract content, 1.52 mPa·s for wort viscosity, and 148.32 mg/L for FAN content in wort. Comparatively, the results conducted from the real laboratory mashing were 8.63% w/w for wort extract content, 1.51 mPa·s for wort viscosity, and 148.88 mg/L for FAN content in wort applying same input values. The presented data from the optimization process using the GA and the subsequent experimental verification on the real mashing process have demonstrated the practical applicability of the proposed approach which confirms the potential to enhance the quality and efficiency of triticale wort production.

Resilience and associated factors in orphaned and separated adolescents in Kenya: Understanding the relationship with care environment and HIV risks

ABSTRACT Orphans are at higher risk of HIV infection and several important HIV risk factors than non-orphans; however, this may be due to a combination of related social, psychological, and economic factors, as well as care environment, rather than orphan status alone. Understanding these complex relationships may aid policy makers in supporting evidence-based, cost-effective programming for this vulnerable population. This longitudinal study uses a causal effect model to examine, through decomposition, the relationship between care environment and HIV risk factors in orphaned and separated adolescents and youths (OSAY) in Uasin Gishu County, Kenya; considering resilience, social, peer, or family support, volunteering, or having one's material needs met as potential mediators. We analysed survey responses from 1105 OSAY age 10–26 living in Charitable Children's Institutions (CCI) (orphanages) and family-based care settings (FBS). Follow-up time was 7–36 months. Care in CCIs (vs. FBS) was associated with a decreased likelihood of engaging in forced, exchange, and consensual sex. Excess relative risks (ERR) attributable to the indirect pathway, mediation, or interaction were not significant in any model. Care environment was not statistically associated with differences in substance use. Our findings support the direct, unmediated, association between institutional care and HIV risk factors.

2D:4D ratio, alexithymia, impulsivity, aggression, and ADHD in men with opioid and methamphetamine use disorders: A comparative analysis with healthy controls

Substance use disorder is an important mental health problem with increasing frequency. This study aims to investigate the 2D:4D ratio, Dr-I values (right-left differences), alexithymia, aggression, impulsivity, and attention deficit hyperactivity disorder (ADHD) symptoms in male patients with methamphetamine use disorder (MUD), opioid use disorder (OUD), and healthy controls. The study included a total of 57 male patients diagnosed with OUD, 42 male patients diagnosed with MUD, and 72 healthy male controls. The study indicated that the OUD and MUD patients were significantly different from the healthy controls in terms of the 2D:4D ratio, alexithymia, impulsivity, aggression, and ADHD. Compared to healthy controls, 2D:4D ratios, and Dr-I values were lower in OUD and MUD patients. OUD and MUD patients had higher scores of alexithymia, impulsivity, aggression, and ADHD compared to healthy controls. There were statistically significant negative correlations between the right-hand 2D:4D ratios and the ADHD, impulsivity, alexithymia and aggression scores and a significant negative correlation between Dr-I scores and the impulsivity scores of MUD and OUD patients. These findings contribute to the existing literature examining the complex relationship between prenatal testosterone exposure, substance use disorders and related psychological traits.

Creating a Decision-Making Program for the Decline Period of Emergency Events

Based on the analysis of the life cycle theory of emergencies, an emergency decision-making method basedon linguistic information and ordering organization is proposed to solve the problem of emergency plan selection duringthe decline period of emergencies. Firstly, language decision theory is introduced to determine the relative importance ofdecision members and the weight of key indicators. Secondly, the extended weighted average operator is used to aggregatethe preference information of alternative solutions and the relative importance of decision members. On this basis, theranking organization method is introduced to deal with the complex relationship between different key indicators andalternative solutions. Finally, the net flow of each alternative is ranked to determine the optimal one. The feasibility andeffectiveness of this method are verified by taking the operation recovery after a fire in a logistics park as an example.

Multivariate analysis of yield and quality traits in sweet potato genotypes (Ipomoea batatas L.)

Improving sweet potato traits and genotype selection based on specific traits of interest is crucial to motivate farmers and rural households to enhance their livelihoods by increasing income and improving nutritional intake. This research offers a thorough analysis of genotype selection guidelines considering multiple traits of sweet potatoes, including vine length (VL), average root length (ARL), average root diameter (ARD), marketable storage root number (MRN), marketable storage root yield (MRY), non-marketable root number (NMRN), non-marketable root weight (NMRW), dry weight of root (DW), beta-carotene content (BC), vitamin C content (VC), starch content (ST), total sugar content (TS), total soluble solid (TSS), and titratable acidity (ACD). The findings showed that the genotype Moz1.15 achieved the highest MRY at 46.46 t/ha, H5.ej.10 displayed the top BC level at 48.94 mg/100 g, and Moz1.9 had the highest VC (23.89 mg/100 g). Furthermore, there was a significant positive correlation between VL and DW, TSS, ST, TS, and VC. Additionally, ARL, ARD, and MRN showed significant positive correlations with MRY. The principal component analysis (PCA) biplot further confirmed how traits were connected with each other. There were four separate clusters of genotypes found, each having its specific significant trait representation. The distance between these clusters ranged from 21.79 to 54.13, while within each cluster, the distances varied from 0 to 17.45. The factor analysis using the multi-trait genotype-ideotype distance index (MGIDI) showed that sweet potato traits played a substantial role in three distinct factors during both growing seasons (2020-21 and 2021-22). This allowed for the selection of genotypes capable of achieving favorable genetic gains ranging from 1.86% to 75.4%, alongside a broad-sense heritability (h2b) spanning from 64.9% to 99.8%. Through the application of the MGIDI index, a number of potential genotypes were identified, among which BARI Mistialu-12 and H9.7.12 stood out as consistently chosen across both years. These promising genotypes exhibited high performance while displaying their strengths and weaknesses in terms of their contributions to the identified factors. This thorough research offers valuable insights into the complex relationship between different traits in sweet potatoes and how genotype selection based on a multi-trait index can significantly assist in shaping upcoming breeding and cultivation strategies.

