bluuebunny/crossref_metadata_2025 huggingface dataset:
TED: Towards Discovering Top-k Edge-Diversified Patterns in a Graph Database
Authors: Huang Kai , Hu Haibo , Ye Qingqing , Tian Kai , Zheng Bolong , Zhou Xiaofang
Published: May 2023

With an exponentially growing number of graphs from disparate repositories, there is a strong need to analyze a graph database containing an extensive collection of small- or medium-sized data graphs (e.g., chemical compounds). Although subgraph enumeration and subgraph mining have been proposed to bring insights into a graph database by a set of subgraph structures, they often end up with similar or homogenous topologies, which is undesirable in many graph applications. To address this limitation, we propose the Top-k Edge-Diversified Patterns Discovery problem to retrieve a set of subgraphs that cover the maximum number of edges in a database. To efficiently process such query, we present a generic and extensible framework called Ted which achieves a guaranteed approximation ratio to the optimal result. Two optimization strategies are further developed to improve the performance. Experimental studies on real-world datasets demonstrate the superiority of Ted to traditional techniques.
Fast Density-Based Clustering: Geometric Approach
Authors: Huang Xiaogang , Ma Tiefeng
Published: May 2023

DBSCAN is a fundamental density-based clustering algorithm with extensive applications. However, a bottleneck of DBSCAN is its O(n2) worst-case time complexity. In this paper, we propose an algorithm called GAP-DBC, which exploits the geometric relationships between points to solve this problem. GAP-DBC introduces an efficient partitioning algorithm to partition the data set with a limited number of range queries and then establishes an initial cluster structure based on the partition. GAP-DBC proceeds to iteratively refine the cluster structure by additional range queries. Finally, the cluster structure is accomplished using an iterative algorithm that utilizes the spatial relationships among points to reduce unnecessary distance calculations. We further demonstrate theoretically that GAP-DBC has an excellent guarantee in terms of computational efficiency. We conducted experiments on both synthetic and real-world data sets to evaluate the performance of GAP-DBC. The results show that our algorithm is competitive with other state-of-the-art algorithms.
Efficient Resistance Distance Computation: The Power of Landmark-based Approaches
Authors: Liao Meihao , Li Rong-Hua , Dai Qiangqiang , Chen Hongyang , Qin Hongchao , Wang Guoren
Published: May 2023

Resistance distance is a fundamental metric to measure the similarity between two nodes in graphs which has been widely used in many real-world applications. In this paper, we study two problems on approximately computing resistance distance: (i) single-pair query which aims at calculating the resistance distance r(s, t) for a given pair of nodes (s, t); and (ii) single-source query which is to compute all the resistance distances r(s, u) for all nodes u in the graph with a given source node s. Existing algorithms for these two resistance distance query problems are often costly on large graphs. To efficiently solve these problems, we first establish several interesting connections among resistance distance, a new concept called v-absorbed random walk, random spanning forests, and a newly-developed v-absorbed push procedure. Based on such new connections, we propose three novel and efficient sampling-based algorithms as well as a deterministic algorithm for single-pair query; and we develop an online and two index-based approximation algorithms for single-source query. We show that the two index-based algorithms for single-source query take almost the same running time as the algorithms for single-pair query with the aid of a linear-size index. The striking feature of all our algorithms is that they are allowed to select an easy-to-hit node by random walks on the graph. Such an easy-to-hit landmark node v can make the v-absorbed random walk sampling, spanning tree sampling, as well as the v-absorbed push more efficient, thus significantly improving the performance of our algorithms. Extensive experiments on 5 real-life datasets show that our algorithms substantially outperform the state-of-the-art algorithms for two resistance distance query problems in terms of both running time and estimation errors.
Efficiently Computing Join Orders with Heuristic Search
Authors: Haffner Immanuel , Dittrich Jens
Published: May 2023

Join order optimization is one of the most fundamental problems in processing queries on relational data. It has been studied extensively for almost four decades now. Still, because of its NP hardness, no generally efficient solution exists and the problem remains an important topic of research. The scope of algorithms to compute join orders ranges from exhaustive enumeration, to combinatorics based on graph properties, to greedy search, to genetic algorithms, to recently investigated machine learning. A few works exist that use heuristic search to compute join orders. However, a theoretical argument why and how heuristic search is applicable to join order optimization is lacking.

In this work, we investigate join order optimization via heuristic search. In particular, we provide a strong theoretical framework, in which we reduce join order optimization to the shortest path problem. We then thoroughly analyze the properties of this problem and the applicability of heuristic search. We devise crucial optimizations to make heuristic search tractable. We implement join ordering via heuristic search in a real DBMS and conduct an extensive empirical study. Our findings show that for star- and clique-shaped queries, heuristic search finds optimal plans an order of magnitude faster than current state of the art. Our suboptimal solutions further extend the cost/time Pareto frontier.
Together is Better: Heavy Hitters Quantile Estimation
Authors: Shahout Rana , Friedman Roy , Ben Basat Ran
Published: May 2023

Stream monitoring is fundamental in many data stream applications, such as financial data trackers, security, anomaly detection, and load balancing. In that respect, quantiles are of particular interest, as they often capture the user's utility. For example, if a video connection has high tail (e.g., 99'th percentile) latency, the perceived quality will suffer, even if the average and median latencies are low.

