Miracle-dz/UniFAR-FTUs huggingface dataset:
Planning Method of Road Network Density Based on Urban Area Position Potential Energy

Authors analyzed the phenomenon of direct proportion relationship between road network density and area position potential energy from the point of view of area position potential energy, and studied the road network density planning method based on urban area position potential energy. At first, the urban road network density was controlled via the total volume control method. Then, authors put forward the algorithms of economic increase coefficient, urban area position potential energy and theoretical area road network density. At last, applying the algorithms above, regarded road network density as control target, authors could plan a city£§s road network density. The results show that the road network density can adapt the imbalance increase of urban internal economy development after controlling total volume, and the method is operational.
The seasonal exchange of carbon dioxide between the atmosphere and the terrestrial biosphere: Extrapolation from site-specific models to regional models

Ecological models of the seasonal exchange of carbon dioxide (CO/sub 2/) between the atmosphere and the terrestrial biosphere are needed in the study of changes in atmospheric CO/sub 2/ concentration. In response to this need, a set of site-specific models of seasonal terrestrial carbon dynamics was assembled from open-literature sources. The collection was chosen as a base for the development of biome-level models for each of the earth's principal terrestrial biomes or vegetation complexes. The primary disadvantage of this approach is the problem of extrapolating the site-specific models across large regions having considerable biotic, climatic, and edaphic heterogeneity. Two methods of extrapolation were tested. 142 refs., 59 figs., 47 tabs
Autosomal recessive osteopetrosis: diagnosis, management, and outcome

Autosomal recessive “malignant” osteopetrosis is a rare congenital disorder of bone resorption. It is caused by the failure of osteoclasts to resorb immature bone.1-3 This leads to abnormal bone marrow cavity formation and clinically to the signs and symptoms of bone marrow failure. Impaired bone remodelling causes bony narrowing of the cranial nerve foramina which results in cranial nerve, especially optic nerve, compression.2Pathologically there is a persistence of the primary spongiosa characterised by cores of calcified cartilage within bone. Abnormal remodelling of primary, woven bone to lamellar bone results in “brittle” bone that is prone to fracture.1 2 Thus fractures, visual impairment, and bone marrow failure are the classical feature of the disease.4 Osteopetrosis has been reported in most ethnic groups although as the disease is very rare it is more frequently seen in ethnic groups where consanguinity is common. Infantile onset osteopetrosis should also be distinguished from the much milder autosomal dominant adult disease and the carbonic anhydrase II deficiency syndrome which is associated with renal tubular acidosis and less severe osteopetrosis.5 6 Affected children usually present within the first year of life and frequently within the first three months.4 7-9Parental concern regarding the child's vision is the most common presenting complaint. Failure to achieve normal visual milestones, roving eye movements, and/or squint are often reported. Other presentations include failure to thrive and recurrent infection, both secondary to the underlying anaemia and bone marrow involvement. Hypocalcaemic seizures, excessive bruising, fractures, nasal congestion, and an abnormal craniofacial appearance are less common presenting complaints.4 7-9 These symptoms are non-specific and while hepatosplenomegaly is invariably present at an early age this may be missed and because of the disease rarity a correct clinical diagnosis is often not initially made. Frequently it is the …
Collaborative Structures in WikiProjects. Wikipedia Users' Activity Case Study

The research presented in the article concerns the development of organizational and cooperation structures from interpersonal interactions in virtual communities sharing knowledge. It is assumed that the activity of communities functioning on the Internet reflects the organization of human communities in the offline space. The main aim of the research was to characterize the structures of cooperation groups that are formed around WikiProjects. The research question was: what is the structure of activity of Wikipedians in WikiProjects in chosen language versions of Wikipedia? The method used in the research is the content analysis method. The author calculated data thanks to the Wikipedia xTools. The numbers of participants involved in WikiProjects were analysed by referring to the Dunbar number concept and the cultural determinants of cooperation, thus referring to the masculinity index (MAS) according to Hofstede. Research has shown that WikiProject members show similarities to the layered community formation proposed by Dunbar. The sizes of groups of active editors are closer to these values. As the WikiProjects group grows, the ratio of declared participants to active participants decreases. The production of value in virtual communities, the ability to work together as a team in physical isolation, is becoming increasingly important. The results of the research may be significant to the knowledge management specialists and virtual communities practitioners.
Estimating Deficient Muscle Activity Using LSTM With Integrated Damping Neurons for EMG-Based Control of Robotic Prosthetic Fingers

