Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation of Se(VI)/Se(IV) ratio in water by the ratio recorded in barite

It is possible that the distribution behaviors of redox‐sensitive elements such as selenium (Se) between authigenic minerals and water can provide information on the oxidation state of the element in the coexistent water during mineral deposition. Considering the similar chemical characteristics of Se(IV) and Se(VI) oxyanions, we propose that the Se(VI)/Se(IV) ratio in a particular precipitate, such as barite, may enable us to estimate the Se(VI)/Se(IV) ratio in the coexistent water. The coprecipitation experiments of Se with barite coupled with the determination of the oxidation state of Se both in the aqueous phase and in barite were conducted by Se K‐edge X‐ray absorption near‐edge structure and high‐performance liquid chromatography connected to inductively coupled plasma‐mass spectrometry, respectively, to investigate the influence of the oxidation state of Se on its immobilization into barite at pH 4.0 and 8.0. The results showed that the Se(VI)/Se(IV) ratio in barite was correlated with the Se(VI)/Se(IV) ratio in water, which in turn can provide physicochemical and biogeochemical information related to the Se(VI)/Se(IV) ratio in water in the environment where barite precipitated.
The Archaeology of China: From the Late Paleolithic to the Early Bronze Age

1. Chinese archaeology: past, present, and future 2. Environment and ecology 3. Foragers and collectors in the Pleistocene-Holocene transition (24,000-9000 cal. BP) 4. Domestication of plants and animals 5. Neolithization: sedentism and food production in the Early Neolithic (7000-5000 BC) 6. Emergence of social inequality: the Middle Neolithic (5000-3000 BC) 7. Rise and fall of early complex societies: the Late Neolithic (3000-2000 BC) 8. Formation of early states in the Central Plain: Erlitou and Erligang (1900/1800-1250 BC) 9. Bronze cultures of the north frontiers and beyond during the early second millennium BC 10. The Late Shang dynasty and its neighbors (1250-1046 BC) 11. Chinese civilization in comparative perspective.
Chromatische Pupillometrie – ein neuer Weg zur funktionellen Glaukomdiagnostik?

Zusammenfassung Die chromatische Pupillometrie erlaubt die Quantifizierung der photorezeptorvermittelten (extrinsischen) und der melanopsinvermittelten (intrinsischen) Antwort der intrinsisch-photosensitiven retinalen Ganglienzellen (ipRGCs). Diese kleine Subpopulation der Ganglienzellen wird beim Glaukom ebenfalls geschädigt, und somit ist die chromatische Pupillometrie für die Glaukomdiagnostik potenziell interessant. Die bisherigen Studien zeigen sowohl eine Verminderung der phasischen Antwort als auch der tonischen Antwort beim Glaukom. Die diagnostische Wertigkeit unterschied sich abhängig von der verwendeten Technik und dem Studiendesign. Der vorliegende Artikel soll vor allem die Grundlagen der chromatischen Pupillometrie und die potenziellen Anwendungen beim Glaukom darstellen. Abstract Chromatic pupillometry allows quantification of photoreceptor-driven (extrinsic) and melanopsin-driven (intrinsic) responses of the intrinsic-photosensitive retinal ganglion cells (ipRGCs). This small subpopulation of retinal ganglion cells is also affected by glaucoma, making chromatic pupillometry a potential diagnostic tool. Studies show reduced phasic and tonic responses in glaucoma patients. The diagnostic value in earlier studies depended on the technical details and the study design. The purpose of this article is to give an introduction into the principles of chromatic pupillometry and to discuss the potential applications in the management of glaucoma.
Die neuen Behandlungselemente der Psychotherapiestrukturreform

