bluuebunny/crossref_metadata_2025 huggingface dataset:
Abstract LB388: Super potent and efficacious ALK/ROS1/TRK inhibitor that overcomes multiple drug-resistant mutations
Authors: Xing Li , Wang Jingbing , Zhu Wei
Published: April 2024

Abnormal activation of ALK, ROS1 and TRK is implicated in tumorigenesis, progression, invasion, and metastasis of malignancies through various mechanisms. The first generation TRK inhibitors including larotrectinib and entrectinib have limited applications due to acquired resistance in clinical settings. Multiple on-target mutations occur at the solvent-front, gatekeeper, and xDFG motif inside the ATP binding pocket of the TRK kinase domain. While some candidates under current clinical development aim to address the solvent-front mutation, they are ineffective against the xDFG mutations (e.g.NTRK1.G667X). There is strong unmet medical needs for next generation TRK inhibitors that can address resistance mutations. We employ the synergistic advantages of domain expertise and modern technology to accelerate early stage drug discovery. On our deep learning (DL) centric AI platform, extensive scientific computing supports large scale virtual screening, generative design, drug property prediction and multi-objective lead optimization. Only the optimized candidate compounds are passed along for synthesis and testing to save time and cost. In Ba/F3-TPM3 cellular assays, LB-2159 showed superior potencies that are hundreds to thousands of fold higher than larotrectinib and/or entrectinib, with IC50 values of 0.02 nM for NTRK1.WT, 0.3 nM for NTRK1.G595R and 0.88 nM for NTRK1.G667C. Similar cellular potencies were demonstrated for ALK and ROS1. In xenograft models LB-2159 at 10 mg/kg bid resulted in complete tumor regression in Ba/F3-TPM3-NTRK1-WT tumors, 96% TGI in Ba/F3-TPM3-NTRK1-G595R tumors, 84% TGI and 100% survival in mice bearing Ba/F3-TPM3-NTRK1-G667C tumors. The pharmacokinetic profiles of LB-2159 are overall favorable, demonstrating extended stability and half life. In vitro safety screens yielded clean profiles without any DDI signal or hERG liability. Preclinical safety evaluations exhibited good tolerance in rats. In multiple dose escalation studies LB-2159 was well tolerated at 50 mg/kg/day and beyond. Empowered by our proprietary AI platform, we have discovered the novel ALK/ROS1/TRK inhibitors that overcome clinically known resistance mutations. Our drug candidate exhibited good tolerance in animals in preclinical safety evaluations. In xenograft models that are resistant to current treatments the compound demonstrates strong tumor suppression efficacy. LB-2159 is advancing through IND enabling studies, and its preclinical profiles support the potential for best-in-class treatment for NTRK fusion-positive solid tumors.

Citation Format: Li Xing, Jingbing Wang, Wei Zhu. Super potent and efficacious ALK/ROS1/TRK inhibitor that overcomes multiple drug-resistant mutations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB388.
Abstract LB445: USP24 suppresses T cell anti-tumor activity by enhancing the stability of the PD-1 protein
Authors: Hsieh Hung-Chia , Hung Jan-Jong , Wang Yi-Ching
Published: April 2024

Persistent programmed cell death-1 (PD-1)/PD-L1 inhibitory signaling disrupts T cell cytotoxic function, leading to T cell exhaustion—a phenomenon strongly associated with advanced cancer progression and immunotherapy failure. Deciphering the mechanisms behind dysregulated PD-1 expression and reactivating tumor-infiltrating lymphocytes (TILs) in the tumor microenvironment (TME) is crucial. Our previous research established that tumor-associated macrophage-derived IL-6 promotes STAT3-dependent PD-1 transcriptional activity and immunotherapy resistance. The current study further explores whether IL-6 abundance in the TME contributes to PD-1 protein stability. Results indicate that IL-6 extended PD-1 protein half-life in T cells, suggesting its role in modulating PD-1 protein stability. Furthermore, analysis of the GEO database showed a positive correlation between the expression of ubiquitin-specific-processing protease (USP) family enzymes and PD-1 in non-responding patients receiving anti-PD-1 therapy. Knockdown experiments revealed that USP24 strongly downregulated PD-1 protein expression and stability. USP24 depletion reversed IL-6-induced PD-1 expression and stability, with USP24 colocalizing with PD-1 at the plasma membrane upon IL-6 stimulation. Immunoprecipitation-Western blot assays confirmed that USP24 directly interacted with PD-1 and countered E3 ligase c-Cbl-mediated ubiquitination. Depleting USP24 suppressed PD-1-mediated immunosuppression both in vitro and in vivo. In lung-specific EGFRL858R/USP24C1698A functional knockout mice, tumor progression was slower, and exhausted tumor-infiltrating PD-1+ TIM-3+ CD8+ T cells reduced compared to EGFRL858R mice. Inhibiting PD-1 stability with an IL-6 neutralizing antibody or the novel inhibitor USP24-i-101 decreased tumor volume and increased activated cytotoxic T cell infiltration in vivo. Moreover, USP24-i-101 enhanced therapeutic effects in combination with anti-CTLA4 immunotherapy. Clinically, our in-house cohort revealed elevated USP24 expression in tumor-infiltrating lymphocytes or peripheral T cells, correlating with worse clinical outcomes and unfavorable responses to immunotherapy in lung cancer patients. This study uncovers USP24 as a novel PD-1 regulator, deubiquitinating PD-1 to enhance protein stability and suppress T cell function. In conclusion, USP24 emerges as a potential target for enhancing anti-tumor immunity and as a biomarker predicting clinical responses to immunotherapy in lung cancer.

