bluuebunny/crossref_metadata_2025 huggingface dataset:
Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation
Authors: Shen Zhiqiang , Cao Peng , Yang Hua , Liu Xiaoli , Yang Jinzhu , Zaiane Osmar R.
Published: August 2023

Consistency regularization and pseudo labeling-based semi-supervised methods perform co-training using the pseudo labels from multi-view inputs. However, such co-training models tend to converge early to a consensus, degenerating to the self-training ones, and produce low-confidence pseudo labels from the perturbed inputs during training.

To address these issues, we propose an Uncertainty-guided Collaborative Mean-Teacher (UCMT) for semi-supervised semantic segmentation with the high-confidence pseudo labels. Concretely, UCMT consists of two main components: 1) collaborative mean-teacher (CMT) for encouraging model disagreement and performing co-training between the sub-networks, and 2) uncertainty-guided region mix (UMIX) for manipulating the input images according to the uncertainty maps of CMT and facilitating CMT to produce high-confidence pseudo labels.

Combining the strengths of UMIX with CMT, UCMT can retain model disagreement and enhance the quality of pseudo labels for the co-training segmentation.

Extensive experiments on four public medical image datasets including 2D and 3D modalities demonstrate the superiority of UCMT over the state-of-the-art.

Code is available at: https://github.com/Senyh/UCMT.
Speeding Up Multi-Objective Hyperparameter Optimization by Task Similarity-Based Meta-Learning for the Tree-Structured Parzen Estimator
Authors: Watanabe Shuhei , Awad Noor , Onishi Masaki , Hutter Frank
Published: August 2023

Hyperparameter optimization (HPO) is a vital step in improving performance in deep learning (DL). Practitioners are often faced with the trade-off between multiple criteria, such as accuracy and latency. Given the high computational needs of DL and the growing demand for efficient HPO, the acceleration of multi-objective (MO) optimization becomes ever more important. Despite the significant body of work on meta-learning for HPO, existing methods are inapplicable to MO tree-structured Parzen estimator (MO-TPE), a simple yet powerful MO-HPO algorithm. In this paper, we extend TPE’s acquisition function to the meta-learning setting using a task similarity defined by the overlap of top domains between tasks. We also theoretically analyze and address the limitations of our task similarity. In the experiments, we demonstrate that our method speeds up MO-TPE on tabular HPO benchmarks and attains state-of-the-art performance. Our method was also validated externally by winning the AutoML 2022 competition on “Multiobjective Hyperparameter Optimization for Transformers”. See https://arxiv.org/abs/2212.06751 for the latest version with Appendix.
ProMix: Combating Label Noise via Maximizing Clean Sample Utility
Authors: Xiao Ruixuan , Dong Yiwen , Wang Haobo , Feng Lei , Wu Runze , Chen Gang , Zhao Junbo
Published: August 2023

Learning with Noisy Labels (LNL) has become an appealing topic, as imperfectly annotated data are relatively cheaper to obtain. Recent state-of-the-art approaches employ specific selection mechanisms to separate clean and noisy samples and then apply Semi-Supervised Learning (SSL) techniques for improved performance. However, the selection step mostly provides a medium-sized and decent-enough clean subset, which overlooks a rich set of clean samples. To fulfill this, we propose a novel LNL framework ProMix that attempts to maximize the utility of clean samples for boosted performance. Key to our method, we propose a matched high confidence selection technique that selects those examples with high confidence scores and matched predictions with given labels to dynamically expand a base clean sample set. To overcome the potential side effect of excessive clean set selection procedure, we further devise a novel SSL framework that is able to train balanced and unbiased classifiers on the separated clean and noisy samples. Extensive experiments demonstrate that ProMix significantly advances the current state-of-the-art results on multiple benchmarks with different types and levels of noise. It achieves an average improvement of 2.48% on the CIFAR-N dataset.
ODEE: A One-Stage Object Detection Framework for Overlapping and Nested Event Extraction
Authors: Ning Jinzhong , Yang Zhihao , Wang Zhizheng , Sun Yuanyuan , Lin Hongfei
Published: August 2023

The task of extracting overlapping and nested events has received significant attention in recent times, as prior research has primarily focused on extracting flat events, overlooking the intricacies of overlapping and nested occurrences. In this work, we present a new approach to Event Extraction (EE) by reformulating it as an object detection task on a table of token pairs. Our proposed one-stage event extractor, called ODEE, can handle overlapping and nested events. The model is designed with a vertex-based tagging scheme and two auxiliary tasks of predicting the spans and types of event trigger words and argument entities, leveraging the full span information of event elements. Furthermore, in the training stage, we introduce a negative sampling method for table cells to address the imbalance problem of positive and negative table cell tags, meanwhile improving computational efficiency. Empirical evaluations demonstrate that ODEE achieves the state-of-the-art performance on three benchmarks for overlapping and nested EE (i.e., FewFC, Genia11, and Genia13). Furthermore, ODEE outperforms current state-of-the-art methods in terms of both number of parameters and inference speed, indicating its high computational efficiency. To facilitate future research in this area, the codes are publicly available at https://github.com/NingJinzhong/ODEE.
Fine-tuned vs. Prompt-tuned Supervised Representations: Which Better Account for Brain Language Representations?
Authors: Sun Jingyuan , Moens Marie-Francine
Published: August 2023

