bluuebunny/crossref_metadata_2025 huggingface dataset:
Cardinality Estimation over Knowledge Graphs with Embeddings and Graph Neural Networks
Authors: Schwabe Tim , Acosta Maribel
Published: March 2024

Cardinality Estimation over Knowledge Graphs (KG) is crucial for query optimization, yet remains a challenging task due to the semi-structured nature and complex correlations of data in typical KGs. In this work, we propose GNCE, a novel approach that leverages knowledge graph embeddings and Graph Neural Networks (GNN) to accurately predict the cardinality of conjunctive queries over KGs. GNCE first creates semantically meaningful embeddings for all entities in the KG, which are then used to learn a representation of a query using a GNN to estimate the cardinality of the query. We evaluate GNCE on several KGs in terms of q-Error and demonstrate that it outperforms state-of-the-art approaches based on sampling, summaries, and (machine) learning in terms of estimation accuracy while also having a low execution time and few parameters. Additionally, we show that GNCE performs similarly well on real-world queries and can inductively generalize to unseen entities, making it suitable for use in dynamic query processing scenarios. Our proposed approach has the potential to significantly improve query optimization and related applications that rely on accurate cardinality estimates of conjunctive queries.
Dias: Dynamic Rewriting of Pandas Code
Authors: Baziotis Stefanos , Kang Daniel , Mendis Charith
Published: March 2024

In recent years, dataframe libraries, such as pandas have exploded in popularity. Due to their flexibility, they are increasingly used in ad-hoc exploratory data analysis (EDA) workloads. These workloads are diverse, including custom functions which can span libraries or be written in pure Python. The majority of systems available to accelerate EDA workloads focus on bulk-parallel workloads, which contain vastly different computational patterns, typically within a single library. As a result, they can introduce excessive overheads for ad-hoc EDA workloads due to their expensive optimization techniques. Instead, we identify source-to-source, external program rewriting as a lightweight technique which can optimize across representations, and offer substantial speedups while also avoiding slowdowns. We implemented Dias, which rewrites notebook cells to be more efficient for ad-hoc EDA workloads. We develop techniques for efficient rewrites in Dias, including checking the preconditions under which rewrites are correct, dynamically, at fine-grained program points. We show that Dias can rewrite individual cells to be 57× faster compared to pandas and 1909× faster compared to optimized systems such as modin. Furthermore, Dias can accelerate whole notebooks by up to 3.6× compared to pandas and 27.1× compared to modin.
LST-Bench: Benchmarking Log-Structured Tables in the Cloud
Authors: Camacho-Rodríguez Jesús , Agrawal Ashvin , Gruenheid Anja , Gosalia Ashit , Petculescu Cristian , Aguilar-Saborit Josep , Floratou Avrilia , Curino Carlo , Ramakrishnan Raghu
Published: March 2024

Data processing engines increasingly leverage distributed file systems for scalable, cost-effective storage. While the Apache Parquet columnar format has become a popular choice for data storage and retrieval, the immutability of Parquet files renders it impractical to meet the demands of frequent updates in contemporary analytical workloads. Log-Structured Tables (LSTs), such as Delta Lake, Apache Iceberg, and Apache Hudi, offer an alternative for scenarios requiring data mutability, providing a balance between efficient updates and the benefits of columnar storage. They provide features like transactions, time-travel, and schema evolution, enhancing usability and enabling access from multiple engines. Moreover, engines like Apache Spark and Trino can be configured to leverage the optimizations and controls offered by LSTs to meet specific business needs. Conventional benchmarks and tools are inadequate for evaluating the transformative changes in the storage layer resulting from these advancements, as they do not allow us to measure the impact of design and optimization choices in this new setting.

In this paper, we propose a novel benchmarking approach and metrics that build upon existing benchmarks, aiming to systematically assess LSTs. We develop a framework, LST-Bench, which facilitates effective exploration and evaluation of the collaborative functioning of LSTs and data processing engines through tailored benchmark packages. A package is a mix of use patterns reflecting a target workload; LST-Bench makes it easy to define a wide range of use patterns and combine them into a package, and we include a baseline package for completeness. Our assessment demonstrates the effectiveness of our framework and benchmark packages in extracting valuable insights across diverse environments. The code for LST-Bench is open source and is available at https://github.com/microsoft/lst-bench/.
RITA: Group Attention is All You Need for Timeseries Analytics
Authors: Liang Jiaming , Cao Lei , Madden Samuel , Ives Zack , Li Guoliang
Published: March 2024

Timeseries analytics is important in many real-world applications. Recently, the Transformer model, popular in natural language processing, has been leveraged to learn high quality feature embeddings from timeseries: embeddings are key to the performance of various timeseries analytics tasks such as similarity-based timeseries queries within vector databases. However, quadratic time and space complexities limit Transformers' scalability, especially for long timeseries. To address these issues, we develop a timeseries analytics tool, RITA, which uses a novel attention mechanism, named group attention, to address this scalability issue. Group attention dynamically clusters the objects based on their similarity into a small number of groups and approximately computes the attention at the coarse group granularity. It thus significantly reduces the time and space complexity, yet provides a theoretical guarantee on the quality of the computed attention. The dynamic scheduler of RITA continuously adapts the number of groups and the batch size in the training process, ensuring group attention always uses the fewest groups needed to meet the approximation quality requirement. Extensive experiments on various timeseries datasets and analytics tasks demonstrate that RITA outperforms the state-of-the-art in accuracy and is significantly faster --- with speedups of up to 63X.
Approximate Sketches
Authors: Tsan Brian , Datta Asoke , Izenov Yesdaulet , Rusu Florin
Published: March 2024