Citation Format: Hung-Chia Hsieh, Jan-Jong Hung, Yi-Ching Wang. USP24 suppresses T cell anti-tumor activity by enhancing the stability of the PD-1 protein [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB445.
Abstract ND11: Chemoproteomic-enabled discovery of VVD-214, a synthetic lethal allosteric inhibitor of WRN helicase
Authors: Kikuchi Shota , Pettazzoni Piergiorgio , Baltgalvis Kristen A. , Lamb Kelsey N. , Symons Kent T. , Wu Chu-Chiao , Hoffman Melissa A. , Snead Aaron N. , Song Xiaodan , Glaza Thomas , Green Jason C. , Rogness Donald C. , Lam Betty , Rodriguez-Aguirre Maria E. , Woody David R. , Eissler Christie L. , Rodiles Socorro , Negron Seth M. , Bernard Steffen M. , Tran Eileen , Pollock Jonathan , Tabatabaei Ali , Contreras Victor , Williams Heather N. , Pastuszka Martha K. , Sigler John J. , Rudolph Markus G. , Classen Moritz , Brugger Doris , Claiborne Christopher , Plancher Jean-Marc , Cuartas Isabel , Seoane Joan , Burgess Laurence E. , Abraham Robert T. , Weinstein David S. , Simon Gabriel M. , Patricelli Matthew P. , Kinsella Todd M.
Published: April 2024

WRN helicase is a promising target for treating cancers with microsatellite instability (MSI) due to its essential role in resolving deleterious non-canonical DNA structures that accumulate in cells with faulty mismatch repair mechanisms. Currently, there are no approved drugs directly targeting human DNA or RNA helicases, in part due to the challenging nature of developing potent and selective compounds to this class of proteins. Here we describe the chemical proteomic-enabled discovery of a clinical-stage, covalent allosteric inhibitor of WRN. The clinical lead, VVD-214, covalently engages cysteine 727 of WRN in a nucleotide cooperative manner and inhibits ATP hydrolysis and helicase activity, resulting in widespread double-stranded DNA breaks, nuclear swelling, and cell death in MSI-high, but not microsatellite stable cells. VVD-214 provided robust tumor regression in multiple MSI-high colorectal cancer cell lines and patient derived xenograft models, including models derived from patients progressing on immune checkpoint therapies. VVD-214 was exceptionally well tolerated, and constitutes a promising oral drug candidate for patients with MSI-high cancers.

Citation Format: Shota Kikuchi, Piergiorgio Pettazzoni, Kristen A. Baltgalvis, Kelsey N. Lamb, Kent T. Symons, Chu-Chiao Wu, Melissa A. Hoffman, Aaron N. Snead, Xiaodan Song, Thomas Glaza, Jason C. Green, Donald C. Rogness, Betty Lam, Maria E. Rodriguez-Aguirre, David R. Woody, Christie L. Eissler, Socorro Rodiles, Seth M. Negron, Steffen M. Bernard, Eileen Tran, Jonathan Pollock, Ali Tabatabaei, Victor Contreras, Heather N. Williams, Martha K. Pastuszka, John J. Sigler, Markus G. Rudolph, Moritz Classen, Doris Brugger, Christopher Claiborne, Jean-Marc Plancher, Isabel Cuartas, Joan Seoane, Laurence E. Burgess, Robert T. Abraham, David S. Weinstein, Gabriel M. Simon, Matthew P. Patricelli, Todd M. Kinsella. Chemoproteomic-enabled discovery of VVD-214, a synthetic lethal allosteric inhibitor of WRN helicase [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr ND11.
The weight of data: an analysis based on the impact on the environment
Authors: Ramirez Lopez Leonardo Juan , Cortes Rodriguez Julian Camilo , Maldonado Engler Ramírez
Published: April 2024