In this work, we consider the problem of approximating the per-item quantiles. Elements in our stream are (ID, value) tuples, and we wish to track the quantiles for each ID. Existing quantile sketches are designed for a plain number stream (i.e., containing just a value). While one could allocate a separate sketch instance for each ID, this may require an infeasible amount of memory. Instead, we consider tracking the quantiles for the heavy hitters (most frequent items), which are often considered particularly important, without knowing them beforehand.

We first present a couple of simple and effective algorithms that serve as baselines, a sampling approach and a sketching approach. Then, we present SQUAD, an algorithm that combines sampling and sketching while improving the asymptotic space complexity. Intuitively, SQUAD uses a background sampling process to capture the behaviour of the quantiles of an item before it is allocated with a sketch, thereby allowing us to use fewer samples and sketches. The algorithms are rigorously analyzed, and we demonstrate SQUAD's superiority using extensive~simulations on real-world traces.
Learned Data-aware Image Representations of Line Charts for Similarity Search
Authors: Luo Yuyu , Zhou Yihui , Tang Nan , Li Guoliang , Chai Chengliang , Shen Leixian
Published: May 2023

Finding line-chart images similar to a given line-chart image query is a common task in data exploration and image query systems, e.g. finding similar trends in stock markets or medical Electroencephalography images. The state-of-the-art approaches consider either data-level similarity (when the underlying data is present) or image-level similarity (when the underlying data is absent).

In this paper, we study the scenario that during query time, only line-chart images are available. Our goal is to train a neural network that can turn these line-chart images into representations that are aware of the data used to generate these line charts, so as to learn better representations. Our key idea is that we can collect both data and line-chart images to learn such a neural network (at training step), while during query (or inference) time, we support the case that only line-chart images are provided. To this end, we present LineNet, a Vision Transformer-based Triplet Autoencoder model to learn data-aware image representations of line charts for similarity search. We design a novel pseudo labels selection mechanism to guide LineNet to capture both data-aware and image-level similarity of line charts. We further propose a diversified training samples selection strategy to optimize the learning process and improve the performance. We conduct both quantitative evaluation and case studies, showing that LineNet significantly outperforms the state-of-the-art methods for searching similar line-chart images.
Hierarchical Residual Encoding for Multiresolution Time Series Compression
Authors: Barbarioli Bruno , Mersy Gabriel , Sintos Stavros , Krishnan Sanjay
Published: May 2023

Data compression is a key technique for reducing the cost of data transfer from storage to compute nodes. Increasingly, modern data scales necessitate lossy compression techniques, where exactness is sacrificed for a smaller compressed representation. One challenge in lossy compression is that different applications may have different accuracy demands. Today's compression techniques struggle in this setting either forcing the user to compress at the strictest accuracy demand, or to re-encode the data at multiple resolutions. This paper proposes a simple, but effective multiresolution compression algorithm for time series data, where a single encoding can effectively be decompressed at multiple output resolutions. There are a number of benefits over current state-of-the-art techniques for time series compression. (1) The storage footprint of this encoding is smaller than re-encoding the data at multiple resolutions. (2) Similarly, the compression latency is generally smaller than re-encoding at multiple resolutions. (3) Finally, the decompression latency of our encoding is significantly faster than single encodings at the strictest accuracy demand.
High-Precision State of Charge Estimation of Urban-Road-Condition Lithium-Ion Batteries Based on Optimized High-Order Term Compensation-Adaptive Extended Kalman Filtering
Authors: Feng Renjun , Wang Shunli , Yu Chunmei , Zhou Heng , Fernandez Carlos
Published: May 2023

It is significant to improve the accuracy of estimating the state of charge (SOC) of lithium-ion batteries under different working conditions on urban roads. In this study, an improved second-order polarized equivalent circuit (SO-PEC) modeling method is proposed. Accuracy test using segmented parallel exponential fitting parameter identification method. Online parameter identification using recursive least squares with variable forgetting factors(VFFRLS). An optimized higher-order term compensation-adaptive extended Kalman filter (HTC-AEKF) is proposed in the process of estimating SOC. The algorithm incorporates a noise-adaptive algorithm that introduces noise covariance into the recursive process in real-time to reduce the impact of process noise and observation noise on the accuracy of SOC estimation. Multiple iterations are performed for some of the processes in the extended Kalman filter(EKF) to compensate for the accuracy impact of missing higher-order terms in the linearization process. Model validation results show over 98% accuracy. The results after comparing with the EKF algorithm show a 4.1% improvement in SOC estimation accuracy under Hybrid Pulse Power Characterization(HPPC) working conditions. 2.7% improvement in accuracy in Dynamic Stress Test(DST) working conditions. 2.1% improvement in Beijing Bus Dynamic Stress Test(BBDST) working conditions. The superiority of the algorithm is demonstrated.
Electrodeposition of Crystalline Si Using a Liquid Zn Electrode in Molten KF–KCl–K2SiF6
Authors: Moteki Wataru , Norikawa Yutaro , Nohira Toshiyuki
Published: May 2023