Sketches are single-pass small-space data summaries that can quickly estimate the cardinality of join queries. However, sketches are not directly applicable to join queries with dynamic filter conditions --- where arbitrary selection predicate(s) are applied --- since a sketch is limited to a fixed selection. While multiple sketches for various selections can be used in combination, they each incur individual storage and maintenance costs. Alternatively, exact sketches can be built during runtime for every selection. To make this process scale, a high-degree of parallelism --- available in hardware accelerators such as GPUs --- is required. Therefore, sketch usage for cardinality estimation in query optimization is limited. Following recent work that applies transformers to cardinality estimation, we design a novel learning-based method to approximate the sketch of any arbitrary selection, enabling sketches for join queries with filter conditions. We train a transformer on each table to estimate the sketch of any subset of the table, i.e., any arbitrary selection. Transformers achieve this by learning the joint distribution amongst table attributes, which is equivalent to a multidimensional sketch. Subsequently, transformers can approximate any sketch, enabling sketches for join cardinality estimation. In turn, estimating joins via approximate sketches allows tables to be modeled individually and thus scales linearly with the number of tables. We evaluate the accuracy and efficacy of approximate sketches on queries with selection predicates consisting of conjunctions of point and range conditions. Approximate sketches achieve similar accuracy to exact sketches with at least one order of magnitude less overhead.
Reactivating and Preserving Interactive Multimedia Artworks: An Analog Performance from the Seventies
Authors: Fiordelmondo Alessandro , Canazza Sergio , Pretto Niccoló
Published: March 2024

Interactive multimedia art shows a complex nature as it is time- and process-based, interconnected with technology, derived by the participation of several authors (artists, technicians, performers, to name a few) and an audience, and it is strongly tied to the moment and space of the original exhibition. These characteristics make the preservation of these artworks a multifaceted process. Building on the foundations developed since the 2000s by international projects focused on preserving and restoring these new art forms, the paper proposes an original model for achieving dynamic preservation, called “the multilevel preservation model”. Since it is no longer possible to guarantee physical integrity for interactive multimedia artworks, dynamic preservation involves recording all the changes that occur to display the artworks in the future. In other words, it makes it possible to record the dynamic authenticity of artworks. The model proposes two fundamental properties: multiple layering, which allows to handle different levels of information about the artwork;
multiple belongingness
, which allows to represent the dynamic authenticity of the artwork at the archival level and achieving dynamic preservation.

The paper demonstrates the high-level implementation of the model by presenting a case study: the reactivation and preservation of an analog video art performance from the 1970s. This case study proposes an interesting real scenario for testing the model, as the reactivation process involved the migration of the entire analog technological apparatus of the artwork into the digital domain.
LEC-MiCs: Low-Energy Checkpointing in Mixed-Criticality Multicore Systems
Authors: Safari Sepideh , Shokri Shayan , Hessabi Shaahin , Lotfi-Kamran Pejman
Published: March 2024

With the advent of multicore platforms in designing Mixed-Criticality Systems (MCSs), simultaneous management of reliability and energy while guaranteeing an acceptable service level for low-criticality tasks is a crucial challenge. To ensure the reliability of the MCSs against transient faults, fault-tolerant techniques are employed which will increase energy consumption. To mitigate the energy overhead, the Dynamic Voltage and Frequency Scaling (DVFS) technique will be exploited. However, this technique might lead to violating the timing constraints of high-criticality tasks. Therefore, this article presents, for the first time, the low-energy checkpointing technique to guarantee the reliability of multiple preemptive periodic mixed-criticality tasks in a multicore platform. In contrast to the previous works in checkpointing technique which consider a specific number of faults that all the tasks in the system should tolerate, in this article, the number of tolerable faults for each execution section of a task and in each voltage and frequency level is determined through proposed formulas to meet the reliability target based on safety standards. Then, our proposed method determines the number of checkpoints and their non-uniform intervals for the normal and overrun sections of each task to reduce energy consumption, respectively. Moreover, the unified demand bound function (DBF) analysis is proposed for analyzing the schedulability of the task set, where each high-criticality task meets its timing and reliability constraints, and low-criticality tasks execute based on their derived guaranteed periods in each operational mode of the system. Experimental results show that our proposed scheme meets the timing and reliability constraints while at the same time, improving the Quality of Service (QoS) of low-criticality tasks and managing energy consumption with an average of 29.49% and 32.78%, respectively.
Dual Homogeneity Hypergraph Motifs with Cross-view Contrastive Learning for Multiple Social Recommendations
Authors: Han Jiadi , Tang Yufei , Tao Qian , Xia Yuhan , Zhang Liming
Published: March 2024