The carbon footprint generated by the information and communications technology (ICT) sector is increasingly significant, emitting greenhouse gases due to high energy consumption, regardless of the way in which energy is generated, the expansion and growth in data centers, as well as the impact generated by the cryptocurrency sector that in the end represents is reflected in greater consumerism, processing, storage, and transport of information that will be somewhere in the world. Current research addresses the problems and the contrast of figures in energy consumption due to the use of a computer, data processing, the role of the user as an internet consumer, the impact of data centers both in carbon footprint, water footprint and soil footprint, the impact of cryptocurrency mining and its contribution to global energy expenditure as well as the ethical debate of new technologies. And finally, the advances in seeking to optimize energy resources, sustainable and conscious for both consumers and service providers, show the trends focused on energy optimization through software and hardware based on a judicious review of research documents.
Autonomous vehicle tracking control for a curved trajectory
Authors: Hasan Hasnawiya , Samman Faizal Arya , Anshar Muh , Sadjad Rhiza S.
Published: April 2024

Recently, research about trajectory tracking of autonomous vehicles has significantly contributed to the development of autonomous vehicle technology, particularly with novel control methods. However, tracking a curved trajectory is still a challenge for autonomous vehicles. This research proposes a state feedback linearization with observer feedback to overcome some difficulties arising from such a path. This approach suits a complex nonlinear system such as an autonomous vehicle. This method also has been compared with the linear-quadratic regulator (LQR) method. So, the goal of this research is to improve the control system performance of autonomous vehicles that are stable enough to navigate a curved path. Moreover, the study shows that the developed control law can track the curved path and solve existing problems. However, improvements are still necessary for the vehicle's performance and robustness.
Enhanced convolutional neural network enabled optimized diagnostic model for COVID-19 detection
Authors: Arul Raj Aaron Meiyyappan , Rajendran Sugumar , Grace Vimal Georgewilliam Sundaram Annie
Published: April 2024

Computed tomography (CT) films are used to construct cross-sectional pictures of a particular region of the body by using many x-ray readings that were obtained at various angles. There is a general agreement in the medical community at this time that chest CT is the most accurate approach for identifying COVID-19 disease. It was demonstrated that chest CT had a higher sensitivity than reverse transcription polymerase chain reaction (RT-PCR) for the detection of COVID-19 illness. This article presents gray-level co-occurrence matrix (GLCM) texture feature extraction and convolutional neural network (CNN)-enabled optimized diagnostic model for COVID-19 detection. In this diagnostic model, CT scan images of patients are given as input. Firstly, GLCM algorithm is used to extract texture features from the CT scan images. This feature extraction helps in achieving higher classification accuracy. Classification is performed using CNN. It achieves higher accuracy than the k-nearest neighbors (KNN) algorithm and multi-layer preceptor (MLP). The accuracy of GLCM based CNN is 99%, F1 score is 99% and the recall rate is also 98%. CNN has achieved better results than MLP and KNN algorithms for COVID-19 detection.
Long-term performance analysis of operational efficiency of a grid-connected solar power plant under Mauritania climate
Authors: Elhassene Issa Cheikh , El Heiba Bamba , Med Mahmoud Teyeb , Aoulmi Zoubir , Youm Issakha , Mahmoud Abdelkader
Published: April 2024

This work examines a solar power plant connected to the Nouakchott electricity grid in Mauritania. Operating since 2013, the 15 MWp plant's reliability and energy yield have been evaluated using a performance index. The assessment involves three phases. First, the plant's meteorological environment and technical indicators are presented. In the second phase, mathematical performance models specified by the International Energy Agency (IEA) are applied to calculate performance indices using data from the data acquisition system (SCADA). The third phase compares actual production data for 2015, 2017, and 2020 with results simulated for PVsyst for the same years. The obtained results are thoroughly analyzed to highlight relevant physical phenomena. The analysis focuses on the plant's 7-year operating period and its impact on performance indicators for electricity production fed into the grid. This study provides insights into the solar power plant's reliability and energy yield, aiding future operational enhancements. It underscores the importance of performance monitoring and assessment in optimizing solar power generation systems.
Hybrid optimal feature selection approach for internet of things based medical data analysis for prognosis
Authors: Bel Felcia , Selvaraj Sabeen
Published: April 2024