To decipher the algorithm underlying the human brain's language representation, previous work probed brain responses to language input with pre-trained artificial neural network (ANN) models fine-tuned on NLU tasks. However, full fine-tuning generally updates the entire parametric space and distorts pre-trained features, cognitively inconsistent with the brain's robust multi-task learning ability. Prompt-tuning, in contrast, protects pre-trained weights and learns task-specific embeddings to fit a task. Could prompt-tuning generate representations that better account for the brain's language representations than fine-tuning? If so, what kind of NLU task leads a pre-trained model to better decode the information represented in the human brain? We investigate these questions by comparing prompt-tuned and fine-tuned representations in neural decoding, that is predicting the linguistic stimulus from the brain activities evoked by the stimulus. We find that on none of the 10 NLU tasks, full fine-tuning significantly outperforms prompt-tuning in neural decoding, implicating that a more brain-consistent tuning method yields representations that better correlate with brain data. Moreover, we identify that tasks dealing with fine-grained concept meaning yield representations that better decode brain activation patterns than other tasks, especially the syntactic chunking task. This indicates that our brain encodes more fine-grained concept information than shallow syntactic information when representing languages.
The First Proven Performance Guarantees for the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) on a Combinatorial Optimization Problem
Authors: Cerf Sacha , Doerr Benjamin , Hebras Benjamin , Kahane Yakob , Wietheger Simon
Published: August 2023

The Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is one of the most prominent algorithms to solve multi-objective optimization problems. Recently, the first mathematical runtime guarantees have been obtained for this algorithm, however only for synthetic benchmark problems.

In this work, we give the first proven performance guarantees for a classic optimization problem, the NP-complete bi-objective minimum spanning tree problem. More specifically, we show that the NSGA-II with population size N >= 4((n-1) wmax + 1) computes all extremal points of the Pareto front in an expected number of O(m^2 n wmax log(n wmax)) iterations, where n is the number of vertices, m the number of edges, and wmax is the maximum edge weight in the problem instance. This result confirms, via mathematical means, the good performance of the NSGA-II observed empirically. It also shows that mathematical analyses of this algorithm are not only possible for synthetic benchmark problems, but also for more complex combinatorial optimization problems.

As a side result, we also obtain a new analysis of the performance of the global SEMO algorithm on the bi-objective minimum spanning tree problem, which improves the previous best result by a factor of |F|, the number of extremal points of the Pareto front, a set that can be as large as n wmax. The main reason for this improvement is our observation that both multi-objective evolutionary algorithms find the different extremal points in parallel rather than sequentially, as assumed in the previous proofs.
A Fast Maximum k-Plex Algorithm Parameterized by the Degeneracy Gap
Authors: Wang Zhengren , Zhou Yi , Luo Chunyu , Xiao Mingyu
Published: August 2023

Given a graph, the k-plex is a vertex set in which each vertex is not adjacent to at most k-1 other vertices in the set. The maximum k-plex problem, which asks for the largest k-plex from a given graph, is an important but computationally challenging problem in applications like graph search and community detection. So far, there is a number of empirical algorithms without sufficient theoretical explanations on the efficiency. We try to bridge this gap by defining a novel parameter of the input instance, g_k(G), the gap between the degeneracy bound and the size of maximum k-plex in the given graph, and presenting an exact algorithm parameterized by g_k(G). In other words, we design an algorithm with running time polynomial in the size of input graph and exponential in g_k(G) where k is a constant. Usually, g_k(G) is small and bounded by O(log(|V|)) in real-world graphs, indicating that the algorithm runs in polynomial time. We also carry out massive experiments and show that the algorithm is competitive with the state-of-the-art solvers. Additionally, for large k values such as 15 and 20, our algorithm has superior performance over existing algorithms.
A Mathematical Runtime Analysis of the Non-dominated Sorting Genetic Algorithm III (NSGA-III)
Authors: Wietheger Simon , Doerr Benjamin
Published: August 2023

The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is the most prominent multi-objective evolutionary algorithm for real-world applications.

While it performs evidently well on bi-objective optimization problems, empirical studies suggest that it is less effective when applied to problems with more than two objectives. A recent mathematical runtime analysis confirmed this observation by proving the NGSA-II for an exponential number of iterations misses a constant factor of the Pareto front of the simple 3-objective OneMinMax problem.

In this work, we provide the first mathematical runtime analysis of the NSGA-III, a refinement of the NSGA-II aimed at better handling more than two objectives.

We prove that the NSGA-III with sufficiently many reference points - a small constant factor more than the size of the Pareto front, as suggested for this algorithm - computes the complete Pareto front of the 3-objective OneMinMax benchmark in an expected number of O(n log n) iterations. This result holds for all population sizes (that are at least the size of the Pareto front). It shows a drastic advantage of the NSGA-III over the NSGA-II on this benchmark. The mathematical arguments used here and in the previous work on the NSGA-II suggest that similar findings are likely for other benchmarks with three or more objectives.
Toward Job Recommendation for All
Authors: Bied Guillaume , Nathan Solal , Perennes Elia , Hoffmann Morgane , Caillou Philippe , Crépon Bruno , Gaillac Christophe , Sebag Michèle
Published: August 2023

This paper presents a job recommendation algorithm designed and validated in the context of the French Public Employment Service. The challenges, owing to the confidential data policy, are related with the extreme sparsity of the interaction matrix and the mandatory scalability of the algorithm, aimed to deliver recommendations to millions of job seekers in quasi real-time, considering hundreds of thousands of job ads. The experimental validation of the approach shows similar or better performances than the state of the art in terms of recall, with a gain in inference time of 2 orders of magnitude. The study includes some fairness analysis of the recommendation algorithm. The gender-related gap is shown to be statistically similar in the true data and in the counter-factual data built from the recommendations.
Fluid Dynamics-Inspired Network for Infrared Small Target Detection
Authors: Chen Tianxiang , Chu Qi , Liu Bin , Yu Nenghai
Published: August 2023