In this study, we propose a novel Si electrodeposition method using a liquid Zn electrode in molten KF–KCl. Electrochemical measurements and electrolysis were conducted in a KF–KCl–K2SiF6 melt at 923 K. Cyclic voltammograms at a liquid Zn electrode revealed that the reduction currents at 0.75–1.0 V vs K+/K were attributed to the formation of Si–Zn liquid alloy. Additionally, Si was deposited through potentiostatic electrolysis at 0.75 V using liquid Zn in a boron nitride (BN) crucible as an electrode. Cross-sectional scanning electron microscopy and energy-dispersive X-ray spectroscopy showed that deposited Si was located at the bottom and side of the interface between Zn and the BN crucible instead of at the interface between Zn and the molten salt, indicating the electrodeposition of Si attributed to Si–Zn liquid alloy formation. The obtained Si was confirmed to be the crystalline form by X-ray diffractometry, and the maximum grain size was approximately 2 mm. Galvanostatic electrolysis at –20 mA cm–2 with varying electrical charges showed that the Si grain size increased with increasing charge, confirming the growth of crystalline Si. Finally, the mechanism of Si electrodeposition on a Zn electrode through Si–Zn alloying was discussed.
Application of the Solute-Solvent EMF Cell to Measure Activity of NiF2 in Molten FLiNaK
Authors: Lichtenstein Timothy , Patricelli Colin V. , Hawthorne Krista L.
Published: May 2023

Electromotive force (emf) measurements made using a combination of solute- and solvent-based electrodes were used to determine the activity of NiF2 in molten FLiNaK eutectic at 823 K across a concentration range of x
NiF2 = 5.2 × 10–4–1.0 × 10–2. The solute emf values were measured using electrodes consisting of Ni wires immersed in FLiNaK with dissolved NiF2 contained in graphite crucibles. The measured emf values were then converted to the FLiNaK Eutectic Potassium Electrode (FEKE) potential and used to quantify the activity of dissolved NiF2. This quantification was based upon comparative measurements of a reference solvent electrode consisting of a K-Bi alloy immersed in pure FLiNaK contained in a boron nitride crucible and a solute electrode. Short cell lives were characteristic of the measurements due to the corrosive nature of the fluoride salts. Quantifying the activity of NiF2 will improve the utility of Ni2+/Ni reference electrodes in molten fluoride salts, which are notoriously difficult electrolytes to work with because of their reactivity. This work demonstrates the general nature of the solute-solvent approach as a repeatable, easily employed method for measuring the activity values of electroactive species in a variety of molten salts to improve understanding of the electroactive species behavior in these systems.
Review— Modern State and Prospects of Electrochemical CO2 Conversion in Molten Salts
Authors: Novoselova Inessa , Omel’chuk Anatoliy
Published: May 2023

An innovative environmentally friendly electrochemical method with using molten salts for direct absorption and transformation of CO2 into solid carbon-containing materials of unique structure and morphology has been developed. The paper presents a review of literature data on electrochemical conversion of carbon dioxide in molten salts as well as the authors’ research results on the direct electrochemical reduction of CO2 and Li2CO3 in chloride melts into different carbonaceous materials with added value. They include: nano-scale solid carbon of various structures and morphologies (carbon nanotubes, fibers, honeycomb-like and scaly carbon nanostructures). Several variants of CO2 electrochemical conversion have been considered, the advantages and disadvantages of each variant have been analyzed. Conditions and mechanisms of electrochemical reduction of CO2 and Li2CO3 to solid carbon are discussed. Promising applications of electrolytic carbon nanomaterials for rechargeable ion batteries, supercapacitors and catalysts are emphasized.
Data Fusion and Processing Technology of Wireless Sensor Network for Privacy Protection
Authors: Shi Lusheng , Li Kai , Zhu Huibo
Published: May 2023

Data fusion and privacy protection technologies are both the research focuses in the field of wireless sensor networks. When the sensor network is in a harsh environment, the sensor nodes will face the danger of malicious entity attack in the data fusion progress. The efficiency and privacy protection of sensor network data fusion are very important. The traditional data fusion privacy protection algorithm has the problems of low data fusion efficiency and low privacy protection level. These problems are to be solved in this study. An improved cluster-based privacy data aggregation (I-CPDA) is proposed, which combines data slicing and false interference data technology. The experimental results of the algorithm show that the data fusion accuracy of the I-CPDA algorithm increases faster than the traditional algorithm with the time interval increasing, and the highest value reaches 90.7%. The fusion accuracy of the traditional CPDA algorithm under the same environment is 68.7%. In the actual test, the interception success rate of the I-CPDA algorithm for data attacks reached 90.74%, while the traditional CPDA was only 76.66. In addition, when the number of nodes in the cluster is 15, the data traffic of the I-CPDA is 56, while the data traffic of the traditional CPDA algorithm in the same environment exceeds 200. Compared with the currently widely used traditional algorithms, the I-CPDA algorithm has obvious advantages in terms of fusion effect, privacy, and efficiency and can be put into practical application.
The Role of an Exoskeleton Simulation of Senescence in Health Sciences Education
Authors: Ramos-Zúñiga Rodrigo , González-Rios Jorge A.
Published: May 2023

