bluuebunny/crossref_metadata_2025 huggingface dataset:
Orca: Scalable Temporal Graph Neural Network Training with Theoretical Guarantees
Authors: Li Yiming , Shen Yanyan , Chen Lei , Yuan Mingxuan
Published: May 2023

Representation learning over dynamic graphs is critical for many real-world applications such as social network services and recommender systems. Temporal graph neural networks (T-GNNs) are powerful representation learning methods and have achieved remarkable effectiveness on continuous-time dynamic graphs. However, T-GNNs still suffer from high time complexity, which increases linearly with the number of timestamps and grows exponentially with the model depth, causing them not scalable to large dynamic graphs. To address the limitations, we propose Orca, a novel framework that accelerates T-GNN training by non-trivially caching and reusing intermediate embeddings. We design an optimal cache replacement algorithm, named MRU, under a practical cache limit. MRU not only improves the efficiency of training T-GNNs by maximizing the number of cache hits but also reduces the approximation errors by avoiding keeping and reusing extremely stale embeddings. Meanwhile, we develop profound theoretical analyses of the approximation error introduced by our reuse schemes and offer rigorous convergence guarantees. Extensive experiments have validated that Orca can obtain two orders of magnitude speedup over the state-of-the-art baselines while achieving higher precision on large dynamic graphs.
Towards Generating Hop-constrained s-t Simple Path Graphs
Authors: Cai Yuzheng , Liu Siyuan , Zheng Weiguo , Lin Xuemin
Published: May 2023

Graphs have been widely used in real-world applications, in which investigating relations between vertices is an important task. In this paper, we study the problem of generating the k-hop-constrained s-t simple path graph, i.e., the subgraph consisting of all simple paths from vertex s to vertex t of length no larger than k. To our best knowledge, we are the first to formalize this problem and prove its NP-hardness on directed graphs. To tackle this challenging problem, we propose an efficient algorithm namedEVE, which exploits the paradigm of edge-wise examination rather than exhaustively enumerating all paths. Powered by essential vertices appearing in all simple paths between vertex pairs,EVE distinguishes the edges that are definitely (or not) contained in the desired simple path graph, producing a tight upper-bound graph in the time cost O(k2|E|). Each remaining undetermined edge is further verified to deliver the exact answer. Extensive experiments are conducted on 15 real networks. The results show thatEVE significantly outperforms all baselines by several orders of magnitude. Moreover, by takingEVE as a built-in block, state-of-the-art for hop-constrained simple path enumeration can be accelerated by up to an order of magnitude.
GeoGauss: Strongly Consistent and Light-Coordinated OLTP for Geo-Replicated SQL Database
Authors: Zhou Weixing , Peng Qi , Zhang Zijie , Zhang Yanfeng , Ren Yang , Li Sihao , Fu Guo , Cui Yulong , Li Qiang , Wu Caiyi , Han Shangjun , Wang Shengyi , Li Guoliang , Yu Ge
Published: May 2023

Multinational enterprises conduct global business that has a demand for geo-distributed transactional databases. Existing state-of-the-art databases adopt a sharded master-follower replication architecture. However, the single-master serving mode incurs massive cross-region writes from clients, and the sharded architecture requires multiple round-trip acknowledgments (e.g., 2PC) to ensure atomicity for cross-shard transactions. These limitations drive us to seek yet another design choice. In this paper, we propose a strongly consistent OLTP database GeoGauss with full replica multi-master architecture. To efficiently merge the updates from different master nodes, we propose a multi-master OCC that unifies data replication and concurrent transaction processing. By leveraging an epoch-based delta state merge rule and the optimistic asynchronous execution, GeoGauss ensures strong consistency with light-coordinated protocol and allows more concurrency with weak isolation, which are sufficient to meet our needs. Our geo-distributed experimental results show that GeoGauss achieves 7.06X higher throughput and 17.41X lower latency than the state-of-the-art geo-distributed database CockroachDB on the TPC-C benchmark.
Matching Roles from Temporal Data: Why Joe Biden is not only President, but also Commander-in-Chief
Authors: Bornemann Leon , Bleifuß Tobias , Kalashnikov Dmitri V. , Nargesian Fatemeh , Naumann Felix , Srivastava Divesh
Published: May 2023

We present role matching, a novel, fine-grained integrity constraint on temporal fact data, i.e., (subject, predicate, object, timestamp)-quadruples. A role is a combination of subject and predicate and can be associated with different objects as the real world evolves and the data changes over time. A role matching states that the associated object of two or more roles should always match across time. Once discovered, role matchings can serve as integrity constraints to improve data quality, for instance of structured data in Wikipedia[3]. If violated, role matchings can alert data owners or editors and thus allow them to correct the error. Finding all role matchings is challenging due both to the inherent quadratic complexity of the matching problem and the need to identify true matches based on the possibly short history of the facts observed so far.