Zusammenfassung Ziel Unsere Studie geht der Frage nach, wie Psychotherapeut:innen die neu eingeführten Behandlungselemente der Psychotherapiestrukturreform von 2017 umsetzen und beurteilen. Methoden Wir führten halbstrukturierte Interviews mit niedergelassenen Psychotherapeut:innen. Die Datenauswertung erfolgte mittels qualitativer Inhaltsanalyse. Ergebnisse Insgesamt 37 Stunden Audiomaterial von 41 Psychotherapeut:innen lagen vor. Es zeigte sich, dass die Akutbehandlung und Rezidivprophylaxe von Psychotherapeut:innen begrüßt werden. Aufgrund mangelnder Terminkapazitäten sowie konzeptioneller Barrieren kommen diese jedoch selten zur Anwendung. Bei der psychotherapeutischen Sprechstunde ergab sich ein heterogenes Meinungsbild. Fazit Einzelne Aspekte der psychotherapeutischen Sprechstunde, Akutbehandlung und Rezidivprophylaxe werden positiv eingeschätzt. Allerdings können die neuen Behandlungselemente nur eingeschränkt umgesetzt werden und sind nicht ausreichend, um die Versorgungslage deutlich zu verbessern.
Updated estimates of biotic diversity and endemism for Madagascar—revisited after 20 years

Abstract The terrestrial and marine biotypes of Madagascar are critical priorities for conservation, with almost unparalleled levels of endemism, species diversity and human threat for a land area of its size. Field inventories and molecular-based research conducted from the mid 1980s to present have greatly expanded knowledge of the country's biota, for some groups with nearly exponential growth in measures of species diversity. I provide updated measures of estimated species diversity between a book with 289 contributors, published in 2003 (The Natural History of Madagascar), and a full-scale update with 539 contributors, published in 2022 (The New Natural History of Madagascar). I compare biodiversity information presented in the new book with data from the earlier book, providing insight into scientific advancements, and revised estimates of species richness and endemism of a range of taxonomic groups.
Vacationscape: Developing Tourist Areas

The third edition of this classic volume integrates the idea of balancing tourism with protection of the resources upon which it depends. The text stresses the role of the community, identifies potential pitfalls, and raises issues of developmental ethics. It includes topics such as environmental impact, sustainability, and ecotourism. Special emphasis is given to the growing need for business to implement environmental protection and ecological integrity as an essential part of economic development. The book is filled with many sketches, functional diagrams, and photographs.
Adverse childhood experiences from family and society contribute to increased risk of depressive symptoms and cognitive impairment: a cross-sectional study

Background Family environments can shape children’s personalities and social networks, rendering distinguishing adverse childhood experiences (ACEs) from family and society essential, but related evidence remains limited. Aims This cross-sectional study aimed to investigate the correlations between intrafamilial and social ACEs, their associations with depressive symptoms and cognitive impairment and the (education-moderated) mediating role of social ACEs. Methods Data for this cross-sectional study were from the China Health and Retirement Longitudinal Study. Nine intrafamilial (0, 1, 2, 3, and 4 or more) and three social (0, 1, and 2 or more) ACEs were identified. Depressive symptoms were assessed using the 10-item Center for Epidemiological Studies Depression Scale. Global cognition, including episodic memory and mental intactness, was calculated as z scores. Binary and ordered logistic regressions, generalised linear models with Gaussian family and identity link, and mediation analysis were used. Results 13 435 participants aged 59.0 (51.0–66.0) were included. Compared with participants with no intrafamilial ACEs, those with 1, 2, 3, and 4 or more intrafamilial ACEs tended to develop more social ACEs, with odds ratios (ORs) of 1.55 (95% confidence interval (CI): 1.36 to 1.76), 2.36 (95% CI: 2.08 to 2.68), 3.46 (95% CI: 3.02 to 3.96) and 6.10 (95% CI: 5.30 to 7.02), respectively. Both intrafamilial and social ACEs were associated with depressive symptoms (OR >3 for four or more intrafamilial ACEs and two or more social ACEs) and global cognition (β=−0.26 for four or more intrafamilial ACEs and β=−0.29 for two or more social ACEs). Social ACEs mediated the associations of intrafamilial ACEs with depressive symptoms and global cognition by 12.3% and 13.1%, respectively. Furthermore, as education levels increased, the impact of intrafamilial ACEs on depressive symptoms was increasingly mediated through social ACEs, while the mediating role of social ACEs between intrafamilial ACEs and cognitive impairment gradually diminished. Conclusions Improving children’s social environments and elevating general education can prevent later-life depressive symptoms and cognitive impairment attributed to ACEs in China.
Research on Airport Bird Recognition Based on the Convolution Neural Network