Background. Education in the formation of human capital in health constantly presents challenges. New tools in the emerging contexts may strengthen empathic attitudes. We developed an educational intervention that included a senescence simulator and assessed its impact on perception and attitudes in healthcare students. Methods. A cross-sectional comparative study was conducted that assessed acquired knowledge and self-perception using a semistructured survey administered before and after the demonstration and intervention using the simulator and reported the experience through the role of the patient and caregiver. The data were analyzed statistically to identify the demographic characteristics and differences between the groups of students. The data were analyzed statistically to identify the demographic characteristics and differences between the groups of students in the responses pre-post intervention, using statistical software (IBM SPSS Statistics 26.0). Results. Of the 256 participants surveyed before the intervention, 93.8% described cognitive deterioration as a significant disability and 53.1% considered the health system to be inadequate in meeting the needs of older individuals. Only 59.8% stated that the current academic training meets the educational requirements for the care of the elderly. In total, 98.9% of the participants reported that the simulator changed their perception by increasing their empathy. In total, 76.2% showed greater sensitivity to older adults and 79.3% reported that the experiential learning consolidated their professionalizing perspective. Among the younger participants (aged 18–20 years), sensitivity and reorientation toward pursuing an associated graduate degree were higher after the intervention (

p
=
0.01

). Conclusions. Educational strategies, such as the senescence simulator, offer an experiential intervention that strengthens the knowledge and attitudes toward older individuals. During the pandemic emergency, it proved to be a useful educational strategy in consolidating caring behavior as a hybrid educational tactic. The senescence simulation enabled the participants to widen their educational and professional schemes to encompass the care of the older population.
Microbiological Indoor and Outdoor Air Quality in Chicken Fattening Houses
Authors: Almatawah Qadreyah A. , Al-Khalaifah Hanan S. , Aldameer Ahmad S. , Ali Abdulmohsen K. , Benhaji Ahmed H. , Varghese Julie S.
Published: May 2023

This study was conducted at one of the largest poultry companies in Kuwait during November and December 2019 to evaluate the microbiological threats of Escherichia coli (APEC), Salmonella spp., and Aspergillus fumigatus to chickens in fattening houses by counting and identifying the microorganisms by culturing and pyrosequencing analysis. During the fattening cycle, the temperature and humidity ranged between 23.6°C and 29°C and 64.1% and 87.1%, respectively. The total bacterial population and Aspergillus fumigatus measured in the indoor and outdoor air exhibited a linear relationship during the fattening cycle. The total bacterial and Aspergillus concentrations determined during the cycle ranged between 150 and 2000 CFU/m3 and 0 and 1000 CFU/m3, respectively. E. coli and Salmonella spp. concentrations determined during the cycle ranged between 1 and 220 CFU/m3 and 4 and 110 CFU/m3, respectively. Pyrosequencing analysis of the air inside the houses at the end of the cycle revealed extensive biodiversity in the microorganisms, detecting 32 bacterial genera and 14 species. The identified species belonging to the genera Corynebacterium, Haemophilus, Streptococcus, Veillonella, and Aspergillus were identified as potentially affecting human and broiler health. The emission of potentially pathogenic bacteria to the outdoor environment from chicken housing can pose a considerable risk to human health and environmental microbial pollution. This study could guide the development of integrated control devices for monitoring microbes in broiler production facilities during chicken collection for transport to slaughterhouses.
Popular Tag Recommendation by Neural Network in Social Media
Authors: Jafari Sadr Mohammad , Mirtaheri Seyedeh Leili , Greco Sergio , Borna Keivan
Published: May 2023

Although “a picture is worth a thousand words,” this may not be enough to get your post seen on social media. This study’s main objective was to determine the best ways to characterize a photo in terms of viral marketing and public appealing. We have to obtain this dataset for this reason from the social media site such as Instagram. A total of 1.4 million hashtags were used in the 570,000 photos that we crawled. Prior to training the text generation module to produce such popular hashtags, we had to determine the components and features of the photo. We trained a multilabel image classification module using a ResNet neural network model for the first section. In order to create hashtags pertaining to their popularity, we trained a cutting‐edge GPT‐2 language model for the second portion. This work differs from others in that, and it initially offered a cutting‐edge GPT‐2 model for hashtag generation using a combination of the multilabel image classification module. The popularity issues and ways to make an Instagram post popular are also highlighted in our essay. Social science and marketing research can both be conducted on this subject. Which content can be considered popular from the perspective of consumers can be researched in the social science setting. As a marketing strategy, end users can help by offering such well‐liked hashtags for social media accounts. This essay adds to the body of knowledge by demonstrating the two possible uses of popularity. Compared to the base model, our popular hashtag generating algorithm creates 11% more relevant, acceptable, and trending hashtags, according to the evaluation that was carried out.
A Weight-Generating Approach of a Deep Neural Network for the Parameter Identification of Dynamic Systems
Authors: Chu Weimeng , Wu Shunan , Fu Fangzhou , Ye Zhe , Wu Zhigang
Published: May 2023