Healthcare is very important application domain in internet of things (IoT). The aim is to provide a novel combined feature selection (FS) methods like univariate (UV) with tree-based methods (TB), recursive feature elimination (RFE) with least absolute shrinkage selection operator (LASSO), mutual information (MI) with genetic algorithm (GA) and embedded methods (EM) with univariate has been applied to internet of medical things (IoMT)based heart disease dataset. The well-suited machine learning algorithms for IoT medical data are logistic regression (LR) and support vector machine (SVM). Each combined method  has been applied to the machine learning algorithms to find the best classifier for prognosis. The various performance metrices has been calculated for all the combined feature selection methods for logistic regression and support vector machine and found that for precise classification could be done using recursive elimination feature selection method with LASSO applied to logistic regression achieved a better performance than all other combined methods with high accuracy, sensitivity and high area under curve. Decision has been taken by data analytics that RFE+LASSO using LR feature selection method will provide an overall better performance for IoT based medical heart disease dataset after comparing all other combined methods with LR and SVM classifiers.
The development of low-cost spin coater with wireless IoT control for thin film deposition
Authors: Abdul Aziz Ahmad Muhajer , Idris Muhammad Idzdihar , Mohammed Napiah Zul Atfyi Fauzan , Shah Zainudin Muhammad Noorazlan , Rashid Marzaini , Bradley Luke
Published: April 2024

A low-cost spin coater with a wireless remote system that can deposit thin films of uniform thickness and quality at a significantly lower cost than traditional methods. The system consists of three main parts, a motorized spindle, a spin-coating head, and a control system connected to the network. The mechanical design on the mechanical part, spin coater system design with ESP32, and implementation of wireless control through visual basic. The network-enabled control system allows for real-time monitoring and adjustment of the deposition process, which can improve efficiency and reproducibility. This low-cost spin coating system represents a promising solution for organizations seeking to access thin film deposition technology at a fraction of the cost of traditional systems. By integrating wireless IoT control into low-cost spin coaters, the impact of this technology on coating uniformity will provide valuable insights for future advancements in this field.
Modeling load sensing pressure and flow control of axial piston pump by analyzing impact of bulk modulus
Authors: Verma Vivek , Kumar Sachin , Anand Apurva
Published: April 2024

This research is focused on investigating the impact of the bulk modulus on the dynamics of variable delivery hydraulic axial piston pump (VAPP). The bulk modulus decreases exponentially with an increase in temperature whereas there is a linear positive relationship with pressure. The research revealed that there is a 6% (1 litre/s) increase in flow rate and a 2.6% (1.5 MPa) decrease in delivery pressure with a 38.75% (0.434 GPa) decrease in bulk modulus. Flow ripple and pressure pulsation are reduced by 39.3% and 43.2% respectively with a corresponding 38.75% decrease in bulk modulus. Pressure pulsation and flow ripple are responsible for the generation of noise and vibrations in the system. Flow rate increase contributes to better response and control of the VAPP. While a reduction in bulk modulus offers improved dynamic performance and overall response of the VAPP, it is noteworthy that a decrease in bulk modulus hurts the pump delivery pressure. The research allows the pump designer to formulate a strategy to optimize the bulk modulus under dynamic operating conditions to achieve optimal pump performance.
Computationally efficient handwritten Telugu text recognition
Authors: Revathi Buddaraju , Prasad M. V. D. , Gattim Naveen Kishore
Published: April 2024

Optical character recognition (OCR) for regional languages is difficult due to their complex orthographic structure, lack of dataset resources, a greater number of characters and similarity in structure between characters. Telugu is popular language in states of Andhra and Telangana. Telugu exhibits distinct separation between characters within a word, making a character-level dataset sufficient. With a smaller dataset, we can effectively recognize more words. However, challenges arise during the training of compound characters, which are combinations of vowels and consonants. These are considered as two or more characters based on associated vattus and dheerghams with the base character. To address this challenge, each compound character is encoded into a numerical value and used as input during training, with subsequent retrieval during recognition. The segmentation issue arises from overlapping characters caused by varying handwritten styles. For handling segmentation issues at the character level arising from handwritten styles, we have proposed an algorithm based on the language's features. To enhance word-level accuracy a dictionary-based model was devised. A neural network utilizing the inception module is employed for feature extraction at various scales, achieving word-level accuracy rates of 78% with fewer trainable parameters.
Dynamic base station allocation for 6G wireless networks through narrow neural network
Authors: Kamble Pradnya , Shaikh Alam N.
Published: April 2024

