bluuebunny/crossref_metadata_2025 huggingface dataset:
Studies on the mechanochemically induced chromene-annulation of meso-tetrakis(pentafluorophenyl)-2,3-dihydroxychlorin: Non-innocence of the grinding aids
Authors: Mohan Eric C. , Atoyebi Adewole O. , Brückner Christian
Published: February 2022

The modification of chlorins to fine-tune their optical properties is important for their application in medical and technical fields. The solution phase, base-induced formation of a chromene-annulated chlorin from the corresponding meso-tetrakis(pentafluorophenyl)-2,3-dihydroxychlorin by intramolecular S[Formula: see text]Ar displacement of an [Formula: see text]-F atom of a meso-aryl group by an adjacent hydroxy group is well-known. Organic transformations performed by grinding the dry reagents together promise to be a much greener and possibly simpler method compared to traditional solution-based reactions. However, the use of such mechanochemical approaches to accomplish porphyrinoid chromophore modifications are virtually unexplored. We report here the translation of the solution-based base-induced formation of a chromene-annulated chlorin to a solid state mechanochemical reaction in a planetary powder mill. The factors determining the success (and failure) of the reaction are delineated. While this report shows that porphyrinoid framework modifications can principally be induced mechanochemically, the mechanochemical reaction is not (yet) competitive with the solution state reaction. The outcome of the reaction is very sensitive to the particular milling conditions, specifically-and importantly as it is unexpected – the grinding aids used. What we previously believed to be inert or merely basic aids affected a significant degradation of the chlorins under the milling conditions, a finding that might generally limit mechanochemical approaches in porphyrinoid chemistry, or at least require careful adjustments.
Two-Dimensional Fast Multipole Indirect Boundary Element Method-Based Solution to P-Wave Scattering by a Mountain with Large-Scale Random Cracks in an Elastic Half-Space
Authors: Liu Zhong-Xian , Huang Zhen-En , Huang Lei , Sun Jun , Du Jian-Mei
Published: February 2022

In this study, the fast multipole indirect boundary element method (FM-IBEM) was adopted for solving the scattering problem of incident P-waves using a two-dimensional (2D) mountain terrain with large-scale random cracks. By adopting the FM-IBEM, this study resolves the bottleneck of the conventional boundary element method (BEM) for computing complex multi-degree-of-freedom models, and for the first time quantitatively analyzes the seismic wave scattering problem of large-scale narrow mountain cracks. After verifying the calculation accuracy, the horizontal and vertical displacement amplitudes of the mountain surface and its adjacent surface were accurately calculated. The results suggest the following: (1) Cracks significantly amplify the displacement amplitude of the mountain surface. The amplification mostly depends on the number of cracks and on the frequency of the incident P-wave. Compared with homogeneous mountains (no cracks), under the most unfavorable circumstances, the horizontal displacement is amplified more than 9.5-fold, while the vertical displacement is amplified more than 6.5-fold. (2) When the wave field passes through the crack area, a large amount of energy is trapped. Moreover, this energy is highly concentrated, which amplifies the local ground motion more than 7-fold. (3) The ground motion generated by the P-wave scattering by a mountain with large-scale random cracks exhibits a significant spatial difference. (4) The resonance of multi-order modes can emerge owing to large-scale random cracks. Compared with homogeneous mountains, the ground motion caused by the P-wave scattering by mountains with large-scale random cracks is much more severe in many cases. This phenomenon must be considered in practical seismic engineering applications.
ASSESSMENT OF FUTURE CLIMATE PROJECTIONS IN ALGERIA USING STATISTICAL DOWNSCALING MODEL
Authors: SAHABI-ABED SALAH
Published: February 2022

In this study, we assess the future changes in minimum temperature (T-min), maximum temperature (T-max), and precipitation (PRCP) for the three periods the 2020s (2011–2040), the 2050s (2041–2070), and the 2080s (2071–2100), with respect to the reference period 1981–2010 over Algeria focusing on a validation of the Statistical DownScaling Model (SDSM). In this approach, to underpin our analysis, we evaluate statistically the SDSM performance by simulating the historical temperatures and precipitation. The NCEP reanalysis data and CanESM2 predictors of three future scenarios, RCP2.6, RCP4.5, and RCP8.5 are used for model calibration and future projection, respectively. The projected climate changes resulting from the application of SDSM show a convincing consistency with those unveiled in previous studies over Algeria based on dynamical regional climate model outputs conducted in the context of Middle East-North Africa region. By the end of the century, the results exhibit strong warming for both extreme temperatures under the worst-case scenario (RCP 8.5), it is more pronounced for the T-max and over the Algerian Sahara region. Under the optimistic scenario (RCP2.6), the strength of the warming is expected to increase for both extreme temperatures. The projected changes of precipitation revealed for all scenarios several discrepancies with significant decrease over the northwest region and central Sahara, while nonsignificant change is projected for the center and eastern coastal regions. Our findings corroborate previous studies using sophisticated tools by demonstrating that Algeria’s climate is expected to warm further in the future. These primary findings could give an overview of the application of the statistical modeling approach using SDSM over a semi-arid and arid vulnerable region like Algeria and would extend our knowledge in the climate-modelling field for the North Africa zone by providing an added value to the existing GCMs and regional climate projections. In addition, reliable information regarding the magnitude of future changes at local scale may be used in impact models to assess changes of other key economic sector variables such as water resources management, energy and agriculture.
Collaborative Rover-copter Path Planning and Exploration with Temporal Logic Specifications Based on Bayesian Update Under Uncertain Environments
Authors: Hashimoto Kazumune , Tsumagari Natsuko , Ushio Toshimitsu
Published: February 2022