To address the first challenge, we introduce several blocking methods both for clean and dirty input data. For the second challenge, the matching stage, we show how the entity resolution method Ditto[27] can be adapted to achieve satisfactory performance for the role matching task. We evaluate our method on datasets from Wikipedia infoboxes, showing that our blocking approaches can achieve 95% recall, while maintaining a reduction ratio of more than 99.99%, even in the presence of dirty data. In the matching stage, we achieve a macro F1-score of 89% on our datasets, using automatically generated labels.
Toward Efficient Homomorphic Encryption for Outsourced Databases through Parallel Caching
Authors: Timothy Tawose Olamide , Dai Jun , Yang Lei , Zhao Dongfang
Published: May 2023

Many applications deployed to public clouds are concerned about the confidentiality of their outsourced data, such as financial services and electronic patient records. A plausible solution to this problem is homomorphic encryption (HE), which supports certain algebraic operations directly over the ciphertexts. The downside of HE schemes is their significant, if not prohibitive, performance overhead for data-intensive workloads that are very common for outsourced databases, or database-as-a-serve in cloud computing. The objective of this work is to mitigate the performance overhead incurred by the HE module in outsourced databases. To that end, this paper proposes a radix-based parallel caching optimization for accelerating the performance of homomorphic encryption (HE) of outsourced databases in cloud computing. The key insight of the proposed optimization is caching selected radix-ciphertexts in parallel without violating existing security guarantees of the primitive/base HE scheme. We design the radix HE algorithm and apply it to both batch- and incremental-HE schemes; we demonstrate the security of those radix-based HE schemes by showing that the problem of breaking them can be reduced to the problem of breaking their base HE schemes that are known IND-CPA (i.e. Indistinguishability under Chosen-Plaintext Attack). We implement the radix-based schemes as middleware of a 10-node Cassandra cluster on CloudLab; experiments on six workloads show that the proposed caching can boost state-of-the-art HE schemes, such as Paillier and Symmetria, by up to five orders of magnitude.
Scaling Up k-Clique Densest Subgraph Detection
Authors: He Yizhang , Wang Kai , Zhang Wenjie , Lin Xuemin , Zhang Ying
Published: May 2023

In this paper, we study the k-clique densest subgraph problem, which detects the subgraph that maximizes the ratio between the number of k-cliques and the number of vertices in it. The problem has been extensively studied in the literature and has many applications in a wide range of fields such as biology and finance. Existing solutions rely heavily on repeatedly computing all the k-cliques, which are not scalable to handle large k values on large-scale graphs. In this paper, by adapting the idea of "pivoting", we propose the SCT*-Index to compactly organize the k-cliques. Based on the SCT*-Index, our SCTL algorithm can directly obtain the k-cliques from the index and efficiently achieve near-optimal approximation. To further improve SCTL, we propose SCTL* that includes novel graph reductions and batch-processing optimizations to reduce the search space and decrease the number of visited k-cliques, respectively. As evaluated in our experiments, SCTL* significantly outperform existing approaches by up to two orders of magnitude. In addition, we propose a sampling-based approximate algorithm that can provide reasonable approximations for any k value on billion-scale graphs. Extensive experiments on 12 real-world graphs validate both the efficiency and effectiveness of the proposed techniques.
FINEX: A Fast Index for Exact & Flexible Density-Based Clustering
Authors: Thiel Konstantin Emil , Kocher Daniel , Augsten Nikolaus , Hütter Thomas , Mann Willi , Schmitt Daniel
Published: May 2023

Density-based clustering aims to find groups of similar objects (i.e., clusters) in a given dataset. Applications include, e.g., process mining and anomaly detection. It comes with two user parameters (ε, MinPts) that determine the clustering result, but are typically unknown in advance. Thus, users need to interactively test various settings until satisfying clusterings are found. However, existing solutions suffer from the following limitations: (a) Ineffective pruning of expensive neighborhood computations. (b) Approximate clustering, where objects are falsely labeled noise. (c) Restricted parameter tuning that is limited to ε whereas MinPts is constant, which reduces the explorable clusterings. (d) Inflexibility in terms of applicable data types and distance functions. We propose FINEX, a linear-space index that overcomes these limitations. Our index provides exact clusterings and can be queried with either of the two parameters. FINEX avoids neighborhood computations where possible and reduces the complexities of the remaining computations by leveraging fundamental properties of density-based clusters. Hence, our solution is efficient and flexible regarding data types and distance functions. Moreover, FINEX respects the original and straightforward notion of density-based clustering. In our experiments on 12 large real-world datasets from various domains, FINEX frequently outperforms state-of-the-art techniques for exact clustering by orders of magnitude.
LightRW: FPGA Accelerated Graph Dynamic Random Walks
Authors: Tan Hongshi , Chen Xinyu , Chen Yao , He Bingsheng , Wong Weng-Fai
Published: May 2023

