One of the recurring economic issues is the challenge of poverty faced by various countries worldwide, including Indonesia. Poverty is evident through a high number of unemployed individuals. Those living in poverty and those facing unemployment generally encounter difficulties in accessing job opportunities, limited business development, income disparities, and lack of workplace protections, especially for women. These issues also affect economically disadvantaged groups in society. This study aims to analyze the impact of job opportunities and minimum wage on unemployment and poverty rates in the Regencies/Cities of Bali Province. The research utilizes secondary data from the period 2012-2021. The study covers 9 Regencies/Cities within Bali Province. Path Analysis is the employed analytical technique. The findings of this research indicate that: 1) Job opportunities have a significant negative impact on unemployment, while minimum wage has a significant positive impact on unemployment. 2) Job opportunities have a positive but insignificant impact on the poverty rate, while minimum wage has a significant negative impact on the poverty rate. Unemployment, on the other hand, has a positive but insignificant impact on the poverty rate. 3) Job opportunities do not have a direct impact on the poverty rate through unemployment, whereas minimum wage directly affects the poverty rate through unemployment. Keyword: Employment Opportunity, Minimum Wage, Unemployment, Poverty Rate.

Abiotic factors are inorganic or physical components of the environment that affect all living organisms and ecosystems in general. These include temperature, lighting, humidity, soil density, air and water composition. This factor is key to the existence of plant organisms and determines their functioning and ability to adapt to the environment. Anthropogenic factors are direct human im-pact on plants or human changes to their habitat. n our studies, these are weather conditions and preparations for the growth and development of pea plants. The results of the experiments showed that the most highly productive and adapted to the growing conditions were the leafy pea variety Cadet and the affine variety Amulet. The use of the preparation Ecolist, VE in both years of the studies gave a reliable increase in the yield of all the studied pea varieties. Keywords: PEA VARIETIES, SEED GERMINATION, PLANT SURVIVAL, WEATHER CONDI-TIONS, PREPARATIONS, PRODUCTIVITY

Micro, Small, and Medium Enterprises (MSMEs) play a pivotal role in Indonesia’s eco-nomic stability, as demonstrated by their resilience during the 1998 economic crisis. This study examines the influence of organizational readiness, top management support, and financial technology on the supply chain performance of MSMEs in Pekanbaru, with the success of Accounting Information Systems (AIS) serving as a mediating factor. A pur-posive sample of 190 MSMEs was analyzed using Structural Equation Modeling-Par-tial Least Squares (SEM-PLS). The findings indicate that organizational readiness, top management support, and the success of Accounting Information Systems significantly enhance supply chain performance. In contrast, financial technology does not have a di-rect impact on supply chain performance. However, organizational readiness, top man-agement support, and financial technology all positively contribute to the success of Ac-counting Information Systems. Theoretical implications of this study reinforce the Re-source-Based View (RBV) and the DeLone & McLean Information System Success Model, emphasizing the importance of organizational readiness and top management support in improving supply chain performance through the success of Accounting In-formation Systems. Additionally, this research contributes to the literature on financial technology within the framework of the Technology Acceptance Model (TAM), high-lighting its potential to enhance supply chain integration and efficiency in MSMEs. These insights provide practical strategies for MSMEs to optimize their supply chain management by effectively leveraging organizational and technological resources.

The title of this article refers specifically to the time spent in Paris by early American collectors of medieval art, but it is also meant to evoke the American response to the medieval antiquities of Paris, to those of France, and to those of Europe in general. Focusing on seven Americans, whose sojourns in Europe – some brief, some extended – roughly span the entire course of the 19th century, this article seeks to understand what made these men turn towards medieval art at a time when most Americans were barely aware of its existence and didn’t care for it if they were. Proceeding chronologically from the earliest collector to the latest, it reexamines the available evidence, using first-hand testimony where possible, in the hope of gleaning some new insights into the impact of Europe on their collecting. Beginning with William Poyntell, who acquired stained glass from the Sainte-Chapelle while in Paris in 1803, it continues with Robert Gilmor, who in 1807 purchased a Book of Hours from the Workshop of the Boucicaut Master; it then turns to two collectors of Italian “Primitices”, Thomas Jefferson Bryan and James Jackson Jarves; to J. Pierpoint Morgan and Henry Walters, the two greatest American collectors of medieval art; and concludes with the sculptor George Grey Barnard, whose precocious appreciation of Romanesque architectural sculpture started a major trend in both collecting and museum design. Although hard evidence for the direct impact of a European experience on the collecting psyche of these men is slight, they all spent “quality time” in Europe, and in all of them, to varying degrees, we can observe reflections of “medieval Paris”. Henry Adams describes the powerful effect of his first taste of Antwerp in 1858 as “education only sensual”. But since it is a sensual appreciation that must underlie the impulse to collect works of art, then we can be permitted to see the direct experience of Europe as having provided these early American collectors with a sense of the reality of the Middle Ages.

