bluuebunny/crossref_metadata_2025 huggingface dataset:
Accelerating Video Captioning on Heterogeneous System Architectures
Authors: Huang Horng-Ruey , Hong Ding-Yong , Wu Jan-Jan , Chen Kung-Fu , Liu Pangfeng , Hsu Wei-Chung
Published: April 2022

Video captioning is a core technology to many important applications, such as AI-assisted medical diagnosis, video question answering, storytelling through videos, and lip-reading. Video captioning employs a hybrid CNN + RNN model. Accelerating such a hybrid model on a heterogeneous system is challenging for two reasons. First, CNN and RNN exhibit very different computing behaviors, making the mapping between computation and heterogeneous devices difficult. Second, data dependency exists between the CNN and RNN within a video frame and between adjacent RNNs across video frames. These data dependencies prohibit the full parallelization of the hybrid model. The issues also include the utilization of accelerator resources, which is critical to maximizing the performance. In this work, we propose a fine-grained scheduling scheme for mapping computation and devices within a video frame, and a pipeline scheduling scheme for exploiting maximum parallelism between the execution of the video frames. In addition, we propose two capacity-guided scheduling methods. On the server, the concurrent kernel execution mechanism is exploited for improving GPU utilization. On the edge platform, we rearrange CNN computation among the CPU and EdgeTPUs guided by the EdgeTPU’s SRAM capacity so that balanced computation is achieved and off-chip memory overhead is minimized. Experimental results show that our scheduling scheme improves video captioning performance by up to 3.24

\( \times \)

with CPU + GPU collaboration over the GPU-only execution. On an edge platform with an ARM CPU and two EdgeTPUs, our CPU + EdgeTPU scheduling exhibits outstanding performance, which achieves up to 54.9

\( \times \)

speedup compared to using ARM CPU only and can perform video captioning of 59 frames per second.
Circular RNA detection and effect of sequence homology
Authors: Sayed Mohammed , Hwang Jae Yeon , Park Juw Won
Published: April 2022

Over the past two decades, researchers have shown an increasing interest in a special form of alternative splicing (AS) that produces a circular form of RNA distinct from the canonical linear form of RNA. Although several circular RNA detection tools have been developed, achieving both high sensitivity and high precision has been quite challenging in this area. Homologous coding sequences (exons) in the same transcript (or isoform) can lead to incorrect assignment of a read to a backsplicing junction instead of a linear-splicing junction, producing a source of false-positives in circular RNA detection. Although this problem has been mentioned in previous research articles, there has been no effort made to better understand the extent to which it affects sensitivity and precision. In this paper, we investigate the frequency of exon sequence homology in three different species (human, mouse and rat) and how this issue can affect accurate detection of circular RNAs from high-throughput sequencing data.
Validating Small Molecule Chemical Probes for Biological Discovery
Authors: Vu Victoria , Szewczyk Magdalena M. , Nie David Y. , Arrowsmith Cheryl H. , Barsyte-Lovejoy Dalia
Published: April 2022

Small molecule chemical probes are valuable tools for interrogating protein biological functions and relevance as a therapeutic target. Rigorous validation of chemical probe parameters such as cellular potency and selectivity is critical to unequivocally linking biological and phenotypic data resulting from treatment with a chemical probe to the function of a specific target protein. A variety of modern technologies are available to evaluate cellular potency and selectivity, target engagement, and functional response biomarkers of chemical probe compounds. Here, we review these technologies and the rationales behind using them for the characterization and validation of chemical probes. In addition, large-scale phenotypic characterization of chemical probes through chemical genetic screening is increasingly leading to a wealth of information on the cellular pharmacology and disease involvement of potential therapeutic targets. Extensive compound validation approaches and integration of phenotypic information will lay foundations for further use of chemical probes in biological discovery.
Blockchain Technology in the Chemical Industry
Authors: Zhou Xiaochi , Kraft Markus
Published: April 2022

This article presents a review of the application of blockchain and blockchain-based smart contracts in the chemical and related industries. We introduce the basic concepts of blockchain and smart contracts and explain how some of their features are enabled. We review several typical or novel applications of blockchain and smart contract technologies and their enabling concepts and underlying technologies. We classify the selected literature into five categories and discuss their motivations and technical designs. We recognize that the trend of decentralization creates a need to use blockchain and smart contracts to implement trust and distributed control mechanisms. We also speculate on future applications of blockchain and smart contracts. We believe that, in the future, blockchains with different consensus mechanisms will be studied and applied to achieve more efficient and practical decentralized systems. Also, blockchain-based smart contracts will be more widely applied to enhance autonomous distributed controls in decentralized systems.
Neuroimmune Interactions in Peripheral Organs
Authors: Klein Wolterink Roel G.J. , Wu Glendon S. , Chiu Isaac M. , Veiga-Fernandes Henrique
Published: April 2022