Graph dynamic random walks (GDRWs) have recently emerged as a powerful paradigm for graph analytics and learning applications, including graph embedding and graph neural networks. Despite the fact that many existing studies optimize the performance of GDRWs on multi-core CPUs, massive random memory accesses and costly synchronizations cause severe resource underutilization, and the processing of GDRWs is usually the key performance bottleneck in many graph applications. This paper studies an alternative architecture, FPGA, to address these issues in GDRWs, as FPGA has the ability of hardware customization so that we are able to explore fine-grained pipeline execution and specialized memory access optimizations. Specifically, we propose LightRW, a novel FPGA-based accelerator for GDRWs. LightRW embraces a series of optimizations to enable fine-grained pipeline execution on the chip and to exploit the massive parallelism of FPGA while significantly reducing memory accesses. As current commonly used sampling methods in GDRWs do not efficiently support fine-grained pipeline execution, we develop a parallelized reservoir sampling method to sample multiple vertices per cycle for efficient pipeline execution. To address the random memory access issues, we propose a degree-aware configurable caching method that buffers hot vertices on-chip to alleviate random memory accesses and a dynamic burst access engine that efficiently retrieves neighbors. Experimental results show that our optimization techniques are able to improve the performance of GDRWs on FPGA significantly. Moreover, LightRW delivers up to 9.55x and 9.10x speedup over the state-of-the-art CPU-based MetaPath and Node2vec random walks, respectively. This work is open-sourced on GitHub at https://github.com/Xtra-Computing/LightRW.
Mining Geospatial Relationships from Text
Authors: Balsebre Pasquale , Yao Dezhong , Cong Gao , Huang Weiming , Hai Zhen
Published: May 2023

A geospatial Knowledge Graph (KG) is a heterogeneous information network, capable of representing relationships between spatial entities in a machine-interpretable format, and has tremendous applications in logistics and social networks. Existing efforts to build a geospatial KG, have mainly used sparse spatial relationships, e.g., a district located inside a city, which provide only marginal benefits compared to a traditional database. In spite of the substantial advances in the tasks of link prediction and knowledge graph completion, identifying geospatial relationships remains challenging, particularly due to the fact that spatial entities are represented with single-point geometries, and textual attributes are frequently missing. In this study, we present GTMiner, a novel framework capable of jointly modeling Geospatial and Textual information to construct a knowledge graph, by mining three useful spatial relationships from a geospatial database, in an end-to-end fashion. The system is divided into three components: (1) a Candidate Selection module, to efficiently select a small number of candidate pairs; (2) a Relation Prediction component to predict spatial relationships between the entities; (3) a KG Refinement procedure, to improve both coverage and correctness of a geospatial knowledge graph. We carry out experiments on four cities' geospatial databases, from publicly-available sources and compare with existing algorithms for link prediction and geospatial data integration. Finally, we conduct an ablation study to motivate our design choices and an efficiency analysis to show that the time required by GTMiner for training and inference is comparable, or even shorter, than existing solutions.
AutoCTS+: Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting
Authors: Wu Xinle , Zhang Dalin , Zhang Miao , Guo Chenjuan , Yang Bin , Jensen Christian S.
Published: May 2023

Sensors in cyber-physical systems often capture interconnected processes and thus emit correlated time series (CTS), the forecasting of which enables important applications. The key to successful CTS forecasting is to uncover the temporal dynamics of time series and the spatial correlations among time series. Deep learning-based solutions exhibit impressive performance at discerning these aspects. In particular, automated CTS forecasting, where the design of an optimal deep learning architecture is automated, enables forecasting accuracy that surpasses what has been achieved by manual approaches. However, automated CTS solutions remain in their infancy and are only able to find optimal architectures for predefined hyperparameters and scale poorly to large-scale CTS. To overcome these limitations, we propose AutoCTS+, a joint, scalable framework, to automatically devise effective CTS forecasting models. Specifically, we encode each candidate architecture and accompanying hyperparameters into a joint graph representation. We introduce an efficient Architecture-Hyperparameter Comparator (AHC) to rank all architecture-hyperparameter pairs, and we then further evaluate the top-ranked pairs to select an architecture-hyperparameter pair as the final model. Extensive experiments on six benchmark datasets demonstrate that AutoCTS+ not only eliminates manual efforts but also is capable of better performance than manually designed and existing automatically designed CTS models. In addition, it shows excellent scalability to large CTS.
INEv: In-Network Evaluation for Event Stream Processing
Authors: Akili Samira , Purtzel Steven , Weidlich Matthias
Published: May 2023

Complex event processing (CEP) detects situations of interest by evaluating queries over event streams. Once CEP is used in networked applications, the distribution of query evaluation among the event sources enables performance optimization. Instead of collecting all events at one location for query evaluation, sub-queries are placed at network nodes to reduce the data transmission overhead. Yet, existing techniques either place such sub-queries at exactly one node in the network, which neglects the benefits of truly distributed evaluation, or are agnostic to the network structure, which ignores transmission costs due to the absence of direct network links.