With the continuous construction of new airports and the increasing number and frequency of various aircraft, bird strike incidents are also rising. Traditional bird repelling equipment usually loses effect after being used for a period of time. Therefore, effective measures must be taken to prevent such incidents. According to the problems in the existing ways and methods of bird-repelling, we proposes a scheme of building an airport bird-repelling linkage system by using the Internet of Things technology. As the core of the system, object detection is particularly important. In order to prevent birds from adapting to bird driving equipment, we explores a better model for bird recognition and achieve training and test accuracies about 94.38%, which is about 5.27% higher than ResNet50. In order to reach faster and better object detection, this model provides a good choice for the future research of bird target detection.
A GPU-Accelerated Population Generation, Sorting, and Mutation Kernel for an Optimization-Based Causal Inference Model

We develop a GPU-accelerated machine learning generative adversarial network model that can be used with observational data for the purpose of constructing causal inferences. The theoretical basis of our machine learning model is novel and is conceptualized to be operable and scalable for high performance computing platforms. Our GPU-accelerated code enables large-scale parallelization of the computation within a common and accessible computing environment. This will expand the reach of our model and empower research in new substantive domains while maintaining the underlying theoretical properties.
Self-Attention-based Data Augmentation Method for Text Classification

Text classification, where textual data is analyzed to gain meaningful information, has many applications in information extraction and data management. Recently, deep-learning models have been applied with success to this problem; however, they require sufficient labeled training data to produce a robust model, and performance suffers in low-resource domains where sufficient training data is unavailable and collecting or creating labeled training data is challenging in terms of cost, energy, and time. To address this problem, we propose an effective data augmentation approach for text classification. Our method employs a self-attention mechanism to augment the text, where we alter and substitute, in some scenarios, words with the highest attention score and, in some cases, words with low scores. Experimental results show that our method performs at least as well as current approaches in most scenarios and outperforms current approaches in some cases by as much as seven percent.
Contrastive Learning of Graph-Kernel-based Representations for Proteins

Protein property prediction is to infer protein properties by finding proteins with similar sequences, structures, surface clefts, and chemical properties. Since the acquisition of protein molecular property labels usually require complex and time-consuming experiments, protein property prediction based on supervised learning requires high costs. Through contrastive learning, our proposed model is capable of self-supervised learning on graphs kernels of proteins. It utilizes a strategy of subgraph removal for data augmentation, and its contrastive estimator maximizes the coherence of subgraphs from the same graphs of proteins while minimizing the coherence of subgraphs from different graphs of proteins. Experiments show that the framework can not only transform graph kernels of proteins into new graph-kernel-based representations for proteins, but also improve the performance of graph neural network encoders on protein property prediction.
Taillight Signal Recognition via Sequential Learning

In autonomous driving, it is crucial to capture the driving intentions of other vehicles on the road, which can then be used for the autonomous driving vehicle to plan a safe route. This study proposes a system to identify the driving intention of other vehicles from their taillight signals. To achieve this goal, both the positions of taillights (i.e., spatial features) and the change of the status of taillights over time (i.e., temporal features) need to be properly extracted and recognized. In our system, a longer sequence of 32 frames is used as input to capture the complete change of taillights. In addition, a transfer-learned classical convolutional neural network and a light-weight WaveNet are adopted to extract spatial and temporal features of the input sequence, respectively. Moreover, the dataset is augmented to ensure the convergence of model training. The experiment results indicate that our system outperforms the state of the art approaches in taillight recognition.
A Bucket-aware Asynchronous Single-Source Shortest Path Algorithm on GPU