Interactions between the nervous and immune systems were recognized long ago, but recent studies show that this crosstalk occurs more frequently than was previously appreciated. Moreover, technological advances have enabled the identification of the molecular mediators and receptors that enable the interaction between these two complex systems and provide new insights on the role of neuroimmune crosstalk in organismal physiology. Most neuroimmune interactions occur at discrete anatomical locations in which neurons and immune cells colocalize. Here, we describe the interactions of the different branches of the peripheral nervous system with immune cells in various organs, including the skin, intestine, lung, and adipose tissue. We highlight how neuroimmune crosstalk orchestrates physiological processes such as host defense, tissue repair, metabolism, and thermogenesis. Unraveling these intricate relationships is invaluable to explore the therapeutic potential of neuroimmune interactions.
Opportunities, Technology, and the Joy of Discovery
Authors: Spear Patricia G.
Published: April 2022

My grandparents were immigrants. My paternal grandfather was illiterate. Yet my parents were able to complete college and to become teachers. I had a conventional upbringing in a small town in Florida, graduating from high school in 1960. I was fortunate enough to graduate cum laude from Florida State University and to earn other credentials leading to faculty positions at outstanding institutions of higher education: the University of Chicago and Northwestern University. At a time when women were rarely the leaders of research groups, I was able to establish a well-funded research program and to make contributions to our understanding of viral entry into cells. My best research was done after I became confident enough to seek productive interactions with collaborators. I am grateful for the collaborators and collaborations that moved our field forward and for my trainees who have gone on to successes in many different careers.
Federated Deep Learning Approaches for the Privacy and Security of IoT Systems
Authors: Alazzam Malik Bader , Alassery Fawaz , Almulihi Ahmed
Published: April 2022

Using federated learning, which is a distributed machine learning approach, a machine learning model can train on a distributed data set without having to transfer any data between computers. Instead of using a centralised server for training, the model uses data stored locally on the device itself. After that, the server uses this model to create a jointly trained model. Federated learning asserts that privacy is preserved because no data is sent. Botnet attacks are detected using on-device decentralised traffic statistics and a deep autoencoder. This proposed federated learning approach addresses privacy and security concerns about data privacy and security rather than allowing data to be transferred or relocated off the network edge. In order to get the intended results of a previously centralised machine learning technique while also increasing data security, computation will be shifted to the edge layer. Up to 98% accuracy is achieved in anomaly detection with our proposed model using features like MAC IP and source/destination/IP for training. Our solution outperforms a standard centrally managed system in terms of attack detection accuracy, according to our comparative performance analysis.
Multiobjective Project Scheduling with Multiple Compression Capabilities of the Multistate Activities with Project Reliability Approach using the Metaheuristic Algorithm
Authors: Shayannia Seyed Ahmad
Published: April 2022

We examine the time-cost-reliability-project balance of the project by considering the limitations of renewable and nonrenewable resources and performing multistate group activities in the same conditions and with the possibility of multiple compression activities. This study aimed to select the best mode for conducting actions in each subset and find the best execution method by determining the number of compression time units of actions to maximize project reliability and minimize project completion risk, time, and cost. Drilling projects will also be implemented for evaluation in the Azar oil field. The exact Epsilon Limitation method was used to solve this problem in low dimensions. The combined genetic metaheuristic algorithm and particle swarming were used to solve high dimensions problems. The low-dimensional Epsilon solution method could clearly balance the objective functions. Also, the Taguchi experimental design method was used to adjust the parameters of the problem algorithm. The correct value of the algorithm parameters is also specified. Based on the results, the hybrid metaheuristic algorithm has less solution time than the exact method. In high dimensions, as it was observed, GAMS software could not solve the problem. Still, the hybrid metaheuristic algorithm solved the problem well in high dimensions.
Fund Performance Evaluation Based on Bayesian Model and Machine Learning Algorithm
Authors: Li Shuanbao , Qu Shenming
Published: April 2022

Based on Bayesian method, this paper constructs a model for estimating fund performance evaluation, and uses machine learning algorithm to construct a sampler that can sample on the basis of conditional distribution. Sampling is used for stress test, so as to give the closeness of all possible test results and data results. The results show that performance evaluation is affected by many factors, and the resistance to risk plays an important role in the whole performance evaluation. At the same time, the Bayesian model in machine learning can quickly and accurately approach the statistical results, which is of great significance for predicting performance evaluation.
Mechanical Properties of Graphene Composite Materials and Mesonumerical Experiments on the Mechanical Properties of Unsaturated Soil‐Rock Mixtures
Authors: Zhang Qinghai , Wu Lihua , Li Jianfei
Published: April 2022