The general learning process of deep learning is extremely time-consuming. Unlike the traditional learning process, a weight-generating approach to quickly generate the weight vectors of a deep neural network model is proposed, which can be used for parameter identification of a dynamic system. Based on the analysis of three trained deep neural network models, which are used to identify the parameters of three different dynamic systems, the statistical relationships between the weight vectors of each hidden layer and its inputs are revealed. Then, the statistical patterns of the weight vectors are imitated by exploiting the statistical patterns of the inputs and these relationships. Then, a weight-generating approach is designed to quickly generate the weight vectors of a deep neural network model. The effectiveness of the weight-generating approach is tested on the tasks of parameter identification for the three dynamic systems. The numerical results are provided to demonstrate the validity and high efficiency of the proposed weight-generating approach.
A Review Paper about Deep Learning for Medical Image Analysis
Authors: Sistaninejhad Bagher , Rasi Habib , Nayeri Parisa
Published: May 2023

Medical imaging refers to the process of obtaining images of internal organs for therapeutic purposes such as discovering or studying diseases. The primary objective of medical image analysis is to improve the efficacy of clinical research and treatment options. Deep learning has revamped medical image analysis, yielding excellent results in image processing tasks such as registration, segmentation, feature extraction, and classification. The prime motivations for this are the availability of computational resources and the resurgence of deep convolutional neural networks. Deep learning techniques are good at observing hidden patterns in images and supporting clinicians in achieving diagnostic perfection. It has proven to be the most effective method for organ segmentation, cancer detection, disease categorization, and computer‐assisted diagnosis. Many deep learning approaches have been published to analyze medical images for various diagnostic purposes. In this paper, we review the work exploiting current state‐of‐the‐art deep learning approaches in medical image processing. We begin the survey by providing a synopsis of research works in medical imaging based on convolutional neural networks. Second, we discuss popular pretrained models and general adversarial networks that aid in improving convolutional networks’ performance. Finally, to ease direct evaluation, we compile the performance metrics of deep learning models focusing on COVID‐19 detection and child bone age prediction.
Block Encryption and Decryption of a Sentence Using Decomposition of the Turan Graph
Authors: Beaula C. , Venugopal P. , Praba B.
Published: May 2023

Encryption and decryption are the two processes in cryptography to conceal and convey important information to an authorized person without third-party interruption in a network. Cryptography is a branch of computer science in which the system has to be updated every second. It mainly depends on mathematical concepts like number theory and algebra. Recently, graph theory concepts are employed in cryptography to make it stronger. The usage of complex graphs in cryptosystems makes it difficult to hack. In this paper, we proposed a cryptosystem using the Turan graph which has a complex graph structure. The advantage of using a Turan graph is that it is a unique multipartite complete graph with more edges than other multipartite complete graphs. This adds robustness to the cryptosystem. The novelty of this paper is the decomposition of the Turan graph into paths and stars and applying edge labeling to them to encrypt and decrypt a sentence of

k

words. The algorithms for encryption and decryption are also proposed in this paper.
Thai Word Segmentation with a Brain‐Inspired Sparse Distributed Representations Learning Memory
Authors: Soisoonthorn Thasayu , Unger Herwig , Maliyaem Maleerat
Published: May 2023

Word segmentation is necessary for many natural language processing, especially Thai language, that is, unsegmented words. However, wrong segmentation causes terrible performance in the final result. In this study, we propose two new brain‐inspired methods based on Hawkins’ approach to address Thai word segmentation. Sparse Distributed Representations (SDRs) are used to model the neocortex structure of the brain to store and transfer information. The first proposed method, THDICTSDR, improves the dictionary‐based approach by utilizing SDRs to learn the surrounding context and combine with n‐gram to select the correct word. The second method uses SDRs instead of a dictionary and is called THSDR. The evaluation uses the BEST2010 and LST20 standard datasets for segmentation words by comparing them with the longest matching, newmm, and Deepcut, which is state‐of‐the‐art in the deep learning approach. The result shows that the first method provides the accuracy, and performances are significantly better than other dictionary bases. The first new method can achieveF1‐Score at 95.60%, comparable to the state‐of‐the‐art and DeepcutF1‐Score at 96.34%. However, it provides a better performanceF1‐Score at 96.78% in learning all vocabularies. In addition, it can achieve 99.48%F1‐Score beyond Deepcut 97.65% in case of all sentences being learnt. The second method has fault tolerance to noise and provides overall result over deep learning in all cases.
A Survey on Learning Objects’ Relationship for Image Captioning
Authors: Runyan Du , Wenkai Zhang , Zhi Guo , Xian Sun
Published: May 2023