Social relations are often used as auxiliary information to address data sparsity and cold-start issues in social recommendations. In the real world, social relations among users are complex and diverse. Widely used graph neural networks (GNNs) can only model pairwise node relationships and are not conducive to exploring higher-order connectivity, while hypergraph provides a natural way to model high-order relations between nodes. However, recent studies show that social recommendations still face the following challenges: 1) a majority of social recommendations ignore the impact of multifaceted social relationships on user preferences; 2) the item homogeneity is often neglected, mainly referring to items with similar static attributes have similar attractiveness when exposed to users that indicating hidden links between items; and 3) directly combining the representations learned from different independent views cannot fully exploit the potential connections between different views. To address these challenges, in this article, we propose a novel method DH-HGCN++ for multiple social recommendations. Specifically, dual homogeneity (i.e., social homogeneity and item homogeneity) is introduced to mine the impact of diverse social relations on user preferences and enrich item representations. Hypergraph convolution networks with motifs are further exploited to model the high-order relations between nodes. Finally, cross-view contrastive learning is proposed as an auxiliary task to jointly optimize the DH-HGCN++. Real-world datasets are used to validate the effectiveness of the proposed model, where we use sentiment analysis to extract comment relations and employ the k-means clustering algorithm to construct the item-item correlation graph. Experiment results demonstrate that our proposed method consistently outperforms the state-of-the-art baselines on Top-N recommendations.
Quintuple-based Representation Learning for Bipartite Heterogeneous Networks
Authors: Zhou Cangqi , Chen Hui , Zhang Jing , Li Qianmu , Hu Dianming
Published: March 2024

Recent years have seen rapid progress in network representation learning, which removes the need for burdensome feature engineering and facilitates downstream network-based tasks. In reality, networks often exhibit heterogeneity, which means there may exist multiple types of nodes and interactions. Heterogeneous networks raise new challenges to representation learning, as the awareness of node and edge types is required. In this article, we study a basic building block of general heterogeneous networks, the heterogeneous networks with two types of nodes. Many problems can be solved by decomposing general heterogeneous networks into multiple bipartite ones. Recently, to overcome the demerits of non-metric measures used in the embedding space, metric learning-based approaches have been leveraged to tackle heterogeneous network representation learning. These approaches first generate triplets of samples, in which an anchor node, a positive counterpart, and a negative one co-exist, and then try to pull closer positive samples and push away negative ones. However, when dealing with heterogeneous networks, even the simplest two-typed ones, triplets cannot simultaneously involve both positive and negative samples from different parts of networks. To address this incompatibility of triplet-based metric learning, in this article, we propose a novel quintuple-based method for learning node representations in bipartite heterogeneous networks. Specifically, we generate quintuples that contain positive and negative samples from two different parts of networks. And we formulate two learning objectives that accommodate quintuple-based learning samples, a proximity-based loss that models the relations in quintuples by sigmoid probabilities and an angular loss that more robustly maintains similarity structures. In addition, we also parameterize feature learning by using one-dimensional convolution operators around nodes’ neighborhoods. Compared with eight methods, extensive experiments on two downstream tasks manifest the effectiveness of our approach.
On the Way to SBOMs: Investigating Design Issues and Solutions in Practice
Authors: Bi Tingting , Xia Boming , Xing Zhenchang , Lu Qinghua , Zhu Liming
Published: March 2024

The increase of software supply chain threats has underscored the necessity for robust security mechanisms, among which the Software Bill of Materials (SBOM) stands out as a promising solution. SBOMs, by providing a machine-readable inventory of software composition details, play a crucial role in enhancing transparency and traceability within software supply chains. This empirical study delves into the practical challenges and solutions associated with the adoption of SBOMs through an analysis of 4,786 GitHub discussions across 510 SBOM-related projects. Through repository mining and analysis, this research delineates key topics, challenges, and solutions intrinsic to the effective utilization of SBOMs. Furthermore, we shed light on commonly used tools and frameworks for SBOM generation, exploring their respective strengths and limitations. This study underscores a set of findings, for example, there are four phases of the SBOM life cycle, and each phase has a set of SBOM development activities and issues; in addition, this study emphasizes the role SBOM play in ensuring resilient software development practices and the imperative of their widespread adoption and integration to bolster supply chain security. The insights of our study provide vital input for future work and practical advancements in this topic.
Identification of Siglec-1-negative alveolar macrophages with proinflammatory phenotypes in chronic obstructive pulmonary disease
Authors: Saito Takuya , Fujino Naoya , Kyogoku Yorihiko , Yamada Mitsuhiro , Okutomo Koji , Ono Yoshinao , Konno Shuichi , Endo Takuto , Itakura Koji , Matsumoto Shuichiro , Sano Hirohito , Aizawa Hiroyuki , Numakura Tadahisa , Onodera Katsuhiro , Okada Yoshinori , Hussell Tracy , Ichinose Masakazu , Sugiura Hisatoshi
Published: March 2024

Alveolar macrophages (AMs) in patients with chronic obstructive pulmonary disease (COPD) orchestrate persistent inflammation in the airway. We find that Siglec-1-negative alveolar macrophages have a wide range of proinflammatory landscapes and a protease-expressing phenotype. Moreover, this subset is associated with the pathogenesis of COPD and responds to viral stimuli.
Silencing of B4Galnt1 Gene Prevents GM2 Accumulation in Tay-Sachs Cells
Authors: Ateş Nurselin , İnci Orhan Kerim , Demir Seçil Akyıldız , Seyrantepe Volkan
Published: March 2024