Graphene, as an emerging two‐dimensional carbon nanomaterial, has attracted widespread attention and in‐depth research by domestic and foreign researchers. With its excellent mechanical properties, graphene is used as a reinforcing material in polymers, ceramics and rubber, and other matrices, which can significantly improve the toughness of the matrix. However, under the action of natural factors such as continuous rainfall or heavy rain, the accumulation layer landslide is often in an unsaturated state, which is very easy to induce disasters. Based on this background, we use micronumerical simulation methods and graphene composite materials to study the mechanical properties of unsaturated soil‐rock mixtures to understand the mechanical properties of unsaturated soil‐rock mixtures and evaluate and predict its stability. This paper takes the soil of a landslide zone in a certain city as the numerical test object. Through the automatic generation technology of the mesostructure model of the soil‐rock mixture, a series of numerical experiments were carried out on the random mesostructure model of the soil‐rock mixture, and the gravel content was analyzed in depth. Experiments show that the model can reasonably describe the volume expansion of the soil‐rock mixture under low confining pressure, the volume compression under high confining pressure, and the phenomenon that the peak strength of the soil‐rock mixture increases with the increase of confining pressure, which verifies that the model is simulating soil and rock. This shows that the stress‐strain relationship curves of different soil‐rock interface parameters, rock content, saturation, and the number of wet and dry cycles all show obvious nonlinear characteristics and strain hardening. This shows that graphene composites can be used in the study of the mechanics of unsaturated soil‐rock mixtures, which is of great significance for improving the landslide environment and enhancing its mechanical properties.
Investigation on the Effect of Geometric Parameter on Reinforced Concrete Exterior Shear Wall‐Slab Connection Using Finite Element Analysis
Authors: Hamicha Aman , Kenea Goshu
Published: April 2022

Finite element modeling is becoming widely applicable for the assessment of structural behavior, which is accurate and reliable, since conducting the full experimental test is time‐consuming and very expensive. Previously conducted research confirmed that there is a good agreement with the experimental tests. In this study, a nonlinear finite element analysis with the ABAQUS software package has been taken to investigate the response of reinforced concrete exterior shear wall‐slab connection subjected to cyclic loading. The structural responses such as load‐carrying capacity, energy dissipation, ductility, and stiffness degradation have been studied. Connection type, an aspect ratio of the thickness of the slab to the thickness of the shear wall (ts/tw), the aspect ratio of the height of the shear wall to the effective width of the slab (H/We), and concrete strength were used as study parameters to evaluate their effect on the structural response of the connection. A total of sixteen models have been selected based on the parametric study and objective of the study. The results show that the exterior shear wall‐slab connection with U‐type connection has high load‐carrying capacity than the other connection types. As the aspect ratios of ts/tw, H/We and concrete strength increase, the ultimate load capacity and energy dissipation capacity increase. In other words, as the number of loading cycles increases, the stiffness of the connection is decreased.
Construction of an Optimal Scheduling Method for Campus Energy Systems Based on Deep Learning Models
Authors: Li Jingyun , Zhao Hong
Published: April 2022

Aiming at the problem of high cost and low efficiency of planning and scheduling caused by load uncertainty of campus energy system, a 3-layer planning and scheduling model based on multivariate load prediction is proposed, mainly including prediction layer, planning layer, and scheduling layer; a long-term and short-term prediction model of multivariate load is constructed based on random forest regression network and long and short-term memory network. With the objective of minimizing the comprehensive planning and scheduling cost and the scheduling operation cost, the optimal comprehensive system cost and configuration scheme are obtained by using improved particle swarm algorithm and CPLEX solver; the equipment status and system cost are analyzed by planning and scheduling under different scenarios. By comparing the planning and scheduling results of the constructed 3-layer model with the conventional two-layer model, the economy and reliability of the 3-layer planning and scheduling model are demonstrated.
Diagnosis of Breast Cancer Pathology on the Wisconsin Dataset with the Help of Data Mining Classification and Clustering Techniques
Authors: Mohammad Walid Theib , Teete Ronza , Al-Aaraj Heyam , Rubbai Yousef Saleh Yousef , Arabyat Majd Mowafaq
Published: April 2022

Breast cancer must be addressed by a multidisciplinary team aiming at the patient’s comprehensive treatment. Recent advances in science make it possible to evaluate tumor staging and point out the specific treatment. However, these advances must be combined with the availability of resources and the easy operability of the technique. This study is aimed at distinguishing and classifying benign and malignant cells, which are tumor types, from the data on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset by applying data mining classification and clustering techniques with the help of the Weka tool. In addition, various algorithms and techniques used in data mining were measured with success percentages, and the most successful ones on the dataset were determined and compared with each other.
3D Virtual Modeling Realizations of Building Construction Scenes via Deep Learning Technique
Authors: Li Weihong
Published: April 2022

The architectural drawings of traditional building constructions generally require some design knowledge of the architectural plan to be understood. With the continuous development of the construction industry, the use of three-dimensional (3D) virtual models of buildings is quickly increased. Using three-dimensional models can give people a more convenient and intuitive understanding of the model of the building, and it is necessary for the painter to manually draw the 3D model. By analyzing the common design rules of architectural drawing, this project designed and realized a building three-dimensional reconstruction system that can automatically generate a stereogram (3 ds format) from a building plan (dxf format). The system extracts the building information in the dxf plan and generates a three-dimensional model (3 ds format) after identification and analysis. Three-dimensional reconstruction of architectural drawings is an important application of computer graphics in the field of architecture. The technology is based on computer vision and pattern recognition, supported by artificial intelligence, three-dimensional reconstruction, and other aspects of computer technology and engineering domain knowledge. It specializes in processing architectural engineering drawings with rich semantic information and various description forms to automatically carry out architectural drawing layouts. The high-level information with domain meanings such as the geometry and semantics/functions of graphics of the buildings can be analyzed for forming a complete and independent research system. As a new field of computer technology, the three-dimensional reconstruction drawings are appropriate for demonstrating the characteristics of architectural constructions.
Analysis and Application of Fractured Carbonate Dual-Media Composite Reservoir Model
Authors: Song Heng , Hu Yuan , Luo Erhui , He Congge , Zeng Xing , Zhang Shaoqi
Published: April 2022