Most infrared small target detection (ISTD) networks focus on building effective neural blocks or feature fusion modules but none describes the ISTD process from the image evolution perspective. The directional evolution of image pixels influenced by convolution, pooling and surrounding pixels is analogous to the movement of fluid elements constrained by surrounding variables ang particles. Inspired by this, we explore a novel research routine by abstracting the movement of pixels in the ISTD process as the flow of fluid in fluid dynamics (FD). Specifically, a new Fluid Dynamics-Inspired Network (FDI-Net) is devised for ISTD. Based on Taylor Central Difference (TCD) method, the TCD feature extraction block is designed, where convolution and Transformer structures are combined for local and global information. The pixel motion equation during the ISTD process is derived from the Navier–Stokes (N-S) equation, constructing a N-S Refinement Module that refines extracted features with edge details. Thus, the TCD feature extraction block determines the primary movement direction of pixels during detection, while the N-S Refinement Module corrects some skewed directions of the pixel stream to supplement the edge details. Experiments on IRSTD-1k and SIRST demonstrate that our method achieves SOTA performance in terms of evaluation metrics.
On AI-Assisted Pneumoconiosis Detection from Chest X-rays
Authors: Akhter Yasmeena , Ranjan Rishabh , Singh Richa , Vatsa Mayank , Chaudhury Santanu
Published: August 2023

According to theWorld Health Organization, Pneumoconiosis

affects millions of workers globally,

with an estimated 260,000 deaths annually. The

burden of Pneumoconiosis is particularly high in

low-income countries, where occupational safety

standards are often inadequate, and the prevalence

of the disease is increasing rapidly. The reduced

availability of expert medical care in rural areas,

where these diseases are more prevalent, further

adds to the delayed screening and unfavourable outcomes

of the disease. This paper aims to highlight

the urgent need for early screening and detection

of Pneumoconiosis, given its significant impact on

affected individuals, their families, and societies as

a whole. With the help of low-cost machine learning

models, early screening, detection, and prevention

of Pneumoconiosis can help reduce healthcare

costs, particularly in low-income countries. In this

direction, this research focuses on designing AI solutions

for detecting different kinds of Pneumoconiosis

from chest X-ray data. This will contribute

to the Sustainable Development Goal 3 of ensuring

healthy lives and promoting well-being for all at all

ages, and present the framework for data collection

and algorithm for detecting Pneumoconiosis

for early screening. The baseline results show that

the existing algorithms are unable to address this

challenge. Therefore, it is our assertion that this

research will improve state-of-the-art algorithms of

segmentation, semantic segmentation, and classification

not only for this disease but in general medical

image analysis literature.
Interactive Machine Learning Solutions for Acoustic Monitoring of Animal Wildlife in Biosphere Reserves
Authors: S. Gouvêa Thiago , Kath Hannes , Troshani Ilira , Lüers Bengt , Serafini Patricia P. , Campos Ivan B. , Afonso André S. , Leandro Sergio M. F. M. , Swanepoel Lourens , Theron Nicholas , Swemmer Anthony M. , Sonntag Daniel
Published: August 2023

Biodiversity loss is taking place at accelerated rates globally, and a business-as-usual trajectory will lead to missing internationally established conservation goals. Biosphere reserves are sites designed to be of global significance in terms of both the biodiversity within them and their potential for sustainable development, and are therefore ideal places for the development of local solutions to global challenges. While the protection of biodiversity is a primary goal of biosphere reserves, adequate information on the state and trends of biodiversity remains a critical gap for adaptive management in biosphere reserves. Passive acoustic monitoring (PAM) is an increasingly popular method for continued, reproducible, scalable, and cost-effective monitoring of animal wildlife. PAM adoption is on the rise, but its data management and analysis requirements pose a barrier for adoption for most agencies tasked with monitoring biodiversity. As an interdisciplinary team of machine learning scientists and ecologists experienced with PAM and working at biosphere reserves in marine and terrestrial ecosystems on three different continents, we report on the co-development of interactive machine learning tools for semi-automated assessment of animal wildlife.
Even If Explanations: Prior Work, Desiderata & Benchmarks for Semi-Factual XAI
Authors: Aryal Saugat , Keane Mark T.
Published: August 2023

Recently, eXplainable AI (XAI) research has focused on counterfactual explanations as post-hoc justifications for AI-system decisions (e.g., a customer refused a loan might be told “if you asked for a loan with a shorter term, it would have been approved”). Counterfactuals explain what changes to the input-features of an AI system change the output-decision. However, there is a sub-type of counterfactual, semi-factuals, that have received less attention in AI (though the Cognitive Sciences have studied them more). This paper surveys semi-factual explanation, summarising historical and recent work. It defines key desiderata for semi-factual XAI, reporting benchmark tests of historical algorithms (as well as a novel, naïve method) to provide a solid basis for future developments.
A Survey on Intersectional Fairness in Machine Learning: Notions, Mitigation, and Challenges
Authors: Gohar Usman , Cheng Lu
Published: August 2023