This article investigates a collaborative rover-copter path planning and exploration with temporal logic specifications under uncertain environments. The objective of the rover is to complete a mission expressed by a syntactically co-safe linear temporal logic (scLTL) formula, while the objective of the copter is to actively explore the environment and reduce its uncertainties, aiming at assisting the rover and enhancing the efficiency of the mission completion. To formalize our approach, we first capture the environmental uncertainties by environmental beliefs of the atomic propositions, under an assumption that it is unknown which properties (or, atomic propositions) are satisfied in each area of the environment. The environmental beliefs of the atomic propositions are updated according to the Bayes rule based on the Bernoulli-type sensor measurements provided by both the rover and the copter. Then, the optimal policy for the rover is synthesized by maximizing a belief of the satisfaction of the scLTL formula through an implementation of an automata-based model checking. An exploration policy for the copter is then synthesized by employing the notion of an entropy that is evaluated based on the environmental beliefs of the atomic propositions, and a path that the rover intends to follow according to the optimal policy. As such, the copter can actively explore regions whose uncertainties are high and that are relevant to the mission completion. Finally, some numerical examples illustrate the effectiveness of the proposed approach.
Learning from Non-experts: An Interactive and Adaptive Learning Approach for Appliance Recognition in Smart Homes
Authors: Codispoti Jackson , Khamesi Atieh R. , Penn Nelson , Silvestri Simone , Shin Eura
Published: February 2022

With the acceleration of Information and Communication Technologies and the Internet-of-Things paradigm,
smart residential environments
, also known as
smart homes
, are becoming increasingly common. These environments have significant potential for the development of intelligent energy management systems and have therefore attracted significant attention from both academia and industry. An enabling building block for these systems is the ability of obtaining energy consumption at the appliance-level. This information is usually inferred from electric signals data (e.g., current) collected by a smart meter or a smart outlet, a problem known as
appliance recognition
. Several previous approaches for appliance recognition have proposed load disaggregation techniques for smart meter data. However, these approaches are often very inaccurate for low consumption and multi-state appliances. Recently, Machine Learning (ML) techniques have been proposed for appliance recognition. These approaches are mainly based on passive MLs, thus requiring pre-labeled data to be trained. This makes such approaches unable to rapidly adapt to the constantly changing availability and heterogeneity of appliances on the market. In a home setting scenario, it is natural to consider the involvement of users in the labeling process, as appliances’ electric signatures are collected. This type of learning falls into the category of
Stream-based Active Learning
(SAL). SAL has been mainly investigated assuming the presence of an
expert
, always available and willing to label the collected samples. Nevertheless, a home user may lack such availability, and in general present a more erratic and user-dependent behavior. In this article, we develop a SAL algorithm, called
K
-Active-Neighbors (KAN), for the problem of household appliance recognition. Differently from previous approaches, KAN jointly learns the user behavior and the appliance signatures. KAN dynamically adjusts the querying strategy to increase accuracy by considering the user availability as well as the quality of the collected signatures. Such quality is defined as a combination of
informativeness
,
representativeness
, and
confidence score
of the signature compared to the current knowledge. To test KAN versus state-of-the-art approaches, we use real appliance data collected by a low-cost Arduino-based smart outlet as well as the ECO smart home dataset. Furthermore, we use a real dataset to model user behavior. Results show that KAN is able to achieve high accuracy with minimal data, i.e., signatures of short length and collected at low frequency.
ASHRAY: Enhancing Water-usage Comfort in Developing Regions using Data-driven IoT Retrofits
Authors: Abbas Samar , Ehsan Ahmed , Ahmed Saad , Khan Sheraz Ali , Jadoon Tariq M. , Alizai Muhammad Hamad
Published: February 2022