Image captioning is a challenging modality transformation task in computer vision and natural language processing, aiming to understand the image content and describe it with a natural language. Recently, the relationship information between objects in the image has been investigated to be of importance in generating a more vivid and readable sentence. Many types of research have been done in relationship mining and learning for leveraging into the caption models. This paper mainly summarizes the methods of relational representation and relational encoding in image captioning. Besides, we discuss the advantages and disadvantages of these methods and provide commonly used datasets for the relational captioning task. Finally, the current problems and challenges in this task are highlighted.
Epidemiological Forecasting Models Using ARIMA, SARIMA, and Holt–Winter Multiplicative Approach for Pakistan
Authors: Riaz Muhammad , Hussain Sial Maqbool , Sharif Saira , Mehmood Qaisar
Published: May 2023

Background of the Study. Statistical models have been extensively used in modeling and forecasting the different fields of agriculture, economics, social sciences, and medical sciences. The transmission of some diseases is a serious life threat around the globe; therefore, proper assessment and modeling need time. Malaria is one of the major life-threatening diseases in Pakistan, and some death cases due to this disease have been reported during the last decade. Methodology. The data have been collected from the Ministry of Health, Rahim Yar Khan, Pakistan, from January 2011 to March 2022. Data were analyzed by applying time series models for prediction purposes. Diagnostic measures such as RMSE, MAE, and MAPE were used to choose the best forecasting model. Results and Discussion. This study aims to forecast malaria cases by choosing the best forecast model. After comparison, it was concluded that the Holt–Winter multiplicative model outperformed the ARIMA and SARIMA models, with the lowest RMSE, MAPE, and MAE compared to other models. Malaria cases in the district Rahim Yar Khan were forecasted by the Holt–Winter multiplicative model, for the month of April 2022 to January 2023. From the forecasting results, the minimum number of cases was found to be 586.75 in June 2022 and the maximum number of cases was found to be 1281.93 in October 2022 among the next ten months. Based on the results, it is paramount for the GOP (Govt. of Pakistan) to enhance the vaccination policy to erase the impacts of malaria cases to flatten the curve.
Assessment of Cassava Utilization Patterns, Postharvest Handling Practices, and Productivity Influencing Factors in South and Southwest Ethiopia
Authors: Bilate Daemo Berhanu , Belew Yohannes Derbew , Mulualem Beyene Tewodros , Gebreselassie Abtew Wosene
Published: May 2023

Cassava (Manihot esculenta Crantz) is mainly produced to supplement food security by providing food for smallholder farmers year round. However, its production is constrained by various factors. Thus, the aim of this research was to assess cassava utilization patterns, postharvest handling practices, and the factors that influence productivity. Data were gathered from primary and secondary sources, and a multistage sampling procedure was used to select 200 HHs. A multiple regression model and descriptive statistics were used to analyze the data. The regression model revealed that the education level, family size, land holding size, cropping system, crop rotation, earthing up rate, maturity, variety type, training, and plant population variables were significantly and positively correlated with cassava productivity. This implies that if any of these variables increases, cassava productivity will increase while the other variables remain constant. Topography and pest variables showed a significant negative correlation, indicating that an undulating topography or being attacked by pests could reduce cassava productivity by 60.00%. The descriptive statistics results for the utilization proportion showed that 51.87% of the farmers utilized for home consumption, 43.68% for the market, and 4.26% for animal feed. The consumption pattern indicated that 46.50% was boiled roots, 15.00% was flour cooked, and 38.50% was boiled roots and flour cooked. As postharvest handling practices showed, 10.00% of the farmers immediately processed to powder, 18.00% immediately processed to sliced (chips), 61.00% left them to root in the soil, and 11.00% did nothing. This indicates that the farmers’ consumption patterns and processing methods are very traditional. Therefore, the study suggested that the farmers’ different practices should be further supported by research through the generation of multiple food forms, postharvest handling practices, and production technology. Proper attention should also be given to address the identified productivity-influencing factors as well as postharvest handling practices. These could sustain the farming system of the crop and help to increase cassava productivity for smallholder farmers.
Crisis Bancaria Global del 2023
Authors: Moreno Rafael , Santana Savka , Ibáñez Fabricio
Published: May 2023

Durante marzo de 2023, colapsaron tres grandes bancos en Estados Unidos: Silvergate Bank, Silicon Valley Bank (SVB) y Signature Bank. En ese mismo mes, a causa de problemas financieros, un importante banco suizo, Credit Suisse, fue adquirido por otro banco de mayor tamaño de la misma plaza, UBS, con el apoyo del regulador suizo. Continuar leyendo...
Abstract ND03: Discovery of ARV-766, an androgen receptor degrading PROTAC® for the treatment of men with metastatic castration resistant prostate cancer
Authors: Snyder Larry , Lee S H. , Neklesa T K. , Chen X , Dong H , Ferraro C , Gordon D A. , Macaluso J , Pizzano J , Wang J , Willard R R. , Vitale N , Peck R , Moore M D. , Crews C M. , Houston J , Crew A P. , Taylor I
Published: May 2023