The widespread adoption of Machine Learning systems, especially in more decision-critical applications such as criminal sentencing and bank loans, has led to increased concerns about fairness implications. Algorithms and metrics have been developed to mitigate and measure these discriminations. More recently, works have identified a more challenging form of bias called intersectional bias, which encompasses multiple sensitive attributes, such as race and gender, together. In this survey, we review the state-of-the-art in intersectional fairness. We present a taxonomy for intersectional notions of fairness and mitigation. Finally, we identify the key challenges and provide researchers with guidelines for future directions.
ICDA: Illumination-Coupled Domain Adaptation Framework for Unsupervised Nighttime Semantic Segmentation
Authors: Dong Chenghao , Kang Xuejing , Ming Anlong
Published: August 2023

The performance of nighttime semantic segmentation has been significantly improved thanks to recent unsupervised methods. However, these methods still suffer from complex domain gaps, i.e., the challenging illumination gap and the inherent dataset gap. In this paper, we propose the illumination-coupled domain adaptation framework(ICDA) to effectively avoid the illumination gap and mitigate the dataset gap by coupling daytime and nighttime images as a whole with semantic relevance. Specifically, we first design a new composite enhancement method(CEM) that considers not only illumination but also spatial consistency to construct the source and target domain pairs, which provides the basic adaptation unit for our ICDA. Next, to avoid the illumination gap, we devise the Deformable Attention Relevance(DAR) module to capture the semantic relevance inside each domain pair, which can couple the daytime and nighttime images at the feature level and adaptively guide the predictions of nighttime images. Besides, to mitigate the dataset gap and acquire domain-invariant semantic relevance, we propose the Prototype-based Class Alignment(PCA) module, which improves the usage of category information and performs fine-grained alignment. Extensive experiments show that our method reduces the complex domain gaps and achieves state-of-the-art performance for nighttime semantic segmentation. Our code is available at https://github.com/chenghaoDong666/ICDA.
Transformers in Time Series: A Survey
Authors: Wen Qingsong , Zhou Tian , Zhang Chaoli , Chen Weiqi , Ma Ziqing , Yan Junchi , Sun Liang
Published: August 2023

Transformers have achieved superior performances in many tasks in natural language processing and computer vision, which also triggered great interest in the time series community. Among multiple advantages of Transformers, the ability to capture long-range dependencies and interactions is especially attractive for time series modeling, leading to exciting progress in various time series applications. In this paper, we systematically review Transformer schemes for time series modeling by highlighting their strengths as well as limitations. In particular, we examine the development of time series Transformers in two perspectives. From the perspective of network structure, we summarize the adaptations and modifications that have been made to Transformers in order to accommodate the challenges in time series analysis. From the perspective of applications, we categorize time series Transformers based on common tasks including forecasting, anomaly detection, and classification. Empirically, we perform robust analysis, model size analysis, and seasonal-trend decomposition analysis to study how Transformers perform in time series. Finally, we discuss and suggest future directions to provide useful research guidance.
A Survey on Masked Autoencoder for Visual Self-supervised Learning
Authors: Zhang Chaoning , Zhang Chenshuang , Song Junha , Yi John Seon Keun , Kweon In So
Published: August 2023

With the increasing popularity of masked autoencoders, self-supervised learning (SSL) in vision undertakes a similar trajectory as in NLP. Specifically, generative pretext tasks with the masked prediction have become a de facto standard SSL practice in NLP (e.g., BERT). By contrast, early attempts at generative methods in vision have been outperformed by their discriminative counterparts (like contrastive learning). However, the success of masked image modeling has revived the autoencoder-based visual pretraining method. As a milestone to bridge the gap with BERT in NLP, masked autoencoder in vision has attracted unprecedented attention. This work conducts a survey on masked autoencoders for visual SSL.
Discovering Sounding Objects by Audio Queries for Audio Visual Segmentation
Authors: Huang Shaofei , Li Han , Wang Yuqing , Zhu Hongji , Dai Jiao , Han Jizhong , Rong Wenge , Liu Si
Published: August 2023

Audio visual segmentation (AVS) aims to segment the sounding objects for each frame of a given video. To distinguish the sounding objects from silent ones, both audio-visual semantic correspondence and temporal interaction are required. The previous method applies multi-frame cross-modal attention to conduct pixel-level interactions between audio features and visual features of multiple frames simultaneously, which is both redundant and implicit. In this paper, we propose an Audio-Queried Transformer architecture, AQFormer, where we define a set of object queries conditioned on audio information and associate each of them to particular sounding objects. Explicit object-level semantic correspondence between audio and visual modalities is established by gathering object information from visual features with predefined audio queries. Besides, an Audio-Bridged Temporal Interaction module is proposed to exchange sounding object-relevant information among multiple frames with the bridge of audio features. Extensive experiments are conducted on two AVS benchmarks to show that our method achieves state-of-the-art performances, especially 7.1% M_J and 7.6% M_F gains on the MS3 setting.
Phytochemical screening and antimicrobial activity of plant leaf extract against enteric bacterial pathogens
Authors: Murali Uthamalingam , Pandeeswari N. , Elango Boojhana , Murugesan Karthikeyan , Abirami K. , Divya V. V. , Sathya T. A. , Basalingappa Kanthesh M. , Mathanmohun Maghimaa
Published: August 2023