In developing countries, majority of the households use overhead water tanks to have running water. These water tanks are exposed to the elements, which usually render the tap water uncomfortable to use, given the extreme subtropical weather conditions. Externally weatherproofing these tanks to maintain the groundwater temperature is short-lived, and only results in a marginal (0.5°C–1°C) improvement in tap water temperature. We propose
Ashray
, an IoT-inspired, intelligent system to minimize the exposure of water to the elements thereby maintaining its temperature close to that of the groundwater.
Ashray
learns the water demand patterns of a household and pumps water into the overhead tank only when necessary. The predictive, machine learning based, approach of
Ashray
improves water comfort by up to 8°C in summers and 3°C in winters, on average.
Ashray
is retrofitted into existing infrastructure with a hardware prototyping cost of $27, whereas it can save up to 16% on water heating costs, through reduction in natural gas consumption, by leveraging groundwater temperature. Moreover, we also consider a transiently-powered
Ashray
,  which uses the energy harvested from the ambient environment, and propose an intermittent data pipeline to improve its prediction accuracy. The transiently-powered
Ashray
is suitable for long-term deployment, requires minimal maintenance and delivers approximately the same performance.
Ashray
has the potential to improve the thermal comfort and reduce energy costs for millions of households in developing countries.
Anomaly Detection in Cybersecurity Datasets via Cooperative Co-evolution-based Feature Selection
Authors: Rashid A. N. M. Bazlur , Ahmed Mohiuddin , Sikos Leslie F. , Haskell-Dowland Paul
Published: February 2022

Anomaly detection from Big Cybersecurity Datasets is very important; however, this is a very challenging and computationally expensive task. Feature selection (FS) is an approach to remove irrelevant and redundant features and select a subset of features, which can improve the machine learning algorithms’ performance. In fact, FS is an effective preprocessing step of anomaly detection techniques. This article’s main objective is to improve and quantify the accuracy and scalability of both supervised and unsupervised anomaly detection techniques. In this effort, a novel anomaly detection approach using FS, called Anomaly Detection Using Feature Selection (ADUFS), has been introduced. Experimental analysis was performed on five different benchmark cybersecurity datasets with and without feature selection and the performance of both supervised and unsupervised anomaly detection techniques were investigated. The experimental results indicate that instead of using the original dataset, a dataset with a reduced number of features yields better performance in terms of true positive rate (TPR) and false positive rate (FPR) than the existing techniques for anomaly detection. For example, with FS, a supervised anomaly detection technique, multilayer perception increased the TPR by over 200% and decreased the FPR by about 97% for the KDD99 dataset. Similarly, with FS, an unsupervised anomaly detection technique, local outlier factor increased the TPR by more than 40% and decreased the FPR by 15% and 36% for Windows 7 and NSL-KDD datasets, respectively. In addition, all anomaly detection techniques require less computational time when using datasets with a suitable subset of features rather than entire datasets. Furthermore, the performance results have been compared with six other state-of-the-art techniques based on a decision tree (J48).
Compressive Sensing Based Distributed Data Storage for Mobile Crowdsensing
Authors: Zhou Siwang , Lian Yi , Liu Daibo , Jiang Hongbo , Liu Yonghe , Li Keqin
Published: February 2022

Mobile crowdsensing systems typically operate centralized cloud storage management, and the environment data sensed by the participants are usually uploaded to certain central cloud servers. Instead, this article addresses the decentralized data storage problem in scenarios where cloud servers or network infrastructures do not work as expected and the sensing data have to be temporarily stored on the mobile devices carried by the participants. Considering that the sensing data are generally correlated, this article investigates a compressive distributed storage scheme for mobile crowdsensing. We notice a key observation: when a participant has a random walk in the target sensing area, his walking/sensing process can be considered as a random sampling for the entire area, although the activity of the participant may only have a local scope. We then propose an encoding algorithm based on compressive sensing theory. Each participant encodes the sensing data in their local trajectory, but the encoded CS measurement is capable of roughly reflecting the entire information of the whole area. While a participant stores a blurred global image of the target sensing area, the entire data can then be collaboratively stored by a certain number of participants. We further present a period-based data recovery algorithm to exploit the inter-period correlations, improving the recovery accuracy. Experimental results using real environmental data demonstrate the performance of the proposed compressive storage scheme. The test datasets and our source codes are available at
https://github.com/siwangzhou/MCS-Storage
.
Federated Dynamic Graph Neural Networks with Secure Aggregation for Video-based Distributed Surveillance
Authors: Jiang Meng , Jung Taeho , Karl Ryan , Zhao Tong
Published: February 2022

Distributed surveillance systems have the ability to detect, track, and snapshot objects moving around in a certain space. The systems generate video data from multiple personal devices or street cameras. Intelligent video-analysis models are needed to learn dynamic representation of the objects for detection and tracking. Can we exploit the structural and dynamic information without storing the spatiotemporal video data at a central server that leads to a violation of user privacy? In this work, we introduce Federated Dynamic Graph Neural Network (Feddy), a distributed and secured framework to learn the object representations from graph sequences: (1) It aggregates structural information from nearby objects in the current graph as well as dynamic information from those in the previous graph. It uses a self-supervised loss of predicting the trajectories of objects. (2) It is trained in a federated learning manner. The centrally located server sends the model to user devices. Local models on the respective user devices learn and periodically send their learning to the central server without ever exposing the user’s data to server. (3) Studies showed that the aggregated parameters could be inspected though decrypted when broadcast to clients for model synchronizing, after the server performed a weighted average. We design an appropriate aggregation mechanism of secure aggregation primitives that can protect the security and privacy in federated learning with scalability. Experiments on four video camera datasets as well as simulation demonstrate that Feddy achieves great effectiveness and security.
Toward Scalable and Privacy-preserving Deep Neural Network via Algorithmic-Cryptographic Co-design
Authors: Zhou Jun , Zheng Longfei , Chen Chaochao , Wang Yan , Zheng Xiaolin , Wu Bingzhe , Chen Cen , Wang Li , Yin Jianwei
Published: February 2022