To overcome the above limitations, we propose INEV graphs for in-network evaluation of CEP queries with rich semantics, including Kleene closure and negation. Our idea is to introduce fine-granular routing of partial results of sub-queries as an additional degree of freedom in query evaluation: We exploit events already disseminated in the network as part of one sub-query, when evaluating another one. We show how to instantiate INEv graphs by splitting a query workload into sub-queries, placing them at network nodes, and forwarding of their results to other nodes. Also, we characterize INEv graphs that guarantee correct and complete query evaluation, and discuss their construction based on a cost model that unifies transmission and processing latency. Our experimental results indicate that INEv graphs can reduce transmission costs for distributed CEP by up to eight orders of magnitude compared to baseline strategies.
Pea Hash: A Performant Extendible Adaptive Hashing Index
Authors: Liu Zhuoxuan , Chen Shimin
Published: May 2023

Hashing index is widely used to support efficient point operations. We observe that there is a conflict between performance and memory utilization goals. Existing hashing indices often have to trade off hash table access latency for better memory utilization. Moreover, many designs support only unique keys, and their performance is often suboptimal with skew workloads.

In this paper, we propose Pea Hash with two techniques to address the above two problems: (i) adaptive hashing strategy that holistically optimizes both access latency and memory utilization, and (ii) data-aware adaptive buckets that accommodate unique keys, and keys with various numbers of duplicates. We develop both an NVM-optimized Pea Hash and a DRAM-based Pea Hash index. Experiments on a machine equipped with Intel Optane DC Persistent memory show that compared to state-of-the-art NVM-optimized hashing indices, the NVM-optimized Pea Hash achieves up to 13.8x performance improvements with similar memory utilization. The DRAM-based Pea Hash outperforms existing in-DRAM hashing index designs, showing the generality of the proposed techniques.
BLOOP: Boolean Satisfiability-based Optimized Loop Pipelining
Authors: Fiege Nicolai , Zipf Peter
Published: May 2023

Modulo scheduling is the premier technique for throughput maximization of loops in high-level synthesis by interleaving consecutive loop iterations. The number of clock cycles between data insertions is called the initiation interval (II). For throughput maximization, this value should be as low as possible; therefore, its minimization is the main optimization goal.

Despite its long historical existence, modulo scheduling always remained a relevant research topic over the years with many exact and heuristic algorithms available in the literature.

Nevertheless, we are able to leverage the scalability of modern Boolean Satisfiability (SAT) solvers to outperform state-of-the-art ILP-based algorithms for latency-optimal modulo scheduling for both integer and rational IIs. Our algorithm is able to compute valid modulo schedules for the whole CHStone and MachSuite benchmark suites, with 99% of the solutions being proven to be throughput optimal for a timeout of only 10 minutes per candidate II. For various time limits, not a single tested scheduler from the state of the art is able to compute more verified optimal solutions or even a single schedule with a higher throughput than our proposed approach. Using an HLS toolflow, we show that our algorithm can be effectively used to generate Pareto-optimal FPGA implementations regarding throughput and resource usage.
Data-Aware Declarative Process Mining with SAT
Authors: Maggi Fabrizio Maria , Marrella Andrea , Patrizi Fabio , Skydanienko Vasyl
Published: May 2023

Process Mining is a family of techniques for analyzing business process execution data recorded in event logs. Process models can be obtained as output of automated process discovery techniques or can be used as input of techniques for conformance checking or model enhancement. In Declarative Process Mining, process models are represented as sets of temporal constraints (instead of procedural descriptions where all control-flow details are explicitly modeled). An open research direction in Declarative Process Mining is whether multi-perspective specifications can be supported, i.e., specifications that not only describe the process behavior from the control-flow point of view, but also from other perspectives like data or time. In this article, we address this question by considering SAT (Propositional Satisfiability Problem) as a solving technology for a number of classical problems in Declarative Process Mining, namely, log generation, conformance checking, and temporal query checking. To do so, we first express each problem as a suitable FO (First-Order) theory whose bounded models represent solutions to the problem, and then find a bounded model of such theory by compilation into SAT.
Understanding Diversity in Session-based Recommendation
Authors: Yin Qing , Fang Hui , Sun Zhu , Ong Yew-Soon
Published: May 2023

Current session-based recommender systems (SBRSs) mainly focus on maximizing recommendation accuracy, while few studies have been devoted to improve diversity beyond accuracy. Meanwhile, it is unclear how the accuracy-oriented SBRSs perform in terms of diversity. In addition, the asserted “tradeoff” relationship between accuracy and diversity has been increasingly questioned in the literature. Toward the aforementioned issues, we conduct a holistic study to particularly examine the recommendation performance of representative SBRSs w.r.t. both accuracy and diversity, striving for better understanding of the diversity-related issues for SBRSs and providing guidance on designing diversified SBRSs. Particularly, for a fair and thorough comparison, we deliberately select state-of-the-art non-neural, deep neural, and diversified SBRSs by covering more scenarios with appropriate experimental setups, e.g., representative datasets, evaluation metrics, and hyper-parameter optimization technique. The source code can be obtained via
github.com/qyin863/Understanding-Diversity-in-SBRSs
. Our empirical results unveil that (1) non-diversified methods can also obtain satisfying performance on diversity, which can even surpass diversified ones, and (2) the relationship between accuracy and diversity is quite complex. Besides the “tradeoff” relationship, they can be positively correlated with each other, that is, having a same-trend (win–win or lose–lose) relationship, which varies across different methods and datasets. Additionally, we further identify three possible influential factors on diversity in SBRSs (i.e., granularity of item categorization, session diversity of datasets, and length of recommendation lists) and offer an intuitive guideline and a potential solution regarding learned item embeddings for more effective session-based recommendation.
Model-Based Analysis and Optimization of Pressurized Alkaline Water Electrolysis Powered by Renewable Energy
Authors: Brauns Jörn , Turek Thomas
Published: May 2023