Punica granatum, a fruit-bearing shrub belonging to the Lythraceae family, offers various benefits to humans, with both its fibers and seeds being valuable. The seeds contain important oil acids such as palmitic, punica, stearic, and oleic acids. In India, P. granatum (pomegranate) leaves have been investigated for their potential antibacterial activity against enteric pathogens. These pathogens are responsible for causing gastrointestinal infections and are often associated with antibiotic resistance. The study aims to explore whether pomegranate leaves possess properties that can combat these enteric pathogens effectively. This research holds promise in identifying a natural and alternative approach to address antibacterial challenges, potentially contributing to the development of novel antimicrobial agents pomegranate is commonly used as a traditional remedy for treating enteric bacterial pathogens. To investigate its antibacterial properties, researchers studied the aqueous extract of P. granatum leaf against enteric pathogens, conducting tests like biochemical examinations and microscopy on the isolates. The phytochemical analysis of P. granatum (pomegranate) leaf aqueous extract revealed the presence of various bioactive compounds. These compounds include amino acids, carbohydrates, alkaloids, steroids, flavonoids, terpenoids, saponins, tannins, and phenols. To evaluate its antimicrobial activity, the researchers used antibiotic discs, including chloramphenicol, vancomycin, and gentamicin, employing the standard Kirby Bauer method. Results showed that vancomycin had the highest zone of inhibition, followed by chloramphenicol and gentamicin, with respective measurements of 20 mm and 19 mm. Chloramphenicol exhibited sensitivity against Escherichia coli (19 mm), while gentamicin showed sensitivity against Salmonella typhi (20 mm), Staphylococcus aureus (19 mm), and E. coli (18 mm). Vancomycin demonstrated sensitivity against S. aureus (21 mm). The aqueous leaf extract displayed significant antibacterial activity at concentrations of 20, 40 mcg, and 60 μg, showing effective zones of inhibition ranging from 15 mm to 22 mm against Pseudomonas aeruginosa, E. coli, S. aureus, Shigella dysentriae, Proteus vulgaris, and S. typhi. In conclusion, the study established that P. granatum leaf extracts possess remarkable antibacterial properties and contain valuable pharmaceutical bioactive compounds. Due to its efficacy in treating gastrointestinal disorders, it is utilized as a natural remedy in traditional medicine.
Documentary Poetry: A Study of Amiri Baraka's "Somebody Blew up America"
Authors: Al-Douri Hamdi Hameed
Published: August 2023

The modern age is prolific of literary movements and literarygenres. Documentary poetry, which can be considered a new genre,combines both primary source material, such as war, political events,terrorism, people in detention and many other events with poetry.Amiri Baraka is a contemporary American poet whose poem"Somebody Blew up America" belongs to this genre. It records theSeptember 11 blowing up of the Trade Centre from a perspectivedifferent from what the American propaganda and mass media tell theworld. The recent paper attempts to shed light on Amiri Baraka'sattitude towards this event, the reasons behind it, the real terroristsand the intentions behind this terrorist event according to this poem.The poet argues that the American government knew beforehand thatthe Trade Centre was going to be blown up and they took no action toprevent the catastrophe and, in this sense, they were partners in thecrime. Furthermore, he accuses the Americans of blowing up the tradecentre. The paper is divided into three sections and a conclusion.Section One is Introductory; it sheds light on documentary poetry, itscharacteristic features and practitioners. Section Two is a biographicalnote of Amiri Baraka paying special attention to his attitude toAmerican politics based on domination, persecution and genocide.Section Three gives a detailed analysis of "Somebody Blew upAmerica" as a documentary poem recording the September 11 blowingup of the Trade Centre with the aim of finding a pretext to invadeother countries. The paper concludes that this event happenedaccording to a well-made plan in cooperation with the Americangovernment and the CIA as partners.
Data-Driven Balanced Truncation for Predictive Model Order Reduction of Aeroacoustic Response
Authors: Rezaian Elnaz , Duraisamy Karthik
Published: August 2023

Rapid prediction of the aeroacoustic response is a key component in the design of aircraft and turbomachinery. In this work, we propose a technique for highly accelerated prediction of aeroacoustic response using a data-driven model reduction approach based on the eigensystem realization algorithm (ERA). Specifically, we create and compare ERA reduced-order models (ROMs) based on the training data generated by solving the linearized and nonlinear Euler equations, and we use them to predict the aeroacoustic response of an airfoil in a purely predictive setting subject to different types of gust loading. Activating each input channel separately in the full-order model (FOM) to generate the Markov sequence for training makes it computationally challenging to use the ERA in aeroacoustics applications. We address this bottleneck by first proposing a multifidelity gappy POD method to reduce the computation cost on the FOM and ROM levels by querying the high-resolution FOM only for the input channels identified by gappy POD. Second, we use tangential interpolation at the ROM level to reduce the size of the Hankel matrix. The proposed methods enable application of the ERA for accurate online acoustic response prediction, and they reduce the offline computation cost of ROMs.
PERANCANGAN ALAT BANTU PANEN GULA AREN
Authors: Jianul Hayat Encep , Setiawan Dede
Published: August 2023

