Related Complications Research Articles

Governments and the Private Energy Sector: Analysis of Energy Sector and Relationship between State

A symbiotic relationship exists between the state and the energy sector that often leads to conflicting relationships between the two. The best example of this complicated relationship is between the state and free markets is the case of the United States, European Union, and Global Energy Sector. The decline in the gas import—due to sanctions placed on the Russian Federation—to Europe from Russia, along with other counties that import gas from Russia, has negatively affected the economies of European counties. The Russian government has restructured the exporting of gas to other counties in order to continue to sustain companies’ growth. This literature review will analyze how sanctions against Russian Federation have affected the energy market and how it will affect entire energy markets.

Molecular Design and High‐Throughput Virtual Screening of Electron Donor and Non‐fullerene Acceptors for Organic Solar Cells

The complicated trilateral relationships among molecular structures, properties, and photovoltaic performances of electron donor and acceptor materials hinder the rapid improvement of power conversion efficiency (PCE) of organic solar cells (OSCs). Herein, the database of 310 donor and non‐fullerene acceptor pairs is constructed and 39 molecular structure descriptors are selected. Four kinds of machine learning (ML) algorithms random forest (RF), extra trees regression, gradient boosting regression trees, and adaptive boosting are applied to predict photovoltaic parameters. The coefficient of determination, Pearson correlation coefficient, mean absolute error, and root mean square error are adopted to evaluate ML performance. The results show that the RF model exhibits the best prediction accuracy. The Gini important analysis suggests the fused ring and aromatic heterocycles are critical fragments in determining PCE. The molecular unit sets are constructed by cutting each donor and acceptor molecules in database. The 31 752 D‐π‐A‐π type donor molecules and 5 455 164 A‐π‐D‐π‐A type acceptor molecules are designed by recombination of molecular units, and 173 212 367 328 donor–acceptor pairs are generated by combining the newly designed donor and acceptor molecules. Based on the predicted PCE using the trained RF model, 42 donor–acceptor pairs exhibit the predicted PCE > 16%, in which the highest PCE is 16.24%.

Differences in stomatal sensitivity to CO2 and light influence variation in water use efficiency and leaf carbon isotope composition in two genotypes of the C4 plant Zea mays.

The ratio of net CO2 uptake (Anet) and stomatal conductance (gs) is an intrinsic measurement of leaf water use efficiency (WUEi) however its measurement can be challenging for large phenotypic screens. Measurements of leaf carbon isotope composition (δ13Cleaf) may be a scalable tool to approximate WUEi for screening because it in part reflects the competing influences of Anet and gs on the CO2 partial pressure (pCO2) inside the leaf over time. However, in C4 photosynthesis the CO2 concentrating mechanism complicates the relationship between δ13Cleaf and WUEi. Despite this complicated relationship, several studies have shown genetic variation in δ13Cleaf across C4 plants. Yet there has not been a clear demonstration of whether Anet or gs are the causal mechanisms controlling WUEi and δ13Cleaf. Our approach was to characterize leaf photosynthetic traits of two Zea mays recombinant inbred lines (Z007E0067 and Z007E0150) which consistently differ for δ13Cleaf even though they have minimal confounding genetic differences. We demonstrate that these two genotypes contrasted in WUEi driven by differences in the speed of stomatal responses to changes in pCO2 and light that lead to unproductive leaf water loss. These findings provide support that differences in δ13Cleaf in closely related genotypes do reflect greater WUEi and further suggests that differences in stomatal kinetic response to changing environmental conditions is a key target to improve WUEi.

Pattern Identification and Acupuncture Prescriptions Based on Real-World Data Using Artificial Intelligence

In traditional East Asian medicine, acupuncture practitioners gather clinical data, identify patterns, and choose appropriate acupoints. The pattern identification procedure is crucial for clinical diagnosis and acupuncture treatment. Understanding the pattern identification process, i.e. gathering and synthesizing clinical information from patient signs and symptoms, is crucial for characterizing the complicated relationships between symptoms and acupoints. Here, we briefly overview recent studies describing the use of a bodily sensation map to identify spatial patterns of “acupoint indications”, an artificial neural network model to characterize the rules connecting symptoms with acupoints, and medical data extrapolated from case reports to reveal associations between diagnoses and acupoint prescriptions. We also propose a method based on pattern identification to optimize acupoint selection for treatment. Artificial intelligence has substantially advanced traditional East Asian medicine by facilitating decision-making and aids understanding of clinical decision-making as it relates to acupuncture treatment.