Fractured carbonate reservoirs are different from conventional reservoirs and generally develop fractures. Conventional reservoir well test performance analysis can only obtain the formation parameters and discharge radius around a single well. For vertical wells in fractured carbonate reservoirs, we can establish a dual-porosity composite reservoir model for well testing interpretation and analysis based on the actual conditions of the reservoir and combined with geological knowledge. The model can be applied to formations with different reservoir properties in the near-well and far-well areas around a single well to analyze the fracture parameters (elastic reserve ratio and channeling coefficient) and the effective flow capacity of formation fluids in the two areas. On this basis, we use the interpretation of actual production wells to verify the accuracy of the model and analyze the influence of reservoir parameters and fracture parameters on the theoretical curve.
Construction and Application of Comprehensive Nursing Information Service Platform Based on Internet of Things Technology
Authors: Nie Xiaoxia
Published: April 2022

This study illustrates the construction and application of comprehensive nursing information service platform based on Internet of Things technology. An Internet-based cardiovascular care management platform, convenient before and after platform application, was constructed; 42 patients with cardiovascular disease were selected as the control group and observation group and 33 nurses participated in the same group, and the intervention lasted for 4 months. The control group received routine admission nursing, and the follow-up nursing nurses received traditional face-to-face teaching, training in the form of assessment. The observation group with the help of the cardiovascular specialist nursing management platform, during the whole process of nursing, nurses also manage patients and receive training through the platform. Experiments show the following: As a result, after the application of the platform, the satisfaction rate of nurses and patients on the platform was high, both reaching more than 92%. After the application of the platform, the scores of various nursing indicators were significantly higher than those before application (all

P
<
0.01

). The construction of a cardiovascular specialist nursing management platform based on IoT technology integrates multiple modules on the medical and patient ends, which not only improves the level of nursing management but also improves the satisfaction of nurses and patients and, at the same time, helps to improve the self-value of nurses.
A Bias-Corrected Method for Fractional Linear Parameter Varying Systems
Authors: Yakoub Zaineb , Naifar Omar , Amairi Messaoud , Chetoui Manel , Aoun Mohamed , Makhlouf Abdellatif Ben
Published: April 2022

This paper proposes an identification algorithm for the fractional Linear Parameter Varying (LPV) system considering noisy scheduling and output measurements. A bias correction technique is provided in order to compensate for the bias caused by the least squares algorithm. This approach was created to estimate either coefficients or fractional-order differentiation, and it has been proven to produce unbiased and reliable results. The suggested method’s performance is assessed by the identification of two fractional models and was compared with Nelder–Mead Simplex method.
Comparative Analysis of Recent Architecture of Convolutional Neural Network
Authors: Saleem Muhammad Asif , Senan Norhalina , Wahid Fazli , Aamir Muhammad , Samad Ali , Khan Mukhtaj
Published: April 2022

Convolutiona neural network (CNN) is one of the best neural networks for classification, segmentation, natural language processing (NLP), and video processing. The CNN consists of multiple layers or structural parameters. The architecture of CNN can be divided into three sections: convolution layers, pooling layers, and fully connected layers. The application of CNN became most demanding due to its ability to learn features from images automatically, involving massive amount of training data and high computational resources like GPUs. Due to the availability of the above-stated resources, multiple CNN architectures have been reported. This study focuses on the working of convolution, pooling, and the fully connected layers of CNN architecture, origin of architectures, limitation, benefits of reported architectures, and comparative analysis of contemporary architecture concerning the number of parameters, architectural depth, and significant contribution.
Cultural Awareness, Listening Comprehension, Listening Motivation, and Attitude among EFL Learners: A Gender-Based Mixed Method Study
Authors: Kamaeva Rima Bizyanovna , Hussien Mohamed , Rassouli Aman , Al-Sudani Ahmed Qassem Ali Sharhan , Zaini Qudsia , Haidari Mir Mohammad Farooq
Published: April 2022

The current research investigated the effects of cultural awareness on listening comprehension and listening motivation of EFL learners. Also, this research investigated the perspectives of EFL students towards having English cultural awareness. Seventy-five EFL learners from three English intact classes were chosen as the subjects of this study. After that, a listening motivation test and a listening comprehension test were given to the three classes. Then, five audio files containing the English cultural-based issues were taught in one class and five audio files containing the Persian cultural contents were instructed to the other experimental class. The third class or the control class was instructed five audio files that did not include any specific cultural-based materials. After teaching all audio files, a listening motivation posttest and a listening comprehension posttest were given to the three groups and an attitude questionnaire was given to the experimental classes to determine their attitudes toward using the effectiveness of cultural awareness on their language learning development. The results of one-sample

t

-test, one-way ANOVA, and the post hoc Sceffe test showed that the experimental classes outflanked the control class in the posttest of listening motivation and listening comprehension. The results indicated that the subjects showed a positive attitude toward having cultural awareness in their English learning process. The outcomes of the study provide some implications for teachers to increase the students’ cultural awareness and to enhance their positive attitudes about the integration of culturally based resources into language education.
Improved Algorithm of Fuzzy Set Gravity Centre in Track System Teaching Field
Authors: Jiang Zhiquan
Published: April 2022