Penelitian ini bertujuan untuk merancang alat bantu panen gula aren yang inovatif menggunakan metode House of Quality (HOQ) guna meningkatkan efisiensi dan efektivitas dalam proses panen gula aren tradisional. Metode HOQ digunakan untuk mengidentifikasi kebutuhan dan preferensi pelanggan serta menerjemahkannya menjadi karakteristik teknik yang spesifik bagi alat bantu panen. Penelitian ini melibatkan survei, wawancara, dan observasi untuk mengidentifikasi kebutuhan penting para pemangku kepentingan dan pengguna akhir yang diurutkan berdasarkan tingkat kepentingannya. Proses perancangan alat bantu panen melibatkan brainstorming dan pembuatan prototipe berbagai konsep, dengan memastikan kesesuaian dengan permintaan pelanggan. Kebutuhan pelanggan yang diidentifikasi dikaitkan dengan fitur teknik yang relevan untuk menciptakan matriks perancangan yang komprehensif. Hasil penelitian menunjukkan bahwa metode HOQ sangat efektif dalam membimbing proses pengambilan keputusan, dan desain alat bantu panen yang dipilih menghasilkan peningkatan produktivitas, kemudahan penggunaan, dan keamanan dalam proses ekstraksi gula aren. Penerapan desain alat bantu panen yang dihasilkan diharapkan dapat meningkatkan hasil dan efisiensi keseluruhan panen gula aren, pada akhirnya alat ini akan memberikan manfaat bagi para petani dan industri gula. Penelitian ini menunjukkan potensi metode HOQ sebagai alat penting dalam pengembangan alat-alat pertanian, dan memberikan kontribusi pada inovasi di bidang ini.
ANALISIS POSISI INDONESIA PADA DINAMIKA LOGISTIK DUNIA MENGGUNAKAN PENDEKATAN COMPLEX NETWORK
Authors: Teri Aripin Wahyu , Ernawati Ernawati , M Fuadi Mamik
Published: August 2023

Sebagai negara yang terdiri dari ribuan pulau dan membentang lebih dari 5.000 km dari barat ke timur, mengetahui posisi Indonesia dalam Jaringan Pengiriman Kapal Liner Global (GLSN) sangatlah penting. Untuk menentukan posisi dinamisnya dalam jaringan ini, akan dilakukan analisis menggunakan data selama tiga tahun dari Alphaliner, yang mencakup lebih dari 157 negara di seluruh dunia. GLSN yang dihasilkan kemudian dibangun dan dievaluasi berdasarkan data tersebut. Secara keseluruhan, studi ini menekankan peran penting pelabuhan-pelabuhan di Indonesia dalam memfasilitasi perdagangan internasional dan menjadikan negara ini sebagai pemain kunci dalam jaringan pengiriman kapal liner global. Studi ini menggunakan sifat topologi dasar untuk menganalisis posisi Indonesia dalam rantai logistik global, khususnya dalam jaringan pengiriman kapal liner global. Hasil penelitian menunjukkan bahwa beberapa pelabuhan di Indonesia memiliki posisi penting yang signifikan dalam GLSN. Namun, dari segi derajat dan kekuatan, pelabuhan Tanjung Priok di Jakarta mendominasi jaringan di pelabuhan Indonesia.
PERBAIKAN KESEHATAN DAN KESELAMATAN KERJA PADA PROSES PEMBUATAN WAJAN ALUMUNIUM DENGAN METODE FISHBONE DIAGRAM
Authors: Kurnia Yusup , Nasarudin Nanang
Published: August 2023

Setiap proses produksi dalam suatu perusahaan tidaklah lepas dari segala bahaya resiko kecelakaan kerja terhadap para tenaga kerja. Metode pengumpulan data dalam penelitian ini adalah wawancara, observasi, dan studi pustaka. Sedangkan teknik analisis data yang digunakan adalah analisis deskriptif. Peneliti menganmbil objek penilitian yaitu tenaga kerja Pabrik Elang Mas Sindangkasih Kabupaten Ciamis.. Dengan metode wawancara tersebut, peneliti mewawancarai manajer Pabrik Elang Mas Sindangkasih Kabupaten Ciamis mengenai penerapan Keselamatan dan Kesehatan Kerja (K3) yang diterapkan pada Pabrik Elang Mas Sindangkasih Kabupaten Ciamis. Berdasarkan hasil penelitian dapat diketahui bahwa penerapan K3 pada Pabrik Elang Mas Sindangkasih Kabupaten Ciamis belum cukup baik dan kurang sesuai peraturan yang berlaku. Berdasarkan hasil penelitian, saran yang dapat diberi adalah hendaknya Pabrik Elang Mas Sindangkasih Kabupaten Ciamis meningkatkan kualitas pada area produksi agar penerapan K3 lebih berjalan dengan efektif.
Internet Addiction and Automatic Thoughts among Young Adults
Authors: Amit  , Sanjay Kumar
Published: August 2023

India is a country of Youth and the development of any nation depends upon the talent the youth population carries. The parents and all stakeholders must inculcate the values and manners to make them resilient and mindful. The present study examines the relationship between Internet Addiction and Automatic Thought Processes among Young Adults. A total sample of 200 young adults was recruited and further equally divided into two groups based on gender. The data was collected from undergraduate young adults studying in arts, commerce, and science stream in various colleges of Hisar, Haryana. The tools used for the study were Internet Addiction Test (IAT) and Automatic Thought Questionnaire (ATQ). The findings of the study reveal that there is a positive correlation between internet addiction and automatic thoughts (Negative). It is concluded that internet addiction leads to negative thoughts and negative thoughts also trigger internet addiction.
10 Anos de Francisco
Authors: Gomes Rita Maria
Published: August 2023

O papa Francisco, que neste ano de 2023 completa 10 anos de pontificado, para a alegria da Igreja e da humanidade, faminta e sedenta de tudo aquilo que possa dar sentido à sua existência e valorizar a sua caminhada pelas estradas da vida, reconhece a dignidade de cada pessoa humana e cuida da casa comum, na perspectiva da ecologia integral.
Francisco, a sinodalidade e as implicações pastorais na Igreja do Brasil
Authors: Kuzma Cesar Augusto
Published: August 2023