It is possible that gut bacteria may have a beneficial effect on cardiovascular health in humans. It may play a major role in the progression of a variety of cardiovascular diseases, including Heart Failure (HF), Atherosclerosis, Coronary Arterial Disease (CAD), Ischemic Heart Disease (IHD), and Others. Dysbiosis of the gut microbiota, along with its direct and indirect impact on gut health, may induce cardiovascular disorders. Although advanced studies have demonstrated the relationship of various metabolites to cardiovascular diseases (CVD) in animals, translating their functional capacity to humans remains a significant area of research. This paper simplifies the demonstration of some compounds, pathways, and components like Trimethylamine N-oxide (TMAO), short-chain fatty acids (SCFAs), and butyrate production. It demonstrates how a change in eating habits causes TMAO and how the impact of different drugs on gut microbiota species and high consumption of Westernized food causes several heartrelated problems, such as atherosclerosis and inflammation that can even become the cause of heart failure. Modulation of the gut microbiome, on the other hand, is a novel therapeutic measure because it can be easily altered through diet and other lifestyle changes. It could then be used to lower the risk of several CVDs.

BackgroundNurses’ turnover has remained a concern in the healthcare sector worldwide. Nurse managers should adopt appropriate leadership styles that promote a positive working environment. The literature revealed that effective leadership...

Currently, there is a surge in the adoption of green construction practices (GCP) to address and attempt to alleviate the adverse effects of construction activities on the natural environment. The current research surrounding sustainability in the industry has witnessed a notable surge in efforts, primarily driven by a growing consciousness of environmental concerns. In this context, it is worth noting that the focus of assessment schemes in developed countries has predominantly gravitated towardeconomic and environmental factors, with relatively less emphasis placed on social performance (SP). Many researchers have underscored this disparity, highlighting the need for a more comprehensive approach encompassing all three sustainability dimensions. Hence, the present research examines the effects of green construction practices on economic performance (ECP) while considering the mediating role of environmental performance (EP) and SP. The results showed no direct impact of GCP on ECP. However, the mediating role of SPand EPin the association between GCP and ECP was found to be statistically significant. The present study provides several implications. This study presents empirical evidence that implementing GCP yields several benefits for contractors, including cost reduction in waste management, legal expenses, and energy consumption. These cost savings effectively offset the initial investment required to implement GCP, enabling construction projects to attain ECP thresholds. This study provides a significant contribution to the expanding corpus of knowledge concerning green building site practices and construction management. It offers novel insights into the influence of a project's EPand SP on its economic feasibility. Furthermore, this research study has effectively showcased the positive impact of GCPon construction projects'environmental construction performance, particularly concerning environmental and social considerations. Integr Environ Assess Manag 2024;20:1396-1406. © 2024 SETAC.

Multiple instance learning (MIL) is a weakly supervised learning paradigm that is becoming increasingly popular because it requires less labeling effort than fully supervised methods. This is especially interesting for areas where the creation of large annotated datasets remains challenging, as in medicine. Although recent deep learning MIL approaches have obtained state-of-the-art results, they are fully deterministic and do not provide uncertainty estimations for the predictions. In this work, we introduce the attention Gaussian process (AGP) model, a novel probabilistic attention mechanism based on Gaussian processes (GPs) for deep MIL. AGP provides accurate bag-level predictions as well as instance-level explainability and can be trained end-to-end. Moreover, its probabilistic nature guarantees robustness to overfit on small datasets and uncertainty estimations for the predictions. The latter is especially important in medical applications, where decisions have a direct impact on the patient's health. The proposed model is validated experimentally as follows. First, its behavior is illustrated in two synthetic MIL experiments based on the well-known MNIST and CIFAR-10 datasets, respectively. Then, it is evaluated in three different real-world cancer detection experiments. AGP outperforms state-of-the-art MIL approaches, including deterministic deep learning ones. It shows a strong performance even on a small dataset with less than 100 labels and generalizes better than competing methods on an external test set. Moreover, we experimentally show that predictive uncertainty correlates with the risk of wrong predictions, and therefore it is a good indicator of reliability in practice. Our code is publicly available.