Introduction. The Tay-Sachs disease (TSD) is a progressive neurodegenerative disorder resulting from genetic mutations in the HEXA gene encoding the α-subunit of β-hexosaminidase A leading to the accumulation of GM2 ganglioside in the central nervous system. Multiple therapeutical strategies have been investigated such as gene therapy for Tay-Sachs patients; however, there is still no cure. In the present study, we suggest a new approach for the treatment of the Tay-Sachs disease with the concept of substrate reduction therapy by using AAV9-mediated RNAi technology targeting the B4Galnt1 gene at the upstream of the enzymatic defect in TSD pathology to decrease GM2 biosynthesis and accumulation in cell models of TSD. Material and Methods. We employed AAV9-mediated shRNA transduction for mice and human Tay-Sachs cells. After transduction, expression levels of ganglioside metabolism genes were analyzed by RT-PCR and GM2 and lysosome-associated membrane protein 1 (LAMP1) protein levels were evaluated by immunocytochemistry analysis. Results. Here, we have shown that AAV9-shRNA transduction effectively reduced B4Galnt1 expression in TSD cells demonstrating a reduction in GM2 accumulation and LAMP1. Discussion. Our data shows that AAV-mediated B4Galnt1-shRNA transduction can ameliorate disease pathologies by decreasing the lysosomal accumulation of GM2 through selectively reducing B4Gant1 activity in cell models of the Tay-Sachs disease. Therefore, we suggest promising novel experimental therapy for this devastating disease using a mouse model in the future.
Improving Yellow Mealworm (Tenebrio molitor) Utilization with Sodium Butyrate in Nile Tilapia Diets: Effects on Growth Performance, Intestinal Histology, Antioxidative Response, and Blood Biomarkers
Authors: El-Desouky Fify F. , Ibrahim Mostafa A. , Abd El-Razek Ibrahim M. , El-Nabawy El-Said M. , Amer Asem A. , Zaineldin Amr I. , Gewaily Mahmoud S. , Dawood Mahmoud A. O.
Published: March 2024

Yellow mealworm (Tenebrio molitor) meal was introduced to aquafeed as a suitable protein source to replace fish meal (FM) and soybean meal and, thereby, consistent aquaculture production. However, mealworms should be added at adequate levels due to the presence of antinutritional factors such as chitin. Consequently, sodium butyrate (SB) is suggested to improve feed quality and ensure aquatic animals’ productivity and welfare. In this study, parallel with the protein source (T. molitor meal or FM), dietary supplementation of SB (1 g/kg) is involved as a factor in the 2 × 2 factorial study. The first and the second diets were formulated using FM as a protein source with or without SB, while the third and fourth diets were prepared by replacing FM with T. molitor meal with or without SB supplementation. After 60 days, fish fed with FM or T. molitor and SB showed improved final body weight and weight gain, while those fed with T. molitor without SB had a reduced protein efficiency ratio. Histological analysis revealed that dietary SB improved intestinal histological features by increasing the height and branching of intestinal villi and immune cell infiltration near intestinal crypts in Nile tilapia‐fed FM or T. molitor. Furthermore, fish‐fed FM or T. molitor and SB had higher Hb, red blood cells, PCV, total protein, and globulin levels than fish‐fed respective test diets without SB supplementation. Dietary SB addition to FM or T. molitor‐based diets also significantly enhanced blood lysozyme and phagocytic activities, catalase, superoxide dismutase, glutathione peroxidase, and reduced MDA levels. Our results demonstrate that T. molitor meal can replace FM without compromising Nile tilapia’s growth performance and health status. Additionally, SB supplementation improved T. molitor meal utilization by Nile tilapia, thereby significantly enhancing the growth, digestion capacity, intestinal histological features, and antioxidative and immune responses. Consequently, dietary T. molitor meal reduces the reliance on FM and improves the sustainability and efficiency of Nile tilapia production.
Ultrasensitive Response Explains the Benefit of Combination Chemotherapy Despite Drug Antagonism
Authors: Patterson Sarah C. , Pomeroy Amy E. , Palmer Adam C.
Published: March 2024

Most aggressive lymphomas are treated with combination chemotherapy, commonly as multiple cycles of concurrent drug administration. Concurrent administration is in theory optimal when combination therapies have synergistic (more than additive) drug interactions. We investigated pharmacodynamic interactions in the standard 4-drug “CHOP” regimen in peripheral T-cell lymphoma (PTCL) cell lines and found that CHOP consistently exhibits antagonism and not synergy. We tested whether staggered treatment schedules could improve tumor cell kill by avoiding antagonism, using in vitro models of concurrent or staggered treatments. Surprisingly, we observed that tumor cell kill is maximized by concurrent drug administration despite antagonistic drug–drug interactions. We propose that an ultrasensitive dose response, as described in radiology by the linear–quadratic (LQ) model, can reconcile these seemingly contradictory experimental observations. The LQ model describes the relationship between cell survival and dose, and in radiology has identified scenarios favoring hypofractionated radiotherapy—the administration of fewer large doses rather than multiple smaller doses. Specifically, hypofractionated treatment can be favored when cells require an accumulation of DNA damage, rather than a “single hit,” to die. By adapting the LQ model to combination chemotherapy and accounting for tumor heterogeneity, we find that tumor cell kill is maximized by concurrent administration of multiple drugs, even when chemotherapies have antagonistic interactions. Thus, our study identifies a new mechanism by which combination chemotherapy can be clinically beneficial that is not contingent on positive drug–drug interactions.
Large Neutral Amino Acids Moderate the Effects of Metabolic Syndrome on Cognitive Performance in Middle-Aged Adults
Authors: Youn Cherry , Caillaud Marie L. , Li Yanrong , Gallagher Isabelle A. , Strasser Barbara , Fuchs Dietmar , Tanaka Hirofumi , Haley Andreana P.
Published: March 2024