The 6G wireless communication system will utilize the terahertz (THz) frequency band (0.1-10 THz) to meet customer demand for increased data rates and ultra-high-speed communication in future applications. The exponential surge in data traffic, which is supported by dynamic resource allocation. To mitigate this challenge, the use of artificial intelligence-based methods, such as narrow neural network (NNN), can help to smooth the performance of the network. In this paper, an NNN-based approach for dynamic base station allocation for 6G wireless networks is proposed 14 different 6G parameters used to train the NNN model, initially achieving an accuracy of 89.5% and an F1 score of 0.72 for 200 users. Results demonstrate the efficacy of the proposed NNN approach for dynamic decision-making in 6G networks and its potential for application in other domains where similar problems exist. Moreover, the proposed narrow neural network model shows improved results with an increase in number of users and decrease in fully connected layers and regularization strength (lambda). The validation accuracy received is 98.9% and 99.6% for thousand users with single fully connected layer, none (linear) activation function and regularization strength lambda values of 0.01 and 0.001.
A new deep learning model with interface for fine needle aspiration cytology image-based breast cancer detection
Authors: Kalita Manjula , Mahanta Lipi B. , Das Anup Kumar , Nath Mananjay
Published: April 2024

Cytological evaluation through microscopic image analysis of fine needle aspiration cytology (FNAC) is pivotal in the initial screening of breast cancer. The sensitivity of FNAC as a screening tool relies on both image quality and the pathologist’s expertise. To enhance diagnostic accuracy and alleviate the pathologist’s workload, a computer-aided diagnosis (CAD) system was developed. A comparative study was conducted, assessing twelve candidate pre-trained models. Utilizing a locally gathered FNAC image dataset, three superior models-MobileNet-V2, DenseNet-121, and Inception-V3-were selected based on their training, validation, and testing accuracies. Further, these models underwent evaluation in four transfer learning scenarios to enhance testing accuracy. While the outcomes were promising, they left room for improvement, motivating us to create a novel deep convolutional neural network (CNN). The newly proposed model exhibited robust performance with testing accuracy at 85%. Our research concludes that the most lightweight, high-accuracy model is the one we propose. We’ve integrated it into our user-friendly Android App, “Breast Cancer Detection System,” in TensorFlow Lite format, with cloud database support, showcasing its effectiveness. Implementing an artificial intelligent (AI)-based diagnosis system with a user-friendly interface holds the potential to enhance early breast cancer detection using FNAC.
Depression recognition over fusion of visual and vocal expression using artificial intelligence
Authors: Gautam Chandan , Raj Aaradhya , Nemade Bhargavee , Gohokar Vinaya
Published: April 2024

Depression is a mental illness that usually goes untreated in people and can have catastrophic consequences, including suicidal thoughts. Counselling services are widely available, but because depression is a stigmatized illness, many people who are depressed decide not to seek help. Therefore, it is essential to develop an automated system that can recognize depression in individuals before it worsens. In this study, a novel approach is proposed for identifying depression using a combination of visual and vocal emotions. Long short-term memory (LSTM) is used to assess verbal expressions and convolutional neural networks (CNN) to analyze facial expressions. The proposed system is trained using features of depression from the distress analysis interview corpus (DAIC) dataset and tested on videos of college students with frontal faces. The proposed approach is effective in detecting depression in individuals, with high accuracy and reliability.
Big five personality with fuzzy approach to feasibility assessment and loan determination for peer-to-peer lending
Authors: Purwanto Iwan , Isnanto Rizal , Widodo Aris Puji
Published: April 2024

Bad credit is an uncollectible receivable because the debtor has difficulty repaying. In May 2023, the number of loans will increase by 3.36%. This is due to the inaccuracy of creditors in assessing prospective debtors. Several methods of valuation of prospective debtors have been widely used, but the use of the test big five personality (TBFP) method for the assessment of prospective debtors has not been found. This study will use TBFP as an input variable that will be calculated using fuzzy-Mamdani. The output of the system is in the form of a recommended percentage (%) of the loan amount. This research needs to be done to provide an assessment of prospective debtors to be more objective so that bad credit problems can be reduced. The results of this study are taken into consideration to be used as input in assessing prospective debtors that are more appropriate so that it has an impact on increasing income. For the community can increase business activities. For the government to help people’s economic activities. Our research still needs to be developed by adding variables such as the financial condition of prospective debtors, psychological values, and loan history. Apart from that, it is necessary to carry out an in-depth study regarding recommendations for loan amounts for bad credit
Enhancing Hadoop distributed storage efficiency using multi-agent systems
Authors: Mahdaoui Rabie , Sais Manar , Abouchabaka Jaafar , Rafalia Najat
Published: April 2024