Single-Source Shortest Path (SSSP) algorithm is a common routine in graph processing and has been extensively studied on Graphics Processing Unit (GPU). Despite the powerful parallelism resources and high memory bandwidth provided by GPU, the performance of the SSSP algorithm is hindered by several bottlenecks, such as irregular memory access, load imbalance, and redundant operations. In this paper, three optimizations are proposed to boost the performance of the SSSP algorithm on GPU, including property-driven reordering, adaptive load balancing, and bucket-aware asynchronous execution. Property-driven reordering is employed to improve the data locality and work efficiency. Adaptive load balancing brings a higher utilization of software and hardware GPU resources. Bucket-aware asynchronous execution presents a bucket-based approach for asynchronous implementation to accelerate the convergence of SSSP search. Extensive experimental results show that our work outperforms the state-of-the-art SSSP implementations, including GPU-based and CPU-based, with an average speedup of 5.09 × and 10.32 × on real-world and synthetic graphs. In addition, our SSSP algorithm indicates good scalability when the graph scale and GPU platform change.
Shrinking cities : international perspectives and policy implications

Section I: Shrinkage in a Global Perspective 1. Introduction Karina Pallagst, Cristina Martinez-Fernandez and Thorsten Wiechmann 2. Theoretical Approaches of "Shrinking Cities" Emmanuele Cunningham-Sabot, Ivonne Audirac, Sylvie Fol and Cristina Martinez-Fernandez Section II: Urban Change and the Role of Shrinkage 3. Shrinking Cities in the United States in Historical Perspective: A Research Note Robert A. Beauregard 4. Shrinking Cities in the Fourth Urban Revolution? Ivonne Audirac 5. The Interdependence of Shrinking and Growing: Processes of Urban Transformation in the USA in the Rust Belt and Beyond Karina Pallagst 6. The Restructuring of Declining Suburbs in the Paris Region Marie-Fleur Albecker and Sylvie Fol 7. Growth Paradigm Against Urban Shrinkage: A Standardised Fight? The Cases of Glasgow (UK) and Saint-Etienne (France) Emmanuele Cunningham-Sabot and Helene Roth 8. Making Places in Increasingly Empty Spaces: Dealing With Shrinkage in Post-Socialist Cities: The Example of East Germany Thorsten Wiechmann, Anne Volkmann and Sandra Schmitz 9. The Nagasaki Model of Community-Governance: Grassroots Partnership with Local Government Hiroshi Yahagi 10. Shrinkage and Expansion in Peri-Urban China: Exploratory Case Study from Jiangsu Province Chung-Tong Wu, Xiao-Lin Zhang, Gong-Hao Cui and Shu-Ping Cui 11. A Cluster of the Four Coal Mining Cities in Korea from a Global Perspective: How Did the People Overcome a Crisis After a Massive Closure of Mines? Dong-Chun Shin 12. From "Up North" to "Down Under": Dynamics of Shrinkage in Mining Communities in Canada and Australia Laura Schatz, David Leadbeater, Cristina Martinez-Fernandez and Tamara Weyman 13. Analytic Implications of the Corporate Town Atenquique and Its Shrinkage, Economic and Environmental Decline Jose G. Vargas-Hernandez 14. Inequality and Urban Shrinkage: A Close Relationship in Brazil Sergio Torres Moraes Section III: Strategic and Policy Implications 15. Emerging Regeneration Strategies in the US, Europe and Japan Jasmin Aber and Hiroshi Yahagi 16. Environmental Sustainability Issues for Shrinking Cities: US and Europe Helen Mulligan 17. Closing Thoughts Cristina Martinez-Fernandez, Karina Pallagst and Thorsten Wiechmann
A Disk Failure Prediction Algorithm Based on Fusion Model

Disk failure prediction is of great significance for ensuring the business continuity and data reliability of cloud data centers. Algorithm performance will directly affect operation and maintenance costs and user experience. Nowadays, many disk manufacturers use the threshold of S.M.A.R. T. data to check disk fault detection or make prediction, but the detection accuracy of this method is low. This paper proposes a disk fault prediction method based on deep learning, using deep residual network to extract high-dimensional hidden features; and using deep neural network combined with integrated tree model tool XGBoost to perform Failure prediction. Moreover, we use AutoML strategy based on improved GA to get the best model structure. Experiments based on Backblaze data show that this method has better prediction performance. Compared with pure XGboost methods, it can better reduce the false alarm rate about 50% and improve the accuracy rate, while maintaining a strong generalization ability.
How Streamers Influence Viewers Purchase Intention in Live Streaming Shopping