Alkaline water electrolysis is a key technology for large-scale hydrogen production. In this process, safety and efficiency are among the most essential requirements. Hence, optimization strategies must consider both aspects. While experimental optimization studies are the most accurate solution, model-based approaches are more cost and time-efficient. However, validated process models are needed, which consider all important influences and effects of complete alkaline water electrolysis systems. This study presents a dynamic process model for a pressurized alkaline water electrolyzer, consisting of four submodels to describe the system behavior regarding gas contamination, electrolyte concentration, cell potential, and temperature. Experimental data from a lab-scale alkaline water electrolysis system was used to validate the model, which could then be used to analyze and optimize pressurized alkaline water electrolysis. While steady-state and dynamic solutions were analyzed for typical operating conditions to determine the influence of the process variables, a dynamic optimization study was carried out to optimize an electrolyte flow mode switching pattern. Moreover, the simulation results could help to understand the impact of each process variable and to develop intelligent concepts for process optimization.
IoT Security Detection Method Based on Multifeature and Multineural Network Fusion
Authors: Zhu Zihao , Zhang Leilei , Liu Jianhua , Ying Xianer
Published: May 2023

IoT security detection plays an important role in securing the IoT ecosystem. The current detection systems suffer from poor fault tolerance and inefficient detection results. To address the IoT security vulnerability, the paper designs a multifeature fusion-based IoT security detection model to simulate an attacker sending test commands to IoT nodes. Firstly, the data collection algorithm is introduced, and the collected dataset is analyzed by three neural network models, namely, RNN, LSTM, and GRU, respectively. The best scoring model is selected as the classifier to identify vulnerabilities and achieve IoT security detection.
Swarm Intelligence to Face IoT Challenges
Authors: Abualigah Laith , Falcone Deborah , Forestiero Agostino
Published: May 2023

The Internet of Things (IoT) paradigm denotes billions of physical entities connected to Internet that allow the collecting and sharing of big amounts of data. Everything may become a component of the IoT thanks to advancements in hardware, software, and wireless network availability. Devices get an advanced level of digital intelligence that enables them to transmit real‐time data without applying for human support. However, IoT also comes with its own set of unique challenges. Heavy network traffic is generated in the IoT environment for transmitting data. Reducing network traffic by determining the shortest route from the source to the aim decreases overall system response time and energy consumption costs. This translates into the need to define efficient routing algorithms. Many IoT devices are powered by batteries with limited lifetime, so in order to ensure remote, continuous, distributed, and decentralized control and self‐organization of these devices, power‐aware techniques are highly desirable. Another requirement is to manage huge amounts of dynamically changing data. This paper reviews a set of swarm intelligence (SI) algorithms applied to the main challenges introduced by the IoT. SI algorithms try to determine the best path for insects by modeling the hunting behavior of the agent community. These algorithms are suitable for IoT needs because of their flexibility, resilience, dissemination degree, and extension.
Acoustic Emission from Concrete: Critical Slowing Down Analysis and Compressive Strength Prediction
Authors: Lv Zhiqiang , Jiang Annan , Tan Zhen
Published: May 2023

This work pursues two primary aims: identifying the precursory point by the CSD analysis of AE series and using acoustic emission parameters to predict the compressive strength of concrete utilizing the artificial neural network (ANN), extreme learning machine (ELM), and support vector machine (SVM). The concrete specimens with a water-cement ratio of 0.45 and 0.55 were tested for compressive strength, and the failure process of concrete was monitored by acoustic emission. The results demonstrate that the advantage of variance was being intuitive, robust, and less affected by the window. The fluctuation and abrupt increase of the variance can be regarded as the critical point and the formation of the main failure surface, which provided warning information for preventing catastrophic failures. The precursory point determined by the variance was 80%–90% of the ultimate strength, consistent with the improved b value (Ib). Then, an approach to predict the compressive strength of concrete was proposed to predict the compressive strength of concrete utilizing the ANN, ELM, and SVM. The results of the ELM were more outstanding than those of the ANN and SVM. The results suggest that it was possible to predict the compressive strength of concrete with a small number of samples using the AE parameters.
Results on Certain Biopolymers Using M‐Polynomial and NM‐Polynomial of Topological Indices
Authors: Mohammed Yasin H. , Suresh M. , Abebe Girum Aklilu , Fufa Samuel A.
Published: May 2023