Prostate cancer is the second leading cause of cancer death in men in the United States. The androgen receptor (AR) plays critical roles in both early disease and advanced prostate cancer. Current therapeutic approaches targeting the androgen/AR axis are initially effective, but castration resistant prostate cancer (CRPC) inevitably develops. CRPC is linked with increased AR activity via gene overexpression, amplification, and gain-of-function mutations. To address these mechanisms of AR-dependent prostate tumor growth, we have developed a novel therapeutic agent, ARV-766, a proteolysis targeting chimera (PROTAC®) that induces a protein-protein interaction between the AR and specific E3 ubiquitin ligase complexes, leading to the ubiquitination of AR and its subsequent degradation via the proteasome. In vitro, ARV-766 degrades AR in various prostate cancer cell lines, including those harboring resistance-conferring, clinically relevant point mutations, with a half-maximal degradation concentration (DC50) of <1 nM in wild type VCaP. Importantly ARV-766 also maintains potency against the AR L702H mutant, which has been associated with resistance to some AR antagonists. In vivo, ARV-766 is orally bioavailable and robustly degrades AR with a >90% observed maximum degradation (Dmax) at efficacious doses. ARV-766 significantly and dose-dependently inhibits tumor growth in murine LNCaP and VCaP xenograft models, including an enzalutamide-insensitive non-castrated VCaP model. These preclinical data supported the clinical development of ARV-766 for the treatment of men with metastatic CRPC. Selected pre-clinical data along with the chemical structure of ARV-766 will be presented.

Citation Format: Larry Snyder, S H. Lee, T K. Neklesa, X Chen, H Dong, C Ferraro, D A. Gordon, J Macaluso, J Pizzano, J Wang, R R. Willard, N Vitale, R Peck, M D. Moore, C M. Crews, J Houston, A P. Crew, I Taylor. Discovery of ARV-766, an androgen receptor degrading PROTAC® for the treatment of men with metastatic castration resistant prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr ND03.
Abstract ND04: BAY 2965501: A highly selective DGK- ζ inhibitor for cancer immuno-therapy with first-in-class potential
Authors: Offringa Rienk , Olesch Catherine , Cichon Frederik , Grees Mareike , Schmees Norbert , Roehn Ulrike , Prinz Florian , Guenther Judith , Stoeckigt Detlef , Erkelenz Michael , Nguyen Thi Thanh Uyen , Boemer Ulf , Werbeck Nicolas , Kunze Marc , Petersen Kirstin , Kirchhoff Dennis
Published: May 2023

The second messenger diacyl glycerol (DAG) plays a key role in T cell receptor downstream signaling and thereby in T-cell activation. Diacylglycerol kinase zeta (DGK-ζ) is a lipid kinase that can down-modulate T-cell activation by catalyzing the conversion of DAG to phosphatidic acid, thereby acting as a ligand independent, intracellular immune checkpoint. Inhibition of DGK- ζ offers the potential to enhance T cell priming against suboptimal tumor antigens and to overcome multiple immune-suppressive mechanisms in the tumor microenvironment.

Bayer AG in collaboration with the German Cancer Research Center (DKFZ) have developed BAY 2965501, a highly selective, orally available DGK-ζ inhibitor which shows a unique binding mode and good DMPK profile. The compound enhances the reactivity of human and mouse T-cells, both in the priming and the effector phase, and overcomes the inhibitory impact of suppressive factors, such as adenosine and prostaglandin E2. Oral dosing of BAY 2965501 shows T cell dependent efficacy in syngeneic, murine tumor models. Furthermore, this results in reactivation of exhausted T cells in vivo. Preclinical safety studies showed only mild findings, so that good clinical tolerability is expected. Collectively, the preclinical data support first in human (FiH) phase I testing of therapeutic potential.

A FiH trial with BAY 2965501 in patients with advanced solid tumors including NSCLC, gastric/GEJ AdCa, ccRCC, and melanoma patients, is currently enrolling patients (NCT05614102). This study will evaluate the safety, tolerability, maximum tolerated or administered dose, pharmacokinetics, pharmacodynamics, and tumor response profile of BAY 2965501.

Citation Format: Rienk Offringa, Catherine Olesch, Frederik Cichon, Mareike Grees, Norbert Schmees, Ulrike Roehn, Florian Prinz, Judith Guenther, Detlef Stoeckigt, Michael Erkelenz, Thi Thanh Uyen Nguyen, Ulf Boemer, Nicolas Werbeck, Marc Kunze, Kirstin Petersen, Dennis Kirchhoff. BAY 2965501: A highly selective DGK- ζ inhibitor for cancer immuno-therapy with first-in-class potential [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr ND04.
Abstract ND11: EO-3021: An antibody drug conjugate targeting CLDN18.2 expressing cancers
Authors: Dornan David
Published: May 2023