An adaptive Copula function-based framework for fault detection in semiconductor wafer fabrication

Wafer fabrication is a complex manufacturing system. Timely identification of the key influencing factors associated with the cause of defects is critical to wafer yield enhancement. However, the massive wafer fabrication process dataset has various data types, uncertain data distribution, and complex correlation relationships. These issues make the statistical sense of correlation association measures, and fault detection (FD) methods face many challenges. Therefore, this study proposes an adaptive Copula function-based framework for FD in semiconductor wafer fabrication. Firstly, the marginal distribution of the wafer fabrication process data was determined. After that, a joint distribution model based on the Copula function was designed for the nonlinear relationship and the nonnormal distribution characteristics. Based on the model mentioned above, the traditional Copula function selection method has a high-dimensional spatial failure problem based on the Euclidean distance theory. Therefore, a high-dimensional spatial distance measurement method based on improved kernel distance measurement (IKDM) is designed to realize the adaptive selection of Copula functions. Afterward, the key influencing factors associated with wafer yield are identified through feature sensitivity analysis and validated with multiple prediction models for comparison. Finally, a case study using actual data from a semiconductor wafer fabrication system is used to validate the effectiveness of the proposed methodological framework.

Identification of intercity ecological synergy regions and measurement of the corresponding policy network structure: A network analysis perspective

The development and governance of modern cities have gradually broken through the boundaries of administrative units. In recent times, with intercity ecological space becoming the focus of a series of development and conservation actions, identifying the intercity ecological synergy regions (maintaining connectivity within the ecological space to ensure the continuity of species migration and landscape) and realizing its collaborative governance has become extremely important to maintain sustainable regional development. Cross-border ecological governance involves many actors and forms a complex relationship network. At present, there are few quantitative evaluation methods for this problem, which limits our ability to manage the ecological space between cities. This paper uses the network method to establish and analyze the related ecological and policy networks in Shenzhen. The study results show that the ecological synergy regions are locally concentrated along the fringes of Shenzhen City, and the links between different levels of actors in the policy network are relatively weak. The action objects of ecological governance designated by different cities form convergence or divergence patterns, making it challenging to organize spatially integrated actions among different cities. Finally, suggestions for structural optimization are proposed concerning the policy network associated with the ecological synergy regions from the aspects of spatial implementation pathways and planning strategies.

The Impact of Vegetation Types on Soil Hydrological and Mechanical Properties in the Hilly Regions of Southern China: A Comparative Analysis

Background: Vegetation roots are considered to play an effective role in controlling soil erosion by benefiting soil hydrology and mechanical properties. However, the correlation between soil hydrology and the mechanical features associated with the variation root system under different vegetation types remains poorly understood. Methods: We conducted dye-tracer infiltration to classify water flow behavior and indoor experiments (including tests on soil bulk density, soil organic carbon, mean weight diameter, soil cohesion, root density, etc.) to interpret variation patterns in three forest systems (coniferous and broad-leaved mixed forest, CBF; coniferous forest, CF; Phyllostachys edulis, PF) and fallow land (FL). Results: Based on the soil dye-tracer infiltration results, the largest dyeing area was observed in CF (36.96%), but CF also had the lowest infiltration rate (60.3 mm·min−1). The soil under CBF had the highest shear strength, approximately 25% higher than other vegetation types. CF exhibited the highest aggregate stability, surpassing CBF by 98.55%, PF by 34.31%, and FL by 407.41%, respectively. Additionally, PF forests showed the greatest root biomass and length. The results of correlation analysis and PCA reveal complex relationships among hydrological and mechanical soil traits. Specifically, soil cohesion does not exhibit significant correlations with hydrological traits such as the dyeing area, while traits like MWD and PAD show either positive or negative associations with hydrological traits. Root traits generally exhibit positive relationships with soil mechanical traits, with limited significant correlations observed with hydrological traits. Conversely, we found that root biomass contributes significantly to the dyeing area (accounting for 51.48%). Conclusions: Our findings indicate that the reforestation system is a successful approach for conserving water and reducing erosion by increasing soil-aggregated stability and shear strength, causing water redistribution to be more homogenized across the whole soil profile.

Positive Psychotherapy to Support Medical Student Education

The purpose of this report is to report on the benefits of using the method of Nossrat Peseschkian, modified by Professor Ivanka Boncheva and applied by her students in a medical environment and especially in the training of medical students. More than ten years of experience working with medical students and applying the postulates of positive psychotherapy help to acquire skills and understand the difference between simply asking questions and getting answers and the skill of “ therapeutic listening”. Understanding transference and countertransference relationships. The concept of the psychosomatic arc is also used to explain the complex relationships between illness and emotion.