Deep Neural Networks (DNNs) have achieved remarkable progress in various real-world applications, especially when abundant training data are provided. However, data isolation has become a serious problem currently. Existing works build privacy-preserving DNN models from either
algorithmic
perspective or
cryptographic
perspective. The former mainly splits the DNN computation graph between data holders or between data holders and server, which demonstrates good scalability but suffers from accuracy loss and potential privacy risks. In contrast, the latter leverages time-consuming cryptographic techniques, which has strong privacy guarantee but poor scalability. In this article, we propose SPNN—a Scalable and Privacy-preserving deep Neural Network learning framework, from an algorithmic-cryptographic co-perspective. From algorithmic perspective, we split the computation graph of DNN models into two parts, i.e., the private-data-related computations that are performed by data holders and the rest heavy computations that are delegated to a semi-honest server with high computation ability. From cryptographic perspective, we propose using two types of cryptographic techniques, i.e., secret sharing and homomorphic encryption, for the isolated data holders to conduct private-data-related computations privately and cooperatively. Furthermore, we implement SPNN in a decentralized setting and introduce user-friendly APIs. Experimental results conducted on real-world datasets demonstrate the superiority of our proposed SPNN.
Federated Learning for Healthcare: Systematic Review and Architecture Proposal
Authors: Antunes Rodolfo Stoffel , André da Costa Cristiano , Küderle Arne , Yari Imrana Abdullahi , Eskofier Björn
Published: February 2022

The use of
machine learning (ML)
with
electronic health records (EHR)
is growing in popularity as a means to extract knowledge that can improve the decision-making process in healthcare. Such methods require training of high-quality learning models based on diverse and comprehensive datasets, which are hard to obtain due to the sensitive nature of medical data from patients. In this context,
federated learning (FL)
is a methodology that enables the distributed training of machine learning models with remotely hosted datasets without the need to accumulate data and, therefore, compromise it. FL is a promising solution to improve ML-based systems, better aligning them to regulatory requirements, improving trustworthiness and data sovereignty. However, many open questions must be addressed before the use of FL becomes widespread. This article aims at presenting a systematic literature review on current research about FL in the context of EHR data for healthcare applications. Our analysis highlights the main research topics, proposed solutions, case studies, and respective ML methods. Furthermore, the article discusses a general architecture for FL applied to healthcare data based on the main insights obtained from the literature review. The collected literature corpus indicates that there is extensive research on the privacy and confidentiality aspects of training data and model sharing, which is expected given the sensitive nature of medical data. Studies also explore improvements to the aggregation mechanisms required to generate the learning model from distributed contributions and case studies with different types of medical data.
Federated Social Recommendation with Graph Neural Network
Authors: Liu Zhiwei , Yang Liangwei , Fan Ziwei , Peng Hao , Yu Philip S.
Published: February 2022

Recommender systems have become prosperous nowadays, designed to predict users’ potential interests in items by learning embeddings. Recent developments of the Graph Neural Networks (GNNs) also provide recommender systems (RSs) with powerful backbones to learn embeddings from a user-item graph. However, only leveraging the user-item interactions suffers from the cold-start issue due to the difficulty in data collection. Hence, current endeavors propose fusing social information with user-item interactions to alleviate it, which is the social recommendation problem. Existing work employs GNNs to aggregate both social links and user-item interactions simultaneously. However, they all require centralized storage of the social links and item interactions of users, which leads to privacy concerns. Additionally, according to strict privacy protection under General Data Protection Regulation, centralized data storage may not be feasible in the future, urging a decentralized framework of social recommendation.

As a result, we design a federated learning recommender system for the social recommendation task, which is rather challenging because of its heterogeneity, personalization, and privacy protection requirements. To this end, we devise a novel frameworkFedratedSocial recommendation withGraph neural network (FeSoG). Firstly,FeSoGadopts relational attention and aggregation to handle heterogeneity. Secondly,FeSoGinfers user embeddings using local data to retain personalization. Last but not least, the proposed model employs pseudo-labeling techniques with item sampling to protect the privacy and enhance training. Extensive experiments on three real-world datasets justify the effectiveness ofFeSoGin completing social recommendation and privacy protection. We are the first work proposing a federated learning framework for social recommendation to the best of our knowledge.
The Future of FPGA Acceleration in Datacenters and the Cloud
Authors: Bobda Christophe , Mbongue Joel Mandebi , Chow Paul , Ewais Mohammad , Tarafdar Naif , Vega Juan Camilo , Eguro Ken , Koch Dirk , Handagala Suranga , Leeser Miriam , Herbordt Martin , Shahzad Hafsah , Hofste Peter , Ringlein Burkhard , Szefer Jakub , Sanaullah Ahmed , Tessier Russell
Published: February 2022