Robotic prosthetic hands can help perform the intended sophisticated movements of the upper limb, which can assist amputees to perform their daily activities. Although a robotic prosthetic hand can be controlled in real-time using the user’s electromyography (EMG), which directly reflects the user’s motion intention, some important EMG signals are usually lost owing to muscle deficiency. This study proposes a muscle activity estimator that is inspired by the muscle synergy across subjects to estimate the activity of the missing muscles in amputees in real-time. The proposed estimator learns muscle synergy from the EMG balance, finger joint angles, and the grasping force of healthy persons. The proposed estimator is developed as an artificial neural network (ANN) with a novel cell structure that combines long-short-term memory and damping neurons to analyze muscle dynamics. Furthermore, to improve the accuracy of learning muscle synergy, the muscles to be input to the estimator are selected by focusing on the enslavement of muscles and anatomical relationships. The effectiveness of the proposed estimator is evaluated by experiments. The results showed that the proposed estimator can contribute well to the realization of the intended sophisticated motions of the user.
Involvement of urban agricultural education students in FFA activities and opportunities

Students in large cities across the United States can better understand agriculture through their involvement in School-Based Agricultural Education (SBAE). Since the passage of the Smith-Hughes Act in 1917, Agricultural Education has been a pathway that encourages students to continue schooling and connect with future careers. We conducted a descriptive study to explore the involvement of 2021-2022 high school Agricultural Education students in the National FFA Organization. Involvement was calculated through self-reported participation in chapter activities, officer positions, FFA degrees, and contests or events. Additionally, the study evaluated students’ curiosity toward stretching or embracing as well as their overall adaptability in different situations. The study occurred in the fall of 2021 with 543 respondents from 18 urban SBAE programs in 12 states completing the online survey. Respondents reported self-perceived moderate curiosity, very strong adaptability, and light involvement in FFA. For urban SBAE students, involvement in FFA was not affected by demographics other than those in the 12th grade were more involved. This could imply study respondents who see the value in the organization’s leadership training or college and career readiness are the most curious and adaptable toward being involved in the organization.
DFU-SIAM a Novel Diabetic Foot Ulcer Classification With Deep Learning

Diabetes affects roughly 537 million people in the world, and it is predicted to reach 783 million by 2045. Diabetic Foot Ulcer (DFU) is a major issue with diabetes that may lead to lower limb amputation. The rapid evolution of DFU demands immediate intervention to prevent the terrible consequences of amputation and related complications.This research introduces a novel approach utilizing deep neural networks and machine learning for the accurate classification of diabetic foot ulcer (DFU) images. The proposed method harnesses the cutting-edge capabilities of Convolutional Neural Networks (CNN) and Vision Image Transformers (ViT) within a Siamese Neural Network (SNN) Architecture. By employing similarity learning, the model efficiently categorizes DFU images into four distinct classes: None, Infection, Ischemia, or Both. The training process involves the use of the DFU2021 dataset, with all ethical clearances duly obtained. Notably, the model exhibits remarkable performance on both the validation and test data, indicating a significant breakthrough in the field of DFU disease image classification. The potential of this innovative model extends beyond classification; it holds promise as an integral component of a comprehensive detection tool and longitudinal treatment protocol validation for DFU disease.
IC Theory and Public Healthcare

The healthcare sector is in crisis worldwide and under pressure from various trends such as digitalization, economic recession, population ageing, and workforce shortfall. This challenges in particular public sector healthcare management’s ability to provide services and meet communities’ growing expectations. Although the value of healthcare organizations is largely based on intellectual capital (IC), such as the experience and skills of professionals, knowledge available through relationships, codified knowledge, and organizational culture, knowledge-related literature in healthcare management has previously focused more on knowledge management activities than on intangibles and their management. Moreover, the public sector is the least examined area in the lC literature. The literature on this specific aspect – IC in public healthcare – has so far focused largely on identifying different aspects of IC rather than recognizing its value in action. To contribute to this knowledge gap, this article studies the role of intellectual capital in public sector healthcare management to elaborate on this critical component of organizational survival, renewal, and performance. This study aims to elucidate what this topical context requires of the application of IC theory and how it can advance the theoretical approach. Based on a literature review and preliminary empirical data gathered through thematic interviews with knowledge management professionals in a public healthcare organization in the throes of major healthcare reform, this article draws on the importance of intellectual capital in the public healthcare sector.
Machine learning algorithms for real-time coal recognition using monitor-while-drilling data

ABSTRACT Accurate coal seam identification is crucial in coal mining to prevent resource wastage and potential damage to coal seams from misplaced explosives. The current industry standard involves drilling past the seam and refilling the hole, a resource-intensive process. Manual seam detection is error-prone, and geophysical logging, performed for only a subset of drill holes, is costly and time-consuming. Monitor-While-Drilling (MWD) data captures drill response metrics like rotary speed and torque, influenced by local geology. These MWD measurements offer insights into geology, including hardness and rock type; They can be used for real-time rock recognition using advanced artificial intelligence techniques. This study focuses on developing tools for precise coal recognition and identification of the top of coal seams using MWD data. Several Machine Learning classifiers are employed, each providing unique data interpretations, and their results are integrated into a more reliable prediction. An artificial neural network is used for rock density regression, which is then used to correct depth offset between geophysical loggings and drill MWD data. The research demonstrates that MWD data can enable real-time coal seam identification, reducing the reliance on time-consuming and expensive geophysical logging. The integrated model accurately identifies the top of coal seams within a ± 20 cm margin.
Coral health and disease in the Pacific : vision for action