With the development of science and technology, modern science and technology are more and more widely used in the field of sports, which not only promotes the scientific development of track and field system teaching but also makes great changes in the way of track and field system teaching. Many methods of sports evaluation using modern science and technology have been accepted by people and have been widely used in teaching, especially in the evaluation of sports achievements. However, there are few scientific and technological methods for the evaluation of sports technology. Track and discipline instructing is an extraordinarily complicated sport. The instructing of bodily schooling forte has accompanied the standard instructing technique for a lengthy time. The focus and evaluation of teaching are usually determined by teachers’ teaching experience. This teaching method is often blind and lack of pertinence and scientific. In order to improve the overall level of track and field system teaching, a scientific and reasonable quantitative mode can be adopted. This paper uses the improved algorithm of fuzzy set centre of gravity set to analyse the existing teaching. The algorithm offers fuzzy interval rating by means of comparison experts, calculates the centre of gravity of the endpoint of interval score, considers the weight of a number influencing elements and the authority of experts, and offers complete decision-making opinions. The algorithm makes full use of expert evaluation opinions and is easy to operate. Evaluation examples verify the scientific and accuracy of the algorithm. The results show that the algorithm conforms to the scoring law of track and field system teaching, has a certain practical effect on the objective evaluation of sports results, and can help teachers clearly understand students’ sports skills.
Toxicological Safety Evaluation in Acute and 21-Day Studies of Ethanol Extract from Solanum lyratum Thunb
Authors: Guo XiaoHua , Weng LianJin , Yi LiTao , Geng Di
Published: April 2022

Solanum lyratum (Solanaceae) is a traditional Chinese medicine widely used to remedy cold fever, damp-heat jaundice, herpes, and nephritis dropsy. Despite its obvious therapeutic advantages, few toxicological studies have involved the efficacy and safety of its long-term treatment. To investigate the acute and subchronic toxicity of the extract of 75% ethanol extract of whole Solanum lyratum (ESL) after oral administration in mice. In acute toxicity experiment, mice were intragastric administration with ESL at doses of 1000, 2000, 3000, 4000, or 5000 mg/kg for 1 day. In a subchronic toxicity experiment, mice were intragastrically administration with ESL at doses of 180, 360, and 720 mg/kg and 0.9% saline for 21 days. Weight gain, hematological, biochemical, and histopathological analysis of vital organs were evaluated. The presence of aristolochic acid I in ESL was studied using UPLC-QTOF-MS. Phytochemical analysis indicated that the presence of aristolochic acid I in ESL was 0.0025 mg/g. This relatively low concentration is not enough to cause toxicity. In the acute toxicity experiment, neither mortality nor clinical alterations were shown, except for the mild transient diarrhea at 5000 mg/kg. So the LD50 value of ESL was assessed to be more than 5000 mg/kg. In the subchronic toxicity experiment, neither mortality nor treatment-related clinical signs were observed. There was a significant increase in body weight, hemoglobin (HB), and urea nitrogen (BUN) after administration with ESL at 180 mg/kg. In addition, the weight of the stomach was increased and the hematocrit (HCT) was decreased after administration with ESL at 360 mg/kg. The changes were not considered treatment-related toxicological effects because the toxicity and histopathological analysis indicate that the extracts are safe for oral administration.
A Comprehensive Review of Computation‐Based Metal‐Binding Prediction Approaches at the Residue Level
Authors: Ye Nan , Zhou Feng , Liang Xingchen , Chai Haiting , Fan Jianwei , Li Bo , Zhang Jian
Published: April 2022

Clear evidence has shown that metal ions strongly connect and delicately tune the dynamic homeostasis in living bodies. They have been proved to be associated with protein structure, stability, regulation, and function. Even small changes in the concentration of metal ions can shift their effects from natural beneficial functions to harmful. This leads to degenerative diseases, malignant tumors, and cancers. Accurate characterizations and predictions of metalloproteins at the residue level promise informative clues to the investigation of intrinsic mechanisms of protein‐metal ion interactions. Compared to biophysical or biochemical wet‐lab technologies, computational methods provide open web interfaces of high‐resolution databases and high‐throughput predictors for efficient investigation of metal‐binding residues. This review surveys and details 18 public databases of metal‐protein binding. We collect a comprehensive set of 44 computation‐based methods and classify them into four categories, namely, learning‐, docking‐, template‐, and meta‐based methods. We analyze the benchmark datasets, assessment criteria, feature construction, and algorithms. We also compare several methods on two benchmark testing datasets and include a discussion about currently publicly available predictive tools. Finally, we summarize the challenges and underlying limitations of the current studies and propose several prospective directions concerning the future development of the related databases and methods.
Cooperation Mode of Soccer Robot Game Based on Improved SARSA Algorithm
Authors: Zhan Wei , Qu Shengqing
Published: April 2022