In this article, we survey existing academic and commercial efforts to provide Field-Programmable Gate Array (FPGA) acceleration in datacenters and the cloud. The goal is a critical review of existing systems and a discussion of their evolution from single workstations with PCI-attached FPGAs in the early days of reconfigurable computing to the integration of FPGA farms in large-scale computing infrastructures. From the lessons learned, we discuss the future of FPGAs in datacenters and the cloud and assess the challenges likely to be encountered along the way. The article explores current architectures and discusses scalability and abstractions supported by operating systems, middleware, and virtualization. Hardware and software security becomes critical when infrastructure is shared among tenants with disparate backgrounds. We review the vulnerabilities of current systems and possible attack scenarios and discuss mitigation strategies, some of which impact FPGA architecture and technology. The viability of these architectures for popular applications is reviewed, with a particular focus on deep learning and scientific computing. This work draws from workshop discussions, panel sessions including the participation of experts in the reconfigurable computing field, and private discussions among these experts. These interactions have harmonized the terminology, taxonomy, and the important topics covered in this manuscript.
FedCTR: Federated Native Ad CTR Prediction with Cross-platform User Behavior Data
Authors: Wu Chuhan , Wu Fangzhao , Lyu Lingjuan , Huang Yongfeng , Xie Xing
Published: February 2022

Native ad is a popular type of online advertisement that has similar forms with the native content displayed on websites. Native ad click-through rate (CTR) prediction is useful for improving user experience and platform revenue. However, it is challenging due to the lack of explicit user intent, and user behaviors on the platform with native ads may be insufficient to infer users’ interest in ads. Fortunately, user behaviors exist on many online platforms that can provide complementary information for user-interest mining. Thus, leveraging multi-platform user behaviors is useful for native ad CTR prediction. However, user behaviors are highly privacy-sensitive, and the behavior data on different platforms cannot be directly aggregated due to user privacy concerns and data protection regulations. Existing CTR prediction methods usually require centralized storage of user behavior data for user modeling, which cannot be directly applied to the CTR prediction task with multi-platform user behaviors. In this article, we propose a federated native ad CTR prediction method named FedCTR, which can learn user-interest representations from cross-platform user behaviors in a privacy-preserving way. On each platform a local user model learns user embeddings from the local user behaviors on that platform. The local user embeddings from different platforms are uploaded to a server for aggregation, and the aggregated ones are sent to the ad platform for CTR prediction. Besides, we apply local differential privacy and differential privacy to the local and aggregated user embeddings, respectively, for better privacy protection. Moreover, we propose a federated framework for collaborative model training with distributed models and user behaviors. Extensive experiments on real-world dataset show that FedCTR can effectively leverage multi-platform user behaviors for native ad CTR prediction in a privacy-preserving manner.
GRNN: Generative Regression Neural Network—A Data Leakage Attack for Federated Learning
Authors: Ren Hanchi , Deng Jingjing , Xie Xianghua
Published: February 2022

Data privacy has become an increasingly important issue in
Machine Learning (ML)
, where many approaches have been developed to tackle this challenge, e.g., cryptography (
Homomorphic Encryption (HE)
,
Differential Privacy (DP)
) and collaborative training (Secure
Multi-Party Computation (MPC)
, Distributed Learning, and
Federated Learning (FL)
). These techniques have a particular focus on data encryption or secure local computation. They transfer the intermediate information to the third party to compute the final result. Gradient exchanging is commonly considered to be a secure way of training a robust model collaboratively in
Deep Learning (DL)
. However, recent researches have demonstrated that sensitive information can be recovered from the shared gradient.
Generative Adversarial Network (GAN)
, in particular, has shown to be effective in recovering such information. However, GAN based techniques require additional information, such as class labels that are generally unavailable for privacy-preserved learning. In this article, we show that, in the FL system, image-based privacy data can be easily recovered in full from the shared gradient only via our proposed
Generative Regression Neural Network (GRNN)
. We formulate the attack to be a regression problem and optimize two branches of the generative model by minimizing the distance between gradients. We evaluate our method on several image classification tasks. The results illustrate that our proposed GRNN outperforms state-of-the-art methods with better stability, stronger robustness, and higher accuracy. It also has no convergence requirement to the global FL model. Moreover, we demonstrate information leakage using face re-identification. Some defense strategies are also discussed in this work.
FedBERT: When Federated Learning Meets Pre-training
Authors: Tian Yuanyishu , Wan Yao , Lyu Lingjuan , Yao Dezhong , Jin Hai , Sun Lichao
Published: February 2022