Claudin 18.2 is tight junction protein with a highly selective cell surface expression profile that is limited to the normal gastric mucosa. Importantly, it is often overexpressed in several types of cancers including gastric, pancreatic, esophageal, ovarian, and lung. There are currently no approved targeted therapies for claudin 18.2-expressing cancers. As such, we designed an antibody drug conjugate, EO-3021/SYSA1801 with the potential to selectively target and kill claudin 18.2-expressing cancer cells. EO-3021 is comprised of a novel tumor-targeting monoclonal antibody that selectively binds claudin 18.2 (Kd = 0.6 nM) that is conjugated to monomethyl auristatin E (MMAE). EO-3021 is internalized in claudin 18.2-expressing cells as well as demonstrates cell killing across a range of cancer cell lines with low (IC50 = 228 ng/ml), medium (IC50 = 7 ng/ml), and high levels of claudin 18.2 (IC50 = 28 ng/ml) expression. More specifically, claudin 18.2-expressing cells undergo G2/M cell cycle arrest and apoptosis upon EO-3021 treatment. EO-3021 demonstrates tumor regressions with a single dose across low, medium, and high claudin 18.2-expressing in vivo models derived from pancreatic (9 mg/kg), gastric (2 mg/kg), and lung cancers (8 mg/kg), respectively. In addition, a single dose of EO-3021 outperforms standard of care chemotherapies, such as cisplatin and gemcitabine, across in vivo models. Nonclinical toxicology studies suggested that EO-3021-mediated toxicities were reversible and manageable as well as afford a therapeutic index suitable for testing in humans. EO-3021/SYSA1801 is currently being evaluated in a Phase I dose escalation study (NCT05009966) in patients with claudin 18.2 positive advanced malignant solid tumors. A case study will be presented where a confirmed and durable partial response (66.7% tumor reduction) was observed following EO-3021 treatment.

Citation Format: David Dornan. EO-3021: An antibody drug conjugate targeting CLDN18.2 expressing cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr ND11.
El barroco maravilloso de Las malas: nuevas aproximaciones a la tradición desde la literatura del presente
Authors: Gamba Ana Marina
Published: May 2023

En su novela Las malas (2019), la autora argentina Camila Sosa Villada propone una compleja inscripción en la tradición literaria latinoamericana, haciendo de esta el lugar de enunciación de una palabra transgresora y disidente. En este artículo pretendemos mostrar cómo este uso de la tradición configura el gesto crítico de la autora, el cual le permite problematizar los límites del lenguaje y su capacidad para “representar” al otro. En sus modalizaciones y actualizaciones críticas del barroco americano y lo real maravilloso, sostenemos, la novela articula un discurso sobre los marcos políticos-culturales de inteligibilidad contemporáneos que impiden el reconocimiento de las vidas trans en tanto duelables.
“La casa de la compasión”: apuntes sobre lo fantástico, la tradición y el mundo trans en un cuento de Camila Sosa Villada
Authors: Moya Micaela
Published: May 2023

Camila Sosa Villada es una autora que ha ganado mucha repercusión en los últimos años, especialmente por su novela Las malas (2019). Toda su producción aborda subjetividades trans porque, como ella misma declara, la suya es una trans-escritura. La crítica que ha estudiado su obra se ha centrado, sobre todo, en evaluar su escritura desde el componente autobiográfico o bien, en destacar su importancia en la creación de una imaginería trans, solidaria con el colectivo LGBTQI+. Sin dejar de lado este último aspecto, este trabajo se propone, a partir del análisis del cuento “La casa de la compasión”, evaluar cómo el mundo trans puede, en la obra de Sosa Villada, articularse también con géneros literarios como el fantástico y ubicarse en series literarias y culturales de distintos tipos. De este modo, buscamos desmontar la falsa y difundida idea de que la literatura trans siempre debe tener un carácter testimonial.
De “ornitólogo nacional” a “escritor universal”: William Henry Hudson en la revista El hornero
Authors: Maranguello Carolina
Published: May 2023

En el marco de la recepción argentina de la obra del escritor y naturalista W. H. Hudson, retomada desde sectores estética e ideológicamente divergentes, se indagará aquí la temprana recuperación de su figura en la revista ornitológica El hornero. En primer lugar, se intentará reponer el singular ejercicio naturalista ensayado por Hudson y observar cómo la heterogeneidad de sus intereses dialoga con las objetivos multifacéticos de la Sociedad Ornitológica del Plata, fundadora de El hornero. En segundo lugar, se abordarán las diferentes estrategias de traducción e incorporación de sus artículos naturalistas, que van modificándose en relación a los cambios experimentados por la recepción de Hudson en la escena literaria nacional, entre un primer interés más estrictamente ornitológico (a partir del agregado de notas científicas y de fotografías inéditas) hasta un interés cultural ampliado que le permite a la revista participar de los homenajes que se radicalizan en torno al Centenario de su nacimiento en 1941.
Less is More: Mitigate Spurious Correlations for Open-Domain Dialogue Response Generation Models by Causal Discovery
Authors: Feng Tao , Qu Lizhen , Haffari Gholamreza
Published: May 2023

In this paper, we conduct the first study on spurious correlations for open-domain response generation models based on a corpus CGDialog curated by ourselves. The current models indeed suffer from spurious correlations and have a tendency to generate irrelevant and generic responses. Inspired by causal discovery algorithms, we propose a novel model-agnostic method for training and inference using a conditional independence classifier. The classifier is trained by a constrained self-training method, coined ConSTrain, to overcome data sparsity. The experimental results based on both human and automatic evaluation show that our method significantly outperforms the competitive baselines in terms of relevance, informativeness, and fluency.