Introduction: Two large neutral amino acids (LNAA), tryptophan and tyrosine, are precursors to cerebral neurotransmitters and are involved in cognitive function. Higher levels of LNAA in young adults are associated with improved cognition, although these associations appear to reverse over time. Given that exposure to metabolic syndrome (MetS) may induce premature cognitive aging, the current project aims to fill the gap in the literature by examining the effect of LNAA on cognitive performance in midlife adults with metabolic risks. Methods: Eighty-eight adults, ages 40–61 years, participated in this cross-sectional study. LNAA metabolites were quantified, MetS components were measured using high-performance liquid chromatography, and MetS components were assessed in the laboratory. Composite verbal memory and executive functioning scores were computed using principal component analysis. We used linear regression models to test the interaction between LNAA and MetS while covarying for sex, age, and education. Results: The kynurenine/tryptophan ratio moderated the relation between MetS and verbal memory, even after adjusting for relevant covariates. Tyrosine metabolites were not significant moderators of the association between MetS and executive functioning. Conclusion: Our findings suggest that the detected weaker memory performance in adults with a high number of MetS components may be related to relative tryptophan depletion and possible decreases in serotonin production. Further investigation is warranted to examine the potential role of LNAA in associations between cognitive performance and metabolic risks over time.
ERRγ1 and Aromatase Expression in Human Placental Tissues from Term Deliveries of Large for Gestational Age Newborns
Authors: Palligas Marcos Abdul , Nemer Cristina Patricia , Cannizzaro Claudia Monica , Baquedano Maria Sonia , Belgorosky Alicia , Saraco Nora
Published: March 2024

Introduction: Being born either large for gestational age (LGA) or small for gestational age has been associated with an increased risk of developing metabolic syndrome in adulthood. However, the mechanism underlying this early programming remained unclear. Estrogen-related receptor gamma (ERRγ) is an orphan nuclear receptor with a high expression in the human placenta, particularly ERRγ1. ERRγ has been proposed to play a central role in controlling genes involved in energy metabolism. In the placenta, ERRγ1 acts as an oxygen-responsive transcription factor, regulating aromatase expression during trophoblast differentiation. Aromatase is an enzyme that catalyzes the synthesis of estrogens from androgens and is located in the syncytiotrophoblast. An adequate estrogen-androgen balance is required for normal pregnancy progression. Our aim was to analyze ERRγ1 and aromatase mRNA in human placenta from term LGA newborns. We propose that ERRγ1 and CYP19A1 expressions in the human placenta of LGA newborns are impaired, which would modify the fetal programming of LGA newborns since an imbalance in the intrauterine estrogen-androgen ratio would occur. Methods: Total RNA was obtained from the placental tissues of LGA (GA: 39–41 weeks, n = 8) and adequate for gestational age (AGA; 39–40 weeks, n = 10) newborns. ERRγ1 and aromatase mRNA variants were analyzed by RT2-PCR. Primers for aromatase analysis were specific for total aromatase (TotalAro) binding in exons 2–3 and for active aromatase (ActAro) in exons 9–10. Aromatase protein was analyzed by Western blot. Results: ERRγ1 mRNA was significantly higher in LGA compared to AGA. TotalAro mRNA was significantly lower in LGA in comparison with AGA control. Similar results were observed with aromatase protein. In contrast, ActAro/TotalAro ratio was higher in LGA compared to the AGA control. Conclusions: High expression of ERRγ1 as well as ActAro/TotalAro ratio in LGA suggests that ERRγ1 is involved in ActAro variant expression and hence disrupted estrogen-androgen balance in the intrauterine environment. We propose that dysregulation of ERRγ1 in the placenta might modify the estrogen-androgen balance in the intrauterine environment in LGA newborns, possibly representing one of the key factors in the regulation of fetal programming.
An efficient floating point adder for low-power devices
Authors: Narayanappa Manjula , Yellampalli Siva S.
Published: March 2024

With an increasing demand for power hungry data intensive computing, design methodologies with low power consumption are increasingly gaining prominence in the industry. Most of the systems operate on critical and noncritical data both. An attempt to generate a precision result results in excessive power consumption and results in a slower system. An attempt to generate a precision result results in excessive power consumption and results in a slower system. For non-critical data, approximate computing circuits significantly reduce the circuit complexity and hence power consumption. For non-critical data, approximate computing circuits significantly reduce the circuit complexity and hence power consumption. In this paper, a novel approximate single precision floating point adder is proposed with an approximate mantissa adder. The mantissa adder is designed with three 8-bit full adder blocks.
Design and implementation of an alcohol detection driver system
Authors: Alabi Oluwaseyi Omotayo , Adeaga Oyetunde Adeoye , Ajagbe Sunday Adeola , Adekunle Esther Oluwayemisi , Adigun Matthew Olusegun
Published: March 2024