The fast growth ofpre-trained models(PTMs) has brought natural language processing to a new era, which has become a dominant technique for variousnatural language processing(NLP) applications. Every user can download the weights of PTMs, then fine-tune the weights for a task on the local side. However, the pre-training of a model relies heavily on accessing a large-scale of training data and requires a vast amount of computing resources. These strict requirements make it impossible for any single client to pre-train such a model. To grant clients with limited computing capability to participate in pre-training a large model, we propose a new learning approach,FedBERT, that takes advantage of the federated learning and split learning approaches, resorting to pre-training BERT in a federated way.FedBERTcan prevent sharing the raw data information and obtain excellent performance. Extensive experiments on seven GLUE tasks demonstrate thatFedBERTcan maintain its effectiveness without communicating to the sensitive local data of clients.
FAtiMA Toolkit: Toward an Accessible Tool for the Development of Socio-emotional Agents
Authors: Mascarenhas Samuel , Guimarães Manuel , Prada Rui , Santos Pedro A. , Dias João , Paiva Ana
Published: February 2022

More than a decade has passed since the development of FearNot!, an application designed to help children deal with bullying through role-playing with virtual characters. It was also the application that led to the creation of FAtiMA, an affective agent architecture for creating autonomous characters that can evoke empathic responses. In this article, we describe the FAtiMA Toolkit, a collection of open-source tools that is designed to help researchers, game developers, and roboticists incorporate a computational model of emotion and decision-making in their work. The toolkit was developed with the goal of making FAtiMA more accessible, easier to incorporate into different projects, and more flexible in its capabilities for human-agent interaction, based upon the experience gathered over the years across different virtual environments and human-robot interaction scenarios. As a result, this work makes several different contributions to the field of Agent-Based Architectures. More precisely, the FAtiMA Toolkit’s library-based design allows developers to easily integrate it with other frameworks, its meta-cognitive model affords different internal reasoners and affective components, and its explicit dialogue structure gives control to the author even within highly complex scenarios. To demonstrate the use of the FAtiMA Toolkit, several different use cases where the toolkit was successfully applied are described and discussed.
Quality-Informed Process Mining: A Case for Standardised Data Quality Annotations
Authors: Goel Kanika , Leemans Sander J. J. , Martin Niels , Wynn Moe T.
Published: February 2022

Real-life event logs, reflecting the actual executions of complex business processes, are faced with numerous data quality issues. Extensive data sanity checks and pre-processing are usually needed before historical data can be used as input to obtain reliable data-driven insights. However, most of the existing algorithms in process mining, a field focusing on data-driven process analysis, do not take any data quality issues or the potential effects of data pre-processing into account explicitly. This can result in erroneous process mining results, leading to inaccurate, or misleading conclusions about the process under investigation. To address this gap, we propose data quality annotations for event logs, which can be used by process mining algorithms to generate quality-informed insights. Using a design science approach, requirements are formulated, which are leveraged to propose data quality annotations. Moreover, we present the “Quality-Informed visual Miner” plug-in to demonstrate the potential utility and impact of data quality annotations. Our experimental results, utilising both synthetic and real-life event logs, show how the use of data quality annotations by process mining techniques can assist in increasing the reliability of performance analysis results.
Psychopathy: Current Knowledge and Future Directions
Authors: Patrick Christopher J.
Published: February 2022

Research on psychopathy has progressed considerably in recent years against the backdrop of important advances in the broader field of clinical psychological science. My major aim in this review is to encourage integration of investigative work on dispositional, biobehavioral, and developmental aspects of psychopathy with counterpart work on general psychopathology. Using the triarchic model of psychopathy as a frame of reference, I offer perspective on long-standing debates pertaining to the conceptualization and assessment of psychopathy, discuss how dispositional facets of psychopathy relate to subdimensions of internalizing and externalizing psychopathology, and summarize findings from contemporary biobehavioral and developmental research on psychopathy. I conclude by describing a systematic strategy for coordinating biobehavioral-developmental research on psychopathy that can enable it to be informed by, and help inform, ongoing research on mental health problems more broadly.
Gene Regulatory Circuits in Innate and Adaptive Immune Cells
Authors: Saini Ankita , Ghoneim Hazem E. , Lio Chan-Wang Jerry , Collins Patrick L. , Oltz Eugene M.
Published: February 2022

Cell identity and function largely rely on the programming of transcriptomes during development and differentiation. Signature gene expression programs are orchestrated by regulatory circuits consisting of cis-acting promoters and enhancers, which respond to a plethora of cues via the action of transcription factors. In turn, transcription factors direct epigenetic modifications to revise chromatin landscapes, and drive contacts between distal promoter-enhancer combinations. In immune cells, regulatory circuits for effector genes are especially complex and flexible, utilizing distinct sets of transcription factors and enhancers, depending on the cues each cell type receives during an infection, after sensing cellular damage, or upon encountering a tumor. Here, we review major players in the coordination of gene regulatory programs within innate and adaptive immune cells, as well as integrative omics approaches that can be leveraged to decipher their underlying circuitry.
(Invited) Electrocatalytic Ammonia Oxidation
Authors: Habib-Zadeh Faezeh , Miller Suzanne , Hamann Thomas , Smith Milton
Published: February 2022