The measurement of port efficiency is a crucial requirement in the maritime sector and has been the subject of numerous studies due to its direct impact on international trade. The methods used to determine port efficiency vary depending on the characteristics of the operations under investigation. Depending on which port operations are assessed, port adequacy can be measured in various ways. In the literature, it is evident that port efficiencies are mostly determined using numerical methods. However, when analyzing port efficiency, measuring it solely through numerical processes can lead to misleading results in some cases. To avoid this, it is necessary to analyze non-numerical internal and external factors that affect port efficiency. In this study, non-numerical internal and external factors influencing port efficiency in liquid chemical cargo ports were identified through a literature review and by experts. The identified factors were evaluated through surveys conducted with experts specializing in liquid chemical cargo ports. The survey results were analyzed using SPSS 21.0 statistical software. According to the results obtained from the surveys, factors such as the location and dimensions of the port area, the adequacy of hinterland connections and logistic facilities, well-organized physical surroundings of the port, the use of technological innovations, and the proper functioning of the quality control system were identified as significant factors affecting port efficiency. In addition to these factors, accurate job analysis in the port, satisfactory wages, sufficient education levels, and morale of port personnel were also found to be crucial factors influencing port efficiency.

This research explores the intricate relationships among influencer marketing strategies, brand image, customer support, and consumer purchase intent within the operational framework of PT. Len Industri (Persero) - Jakarta Office. Through path analysis, the study discerns direct and indirect effects, revealing significant findings. Marketing Strategies exhibit a notable direct impact on both Customer Support and Consumer Purchase Intent, underscoring the centrality of effective marketing initiatives in shaping customer interactions and purchase decisions. Conversely, Brand Image reveals a non-significant direct effect on Customer Support but demonstrates a marginally significant impact on Consumer Purchase Intent, indicating a nuanced relationship that warrants further investigation. The indirect effects analysis highlights the substantial influence of Marketing Strategies on Consumer Purchase Intent mediated by Customer Support. These findings underscore the importance of a holistic approach, integrating marketing strategies, brand image cultivation, and effective customer support to enhance overall consumer experiences and drive purchase intent in a competitive market landscape.

The goal of this analysis is to investigate and assess the impact of governance structures on the disclosure of environmental accounting on financial results and firm valuation. The research was carried out on companies listed on the Iraq Stock Exchange in the hospitality sub-sector for the duration of 2015–2019. Sampling is achieved by purposeful sampling. Path analysis and moderated regression analysis are the analysis techniques utilized (MRA). The findings of the direct impact test analysis indicate that the structures of corporate governance that are proxies of global investment and systemic ownership do not influence financial efficiency and corporate valuation. The number of meetings of the audit committee has a considerable impact on financial results and firm value. Financial success has a significant influence on the valuation of firms. Financial results at firm valuation should not be mediated by financial results at firm valuation. Financial performance has been found to be moderated (weaken) by environmental accounting accounts as compared to the company's valuation. The study also recommends that environmental accounting transparency will weaken financial results relative to firm valuation, which requires the company to improve disclosures in environmental accounting to moderate financial results toward firm valuation.

This study investigates the intricate relationships among team dynamics, employee performance, and well-being in the context of a hybrid work environment, with a specific focus on PT Semen Baturaja (Persero) Tbk. Utilizing path analysis, the study explores both direct and indirect effects to unveil the dynamics shaping organizational practices related to hybrid work. The research reveals a significant direct impact of individual employee performance on both team dynamics and the establishment of a hybrid work environment. Team dynamics are identified as a crucial mediator, emphasizing their pivotal role in translating individual contributions into organizational strategies for hybrid work adaptation. However, the study finds that the direct influence of employee well-being on team dynamics and subsequently on the hybrid work environment may not be statistically significant. These findings offer nuanced insights for organizations aiming to optimize their work environment, suggesting tailored strategies to leverage team dynamics and individual performance for successful hybrid work implementation while considering the multifaceted nature of employee well-being.

Target identification is a critical step in biomedical research because it lays the groundwork for the development of new therapies and drugs. Genetic research, including genome-wide association studies (GWAS), genomic sequencing, functional genomics, and data integration, is crucial for understanding disease genetics and potential treatment targets. Transcriptomics and proteomics give data on gene and protein expression, making it easier to identify targets in dysregulated diseases. Target identification is essential for drug discovery, precision medicine, lowering medication attrition, increasing therapeutic efficacy, and, eventually, transforming patient care and drug development. Target validation is a critical stage in drug development because it verifies that revealed molecular targets play a substantial role in disease progression and are therefore suitable for treatment. It employs a range of approaches, including genetic validation, pharmacological validation, and animal model validation. Target validation assures that discovered targets are physiologically relevant, druggable, and have a direct impact on disease processes, thereby reducing pharmaceutical attrition, promoting precision medicine, and hastening therapeutic development. Historically, target identification relied on limited knowledge, typically through candidate-based techniques based on assumptions or prior observations. Target validation experiments looked into how gene knockdown or RNA interference affected illness symptoms. Genomics, proteomics, and functional genomics have all made advances in recent years, as have high-throughput screening and data integration. CRISPR-based technologies and high-throughput sequencing have assisted in the validation of targets. Single-cell validation, machine learning and artificial intelligence, advanced in vitro models like organoids, and patient-derived models will all help to make future assessments of target relevance and treatment responses more precise and individualized. These developments have the potential to dramatically revolutionize research target identification and validation.