A technology called an alcohol detection driver system is used to stop drunk driving by identifying alcohol in a motorist's breath or blood. This technology correctly measures the amount of alcohol a driver has in their system using sensors and algorithms, and it stops the car from starting if the amount is more than the legal limit. The number of fatal accidents and traffic fatalities caused by drinking could be greatly decreased thanks to this technology. The main focus of this project is to carry out the experiment in lowering the number of alcohol-related incidents on the road. Alcohol detection devices come in a variety of forms right now, including ignition interlocks, passive alcohol sensors, and in-car breathalyzers. Although these systems have reduced the number of drunk driving accidents, there remain questions about their efficiency, dependability, and cost. According to the sensor's specs, the output voltage of the MQ-3 sensor reduces by 69% during the sensor's recovery period of 30 seconds at 69% of baseline resistance. To assess the long-term viability and efficiency of these systems in lowering alcohol-related accidents and enhancing traffic safety, more research is required.
A novel ensemble deep network framework for scene text recognition
Authors: Dasari Sunil Kumar , Mehta Shilpa , Steffi Diana
Published: March 2024

In recent years, scene text recognition (STR) has always been considered a sequence-to-sequence problem. Attention-based techniques have a greater potential for context-semantic modelling, but they tend to overfit inadequate training data. STR is one of the most important and difficult challenges in image-based sequence recognition. A novel framework ensemble deep network (EDN) is proposed, EDN comprises customized convolutional neural network (CNN), and deep autoencoder. Customized CNN is designed by introducing the optimal spatial transformation module for optimizing the input of irregular text to read for same size. Further, deep autoencoder is introduced with effective attention mechanism utilizing the inherent features. The proposed ensemble deep network-proposed system (EDN-PS) approach outperforms the existing state-of-art techniques for both irregular and regular scene-texts and upon further simulations, the proposed model generates better results for IIIT5K, ICDAR-13, ICDAR-15, and CUTE dataset in comparison with the existing system hence our proposed EDN-PS model outperforms the existing state-of-art methods.
Extended distribution of Clematis wightiana Wall. (Ranunculaceae) in the Indian State of Arunachal Pradesh – a hitherto endemic species of the Western Ghats, India
Authors: Debasmita Dutta Pramanick  , Manas Bhaumik
Published: March 2024

The genus Clematis L., one of the largest genera of the family Ranunculaceae, is widely distributed in the world but intense diversity in subtropical and temperate regions of the Northern Hemisphere (especially Asia and China) made this area as centre of diversity of the genus with 147 species of which 93 taxa are endemic. In India, the genus is represented by 49 taxa of which 13 are endemic to the country. The present paper reports extended distribution of Clematis wightiana Wall., earlier reported as endemic to the Western Ghats, from the Indian state of Arunachal Pradesh and as a new distribution record for the state. Detailed description, flowering & fruiting data, herbarium image, distribution, and ecology of the species are provided.
ACURATE neo2 Versus SAPIEN 3 Ultra Transcatheter Heart Valve in Severe Aortic Valve Calcification: A Propensity-Matched Analysis
Authors: Eckel Clemens , Kim Won-Keun , Sötemann Dagmar , Grothusen Christina , Tiyerili Vedat , Dohmen Guido , Renker Matthias , Charitos Efstratios I. , Hamm Christian W. , Choi Yeong-Hoon , Elsässer Albrecht , Möllmann Helge , Blumenstein Johannes
Published: March 2024

BACKGROUND:

Comparative data on transcatheter self-expanding ACURATE neo2 (NEO2) and balloon-expandable SAPIEN 3 Ultra prostheses in technically challenging anatomy of severe aortic valve calcified aortic annuli are scarce.

METHODS:

A total of 1987 patients with severe native aortic stenosis treated with the self-expanding NEO2 (n=1457) or balloon-expandable SAPIEN 3 Ultra (n=530) from January 2017 to April 2023 were evaluated. The primary end point was procedural outcome according to the Valve Academic Research Consortium 3 definitions. Propensity matching defined 219 pairs with severe calcification (calcium density cutoff, 758 AU/cm
2
) of the native aortic valve.

RESULTS:

Technical success (90.4% versus 91.8%; risk difference, 1.4% [95% CI, −4.4 to −7.2];
P
=0.737) and device success at 30 days (80.8% versus 75.8%; risk difference, −5.0% [95% CI, −13.2 to 3.1];
P
=0.246) were comparable between NEO2 and SAPIEN 3 Ultra. The rate of severe prosthesis-patient mismatch (1.1% versus 10.1%; risk difference, 10.0% [95% CI, 4.0–13.9];
P
<0.001) and mean transvalvular gradient ≥20 mm Hg (2.8% versus 14.3%; risk difference, 11.5% [95% CI, 5.8–17.1];
P
<0.001) was lower with NEO2. The rate of more-than-mild paravalvular leakage or valve-in-valve due to paravalvular leakage was significantly higher (6.2% versus 0.0%; risk difference, 6.2% [95% CI, −10.1 to −2.7];
P
=0.002), and there was a tendency for a higher rate of device embolization or migration (1.8% versus 0.0%; risk difference, −1.8% [95% CI, −4.1 to 0.4];
P
=0.123) with NEO2. Multivarate regression revealed no independent impact of transcatheter heart valve selection on device success (odds ratio, 0.93 [95% CI, 0.48–1.77];
P
=0.817).