Ammonia is receiving increasing interest as a carbon-free renewable energy storage medium. Scientific challenges remain to efficiently produce NH3 by fixing atmospheric N2 using renewable energy and hydrogen as well as utilizing the energy stored in NH3. One way the to release the energy stored in NH3 is electrolysis to produce N2 and H2, where the evolved H2 can be used in a fuel cell. The electrolysis of NH3 is currently limited by the nitrogen evolution reaction (NER) at the anode. While there have been numerous reports on heterogeneous catalysts for electrocatalytic oxidation of NH3, with limited success, homogenous molecular catalysts are non-existent. In this talk, transition metal complexes will be presented which show the first examples of catalytic activity in presence of ammonia at room temperature and ambient pressure. We will show the catalytic activity can be modulated via ligand design. Gas chromatography measurements reveals production of N2 and H2 in approximately 1:3 ratio following bulk electrolysis of ammonia showing stable catalysis and high faradaic efficiencies. Results from absorption spectroscopy, NMR and various electrochemical measurements will be presented which elucidate the key steps of the catalytic cycle. These assignments will be supported with N isotope labeling experiments followed with Mass Spectroscopy. In addition, spectroscopic and electrochemical measurements of proposed intermediate species in formation of the N-N bond will be presented. From these combined results, a tentative mechanism of the catalytic cycle will be proposed and the implications discussed.
Integrating Ab-Initio Simulations and Experimental Characterization Methods for Understanding Chemistry at Complex Photoelectrochemical Interfaces
Authors: Pham Tuan Anh , Zhang Xueqiang , Wood Brandon C. , Ptasinska Sylwia , Ogitsu Tadashi
Published: February 2022

The generation of hydrogen from water and sunlight through photoelectrochemical cells (PECs) offers a promising approach for producing scalable and sustainable carbon-free energy. However, the design of high-performance PECs requires a detailed understanding of physicochemical properties of the interface between semiconductor photoelectrode materials and liquid water, which is often difficult to probe under operating conditions. In this presentation, we discuss how high-level first-principles simulations can be integrated with advanced experimental characterization techniques to unravel the key chemical and electronic properties of solid/liquid electrochemical interfaces. Specific discussion focuses on the combination of X-ray photoelectron spectroscopy (XPS) simulations and near-ambient-pressure XPS experiments for the identification of solid/liquid interfacial chemical composition and speciation. We also show how first-principles molecular dynamics simulations can be coupled to many-body perturbation theory to directly probe the link between interfacial electronic properties and local chemistry, which is necessary for devising meaningful strategies to improve PEC performance and durability. Finally, we illustrate the benefit of this combined approach towards insights into native oxide chemistry at prototypical InP/water and GaP/water interfaces. We show that GaP exhibits a larger water-induced band edge shift and a broader XPS spectrum than InP, which can be traced to fundamental differences in the surface oxide chemistry by direct comparison between simulations and experiments.This example suggests a more general roadmap for obtaining a realistic and reliable description of the chemistry of complex interfaces by combining state-of-the-art computational and experimental techniques.
Activation of PKC results in improved contractile effects and Ca2+ cycling by inhibition of PP2A-B56α
Authors: Pluteanu Florentina , Boknik Peter , Heinick Alexander , König Christiane , Müller Frank U. , Weidlich Adam , Kirchhefer Uwe
Published: February 2022

The importance of the serine-41 phosphorylation site on B56α in reducing PP2A activity was demonstrated for the first time using a transgenic mutation model. The direct activation of PKC inhibits PP2A, leading to increased phosphorylation of MyBP-C in cardiac myocytes. The increased phosphorylation of contractile proteins is influenced by the PKC-phosphoB56α-PP2A signaling cascade resulting in an improved intracellular Ca2+ handling as well as an enhanced contractility and relaxation. The PKC-mediated inhibition of PP2A also leads to a modulation of the LTCC activation and inactivation kinetics. This study highlights the importance of exploring regulatory subunits of PP2A.
Sex-specific effects of prenatal hypoxia on the cardiac endothelin system in adult offspring
Authors: Hula Nataliia , Vu Jennie , Quon Anita , Kirschenman Raven , Spaans Floor , Liu Ricky , Cooke Christy-Lynn M. , Davidge Sandra T.
Published: February 2022

We demonstrated that prenatal hypoxia (a common condition of pregnancy) can have profound differential effects on treatment strategies in adult cardiovascular disease. Our data using a rat model of prenatal hypoxia demonstrated that, as adults, although inhibition of endothelin (ET)A receptors before an ex vivo cardiac ischemic insult improved recovery in females, it strikingly prevented recovery in males. Our data indicate a sex-specific effect of prenatal hypoxia on the cardiac ET-1 system in adult offspring.
Fluid Charging and Gas Reservoir Pressure Evolution of Longmaxi Formation Shale in Southeastern Chongqing
Authors: Chen Cen , Meng Cheng , You Qidong , Luo Chao , Chen Yang , Xiong Zhuang , Xu Pengjing
Published: February 2022