Distributed storage systems play a pivotal role in modern data-intensive applications, with Hadoop distributed file system (HDFS) being a prominent example. However, optimizing the efficiency of such systems remains a complex challenge. This research paper presents a novel approach to enhance the efficiency of distributed storage by leveraging multi-agent systems (MAS). Our research is centered on enhancing the efficiency of the HDFS by incorporating intelligent agents that can dynamically assign storage tasks to nodes based on their performance characteristics. Utilizing a decentralized decision-making framework, the suggested approach based on MAS considers the real-time performance of nodes and allocates storage tasks adaptively. This strategy aims to alleviate performance bottlenecks and minimize data transfer latency. Through extensive experimental evaluation, we demonstrate the effectiveness of our approach in improving HDFS performance in terms of data storage, retrieval, and overall system efficiency. The results reveal significant reductions in job execution times and enhanced resource utilization, there by offering a promising avenue for enhancing the efficiency of distributed storage systems.
Patient-patient interactions visualization for drug side effects in patients’ reviews
Authors: Salah Zaher , Elsoud Esraa , Salah Kamal , Al-Sit Waleed T. , Maaya'a Manal , Al Khawaldeh Ahmad
Published: April 2024

This paper describes the patient-patient interactions (PPIs) graph extraction framework from patient’s review transcripts. The concept is to visualise patients as nodes and interactions representing links. Links are made based on review text similarity. Nodes are categorized as positive or negative according to the patient’s attitude toward a given drug. Attitudes are then utilized to categorize the links as supporting or opposing the use of a certain drug. If both patients share the same attitude: negative (severe side effect) or positive (moderate side effect), the relationship is considered supportive; if not, the link is considered opposed. Resulting graph represent a drug as a dispute between two factions arguing on related drug. The framework is explained and evaluated using a dataset included 3,763 patients’ reviews linked to 255 different drugs, -predictive-value (0.37). We argue that, this is caused by derogatory jargon that is an expected feature of patient’s review. The true-negative-recognition-rate is 0.70 and true-positive-recognition-rate is 0.54. Total-average-accuracy, which is independent of class priors, is 0.66. Results show that, it is possible to use text proximity measures and sentiment analysis to capture PPIs structure.
Large file encryption in a reduced-round permutation-based AES file management system
Authors: Baladhay Jerico S. , Gamido Heidilyn V. , De Los Reyes Edjie M.
Published: April 2024

In the rapid evolving digital landscape, the imperative to ensure data security has never been more crucial. This paper addresses the pressing challenges in data security by introducing a file encryption management system, leveraging a modified advanced encryption standard (AES) algorithm with reduced round iterations and bit permutation. This system aims to comprehensively secure various file types, providing a dependable solution for file exchange. Our findings reveal substantial improvements in both encryption and decryption processes using the reduced-round permutation-based AES (RRPBA). The adapted algorithm demonstrates a significant 38.8% acceleration in encryption time and a remarkable 44.86% improvement in decryption time, positioning it as a pivotal component for efficient file operations within the management system. Moreover, the throughput assessments showcase a remarkable 33.73% improvement in encryption and 23.72% in decryption, outperforming the original AES, emphasizing the algorithm's superior computational effectiveness, signaling positive implications for future high-performance applications. In conclusion, the study not only addresses critical security challenges but also presents a viable solution with tangible speed advantages for file encryption and decryption processes within digital file management systems.
A Shift in Artistic Practices through Artificial Intelligence
Authors: Tatar Kivanç , Ericson Petter , Cotton Kelsey , Del Prado Paola Torres Núñez , Batlle-Roca Roser , Cabrero-Daniel Beatriz , Ljungblad Sara , Diapoulis Georgios , Hussain Jabbar
Published: April 2024

The explosion of content generated by artificial intelligence (AI) models has initiated a cultural shift in arts, music, and media, whereby roles are changing, values are shifting, and conventions are challenged. The vast, readily available dataset of the Internet has created an environment for AI models to be trained on any content on the Web. With AI models shared openly and used by many globally, how does this new paradigm shift challenge the status quo in artistic practices? What kind of changes will AI technology bring to music, arts, and new media?
Experiential AI: Between Arts and Explainable AI
Authors: Hemment Drew , Murray-Rust Dave , Belle Vaishak , Aylett Ruth , Vidmar Matjaz , Broz Frank
Published: April 2024