Topological indices are numerical descriptors that aid in the prediction of chemical molecules’ physiochemical properties and biological actions. It is often helpful to forecast numerous physiochemical attributes and biological actions of molecules in chemometrics, bioinformatics, and biomedicine. In this paper, we establish the M‐polynomial and NM‐polynomial of some very familiar biopolymers, which are xanthan gum, gellan gum, and polyacrylamide. The uses of these biopolymers can increasingly take the place of traditional admixtures for the application of soil stability and enhancement. We recover the important topological degree‐based indices. Also, we give diverse graphs of topological indices and their relations with the parameters of structures.
Strategies for Medical Device Development: User and Stakeholder Perceptions
Authors: Tsai I-Ching , Wang Ching-Da , Chen Peng-Ting
Published: May 2023

Medical device development involves user safety, and it is governed by specific regulations. The failure of medical device developers to consider the influence of users, the environment, and related organizations on product development during the design and development process can result in added risks to the use of medical technologies. Although many studies have examined the medical device development process, there has been no systematic and comprehensive assessment of the key factors affecting medical device development. This research synthesized the value of medical device industry stakeholders’ experiences through a literature review and interviews with industry experts. Then, it establishes an FIA‐NRM model to identify the key factors affecting medical device development and suggests appropriate pathways for improvement. Results indicate that the development of medical devices should begin with stabilizing organizational characteristics, followed by strengthening technical capability and use environment, and finally, consideration should be given to the user action of medical devices. The results provide medical device developers with optimal development pathways and resource allocation recommendations to support developers in developing medical device development strategies as well as ensuring the safety and effectiveness of the products for end users.
A Study Based on Network Pharmacology Decoding the Multi‐Target Mechanism of Duhuo Jisheng Decoction for the Treatment of Intervertebral Disc Degeneration
Authors: Liu Hao , Li Yumin , Li Zhujun , Li Jie , Zhang Qiongchi , Cao Shuai , Li Haopeng
Published: May 2023

Intervertebral disc degeneration (IDD) poses a grim public health impact. Duhuo Jisheng Decoction (DJD), a traditional Chinese medicine formula, has recently received significant attention for its efficacy and safety in treating IDD. However, the pathological processes of IDD in which DJD interferes and molecular mechanism involved are poorly understood, which brings difficulties to the clinical practice of DJD for the treatment of IDD. This study systematically investigated the underlying mechanism of DJD treatment of IDD. Network pharmacology approaches were employed, integrating molecular docking and random walk with restart (RWR) algorithm, to identify key compounds and targets for DJD in the treatment of IDD. Bioinformatics approaches were used to further explore the biological insights in DJD treatment of IDD. The analysis identifies AKT1, PIK3R1, CHUK, ALB, TP53, MYC, NR3C1, IL1B, ERBB2, CAV1, CTNNB1, AR, IGF2, and ESR1 as key targets. Responses to mechanical stress, oxidative stress, cellular inflammatory responses, autophagy, and apoptosis are identified as the critical biological processes involved in DJD treatment of IDD. The regulation of DJD targets in extracellular matrix components, ion channel regulation, transcriptional regulation, synthesis and metabolic regulation of reactive oxygen products in the respiratory chain and mitochondria, fatty acid oxidation, the metabolism of Arachidonic acid, and regulation of Rho and Ras protein activation are found to be potential mechanisms in disc tissue response to mechanical stress and oxidative stress. MAPK, PI3K/AKT, and NF‐κB signaling pathways are identified as vital signaling pathways for DJD to treat IDD. Quercetin and Kaempferol are assigned a central position in the treatment of IDD. This study contributes to a more comprehensive understanding of the mechanism of DJD in treating IDD. It provides a reference for applying natural products to delay the pathological process of IDD.
Kinetics and Isotherm Studies for Adsorption of Gentian Violet Dye from Aqueous Solutions Using Synthesized Hydroxyapatite
Authors: Ali Dalia A. , Saad Fatma A. , Elsawy Hoda A.
Published: May 2023

Water is the most important resource for life, but it has been greatly exhausted over the past century as a result of the human population and environmentally harmful activities. The excessive quantity of dyes exists in the wastewater produced from the textile industries which is the main reason for serious human health and environmental problems. There are many dye removal techniques, and the most promising one is the adsorption technique. The novelty of this research is using unmodified synthesized hydroxyapatite (HAp) as an adsorbent for the removal of gentian violet (GV) dye from aqueous solutions as there are no sufficient data in the literature about using it in the adsorption of GV dye from aqueous solutions. Unmodified HAp was synthesized by a combined precipitation microwave method. The prepared adsorbent was characterized by scanning electron microscopy (SEM), energy dispersive X-ray (EDX), X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, and zeta potential analyses. The kinetic study showed that the pseudo-second-order (PSO) model was the best fitted model with the experimental data. Analysis of adsorption isotherms using different models showed that this adsorption system was better described by the Halsey isotherm with a maximum adsorption capacity (qmax)  of 1.035 mg/g. The effects of experimental factors such as initial solution pH, initial dye concentration, adsorbent dose, and contact time were studied during the investigation of GV dye removal efficiency. The experimental results indicated that the maximum adsorption efficiency (99.32%) of the GV dye using HAp adsorbent was achieved at the following conditions: contact time = 90 min, pH = 12, initial GV dye concentration = 3 mg/L, and adsorbent dose = 1 g/L. The adsorption mechanism of the GV dye using HAp might be explained by the electrostatic interaction between the negatively charged surface of the HAp and the positively charged group of the GV dye. Thermodynamics study was performed on the adsorption process of GV dye from aqueous solutions using the synthesized HAp which revealed that this process was endothermic and spontaneous due to positive values of ΔH and ΔS and negative values of ΔG.
Optimal Combinations of Control Strategies and Cost‐Effectiveness Analysis of Dynamics of Endemic Malaria Transmission Model
Authors: Edossa Dereje Gutema , Wedajo Alemu Geleta , Koya Purnachandra Rao
Published: May 2023