Capabilities of Microbial Consortia from Disparate Environment Matrices in the Decomposition of Nature Organic Matter by Biofiltration

Dissolved organic matter (DOM) plays a pivotal role in drinking water treatment, influencing the performance of unit processes and final water quality (e.g. disinfection byproduct risk). Biofiltration is an effective method of reducing DOM, but currently lacks a comprehensive appreciation of the association between microbial profiles and biofiltration performance. In this study, bench-scale biofiltration units inoculated with microbial consortia from river and soil matrices were operated successively for comparing their efficacy in terms of DOM removal. The results showed that biofiltration units receiving soil microbes were significantly superior (p < 0.05) to those receiving river inoculated microbes in terms of decomposing DOM recalcitrant fractions and reducing DBP formation potential, resulting in DOC and DBP precursor removals of up to 58.4 % and 87.9 %, respectively. Characterization of the taxonomic composition revealed that differences in the microbial assembly of the two biofilter groups were subject to deterministic rather than stochastic factors. Furthermore, more complicated interspecific relationships and niche structures in soil inoculated biofilters were deciphered by co-occurrence network, providing a plausible profile on a taxonomic division of labor in DOM stepwise degradation. Accordingly, the contribution of microbial compositions was found to be of greater importance than the GAC mass and biomass attached to the media. Thus, this study has advanced the understanding of microbial-mediated DOM decomposition in biofiltration, and also provided a promising strategy for enhancing the process for water use via developing appropriate engineered consortia of bacteria.

Integrated modeling of crowd groups based on spatial distribution of members

An integrated framework supporting both leader-follower and leaderless groups is proposed. The concept of subgroup is supported so that chains of groups can exist. This allows to model complicated group relationships in crowd simulations. To model group related dynamics, rules are proposed. A detailed explanation of implementation of these rules is given. To validate the proposed framework, two sets of numerical experiments are conducted, using experimental data collected by previous researchers. In one set of experiments, small leader-follower groups without subgroups are considered. In the other set, groups are leaderless and can be small or large with zero or more subgroups. The impact of crowd groups on fundamental diagrams is also experimented. The conducted experiments indicate that the proposed framework can reproduce results that are quite comparable to practical values, thus the framework’s efficacy is validated within a certain range.

Stubble burning: What determines this fire?

Despite the recognized impact of agricultural fires on air pollution and public health globally, with significant consequences observed in regions such as China, India, and Turkiye, there remains a critical gap in understanding their underlying causes. This study aims to identify the key factors influencing agricultural fires in Turkiye between 2012 and 2021. This research conducts a thorough district-level evaluation using extensive micro-level data, remote sensing datasets, and robust methodologies, including fixed effects and spatial panel data methods. The study finds that variables such as the cultivation of second-crop corn and regional conflicts positively correlate with fire occurrences. In contrast, factors like cotton cultivation, livestock presence, and forested areas are inversely related to fire incidents. These findings underscore the complicated relationship between agricultural practices, environmental factors, and socio-economic conditions influencing fire occurrences. The study's outcomes offer significant policy implications, highlighting the necessity for customized strategies to curb agricultural fires. These strategies encompass promoting alternative crop patterns, tackling socio-economic determinants, and accommodating regional specificities.