O artigo que apresentamos faz uma reflexão sobre os 10 anos do Pontificado do Papa Francisco e procura discorrer sobre o significado de Francisco, sua visão de Igreja, reformas, sinodalidade e as possíveis implicações pastorais e desafios correspondentes. Como objetivo, o artigo traz as novidades deste pontificado e as suas principais compreensões eclesiológico-pastorais, na intenção de apontar para a percepção de Francisco na realidade da Igreja brasileira. Metodologicamente, seguiremos por uma pesquisa bibliográfica qualitativa e exploratória, atendendo as questões de forma crítica e dialogal. O trabalho apresentado se divide em três partes: os 10 anos do Pontificado de Francisco, seguindo para as ressonâncias deste pontificado na realidade latino-americana e brasileira, em especial, o que nos conduz aos desafios para a Igreja em si mesma e para aquilo que ela se projeta para fora. Este caminho nos oferece um convite para aprofundar sobre a sinodalidade e missionariedade, vistas dentro do contexto brasileiro, como dimensões constitutivas do ser Igreja.
Is continue positive airway pressure causing neonatal gastric perforation?
Authors: Balasubramaniam Sayanthan , Samarakkody Udaya , Jayaratnam Sridharan , Bailey-Wilde Miranda
Published: August 2023

Objectives:

Neonatal gastric perforation (NGP) is a rare surgical emergency that causes significant morbidity and mortality and costs thousands of health dollars. We aim to assess risk factors, outcomes, and preventive measures for NGP.

Material and Methods:

We retrospectively reviewed clinical notes, charts and operative findings for all NGPs from 2000 to 2022 in the Neonatal Intensive Care Unit. The demography, gestational age at birth, age of perforation, potential risk factors, intraoperative findings, incision site and outcome were analysed. Study ethically approved.

Results:

A total of 8 NGPs were sampled. The gestational age ranged from 24–35 (mean of 28.4) weeks. The birth weight was 700–3030 g (1402 g). Seven had respiratory distress and received respiratory support. Six were on continuous positive airway pressure (CPAP), and one was intubated. One baby was on room air and had a <1 cm perforation in the posterior gastric wall by nasogastric tube (NGT). The intubated baby had perforation due to necrotising enterocolitis (NEC) involving the posterior gastric wall (size – 1.5 cm). The remaining six babies had CPAP-related NGP (Estimated P < 0.05) found at the greater curvature (Size >2 cm). Six had left upper quadrant surgical incisions, and two had right-sided incisions requiring an extension. The NGP mortality rate is 1 out of 8 (12.5%).

Conclusion:

CPAP is the leading cause of NGP in low body weight and premature babies. NGT and NEC also cause perforation. Clinical assessment is key to appropriate surgical incisions and minimize the mortality rate.
Biological effects induced by colchicine in Ocimum basilicum L.
Authors: Morosan Ioana-Claudia , Ivanescu Lacramioara Carmen , Olaru Stefan Mihaita , Zamfirache Maria-Magdalena
Published: August 2023

Colchicine is an alkaloid with antimitotic properties, which has been used for polyploidy induction in plant breeding. However, rather limited information is known about the potential biological effects that this compound might induce in plant cells in vivo. This study aims to explore different methods of colchicine treatment using two Ocimum basilicum cultivars: ‘Genovese’ and ‘Aromat de Buzau’ and to determine and compare the induced biological effects. We found that colchicine treatment did not significantly affect the photosynthetic efficiency of treated plants or most seed germination parameters, except for the seedling vigor index. However, it had a significant impact on some morpho-anatomical indices of the plants. Specifically, colchicine treatment resulted in the occurrence of heteromorphic cells and weak xylem lignification in the epicotyl’s structure. In addition, heteromorphic stomata and epidermal cells appeared on both leaf surfaces. Furthermore, we observed tector hair morphogenesis and elongation may be stimulated as possible consequences of chemical stress induced by colchicine treatment.
Multivariate analysis of phenotypic variation and inter-character association in eggplant (Solanum melongena L.) accessions
Authors: Olusola Babatunde Kehinde  , Ayodeji Ayomide Famogbiele  , Christopher Olusanya Alake  , Mufutau Olaoye Atayese  , Oyeboade Adebiyi Oyetunde
Published: August 2023

The agronomic and nutritional values of eggplant (Solanum melongena) can be increased by harnessing its genetic variability. In this work, we aimed to study the variation and inter-character association among eggplant genotypes, with the ultimate goal of identifying genotypes that are useful for breeding. The study was carried out at the Federal University of Agriculture, Abeokuta, Nigeria in 2013, using seven eggplant genotypes. Data were collected on seventeen agro-morphological characters and subjected to principal component analysis (PCA), single linkage cluster analysis (SLCA), correlation analysis, and Genotype × Yield-Trait-biplot (GYT-biplot) procedure. PCA revealed that plant, fruit and yield characters were relevant in discriminating the genotypes while SLCA classified the genotypes into four clusters. Correlation analyses revealed a strong, significant but negative correlation between fruit breadth and yield traits while the GYT-biplot procedure delineated the genotypes into three sectors. We suggest that reduced fruit breadth should be selected for in order to obtain improved yield in eggplant. Genotypes 10014-101a, 97068-091a, and 12078-092e should be further investigated because they are in distinct clusters and, they are the vertex genotypes in sectors 1, 2 and 3.
Rapid Formation of Non-canonical Phospholipid Membranes by Chemoselective Amide-forming Ligations with Hydroxylamines
Authors: Chen Jiyue , Brea Roberto J. , Fracassi Alessandro , Cho Christy J. , Wong Adrian M. , Salvador-Castell Marta , Sinha Sunil K. , Budin Itay , Devaraj Neal K.
Published: August 2023