This paper investigates the factors influencing purchase intention on live streaming shopping platforms from a streamers' perspective based on the SOR framework. This study uses a non-probability convenience sampling method by using an online survey distributed to live streaming shopping viewers through the comment section and direct messages to random viewers. The analysis revealed streamers that have experienced, skilled, and knowledgeable are effective in influencing the viewers' persuasive perception and developing viewers' trust and could drive viewers' intention to purchase in live streaming shopping. This research focused on the streamers' perspective that affects purchase intention in the context of live streaming shopping in Indonesia. Future research may add other variables related to the streamers' perspective with regional and cultural differences. As streamers' perspectives affect viewers' purchase intention through trust in sellers and perceived persuasiveness, this study provides insights into sellers who sell their products through live streaming platforms. Previous research focused on customer motivation in purchase intention and streamer-consumer interaction using different variables. Therefore, our research seeks to analyze other variables and combine perceived persuasiveness and trust in sellers into mediating variables that affect purchase intention in live streaming shopping.
Do agents dream of abiding by the rules?: Learning norms via behavioral exploration and sparse human supervision

In recent years, several normative systems have been presented in the literature. Relying on formal methods, these systems support the encoding of legal rules into machine-readable formats, enabling, e.g. to check whether a certain workflow satisfies or agents abide by these rules. However, not all rules can be easily expressed (see for instance the unclear boundaries between tax planning and tax avoidance). The paper introduces a framework for norm identification and norm induction that automates the formalization of norms about non-compliant behavior by exploring the behavioral space via simulation, and integrating inputs from humans via active learning. The proposed problem formulation sets also a bridge between AI & law and more general branches of AI concerned by the adaptation of artificial agents to human directives.
Gym Exercises Monitoring with Smart Gloves: Exercise Recognition, Repetition Counting, and Imbalance Quantification

Fitness training attracts a lot of attention, as it tumbles the health risks in a participant. Most fitness training activities involve the interaction of hand and workout equipment. Therefore, we present a pressure-sensing smart glove system to recognize fitness exercises, count repetitions, and quantify imbalance. The proposed smart glove with 93 sensing points at the wrist and palm can attain pressure-distributed time series data. We evaluate the system’s performance using the collected exercise data from 20 participants. Support vector classifier attains overall 96 accuracy of exercise recognition. For counting repetitions, the overall mean absolute error of 1.78 is calculated for ten types of exercises. Using the ratio index technique, we compute the upper-threshold (+0.6) and lower-threshold (-0.6) to quantify the imbalance of lifted weights.
Study on Rainfall Runoff Prediction of Basin Based on Digital Twin

The precise prediction of rainfall runoff is of great significance to flood control in the basin. A digital twin-based prediction model combined with a long short-term memory (LSTM) network is proposed to achieve accurate prediction of rainfall runoff values. The digital twin model combines physical space and virtual space. According to the relevant hydrological data in the physical space, to obtain the runoff values through the LSTM prediction model and the historical data is updated in real time, reflecting the real-time and consistency between the physical space and the virtual space. Our research experiments are carried out in the Huaihe River basin, with 16 years of meteorological observations and runoff observations from 2001 to 2016 to train runoff prediction model, and the trained model is applied to the prediction of runoff. The prediction results are responded to in the twin basin scenario, and the meteorological characteristics and runoff changes are visualized in real time. The results also drive the risk assessment and conduct early warning response. The experiment shows that the proposed method achieves the combination of physical entities and data-driven, to effectively visualize the digital twin scenes. Therefore, compared with the traditional rainfall runoff prediction, it shows more intuitive and improve the perceptual response capability.