The Impact Of Innovation To Company Policy Through Operation Research

In the ever-evolving corporate landscape, understanding how innovation influences company policies is significant for organizational success. This research investigates the complicated relationship between innovation—specifically, organizational, technological, and process innovation—and the management of critical policy domains: HR policies, compliance policies, and technological adoption policies. Leveraging surveyed data analysis, this study uncovers profound insights into the dynamic interplay between innovation strategies and policy adaptations. The study's methodology used quantitative data analysis, utilizing surveys and impact ratings to measure the influence of various innovation types on company policies. The analysis indicates that organizational innovation shows substantial influence over HR policies, underscoring the necessity of aligning HR management with the growing culture and structure of organizations undergoing transformation. Technological innovation shows as a significant motivate for adaptations in compliance policies, highlighting the critical importance of policy alignment with dynamic regulatory landscapes. Process innovation, although exhibiting a moderate impact, show cases its practical relevance in enhancing operational efficiency across all policy areas. This research extends existing literature by providing empirical evidence of innovation's multifaceted impact on policies and introduces novel perspectives, especially the influence of technological innovation on compliance policies. The implications of this study contribute in academia and practice alike, emphasizing the significance of proactive policy alignment with innovation strategies. As organizations increasingly harness innovation to remain competitive and adaptive, the research findings offer valuable guidance for optimizing the synergy between innovation initiatives and policy management, ensuring regulatory compliance, and enhancing operational efficiency.

Synchronously in vivo real-time monitoring bacterial load and temperature with evaluating immune response to decipher bacterial infection.

Determining the precise course of bacterial infection requires abundant in vivo real-time data. Synchronous monitoring of the bacterial load, temperature, and immune response can satisfy the shortage of real-time in vivo data. Here, we conducted a study in the joint-infected mouse model to synchronously monitor the bacterial load, temperature, and immune response using the second near-infrared (NIR-II) fluorescence imaging, infrared thermography, and immune response analysis for 2 weeks. Staphylococcus aureus (S. aureus) was proved successfully labeled with glucose-conjugated quantum dots in vitro and in subcutaneous-infected model. The bacterial load indicated by NIR-II fluorescence imaging underwent a sharp drop at 1 day postinfection. At the same time, the temperature gap detected through infrared thermography synchronously brought by infection reached lowest value. Meanwhile, the flow cytometry analysis demonstrated that immune response including macrophage, neutrophil, B lymphocyte, and T lymphocyte increased to the peak at 1 day postinfection. Moreover, both M1 macrophage and M2 macrophage in the blood have an obvious change at ~1 day postinfection, and the change was opposite. In summary, this study not only obtained real-time and long-time in vivo data on the bacterial load, temperature gap, and immune response in the mice model of S. aureus infection, but also found that 1 day postinfection was the key time point during immune response against S. aureus infection. Our study will contribute to synchronously and precisely studying the complicated complex dynamic relationship after bacterial infection at the animal level.

UNLOCKING CONSUMER BEHAVIOR IN AGE OF ARTIFICIAL INTELLIGENCE: A STUDY ON TAILORED DATA, PRIVACY RESERVATIONS, AND PURCHASE INTENTIONS

This study investigates the complicated relationship between consumers and artificial intelligence (AI), precisely exploring the impact of tailored data (AI) on purchasing decisions. Drawing from a diverse sample of social media users in top five HEC ranked universities, the study investigates how factors such as privacy reservations and positive past experiences intersect with AI-driven personalization, impact consumer attitudes and willingness to make purchases online. The researcher applied survey method to collect data from 346 participants, primarily social media users, with the focus on those regularly engaging with the social media advertising and AI-based applications. This research tested three hypotheses wherein findings show significant influence of tailored data on consumer purchasing willingness and positive past experiences with AI ads. The privacy concerns, however, were found to have an insignificant impact on the online buying decisions. In this linking, these insights contribute to broader discourse on AI's role in consumer behavior, offering practical implications for various businesses navigating integration of artificial intelligence technologies in marketing strategies.