Diseases occur globally in most coral reef habitats whether near human population centers or remotely offshore. They generally affect a low proportion of the susceptible species, although localized outbreaks have produced significant mortalities to scleractinian corals, gorgonians, sea urchins, reef fish, sponges, algae and other coral reef organisms (Peters, 1993; Harvell et al., 1999; Williams and Bunkley-Williams, 2000). There are now over 30 named diseases in the Caribbean basin affecting 45 zooxanthellate scleractinian corals, three hydrozoan corals, ten octocorals, two zoanthids, nine sponges and two crustose coralline algae (Green and Bruckner, 2000; Weil et al., 2006), and at least seven major diseases from the IndoPacific, along with about 30 additional conditions that are associated with compromised health in scleractinian corals. While an apparently unprecedented increase in disease occurred in the Caribbean since the 1980s, much less is known about the status of disease in the IndoPacific and Red Sea. Surveys over the last decade in Australia, Palau, East Africa, the Philippines and other locations have revealed new diseases, suggesting a rapid emergence of disease, or at least a realization of their presence, throughout the Indo-Pacific. Between 1972 and 2005 coral diseases were reported on 39 genera and 148 species worldwide, with observations in 63 countries. Although Pacific reefs have a higher diversity of reef-building corals than the Atlantic and harbor 92% of the world’s coral reefs (Spalding and Greenfell, 1997), only 14% of the global observations of coral disease were from the Indo-Pacific during this period (Green and Bruckner, 2000, Sutherland et al., 2004, GCDD, 2007), and 58% of all coral disease records are for BBD, WBD and WP. The Caribbean has historically been referred to as a “hotspot” for disease, largely because of the rapid emergence, high prevalence, wide distribution, large numbers of host species, and virulence of diseases in this region. There are eight major diseases (BBD, WP, WBD, YBD, DSD, WPX, ASP and tumours) that have been reported from throughout the western Atlantic along with another 32 conditions (including different “types” of the major diseases) that have been reported since 1972. WBD, BBD and WP were first reported from the Caribbean in the 1970s from a small number of countries, with observations expanding to new locations during the 1980s including reports of WBD from about half of the Caribbean nations. During this period, BBD and WP caused localized mortality, while WBD contributed to a
Evaluation of the Relationship between Energy Consumption, Economic Growth, and Carbon Emissions in the Context of the N-Shaped Environmental Kuznets Curve: Newly Industrialized Countries (NICs)

Climate change and environmental degradation have become an issue that has focused on the whole world in recent years. In many studies on this subject, the existence of an inverted U-shaped Kuznets Curve has been analyzed. In this study, a model was established to analyze the N-shaped Kuznets Curve in order to see the long-term position of the relation between economic growth andenvironmental degradation. New industrialized Countries (NICs), which have a substantial effect on world carbon emissions, are also discussed in the study. Panel data analysis was used to examine the relationship between environmental degradation and economic growth over the period from 1970 to 2019.In the analysis, first of all, the cross-section dependence (CSD) was tested and it was decided that the second-generation tests would be continued according to the results. In the subsequent Westerlund hand-integration analysis, it was concluded that there was co-integration between the series and that they would act together in the long run. Finally, the CCE method, which is one of the long-term estimators, was used and analyzes were carried out by establishing three different models. As a result of the analysis, it was concluded that energy consumption positively affects carbon emissions, the existence of an inverted U-shaped Kuznets Curve for the entire panel and the existence of an N-shaped Kuznets Curve throughout the panel could not be proven.
The China population and labor yearbook

CONTENTS List of Tables List of Figures Preface Acknowledgements List of Contributors Chapter One Demographic Changes During the Period of Twelfth Five-Year Plan and Predictions for Population Development Hu Ying, Cai Fang, and Du Yang Chapter Two Low Fertility and Related Theoretical Issues in China Guo Zhigang Chapter Three How China Tackled the Global Financial Crisis Cai Fang, Du Yang, and Wang Meiyan Chapter Four Public Investment and Employment: An Empirical Analysis Wang Dewen Chapter Five Growing Pains: What Employment Dilemma Does China Face at Its Lewis Turning Point? Cai Fang Chapter Six The Impact of the Financial Crisis on Employment in Small and Medium Enterprises Wu Yaowu and Du Yang Chapter Seven Changes in Industrial Location and Labor Flows in China Cai Fang, Wang Meiyan, and Qu Yue Chapter Eight Population, Industrial Development, and Employment in Chinese Urbanization Du Yang, Wang Meiyan Chapter Nine Has Labor Migration Really Not Narrowed the Rural-Urban Income Gap? Cai Fang and Wang Meiyan Chapter Ten Emission Reduction Compatible with Economic and Employment Growth Cai Fang, Du Yang, and Wang Meiyan Chapter Eleven The New Elements of China's Labor Market in the Post-Financial-Crisis Era Cai Fang, Wang Meiyan Chapter Twelve Reform of the Hukou System and Unification of Rural-Urban Social Welfare Cai Fang Index
Cannabis and Other Substance Misuse: Implications and Regulations