Fluid charging and formation pressure evolution are of great significance for shale gas exploration and development. Taking the Longmaxi formation shale in southeast Chongqing as the research object, the fluid activity and pressure evolution process were studied by means of thin section observation, fluid inclusion analysis and testing, and basin numerical simulation. The results show that the area of the southeast of Chongqing, the Longmaxi formation of dike filling minerals, is mainly quartz vein and calcite part of the veins and arteries, and vein body captures the rich organic inclusions in minerals, sees fgas-liquid two-phase brine inclusions and methane inclusions, combining with the temperature and salinity of brine inclusions, and shows that the area has experienced two phases of oil and gas filling, the first period of gas-liquid fluid. The second stage is gas fluid. Combined with burial history and thermal maturation history, it is found that the filling time of oil and gas is late Cretaceous and Paleocene, respectively. In the Cretaceous, the pressure coefficient of Longmaxi formation reached the maximum value of 2.2 and remained above 1.5 overpressure after pressure unloading in the later period. In terms of the frequency of inclusions, the charging amount of the first stage is relatively large, and the charging time corresponds to the peak period of the palaeo-formation pressure. At the same time, fluid charging can timely supplement the pressure after unloading, which is conducive to high and stable production of shale gas.
Free Triiodothyronine Is Associated with Poor Outcomes after Acute Ischemic Stroke
Authors: Song Yue , Yang Changqiang , Wang Hua
Published: February 2022

Aims. It is unclear whether thyroid hormones are associated with functional outcomes after ischemic stroke. We aimed to investigate the impact of serum levels of thyroid hormones at admission on functional outcomes at 3 months after acute ischemic stroke. Methods. A total of 480 consecutive patients with ischemic stroke who were admitted to our hospital within 48 h of onset were enrolled. The levels of thyroid hormones, including thyroid-stimulating hormone, free triiodothyronine (FT3), and free thyroxine, were measured at admission, and functional outcomes were assessed at 3 months using the modified Rankin Scale (mRS), with scores ranging from 0 to 6. Poor outcome was defined as mRS score ≥3. Results. FT3 levels at admission were considerably lower in patients with poor outcomes than in those with good outcomes at 3 months (3.53 ± 0.70 pmol/L vs. 4.04 ± 0.68 pmol/L;

P
<
0.001

). Lower FT3 levels were observed in patients with higher mRS scores. Multivariable logistic regression analysis revealed that FT3 levels were significantly associated with a risk of poor outcomes at 3 months, independent of conventional risk factors such as age, National Institutes of Health Stroke Scale score, and recanalized therapy. In addition, patients in FT3 levels in the lowest quartile had a 2.56-fold higher risk of poor outcomes than those with FT3 levels in the highest quartile (odds ratio = 2.56, 95% confidence interval = 1.15–5.69,

P
=
0.021

). The sensitivity and specificity of FT3 level ≤3.69 pmol/L for predicting poor outcomes were 62.70% and 72.03%, respectively. Conclusion. Our study suggests that FT3 levels at admission are significantly and independently associated with a risk of poor outcomes after ischemic stroke and that lower FT3 levels can be considered as a prognostic biomarker for poor outcomes at 3 months.
Mechanical Analysis and Clinical Application of Butterfly-Shaped Patellar Claw
Authors: Liu Yi
Published: February 2022

Memory alloy patella claws for treating patella fractures have been used for more than 30 years with many desirable features including fast healing, quick recovery, and avoidance of top abrasion of Kirschner wires and other complications. However, there are many models and it is difficult to choose the accurate claw for the patient. In this study, a finite element model of the butterfly-shaped patellar claw made of shape memory alloy was established, its mechanical structure was analyzed, and its clinical application was monitored. We used Solidworks Simulation software for modeling and mainly analyzed the force of the compression ring of the butterfly-shaped patellar claw. Clinically, we chose a closed fresh patella fracture case. After finite element analysis, the maximum stress that the compression ring of the butterfly-shaped patellar claw can withstand is 568.1 MPa. In this range, it always has elastic deformation resistance. The butterfly-shaped patella claw is fixed on the patella and will not break when subjected to a maximum force of 150 N on the encircling arm, and at the same time, there will be no pressure failure due to plastic deformation. A total of 27 cases were clinically used for the assessment of the clinical efficacy of the newly designed butterfly-shaped patella claws. The average follow-up time was 15.5 months, and the average fracture healing time was 8-12 weeks. All patients can get out of bed with crutches within 2 to 3 days after surgery. Among them, there were 15 cases with excellent functional ratings, 10 cases with good ratings, 2 cases with acceptable ratings, and no cases with poor ratings. The designed butterfly-shaped patella claws can provide an effective method for the treatment of patella fractures.