PrimitiveNet: decomposing the global constraints for referring segmentation

In referring segmentation, modeling the complicated constraints in the multimodal information is one of the most challenging problems. As the information in a given language expression and image becomes increasingly abundant, most of the current one-stage methods that directly output the segmentation mask encounter difficulties in understanding the complicated relationships between the image and the expression. In this work, we propose a PrimitiveNet to decompose the difficult global constraints into a set of simple primitives. Each primitive produces a primitive mask that represents only simple semantic meanings, e.g., all instances from the same category. Then, the output segmentation mask is computed by selectively combining these primitives according to the language expression. Furthermore, we propose a cross-primitive attention (CPA) module and a language-primitive attention (LPA) module to exchange information among all primitives and the language expression, respectively. The proposed CPA and LPA help the network find appropriate weights for primitive masks, so as to recover the target object. Extensive experiments have proven the effectiveness of our design and verified that the proposed network outperforms current state-of-the-art referring segmentation methods on three RefCOCO datasets.

Neighborhood inequality in government fines: The case of parking tickets in 16 U.S. cities

A common way citizens interact with the legal system is through parking tickets. Existing literature suggests that there is a relationship between parking tickets and neighborhood characteristics, particularly higher rates in economically vulnerable and historically marginalized neighborhoods. The current research is limited to a select number of cities. We seek to examine if these relationships exist at a larger geographical scale. Specifically, we investigate the spatial distribution of parking tickets across 16 U.S. cities. Employing multivariate regression models, we examine the relationship between neighborhood parking ticket rates and racial/ethnic and socioeconomic composition at the block group level. We found that percent Hispanic and percent poor are positively associated with total parking tickets, and parking tickets for violations, days of the week, and times of the day that may be more likely to be issued to neighborhood residents. While percent Black exhibits no association with total parking tickets, it is positively associated with tickets issued by police officers, revealing the complicated relationship between infrastructure, policing, and race.

Exploring the influence of workplace culture on employee satisfaction in Star hotels of Delhi

This study explores the complicated relationship between employee satisfaction, work life balance and workplace culture in the context of Delhi's fivebstar hotels. Given the fierce competition and focus on providing outstanding guest experiences in the hospitality sector, it is critical to comprehend how workplace culture contributes to employee satisfaction. Using a mixedbmethods approach, information was gathered from staff members including top level management to lower level staff at Delhi's five star hotels. A well-structured questionnaire has been used for collection of data. The study looks at important aspects of workplace culture, such as work life balance initiatives, freedom of speech, communication techniques, employee recognition programs, and organizational ideals, and how this affect worker satisfaction. According to preliminary research, having a strong workplace culture with open communication, encouraging leadership, chances for professional development, and a friendly work atmosphere greatly raises levels of satisfaction. The study provides insightful information about the processes through which workplace culture shapes employee perceptions and experiences, emphasizing the significance of fostering a positive and inclusive culture in star hotels to improve employee satisfaction, retention, and overall organizational performance. On the other hand, negative aspects like a lack of communication, hierarchical barriers, inadequate recognition, and a poor work life balance negatively influence job satisfaction among hotel employees has been incorporated.

Untying black boxes with clustering-based symbolic knowledge extraction

Machine learning black boxes, exemplified by deep neural networks, often exhibit challenges in interpretability due to their reliance on complicated relationships involving numerous internal parameters and input features. This lack of transparency from a human perspective renders their predictions untrustworthy, particularly in critical applications. In this paper, we address this issue by introducing the design and implementation of CReEPy, an algorithm for symbolic knowledge extraction based on explainable clustering. Specifically, CReEPy leverages the underlying clustering performed by the ExACT or CREAM algorithms to generate human-interpretable Prolog rules that mimic the behaviour of opaque models. Additionally, we introduce CRASH, an algorithm for the automated tuning of hyper-parameters required by CReEPy. We present experiments evaluating both the human readability and predictive performance of the proposed knowledge-extraction algorithm, employing existing state-of-the-art techniques as benchmarks for comparison in real-world applications.