Football is one of the most popular sports in the world, and its competition has received increasing people’s attention. With the increasing number of robot football competitions, more strategic planning is needed for robot football matches. In a tournament, each player has their own task and must have the skills to complete it. In this paper, we use the RoboCup2D platform to give details of the server and client roles, introduce the agent model in RoboCup2D, and compare the plan design and scheme design presented in the current study using the SARSA algorithm, one of the augmented methods classified as TD learning metrics. In addition, heuristic information was introduced and implemented to enhance learning through the sharing of

Q

values between participants and reinforcement learning. A comparative analysis of the feasibility of the SARSA algorithm in the context of its application in RoboCup2D was carried out, and the experimental results proved that our algorithm was effective in improving the team’s offensive and defensive capabilities.
¿Existe una relación entre la esquizofrenia y la violencia y los homicidios?
Authors: Romero Martínez Ángel
Published: April 2022

La cultura popular ha empleado la esquizofrenia y otras enfermedades mentales como un recurso narrativo útil. Sin embargo, esto puede llegar a estigmatizar a las personas que las padecen, creando una imagen errónea. Por ejemplo, la película Joker (2019) puede malinterpretarse y concluir que las personas con esquizofrenia son potenciales homicidas. Es por ello por lo que debemos prestar atención a las conclusiones de la comunidad científica respecto a la relación de la esquizofrenia con la violencia y los homicidios. En este sentido, las evidencias científicas con las que contamos hasta el momento no nos permiten asumir que la esquizofrenia sea la causa principal de la violencia y, mucho menos, del homicidio. De hecho, son necesarias más variables, no directamente relacionas con la enfermedad, como el consumo de drogas, la psicopatía o el maltrato durante la infancia para que exista una relación entre este enfermedad y la violencia o el homicidio.
Colonización nasal por Staphylococcus aureus y Staphylococcus aureus resistente a meticilina (SARM) en auxiliares de enfermería
Authors: Fajardo Zapata Alvaro Luis , Gaines Acuña Sebastian
Published: April 2022

Introducción: Staphylococcus aureus, es la principal causa de bacteriemia infecciosa y endocarditis, así como de infecciones osteoarticulares, de piel y tejidos blandos, su reservorio principal es la mucosa nasal. Los trabajadores de la salud son una fuente importante de transmisión de S. aureus y S. aureus resistente a la meticilina (SARM). Objetivo: determinar la presencia de Staphylococcus aureus y SARM en la fosa nasal de auxiliares de enfermería en la ciudad de Bogotá. Materiales y métodos: estudio descriptivo de corte transversal, en auxiliares de enfermería de diferentes instituciones hospitalarias y clínicas en la ciudad de Bogotá, Colombia. Se realizó un muestreo aleatorio. El tamaño de la muestra fue de 491 hisopados de la fosa nasal derecha de igual número de auxiliares de enfermería que al momento del estudio se encontraban laborando a nivel clínico. Se tomó un intervalo de confianza del 95% y error máximo admisible del 5%, se consideró el valor de p= 0,5. Se realizó un estudio de frecuencias y determinación de prevalencias mediante un análisis univariado. Resultados: la presente investigación encontró que el 28,5% de los participantes fueron portadores del Staphylococcus aureus y el 6,1% fueron SARM. Conclusiones: la colonización por Staphylococcus aureus y SARM es frecuente en auxiliares de enfermería.
Expansion of Gastric Intestinal Metaplasia with Copy Number Aberrations Contributes to Field Cancerization
Authors: Kumagai Ken , Shimizu Takahiro , Takai Atsushi , Kakiuchi Nobuyuki , Takeuchi Yasuhide , Hirano Tomonori , Takeda Haruhiko , Mizuguchi Aya , Teramura Mari , Ito Takahiko , Iguchi Eriko , Nikaido Mitsuhiro , Eso Yuji , Takahashi Ken , Ueda Yoshihide , Miyamoto Shin'ichi , Obama Kazutaka , Ogawa Seishi , Marusawa Hiroyuki , Seno Hiroshi
Published: April 2022

Intestinal metaplasia (IM) is a risk factor for gastric cancer following infection with Helicobacter pylori. To explore the susceptibility of pure gastric IM to cancer development, we investigated genetic alterations in single IM gastric glands. We isolated 50 single IM or non-IM glands from the inflamed gastric mucosa of 11 patients with intramucosal gastric carcinoma (IGC) and 4 patients without IGC; 19 single glands in the noninflamed gastric mucosa of 11 individuals from our cohort and previous dataset were also included as controls. Whole-exome sequencing of single glands revealed significantly higher accumulation of somatic mutations in various genes within IM glands compared with non-IM glands. Clonal ordering analysis showed that IM glands expanded to form clusters with shared mutations. In addition, targeted-capture deep sequencing and copy number (CN) analyses were performed in 96 clustered IM or non-IM gastric glands from 26 patients with IGC. CN analyses were also performed on 41 IGC samples and The Cancer Genome Atlas-Stomach Adenocarcinoma datasets. These analyses revealed that polyclonally expanded IM commonly acquired CN aberrations (CNA), including amplification of chromosomes 8, 20, and 2. A large portion of clustered IM glands typically consisted of common CNAs rather than other cancer-related mutations. Moreover, the CNA patterns of clustered IM glands were similar to those of IGC, indicative of precancerous conditions. Taken together, these findings suggest that, in the gastric mucosa inflamed with H. pylori infection, IM glands expand via acquisition of CNAs comparable with those of IGC, contributing to field cancerization.