CONCLUSIONS:

In patients with severely calcified annuli, supraannular implantation of NEO2 showed hemodynamic advantages. Nevertheless, NEO2 was associated with a higher incidence of relevant paravalvular leakage and a numerically higher rate of device embolization than SAPIEN 3 Ultra in this particular patient group.
Trends and Disparities in Cardiovascular Death in Sarcoidosis: A Population‐Based Retrospective Study in the United States From 1999 to 2020
Authors: Tan Min Choon , Yeo Yong Hao , Mirza Noreen , San Boon Jian , Tan Jian Liang , Lee Justin Z. , Mazzarelli Joanne K. , Russo Andrea M.
Published: March 2024

Background

Despite significant cardiac involvement in sarcoidosis, real‐world data on death due to cardiovascular disease among patients with sarcoidosis is not well established.

Methods and Results

We queried the Centers for Disease Control and Prevention's Wide‐Ranging Online Data for Epidemiologic Research database for data on patients with sarcoidosis aged ≥25 years from 1999 to 2020. Diseases of the circulatory system except ischemic heart disease were listed as the underlying cause of death, and sarcoidosis was stated as a contributing cause of death. We calculated age‐adjusted mortality rate (AAMR) per 1 million individuals and determined the trends over time by estimating the annual percentage change using the Joinpoint Regression Program. Subgroup analyses were performed on the basis of demographic and geographic factors. In the 22‐year study period, 3301 cardiovascular deaths with comorbid sarcoidosis were identified. The AAMR from cardiovascular deaths with comorbid sarcoidosis increased from 0.53 (95% CI, 0.43–0.65) per 1 million individuals in 1999 to 0.87 (95% CI, 0.75–0.98) per 1 million individuals in 2020. Overall, women recorded a higher AAMR compared with men (0.77 [95% CI, 0.74–0.81] versus 0.58 [95% CI, 0.55–0.62]). People with Black ancestry had higher AAMR than people with White ancestry (3.23 [95% CI, 3.07–3.39] versus 0.39 [95% CI, 0.37–0.41]). A higher percentage of death was seen in the age groups of 55 to 64 years in men (23.11%) and women (21.81%), respectively. In terms of US census regions, the South region has the highest AAMR from cardiovascular deaths with comorbid sarcoidosis compared with other regions (0.78 [95% CI, 0.74–0.82]).

Conclusions

The increase of AAMR from cardiovascular deaths with comorbid sarcoidosis and higher cardiovascular mortality rates among adults aged 55 to 64 years highlight the importance of early screening for cardiovascular diseases among patients with sarcoidosis.
OneMax Is Not the Easiest Function for Fitness Improvements
Authors: Kaufmann Marc , Larcher Maxime , Lengler Johannes , Zou Xun
Published: March 2024

We study the (1:s+1) success rule for controlling the population size of the (1,λ)-EA. It was shown by Hevia Fajardo and Sudholt that this parameter control mechanism can run into problems for large s if the fitness landscape is too easy. They conjectured that this problem is worst for the OneMax benchmark, since in some well-established sense OneMax is known to be the easiest fitness landscape. In this paper, we disprove this conjecture. We show that there exist s and ɛ such that the self-adjusting (1,λ)-EA with the (1:s+1)-rule optimizes OneMax efficiently when started with ɛn zero-bits, but does not find the optimum in polynomial time on Dynamic BinVal. Hence, we show that there are landscapes where the problem of the (1:s+1)-rule for controlling the population size of the (1,λ)-EA is more severe than for OneMax. The key insight is that, while OneMax is the easiest function for decreasing the distance to the optimum, it is not the easiest fitness landscape with respect to finding fitness-improving steps.
The Spiraling Cognitive–Emotional Brain: Combinatorial, Reciprocal, and Reentrant Macro-organization
Authors: Pessoa Luiz
Published: March 2024

This article proposes a framework for understanding the macro-scale organization of anatomical pathways in the mammalian brain. The architecture supports flexible behavioral decisions across a spectrum of spatiotemporal scales. The proposal emphasizes the combinatorial, reciprocal, and reentrant connectivity—called CRR neuroarchitecture—between cortical, BG, thalamic, amygdala, hypothalamic, and brainstem circuits. Thalamic nuclei, especially midline/intralaminar nuclei, are proposed to act as hubs routing the flow of signals between noncortical areas and pFC. The hypothalamus also participates in multiregion circuits via its connections with cortex and thalamus. At slower timescales, long-range behaviors integrate signals across levels of the neuroaxis. At fast timescales, parallel engagement of pathways allows urgent behaviors while retaining flexibility. Overall, the proposed architecture enables context-dependent, adaptive behaviors spanning proximate to distant spatiotemporal scales. The framework promotes an integrative perspective and a distributed, heterarchical view of brain function.
Diet variations across remote populations of a widely distributed snake species, the Asp viper (Vipera aspis aspis, Linnaeus, 1758)
Authors: Laurence Florian , Bonnet Xavier , Ursenbacher Sylvain , Guiller Gaetan , Billy Gopal , Naulleau Guy , Vidal Nicolas
Published: March 2024