Experiential artificial intelligence (AI) is an approach to the design, use, and evaluation of AI in cultural or other real-world settings that foregrounds human experience and context. It combines arts and engineering to support rich and intuitive modes of model interpretation and interaction, making AI tangible and explicit. The ambition is to enable significant cultural works and make AI systems more understandable to nonexperts, thereby strengthening the basis for responsible deployment. This paper discusses limitations and promising directions in explainable AI, contributions the arts offer to enhance and go beyond explainability and methodology to support, deepen, and extend those contributions.
A Comparison of White Matter Brain Differences in Monolingual and Highly Proficient Multilingual Speakers
Authors: Midrigan-Ciochina Ludmila , Vodacek Kayla P. , Sewell Cristina , Corina David P.
Published: April 2024

Language processing relies on the communication between brain regions that is achieved through several white matter tracts, part of the dorsal, ventral, and medial pathways involved in language processing and control (Coggins et al., 2004; Friederici & Gierhan, 2013; Hickok & Poeppel, 2007; Luk et al., 2011). While changes in white matter tract morphology have been reported as a function of second language learning in bilinguals, little is known about changes that may be present in multilanguage users. Here we investigate white matter morphometry in a group of highly proficient multilinguals, (individuals with proficiency in four or more languages), compared to a group of monolinguals. White matter morphometry was quantified using a fixel-based analysis (Raffelt et al., 2015; Raffelt et al., 2017; Tournier et al., 2007). Higher fiber cross-section and lower fiber density values were observed for the multilinguals, in the dorsal pathways (superior longitudinal fasciculus and arcuate fasciculus) and the ventral pathway, including the inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, and the uncinate fasciculus. Segments of the corpus callosum, the fornix, and the cortico-spinal tract showed decreases in all three morphometry measures for multilinguals. The findings suggest differential efficiencies in neural communication between domain-specific language regions and domain-general cognitive processes underlying multilingual language use. We discuss the results in relation to the bilingual Anterior to Posterior and Subcortical Shift (BAPSS) hypothesis (Grundy et al., 2017) and the Dynamic Restructuring Model (Pliatsikas, 2020).
Variations on a Theme: Heidegger and Judaism
Authors: Herskowitz Daniel M.
Published: April 2024

This essay surveys a number of prominent, recurring, and new directions in the growing scholarly discourse on the theme “Heidegger and Judaism” arranged under three headings. The first, the contrastive framing, encompasses cases in which the relationship between Heidegger and Judaism is perceived as antithetical. The second, the conjunctive framing, encompasses views claiming the existence of affinities and parallels between Heidegger and Judaism, grouped under three subheadings: “Heidegger and biblical thinking,” “Heidegger and Kabbalah,” and “Heidegger and the Jewish nation.” The third, historical perspectives, uses the approach of intellectual history to explore visions of Judaism that are developed as part of engagements with Heidegger’s philosophy.
Brokenness of Being and Errancy of Ontological Untruth: Susan Taubes’s Criticism of Heidegger’s Seinsdenken
Authors: Wolfson Elliot R.
Published: April 2024

In this study, I examine Susan Taubes’s criticism of Heidegger’s Seinsdenken that pivots around her contention that he absolutized the nothingness of being in a manner that is analogous to but yet significantly different than the role assigned to the Godhead on the part of many mystical visionaries. The common denominator is in Heidegger’s insistence on being to the neglect of fully engaging with the rhythms of life. As a consequence, there is no purchase on the chaotic, which falls outside the purview of what can be signified, and in the absence of the unsignifiable, there can be no signification. Every attempt to reconstruct the broken world into a totality contains the flaw of an ontological untruth. The effort at totalization is not only the methodological weakness of tragedy and mysticism, but it is also the nihilistic basis of the role accorded to the nothingness of being in Heidegger’s Seinsdenken. The erroneous nature of the world is not denied by Susan, but she struggled to uphold the ontological untruth that what is most wrong in our existence is what is most precious. The paradoxical retrieval of the truth of this untruth affords the poetic soul the opportunity to confront the limit by breaking it, an infringement of boundary that is redolent equally with the enduring transience of love and the transient endurance of death.
Gagarin Sixty Years Later: Earth and Place after Heidegger and Levinas
Authors: Cools Arthur
Published: April 2024

In this article I re-examine the well-known distinction between rootedness and uprootedness that Emmanuel Levinas draws in his short text “Heidegger, Gagarin and Us” (1961). This distinction addresses the relation between men and place either as an attachment to place (paganism, Heidegger) or as a freedom with regard to place (Judaism, Gagarin). I question this opposition from a contemporary perspective in environmental philosophy, namely from the growing awareness of the interconnectedness between place and Earth. I contend that this new perspective changes the understanding of dwelling today because of Earth’s exteriority with regard to place. I argue that this exteriority is neither infinite nor a totality.
Person indexation in Dpal.ri Smar
Authors: ZHAO Haoliang
Published: April 2024