In this study, we propose and analyze a determinastic nonlinear system of ordinary differential equation model for endemic malaria disease transmission and optimal combinations of control strategies with cost effective analysis. Basic properties of the model, existence of disease‐free and endemic equilibrium points, and basic reproduction number of the model are derived and analyzed. From this analysis, we conclude that if the basic reproduction number is less than unity, then the disease‐free equilibrium point is both locally and globally asymptotically stable. The endemic equilibrium will also exist if the basic reproduction number is greater than unity. Moreover, existence and necessary condition for forward bifurcation is derived and established. Furthermore, optimal combinations of time‐dependent control measures are incorporated to the model. By using Pontryagin’s maximum principal theory, we derived the necessary conditions of optimal control. Numerical simulations were conducted to confirm our analytical results. Our findings were that malaria disease may be controlled well with strict application of the combination of prevention of drug resistance, insecticide‐treated net (ITN), indoor residual spray (IRS), and active treatment. The use of a combination of insecticide‐treated net, indoor residual spray, and active treatment is the most optimal cost‐effective and efficacious strategy.
Enhancing Disease Classification in Abdominal CT Scans through RGB Superposition Methods and 2D Convolutional Neural Networks: A Study of Appendicitis and Diverticulitis
Authors: Lee Gi Pyo , Park So Hyun , Kim Young Jae , Chung Jun-Won , Kim Kwang Gi
Published: May 2023

The primary symptom of both appendicitis and diverticulitis is a pain in the right lower abdomen; it is almost impossible to diagnose these conditions through symptoms alone. However, there will be misdiagnoses happening when using abdominal computed tomography (CT) scans. Most previous studies have used a 3D convolutional neural network (CNN) suitable for processing sequences of images. However, 3D CNN models can be difficult to implement in typical computing systems because they require large amounts of data, GPU memory, and extensive training times. We propose a deep learning method, utilizing red, green, and blue (RGB) channel superposition images reconstructed from three slices of sequence images. Using the RGB superposition image as the input image of the model, the average accuracy was shown as 90.98% in EfficietNetB0, 91.27% in EfficietNetB2, and 91.98% in EfficietNetB4. The AUC score using the RGB superposition image was higher than the original image of the single channel for EfficientNetB4 (0.967 vs. 0.959, p = 0.0087). The comparison in performance between the model architectures using the RGB superposition method showed the highest learning performance in the EfficientNetB4 model among all indicators; accuracy was 91.98% and recall was 95.35%. EfficientNetB4 using the RGB superposition method had a 0.011 (p value = 0.0001) AUC score higher than EfficientNetB0 using the same method. The superposition of sequential slice images in CT scans was used to enhance the distinction in features like shape, size of the target, and spatial information used to classify disease. The proposed method has fewer constraints than the 3D CNN method and is suitable for an environment using 2D CNN; thus, we can achieve performance improvement with limited resources.
Clinical Outcomes and Predictors of Late Recurrence in Young Patients with Atrial Fibrillation after Catheter Ablation
Authors: Xu Haonan , Chen Xiaowei , Zhang Yubin , Zhu Kui , Zhao Jiangtao , Qin Fen , Tao Hailong
Published: May 2023