There are some indexes that affect the quality of life of countries. Economic crises, pandemics, natural events, etc. affect these indexes. The COVID-19 pandemic also had a direct impact on all these indexes. In this study, the impact of the COVID-19 pandemic on the quality of life of countries was investigated. In this context, 29 different artificial neural networks were trained between 2012 and 2019 with the data of 29 countries in the European continent, consisting of a total of six indexes. The countries' quality of life indexes for 2020 were predicted and compared with the quality of life indexes realized in 2020. The study was evaluated according to the performance criteria R, R2, RMSE and MAPE in the range "very good, good, valid and invalid" and showed “very good” results. In this study, it was found that the country with the highest decrease in quality of life after the pandemic was Germany and the country with the highest increase was Bulgaria. In Turkey and Spain, the change in quality of life indexes was close to zero. In accordance with the results, evaluations were made and suggestions were made.

Influence of sedimentary processes and fault tectonics on the evolution of submarine canyons in the East Andaman Basin: Insights from high-resolution seismic data analysis

Horngear and carrier design for braiding tailorable composite preforms

Autistic children who have experienced adverse childhood experiences (ACEs) may have barriers to receiving special education or other developmental services-thus, impacting educational outcomes. Our objective was to model such a pathway using the 2016-2021 National Survey of Children's Health datasets. We extracted data for school outcomes, use of special education and autism-related specialty services and sociodemographic characteristics among autistic children within the data. Associations between sociodemographics and ACEs (categorized as 0, 1-3, and 4+) were tested using design-based X2 tests. We then used structural equation modeling to map the quasi-causal pathways. The sample for our analysis included 4717 autistic children-38.94% were aged 6-10 years, 35.73% of children aged 11-14 years, and 25.32% were between 15 and 17 years-with 88.70% living in metropolitan areas. The X2 showed significant relationships between ACEs and age, ethnoracial groups, and urbanicity among others. The SEM showed ACEs were directly associated with poorer school outcomes (β = -0.14 (0.04), p = 0.002) and through their inverse relationship with support services (β = -0.08 (0.04), p = 0.023)- when support services were increased, school outcomes improved (β = 0.62, p < 0.001). Findings suggested ACEs have a significant direct and indirect impact on school outcomes of autistic children, and 10.76% of children who are autistic have experienced four or more ACEs-which were more likely to occur with severe autism symptomatology and in rural areas. Results highlight the need for communities to recognize the potential long-term impact of ACEs on the academic outcomes of autistic children.

Double-layer multi-criteria group decision-making approach using neutralized possibility degree-based decision matrix with fuzzy information

The research aims to explain and clarify the nature, importance and nature of the factors that have a direct impact on the material requirements planning system and affect production capacity planning and the relationship of the two to each other in Iraqi industries. The most important findings of the research are that it is possible to develop a model that includes the most important factors affecting the production capacity system and the materials requirements planning system, which would raise the efficiency and effectiveness of the production system in Iraqi industries.

In the wave of the novel coronavirus (COVID-19) epidemic, there is a need to investigate the mental health status of Chinese university students. To examine the effects of witnessing cyberbullying on psychological distress and the mediating role of coping on these effects in Chinese university students, which is yet to be examined. 306 Chinese university students were enrolled in this cross-sectional study. Linear regression was conducted to analyze the relationship between depression, anxiety and being a bystander to cyberbullying, whereas the bootstrapping approach tested the mediation effects of coping strategies. Results indicated that 27.12% of subjects (95% CI: 22.22%, 32.48%) suffered from anxiety and 44.12% (95% CI: 38.47%, 49.88%) were depressed. Meanwhile, 89.87% of subjects were involved in witnessing cyberbullying. Being a bystander to cyberbullying was weakly but significantly associated with anxiety (β= 0.195, 95% CI: 0.068, 0.292) and depression (β= 0.223, 95% CI: 0.113,0.333). Negative coping partially mediated between witnessing cyberbullying and anxiety, with mediation effect values of 0.04 (P< 0.05). While being a bystander to cyberbullying was only directly associated with depression. Based on our findings, college students in Hunan, China, are experiencing anxiety, depression and cyberbullying after COVID-19. Being a bystander to cyberbullying is associated with coping strategies, anxiety, and depression. Cyberbullying has a direct impact on anxiety, but it also influences anxiety through mediating effects on coping.