Abusing controlled substances, including cannabis and various drugs, can result in severe intoxication and even death. Therefore, a comprehensive postmortem analysis is crucial for understanding the underlying causes of such fatalities. This narrative review discusses the characteristics of commonly abused controlled substances, the methodologies employed in postmortem analysis, lethal dosage levels, mechanisms of toxicity, side effects, and existing regulations. The focus centers on seven prevalent groups of controlled substances, namely cannabis, opioids, amphetamine-type stimulants, cocaine, new psychoactive substances, and hallucinogens. These groups have been linked to an increased risk of fatal overdose. Most substances in these groups exert neurotoxic effects by targeting the central nervous system (CNS). Consequently, strict regulation is essential to mitigate the potential harm posed by these substances. To combat abuse, prescribers must adhere to guidelines to ensure their prescribed medications comply with the outlined regulations. Through an enhanced understanding of controlled substance abuse and its consequences, more effective strategies can be developed to reduce its prevalence and associated mortality.
Flexible Carbon Neutralization Strategy: Customized Dynamic Server Management for Energy Efficiency Optimization

In recent years, large data centers have increased significantly as data usage has increased because of digital innovations. However, data centers are 24-hour operation facilities that consume large amounts of power, thus causing environmental problems. Recently, research has been conducted using deep learning methods from various perspectives to predict traffic and reduce power consumption in data centers and servers. However, the traffic processed by servers is highly variable, which is a factor that makes server management difficult. Thus, the traffic processed by servers is irregular, and more research is required on dynamic server management. This study proposes Customized Dynamic Server Management (CDSM) based on Long-Term Short Memory (LSTM), a neural network that is effective in predicting time-series data, to address the aforementioned problem. The proposed method can more effectively save the power used by servers, thereby managing servers more reliably and efficiently than before in the current operating environment. To validate the proposed model, we collected the traffic data at six Wikipedia data centers. We then analyzed the relationship between each traffic data using statistical analysis and conducted experiments. Furthermore, we calculated the server power consumption based on the actual power consumption according to the CPU usage of different servers provided by SPECpower, a benchmark for evaluating server power efficiency. Additionally, we calculated the amount of computation required for the program and deep learning model of the proposed. Based on this, practical results were derived considering the trade-off between the server’s power saving and computation performance. The experiment results showed that the server power consumption could be reduced by an average of 68% and a minimum of 32% with CDSM compared to without. This shows that CDSM can effectively reduce server power, hence saving energy in data centers and contributing to carbon neutrality.
Anticoagulation: The Art of Improving Practice

Following our 1st Audit cycle in January 2019, we introduced a proforma for documentation of anticoagulant initiation at The Queen Elizabeth Hospital (QEH), King’s Lynn. A proforma consisting of six criteria was used in patients commenced on anticoagulation therapy to standardise the practice of prescribing. These six criteria consisted of coagulation profile, creatinine clearance, bridging therapy-if indicated, counselling before the start of therapy, referral to an anticoagulation clinic and finally the duration of therapy. The aim of this audit is to assess the effectiveness of utilising a standardised proforma in patients commenced on anticoagulation therapy.
Numerical approach for the optimal design of dewatering schemes for deep foundation pits

In recent years, underground spaces have expanded widely in the urban zone. The appropriate estimation number of dewatering well with economical costs and securing soil stability at construction sites is challenging in designing pumping schemes. This paper aims to present the numerical approach for the optimal number of wells for designing a dewatering deep foundation pit. First, the estimated hydraulic conductivity was selected among the Theis, Cooper-Jacod, and reverse groundwater modeling methods. The estimated hydraulic conductivity via reverse groundwater modeling shows the lowest error with the monitoring pumping test. Second, hydraulic conductivity was input to groundwater modeling to estimate the groundwater level of four pumping schemes with the same total pumping rate (20 wells, 17 wells, 14 wells, and 13 wells). The number of wells was optimal from four random pumping schemes concerning the cost and the drawdown outside the pit. As a result, the pumping scheme with 16 wells is suitable, satisfying the cost and safety outside the drawdown of the foundation pit. The numerical approach could apply to estimate the number of wells for dewatering projects with economic costs and less impact on surrounding construction sites.