Background. Catheter ablation (CA) is an established treatment for atrial fibrillation (AF), but the recurrence of AF is not neglected. Young patients with AF were generally more symptomatic and intolerant to long‐term drug treatment. We aim to explore clinical outcomes and predictors of late recurrence (LR) in AF patients younger than 45 years after CA to better manage them. Methods. We retrospectively studied 92 symptomatic AF patients who accepted CA from September 1, 2019, to August 31, 2021. Baseline clinical data (including N‐terminal prohormone of brain natriuretic peptide, NT‐proBNP), ablation outcomes, and follow‐up outcomes were collected. Patients were followed up at 3, 6, 9, and 12 months. Follow‐up data were available for 82/92 (89.1%) patients. Results. One‐year arrhythmia‐free survival was 81.7% (67/82) in our study group. Major complications occurred in 3/82 (3.7%) patients with an acceptable rate. The value of ln(NT‐proBNP) (P = 0.025, odds ratio [OR] = 1.977, 95% confidence interval [CI] 1.087‐3.596) and a family history of AF (P = 0.041, HR = 9.269, 95% CI 1.097‐78.295) could independently predict AF recurrence. The ROC analysis of ln(NT‐proBNP) showed that NT‐proBNP greater than 200.05 pg/ml (area under the curve: 0.772, 95% CI 0.642‐0.902, P = 0.001, sensitivity 0.800, specificity 0.701) was the cut‐off point for predicting late recurrence. Conclusions. CA is a safe and effective treatment for AF patients younger than 45 years. Elevated NT‐proBNP level and a family history of AF could be used as predictors for late recurrence in young patients. The result of this study may help us take more comprehensive management of those with high‐recurrence risks to reduce disease burden and improve quality of life.
Cognitive Rehabilitation: Mild Traumatic Brain Injury and Relevance of OTPF
Authors: Vas Asha , Luedtke Anna , Ortiz Eryn , Mackie Natalie , Gonzalez Samantha
Published: May 2023

There is increased awareness of the long-term cognitive sequelae of mild traumatic brain injury (mTBI). Therefore, researchers and clinicians have developed and tested cognitive training protocols to address these challenges. The current review summarized literature that examined existing cognitive rehabilitation/training programs. Specifically, the review listed the impact of these programs on functional domains informed by the Occupational Therapy Practice Framework (OTPF). Literature between the years 2008 and 2022 was gathered from nine databases. Results indicate that several cognitive rehabilitation programs have proven to positively influence domains of occupation, client factors, performance, and context. Occupational therapy practitioners have an opportunity to engage in mTBI management. Furthermore, adopting domains of OTPF may guide assessments, treatment planning, and long-term follow-up.
Adaptive Optimal Control of Hybrid Electric Vehicle Power Battery via Policy Learning
Authors: Zhu Qinglin , Sun Huanli , Zhao Ziliang , Liu Yixin , Zhao Jun
Published: May 2023

An online policy learning algorithm is used to solve the optimal control problem of the power battery state of charge (SOC) observer for the first time. The design of adaptive neural network (NN) optimal control is studied for the nonlinear power battery system based on a second‐order (RC) equivalent circuit model. First, the unknown uncertainties of the system are approximated by NN, and a time‐varying gain nonlinear state observer is designed to address the problem that the resistance capacitance voltage and SOC of the battery cannot be measured. Then, to realize the optimal control, a policy learning‐based online algorithm is designed, where only the critic NN is required and the actor NN widely used in most design of the optimal control methods is removed. Finally, the effectiveness of the optimal control theory is verified by simulation.
Updates on the Status of Carbapenem-Resistant Enterobacterales in Lebanon
Authors: Fadlallah Mahdi , Salman Ahmad , Salem-Sokhn Elie
Published: May 2023

Carbapenem-resistant Enterobacterales (CRE) pathogens have been increasingly isolated and reported in Lebanon. Several studies have been published over the last two decades about the CRE situation in the country. However, compared to the worldwide data, those studies are scarce and mostly restricted to single center studies. In this review, we aim to present a comprehensive and reliable report illustrating the current situation regarding CRE in Lebanon. Variable studies have shown an increasing pattern of carbapenem resistance in Enterobacterales since the first reports of CRE isolates in 2007 and 2008. Escherichia coli and Klebsiella pneumoniae were the most detected ones. The OXA-48 class D carbapenemases were the most prevalent carbapenemases among CRE isolates. Moreover, the emergence of other carbapenemases like the NDM class B carbapenemase has been noticed. Strict infection control measures in hospitals, including the identification of CRE carriers, are needed in Lebanese hospitals since carriage is a potential risk for the spread of CRE in healthcare settings. The dissemination of CRE in the community is noticed and attributed to multiple causes, such as the refugee crisis, water contamination, and antimicrobial misuse. In conclusion, strict infection control measures in healthcare settings, in addition to accurate antimicrobial stewardship program implementation, are urgently needed.
A Novel Subspace Decomposition with Rotational Invariance Technique to Estimate Low-Frequency Oscillatory Modes of the Power Grid
Authors: Samal Sudhansu Kumar , Khadanga Rajendra Kumar
Published: May 2023

This paper proposes modified Karhunen–Loeve transform with total least square estimation of signal parameters using rotational in-variance technique (MKLT-TLS-ESPRIT) to approximate the low-frequency oscillatory modes. MKLT decreases the impact of highly correlated additive colored Gaussian noise (ACGN) from the signal by differentiating the correlation matrix w.r.t from the final time instance. A quantitative study of the suggested method with other estimation methods is used to evaluate the effectiveness of the proposed method. Monte Carlo simulations with 50,000 runs are conducted to test the robustness of the estimation scheme for MKLT-TLS-ESPRIT. The evaluation of the efficiency of the proposed method in real-time perspective, the two-area system, and New England sixty-eight bus test system has been considered. The analysis shows that the suggested methodology correctly measures the interarea modes and lowers their mean and standard deviation to a minimum value.