There has been increasing interest in methods to generate synthetic lipid membranes as key constituents of artificial cells or to develop new tools for remodeling membranes in living cells. However, the biosynthesis of phospholipids involves elaborate enzymatic pathways that are challenging to reconstitute in vitro. An alternative approach is to use chemical reactions to non-enzymatically generate natural or non-canonical phospholipids de novo. Previous reports have shown that synthetic lipid membranes can be formed in situ using various ligation chemistries, but these methods lack biocompatibility and/or suffer from slow kinetics at physiological pH. Thus, it would be valuable to develop chemoselective strategies for synthesizing phospholipids from water-soluble precursors that are compatible with synthetic or living cells Here, we demonstrate that amide-forming ligations between lipid precursors bearing hydroxylamines and α-ketoacids (KAs) or potassium acyltrifluoroborates (KATs) can be used to prepare non-canonical phospholipids in physiologically relevant conditions. The generated amide-linked phospholipids spontaneously self-assemble into cell-like micron-sized vesicles similar to natural phospholipid membranes. We show that lipid synthesis using KAT ligation proceeds extremely rapidly, and the high selectivity and biocompatibility of the approach facilitates the in situ synthesis of phospholipids and associated membranes in living cells.
Fitting q-Gaussians onto Anatase TiO2 Raman Bands
Authors: Sparavigna Amelia Carolina
Published: August 2023

In previous studies, we used the Tsallis q-Gaussian functions for the analysis of Raman spectra, providing several successful examples. Here we apply q-Gaussians to anatase TiO2 Raman spectra from literature, and from RRUFF and ROD databases. We will discuss, in particular, the Raman band about 515 cm−1, and its decomposition in A1g and B1g modes.
Molecular rhodium complex within N-rich porous polymer macroligand as heterogeneous catalyst for the visible-light driven CO2 photoreduction
Authors: Newar Rajashree , Ghosh Ashta C. , Rajapaksha Remy , Samanta Partha , Wisser Florian M. , Canivet Jerome
Published: August 2023

The heterogenization of molecular catalysts within a porous solid acting as macroligand can advantageously open access to enhanced stability and productivity, and thus to more sustainable catalytic process. We report here porous organic polymer (POP) made through metal-free polymerization using bipyridine repeating units. This N-rich POP is an efficient macroligand for the heterogenization of molecular rhodium complexes. The intrinsic catalytic activity of the heterogenized catalyst is slightly higher than that of its homogeneous molecular counterpart for formic acid production as unique carbon containing product. The heterogenization of the rhodium catalysts enables recycling for a total productivity up to 8.3 grams of formic acid per gram of catalyst after 7 cycles of reaction using visible light as sole energy source.
Solvation Structure and UV-Visible Absorption Spectra of the Nitrate Anion at the Air-Ice Interface by First Principles Molecular Simulations
Authors: Berrens Margaret , Chen Zekun , Chan Kam Tung , Anastasio Cort , Donadio Davide
Published: August 2023

Nitrate is a significant contaminant in Polar snow. Its photolysis in environmental sunlight generates reactive nitrogen, which impacts the oxidative capacity of the atmosphere, influencing the fate and lifetimes of pollutants. The photolysis of nitrate can produce either $\mathrm{NO_2}$ or $\mathrm{NO_2^-}$, with recent experiments suggesting that the process is accelerated at the air-ice interface compared to the bulk solution. In this study, we employed multiscale modeling approaches to investigate the enhanced photoreactivity of nitrate at the ice surface in the presence of two different cations. We characterized the solvation shell of $\mathrm{NO_3^-}$ and explored its pairing with cations in water and ice using {\sl ab initio} molecular dynamics and enhanced sampling. Molecular trajectories were used to calculate light absorption spectra at different solvation conditions and finite temperature. Our analysis revealed that the pairing of nitrate with cations may alter the molar absorption coefficient of nitrate at the air-ice interface affecting the rate of photolysis observed in experiments of ammonium nitrate deposited on snow.
The effect of [n]-helicene length on crystal packing
Authors: Schmidt Julia , Wolpert Emma , Sparrow Grace , Johnson Erin , Jelfs Kim
Published: August 2023

Chiral $\pi$-conjugated organic molecules hold potential for emerging technologies as they are capable of introducing novel functionalities into electronic devices owing to their strong chiroptical properties. However, capitalizing on chiral molecules for electronic devices is reliant on their molecular packing—a factor that impacts their charge-transport properties. The solid-state behaviour of molecules is sensitive to subtle differences in molecular interactions, chirality, and shape, but these relationships are not fully understood. Here, we employ crystal structure prediction as a tool to probe the lattice-energy landscape for a family of chiral organic molecules: [$n$]helicenes, where $n$ ranges from 3 to 12. Our results show excellent agreement between the crystal structure prediction landscapes and experimentally reported structures. By analysing the packing motifs within the polymorph landscapes, we begin to develop an understanding of how helicene length affects the shape and $\pi-\pi$ stacking interactions seen in the polymorphs. Further, we propose how helicene length can be used as a tool to design new functional organic electronics.