Significance:

This study contributes to our understanding of gastric intestinal metaplasia as a risk factor for gastric adenocarcinoma via their multifocal expansion and acquisition of CNAs and somatic mutations.
Epithelial–Mesenchymal Plasticity in Tumor Immune Evasion
Authors: Mullins Riley D.Z. , Pal Ananya , Barrett Thomas F. , Heft Neal Molly E. , Puram Sidharth V.
Published: April 2022

Epithelial–mesenchymal transition (EMT) is a fundamental process that occurs during embryogenesis and tissue repair. However, EMT can be hijacked by malignant cells, where it may promote immune evasion and metastasis. Classically considered a dichotomous transition, EMT in cancer has recently been considered a plastic process whereby malignant cells display and interconvert among hybrid epithelial/mesenchymal (E/M) states. Epithelial–mesenchymal plasticity (EMP) and associated hybrid E/M states are divergent from classical EMT, with unique immunomodulatory effects. Here, we review recent insights into the EMP-immune cross-talk, highlighting possible mechanisms of immune evasion conferred by hybrid E/M states and roles of immune cells in EMP.
Identification and Management of Pathogenic Variants inBRCA1,BRCA2, andPALB2in a Tumor-Only Genomic Testing Program
Authors: Bychkovsky Brittany L. , Li Tianyu , Sotelo Jilliane , Tayob Nabihah , Mercado Joanna , Gomy Israel , Chittenden Anu , Kane Sarah , Stokes Samantha , Hughes Melissa E. , Kim Ji Seok , Umeton Renato , Awad Mark M. , Konstantinopoulos Panagiotis A. , Yurgelun Matthew B. , Wolpin Brian M. , Taplin Mary-Ellen , Newmark Randall E. , Johnson Bruce E. , Lindeman Neal I. , MacConaill Laura E. , Garber Judy E. , Lin Nancy U.
Published: April 2022

Purpose:

Tumor-only genomic testing can uncover somatic and germline pathogenic variants [pathogenic/likely pathogenic (P/LP)] in cancer predisposition genes. We describe the prevalence of P/LPs in BRCA1/2 and PALB2 (B1B2P2) across malignancies and the frequency of clinical germline testing (CGT) in patients with P/LPs in B1B2P2 identified on tumor-only testing.

Experimental Design:

Among 7,575 patients with cancer tested between 2016 and 2018 with the OncoPanel tumor-only sequencing assay, we characterized P/LP frequencies by tumor type, receipt of CGT prior to or within 12 months after OncoPanel, and factors associated with CGT.

Results:

272 (3.6%) patients had OncoPanel-detected P/LPs in B1B2P2: 37.5% of P/LPs were in BRCA-related cancers; the remainder were in non-BRCA tumors. P/LPs were detected in ≥5% of breast, pancreatic, prostate, ovarian, nonmelanoma skin, endometrial, small cell lung, and colorectal cancers. 37.9% of patients with P/LPs received CGT prior to OncoPanel; an additional 10.7% underwent CGT within 12 months of OncoPanel. Among 132 with CGT, 88.6% had ≥1 clinical factor for CGT compared with 47.1% who did not undergo CGT. Patients with BRCA tumors were more likely to have CGT compared with those without (81.4% vs. 29.0%, P < 0.0001). Among patients with CGT, 70.5% (93/132) of P/LPs were germline.

Conclusions:

Tumor-only genomic testing identified P/LPs in B1B2P2 in 3.6% of patients. 52.9% of patients with tumor-detected P/LPs and without CGT did not meet personal or family history criteria for CGT. In addition, some patients with tumor-detected P/LPs were not referred for CGT, especially those with non-BRCA tumors. Given implications for treatment selection and familial cancer risk, processes to reliably trigger CGT from tumor-genomic findings are needed.
Ringmajanduslike praktikate juurutamise võimaldajad ja barjäärid: uuringu aruanne.
Authors:   Tallinn University of Technology, Gertslberger Wolfgang , Küttim Merle , Tuisk Tarmo , Hurt Ulrika , Niine Tarvo , Ahmadov Tarlan , Metsmaa Margit , Liiv Jana , Andrijaškina Anastassia , Bavõkina Jekaterina , Pentsa Hanna
Published: April 2022

This research study focused on the business models related to the circular economy of the four industries and their enablers and barriers have been studied. The research was conducted from September to December 2021 in Estonia by Tallinn University of Technology Sustainable Value Chain Management Working Group for the Ministry of Economic Affairs and Communications. The industries covered by the study were: 1) the computer, electronic and optical equipment industries; 2) chemicals and chemical industry, except plastics industry; 3) the electrical equipment industry; 4) metal industry.
Prediction of Recurrent Ischemic Stroke Using Registry Data and Machine Learning Methods: The Erlangen Stroke Registry
Authors: Vodencarevic Asmir , Weingärtner Michael , Caro J. Jaime , Ukalovic Dubravka , Zimmermann-Rittereiser Marcus , Schwab Stefan , Kolominsky-Rabas Peter
Published: April 2022

Background:

There have been multiple efforts toward individual prediction of recurrent strokes based on structured clinical and imaging data using machine learning algorithms. Some of these efforts resulted in relatively accurate prediction models. However, acquiring clinical and imaging data is typically possible at provider sites only and is associated with additional costs. Therefore, we developed recurrent stroke prediction models based solely on data easily obtained from the patient at home.