Documenting intra-specific diet variations among remote populations, thus across different habitats, is important to address evolutionary (e.g., phenotypic plasticity) and conservation issues (e.g., capacity to adapt to changing prey availability). We compared the diet of different populations of Asp vipers (Vipera aspis aspis; Linnaeus, 1758) living in contrasted habitats of France and Switzerland. We sampled 1680 individuals in five geographical areas, broadly encompassing the distribution range of the species (West to East and North to South): Group 1 = Western Central France; Group 2 = Central France; Group 3 = West France; Group 4 = South France, and Group 5 = Switzerland. We compared mean total body size (TL) of snakes and their diet. We found strong similarities among the groups with a predominance of micromammals in the diet. In mountainous area (Group 5), however, snakes included a substantial number of reptiles and fed occasionally on amphibians and birds. The diet varied ontogenetically (juveniles often consuming lizards, adults heavily feeding on mammals) and sexually (females tending to feed more extensively on mammals). Overall, our results suggest that Asp vipers maintain a diet largely based on micromammals (mostly voles), at least across their continental distribution range.
Checklist of the Jurassic wood (updated March 2024)
Authors: GOU XU-DONG , FENG ZHUO
Published: March 2024

A list of all known fossil wood taxa reported from the Jurassic, up to March of 2024, is given. The list contains 54 genera and 203 species of gymnospermous wood taxa described from 36 countries across seven continents. 35 genera and 98 species were reported from Asia, 25 genera and 58 species from Europe, 10 genera and 15 species from Africa, 7 genera and 11 species from North America, 12 genera and 28 species from South America, and 4 genera and 5 species from Australia and Antarctica. 23 genera and 44 species were documented from the Lower Jurassic, 36 genera and 82 species from the Middle Jurassic, and 24 genera and 67 species from the Upper Jurassic. Systematically: two genera and two species belong to Cycadales; one monotypic genus belongs to Bennettitales; four genera and seven species belong to Ginkgoales; 45 genera and 191 species of Coniferales; and two genera and two species are of Incertae sedis.
Rissoidae (Mollusca, Gastropoda, Rissooidea) from taphocoenotic assemblages of the Strait of Sicily (Mediterranean Sea) with the description of two new species
Authors: AMATI BRUNO , TAVIANI MARCO , OLIVERIO MARCO
Published: March 2024

Twenty-one rissoid species have been found in four bathyal samples from the Strait of Sicily, represented exclusively by empty shells. Some of these species are shallow water dwellers, associated with algal facies, others are typical of deep-sea (bathyal-abyssal). Two species are described as new (Alvania ismar n. sp. and Manzonia schmidti n. sp., both of probable Plio-Pleistocene age) and two have not been identified at the species level, due to the small number of specimens found and their poor state of conservation. Taxonomical remarks on some taxa are provided.
Policy Solutions: Education policy reform for the hip-hop era
Authors: Collins Jonathan E.
Published: March 2024

Fifty years after the birth of hip-hop, Jonathan E. Collins considers how hip-hop can transform U.S. education. Many schools have barred hip-hop and disciplined students who use it. But these restrictions have been built on flimsy premises that ignore the beauty and complexity of the art form. Embracing and incorporating hip-hop can improve student engagement, particularly among Black students, and help schools forge stronger connections with the community.
Efficacy of Theta Burst Stimulation in Bipolar Depression: A Systematic Review and Meta-analysis
Authors: Malik Gunjan , Mishra Preeti , Godi Sangha Mitra , Garg Shobit , Tikka Sai Krishna
Published: March 2024

Purpose:

Repetitive transcranial magnetic stimulation (rTMS) has emerged as a promising alternative therapeutic modality for bipolar depression. Theta Burst Stimulation (TBS) a novel variant of rTMS is currently evolving as an effective and safe therapeutic tool in the management of bipolar depression, although it is still in the initial phase. In the present systematic review and meta-analysis, data from various studies investigating the effectiveness and tolerability of TBS in individuals with bipolar depression were synthesized.

Data collection and analyses:

Eight studies assessing the efficacy of TBS as a treatment for bipolar depression were included after a systematic search. The quality of the included studies was assessed using standard methods. Synthesized data included three uncontrolled clinical trials (non-randomized controlled trials [RCTs]; n = 48) and five RCTs ( n = 201). While four studies used intermittent TBS, two studies used continuous TBS and bilateral TBS, each. Data from the five RCTs meta-analyzed using the random effects model showed the pooled odds ratio and effect size for response in the active group compared to the sham group were 1.01 (95%CI: 0.56, 1.83) and 1.64 (95%CI: –0.46, 3.74), respectively. Although both models showed no/minimal heterogeneity or publication bias, they were not statistically significant. A supplementary trial sequential analysis confirmed the need for more studies and suggested that the total sample required for drawing meaningful inferences was 918 samples.

Conclusion:

The available evidence suggests that more studies are required to draw conclusive inferences for the efficacy of TBS in the therapeutic management of bipolar depression.
Manufacturing of aluminium/copper/nickel(powder) metal intermetallic composite by accumulative roll bonding and subsequent heat treatment
Authors: Xia Lingqin , Fang Yihang , Chen Guang , Wang Ruiquan , Wu Tao
Published: March 2024

This study investigates the annealing effect on the tensile properties of aluminium (Al)/copper (Cu)/nickel (Ni)(powder) composite fabricated utilising the accumulative roll bonding (ARB) technique up to 11 passes. The results showed that the composite transformed into a layer/powder-reinforced fabric by increasing the applied strain. After the 11th pass, the Al and Cu layers and Ni powder were uniformly distributed in the composite. The tensile strength decreased for the second pass and increased as the ARB process proceeded. Upon annealing temperature and time increase, the tensile strength of the 3rd pass increased and for the 11th pass decreased. Also, as the annealing temperature and duration extended, the elongation for the 3rd and 11th passes fluctuated and remained almost unchanged.