BMI mediates the association of serum uric acid with bone health: a cross-sectional study of the National Health and Nutrition Examination Survey (NHANES)

BackgroundThe associations between serum uric acid and osteoporosis or osteopenia remain controversial, and few studies have explored whether BMI acts as a mediators in the association between the SUA and OP/ osteopenia.ObjectiveTo explore the relationship between serum uric acid and osteoporosis or osteopenia among US adults.MethodsA cross-sectional study was conducted to examine the association between serum uric acid and osteoporosis or osteopenia from four cycles of NHANES. Binary logistic regression models and restricted cubic spline models were used to evaluate the association between serum uric acid and osteoporosis or osteopenia, and interaction analysis was used to test the differences between subgroups. Mediation analysis was utilized to investigate whether BMI acts as a mediator in the association between SUA and OP/ osteopenia.Results12581 participants aged ≥ 18 years were included. A U-shape nonlinear relationship between SUA and osteoporosis or osteopenia in all people was found (P < 0.0001, P for nonlinear = 0.0287). There were significant interactions in age subgroups (P for interaction = 0.044), sex subgroups (P for interaction = 0.005), and BMI subgroups (P for interaction = 0.017). We further assessed the subgroups and found the optimal range of serum uric acid levels with a lower risk of osteoporosis or osteopenia was 357–535 µmol/L in males, 327–417 µmol/L in people aged ≥ 50 years, above 309 µmol/L in people aged < 50 years, 344–445 µmol/L in people with BMI ≥ 30, and above 308 µmol/L in people with BMI < 30. BMI fully mediated the association of SUA and OP/osteopenia, with a value of -0.0024(-0.0026–-0.0021). These results were robust in sensitivity analyses.ConclusionsA complicated relationship between SUA and bone health in different populations was observed. Maintaining SUA within a specific range may be beneficial to bone health. In addition, BMI may play an important role in the association between SUA and bone health, but considering the limitations of this study, further prospective research is required.

Branched-Chain Amino Acids in Liver Diseases: Complexity and Controversy.

Branched-chain amino acids (BCAAs), as essential amino acids, engage in various physiological processes, such as protein synthesis, energy supply, and cellular signaling. The liver is a crucial site for BCAA metabolism, linking the changes in BCAA homeostasis with the pathogenesis of a variety of liver diseases and their complications. Peripheral circulating BCAA levels show complex trends in different liver diseases. This review delineates the alterations of BCAAs in conditions including non-alcoholic fatty liver disease, hepatocellular carcinoma, cirrhosis, hepatic encephalopathy, hepatitis C virus infection, and acute liver failure, as well as the potential mechanisms underlying these changes. A significant amount of clinical research has utilized BCAA supplements in the treatment of patients with cirrhosis and liver cancer. However, the efficacy of BCAA supplementation in clinical practice remains uncertain and controversial due to the heterogeneity of studies. This review delves into the complicated relationship between BCAAs and liver diseases and tries to untangle what role BCAAs play in the occurrence, development, and outcomes of liver diseases.

The Evolution of Board-Certified Emergency Physicians and Staffing of Emergency Departments in Israel.

Emergency medicine (EM) was recognized as a specialty in Israel in 1999. Fifty-nine of the 234 (25%) attending physicians working in emergency departments (ED) nationwide in 2002 were board-certified emergency physicians (EP). A 2012 study revealed that 123/270 (45%) of ED attendings were EPs, and that there were 71 EM residents. The EPs primarily worked midweek morning shifts, leaving the EDs mostly staffed by other specialties. Our objective in this study was to re-evaluate the EP workforce in Israeli EDs and their employment status and satisfaction 10years after the last study, which was conducted in 2012. We performed a three-part, prospective cross-sectional study: 1) a survey, sent to all EDs in Israel, to assess the numbers, level of training, and specialties of physicians working in EDs; 2) an anonymous questionnaire, sent to EPs in Israel, to assess their demographics, training, employment, and work satisfaction; and 3) interviews of a convenience sample of EPs analyzed by a thematic approach. There were 266 board-certified EPs, 141 (53%) of whom were employed in EDs full-time or part-time. Sixty-two non-EPs also worked in EDs. The EPs were present in the EDs primarily during weekday morning shifts. There were 273 EM residents nationwide. A total of 101 questionnaires were completed and revealed that EPs working part-time in the ED worked fewer hours, received higher salaries, and had more years of experience compared to EPs working full time or not working in the ED. Satisfaction correlated only with working part time. Meaningful work, diversity, and rewarding relationships with patients and colleagues were major positive reasons for working in the ED. Feeling undervalued, carrying a heavy caseload, and having complicated relationships with other hospital departments were reasons against working in the ED. Our study findings showed an increase in the number of trained and in-training EPs, and a decrease in the percentage of board-certified EPs who persevere in the EDs. Emergency medicine in Israel is at a crossroads: more physicians are choosing EM than a decade ago, but retention of board-certified EPs is a major concern, as it is worldwide. We recommend taking measures to maintain trained and experienced EPs working in the ED by allowing part-time ED positions, introducing dedicated academic time, and diversifying EP roles, functioning, and work routine.