Methods:

Data from 384 patients with ischemic stroke were obtained from the Erlangen Stroke Registry. Patients were followed at 3 and 12 months after first stroke and then annually, for about 2 years on average. Multiple machine learning algorithms were applied to train predictive models for estimating individual risk of recurrent stroke within 1 year. Double nested cross-validation was utilized for conservative performance estimation and models’ learning capabilities were assessed by learning curves. Predicted probabilities were calibrated, and relative variable importance was assessed using explainable artificial intelligence techniques.

Results:

The best model achieved the area under the curve of 0.70 (95% CI, 0.64–0.76) and relatively good probability calibration. The most predictive factors included patient’s family and housing circumstances, rehabilitative measures, age, high calorie diet, systolic and diastolic blood pressures, percutaneous endoscopic gastrotomy, number of family doctor’s home visits, and patient’s mental state.

Conclusions:

Developing fairly accurate models for individual risk prediction of recurrent ischemic stroke within 1 year solely based on registry data is feasible. Such models could be applied in a home setting to provide an initial risk assessment and identify high-risk patients early.
Thromboinflammation in Brain Ischemia: Recent Updates and Future Perspectives
Authors: De Meyer Simon F. , Langhauser Friederike , Haupeltshofer Steffen , Kleinschnitz Christoph , Casas Ana I.
Published: April 2022

Despite decades of promising preclinical validation and clinical translation, ischemic stroke still remains as one of the leading causes of death and disability worldwide. Within its complex pathophysiological signatures, thrombosis and inflammation, that is, thromboinflammation, are highly interconnected processes leading to cerebral vessel occlusion, inflammatory responses, and severe neuronal damage following the ischemic event. Hence, we here review the most recent updates on thromboinflammatory-dependent mediators relevant after stroke focusing on recent discoveries on platelet modulation, a potential regulation of the innate and adaptive immune system in thromboinflammation, utterly providing a thorough up-to-date overview of all therapeutic approaches currently undergoing clinical trial.
Consilience in Urban Design (?): The Conceptual Convergence of Strange Bedfellows
Authors: Arefi Mahyar
Published: April 2022

This article unfolds how the predominant binary view towards urban design offers an alternative tripartite interpretation. This less orthodox but more realistic probing of the field's methods of praxis (hybridity, spontaneity, and continuity) “dissolves” its dominant formal-informal binary categorizations. Hybridity represents the synergistic city-nature relationship based on visually persuasive methods of design by mimicking natural processes. Spontaneity captures how local creative impromptu small-scale DIY design intervention efforts fill possible gaps in formal top-down urban design projects. Continuity highlights the complementary (rather than polarized) relationship between formal and informal urbanism.
Fostering Natural World Engagements: Design Lessons and Issues from the My Naturewatch Training Program
Authors: Phillips Robert , Abbas-Nazari Amina
Published: April 2022

Nature's welfare is intertwined with humankinds' welfare, requiring mass citizen-led action. According to wildlife advocate David Attenborough, “we share responsibility for the future of life on earth, [we all have] the power to change.” To this end, the My Naturewatch project (MNW) follows research-through-design approaches: It deploys do-it-yourself (DIY) devices to support new methods of engagement between nature, technology, and humans. The MNW cameras help participants to capture images of their “back garden” wildlife. Through the MNW project, we (the authors) position cameras as agents, enabling “designed engagement(s)” that engender agency, serendipity, and impact.

This article recounts a training scheme used in the MNW project to provide nature organizations with methods to foster public engagement through DIY, accessible digital technologies. The scheme encourages appropriation that suits contextual, environmental, and organizational requirements. The authors unpack the experiences and issues realized (through practice) by 14 nationally acclaimed wildlife and conservation organizations, independently running workshops using the MNW project tools. Our findings report the issues and opportunities of designed community engagements for practitioners engaged with defining more sustainable practices.
He Zhen and the Decolonialization of Feminism
Authors: Rošker Jana S.
Published: April 2022

Culturally conditioned differences between women must be seen and theorized about in order to avoid essentialist generalizations about “women’s issues.” In this context, the idea of a universal patriarchal order is questioned, as is the idea of a general, universally equivalent type of feminism. Through analysis of the political philosophy of He Zhen (1884–circa 1920), this paper aims to present a Chinese alternative to liberal feminism based on the assumption that the Western feminist movement might not be a suitable means of abolishing women’s oppression in China, because it is culturally conditioned and rooted in the particulars of Western social history.