Digital literacy and financial inclusion in virtual economies: Analyzing the role of education in shaping attitudes toward cryptocurrencies

The development of cryptocurrencies has altered traditional economic institutions and financial inclusion. This study investigates the complicated relationships between education, digital literacy, cryptocurrency views, and financial inclusion in virtual economies, addressing a key gap in comprehending the disruptive dynamics of emerging financial technologies. Undergraduate, postgraduate, and PhD students from private universities in Delhi NCR were polled using a standardized questionnaire with a 5-point Likert scale. According to the study, higher education boosts digital literacy, cryptocurrency opinions and financial inclusion. Although education is critical for financial inclusion, the link between positive attitudes about cryptocurrencies and financial inclusion remains uncertain. As virtual currencies expand, these results urge policymakers and educators to concentrate on digital literacy and positive attitudes in order to promote inclusive financial ecosystems.

British Colonial Education in the Indian Subcontinent (1757-1858): Attitude of Muslims

This article investigates the British colonial education imparted in the Indian Subcontinent during the priod 1757-1858, keeping in view the Muslim perspective. The objective is to comprehend the complex and diverse ways in which Muslims responded to British education throughout the colonial era, which were influenced by revolutionary and reactionary movements ignited by British domination. Muslims developed a complicated relationship with colonial education as a result of tensions heightened by the repression of indigenous concerns and the disregard for the acquisition of Islamic values. This study aims to comprehend the purpose of British education in the Indian subcontinent, analyses competing Muslim ideologies surrounding colonial education, uncovers factors influencing Muslim attitudes toward education during the British rule, and also analyses the differences in how different groups of Muslims responded to British education throughout British colonialism. For this purpose, this study uses a qualitative research methodology to investigate historical sources and determine the factors contributing to the hostile and defensive views of Indian Muslims regarding colonial education. The findings demonstrate the complex effects of British colonial policies, underlining the conflicts brought about by the disregard for Islamic knowledge and the fallout from the Sepoy Revolution. The study depicts varying responses to western education and competing ideologies among Muslims in India and underscores the significance of placing Muslim views in the larger sociopolitical context of colonial India. It also emphasises how critical it is to comprehend Muslim viewpoints on colonial education to dispel myths and promote a nuanced awareness of the historical legacies that continue to influence educational practices in post-colonial cultures.

Universal materials model of deep-learning density functional theory Hamiltonian

Realizing large materials models has emerged as a critical endeavor for materials research in the new era of artificial intelligence, but how to achieve this fantastic and challenging objective remains elusive. Here, we propose a feasible pathway to address this paramount pursuit by developing universal materials models of deep-learning density functional theory Hamiltonian (DeepH), enabling computational modeling of the complicated structure–property relationship of materials in general. By constructing a large materials database and substantially improving the DeepH method, we obtain a universal materials model of DeepH capable of handling diverse elemental compositions and material structures, achieving remarkable accuracy in predicting material properties. We further showcase a promising application of fine-tuning universal materials models for enhancing specific materials models. This work not only demonstrates the concept of DeepH’s universal materials model but also lays the groundwork for developing large materials models, opening up significant opportunities for advancing artificial intelligence-driven materials discovery.