The article describes the person indexation system of Dpal.ri Smar, a little-studied Trans-Himalayan language spoken in Chab.mdo, Tibet. It gives paradigms of intransitive and transitive verbs, and categorizes verbs into different conjugations, revealing complex morphophonological rules. Additionally, it compares the system to Rgyalrongic languages, and explores the origin of the second person suffix “-əm” which is unique in the Trans-Himalayan family.
What triggered the human-wildlife conflicts in Bangladesh between forest proximity people and wildlife? A responsive study
Authors: Islam Md. Ziaul , Wang Shuwei
Published: April 2024

Reflecting upon the distressing situation of human-wildlife conflict (HWC), this study has identified numerous factors that exacerbate the HWC issue in Bangladesh, including a lack of awareness, a prevalent disregard for the law, inadequate punitive measures, institutional weaknesses, socio-economic challenges, a burgeoning population, a refugee crisis, rampant deforestation, encroachment upon natural habitats, migratory challenges faced by wildlife, and the influence of national and global politics. It is evident that these multifaceted elements significantly contribute to the escalation of HWC. The regions of Chittagong-Hill Tracts (CHT), the Sundarbans, and central-northern Bangladesh are facing a pressing HWC issue that demands our immediate attention. Tragically, over the past two decades, more than 118 elephants have lost their lives, while these creatures claimed 13 human lives in 2017–2018. Furthermore, tigers, an emblem of strength and beauty, have been responsible for the demise of approximately 50 individuals between 2009 and 2010. Astonishingly, the year 2022 has already witnessed a count of 375 HWCs. Therefore, this study delves deeper into the root causes of HWC in Bangladesh and proposes effective preventive measures to safeguard the endangered species, specifically the Asian elephants (Elephas maximus) and Bengal tigers (Panthera tigris tigris), who bear the brunt of these conflicts.
Storm Gust Prediction with the Integration of Machine Learning Algorithms and WRF Model Variables for the Northeast United States
Authors: Jahan Israt , Cerrai Diego , Astitha Marina
Published: April 2024

Wind gusts are often associated with severe hazards and can cause structural and environmental damages, making gust prediction a crucial element of weather forecasting services. In this study, we explored the utilization of machine learning (ML) algorithms integrated with numerical weather prediction outputs from the Weather Research and Forecasting (WRF) Model, to align the estimation of wind gust potential with observed gusts. We have used two ML algorithms, namely, random forest (RF) and extreme gradient boosting (XGB), along with two statistical techniques: generalized linear model with identity link function (GLM-Identity) and generalized linear model with log link function (GLM-Log), to predict storm wind gusts for the northeast (NE) United States. We used 61 simulated extratropical and tropical storms that occurred between 2005 and 2020 to develop and validate the ML and statistical models. To assess the ML model performance, we compared our results with postprocessed gust potential from WRF. Our findings showed that ML models, especially XGB, performed significantly better than statistical models and Unified Post Processor for the WRF (WRF-UPP) Model and were able to better align predicted with observed gusts across all storms. The ML models faced challenges capturing the upper tail of the gust distribution, and the learning curves suggested that XGB was more effective than RF in generating better predictions with fewer storms.
Toward a South American High-Impact Weather Reports Database
Authors: Salio Paola , Bechis Hernán , Ribeiro Bruno Z. , de Lima Nascimento Ernani , Galligani Vito , Garcia Fernando , Alvarenga Lucas , Alvarezs Imaz Maria de los Milagros , Baissac Daiana Marlene , Barle María Florencia , Bastías-Curivil Cristian , Benedicto Marcos , Cancelada Maite , Carvalho da Costa Izabelly , D’Amen Daniela , de Elia Ramon , Diaz David Eduardo , Duarte Páez Anthony , González Sergio , Goede Vitor , Goñi Julián , Granato Agustín , Machado Lopes Murilo , Mederos Matias , Menalled Matias , Mezher Romina , Mingo Vega Eduardo José , Nicora María Gabriela , Pini Lucía , Rondanelli Roberto , Ruiz Juan Jose , Santayana Nestor , Santos Laís , Schild Guilherme , Simone Inés , Valenzuela Raul , Velazquez Yasmin Romina , Vidal Luciano , Villagrán Asiares Constanza Inés
Published: April 2024

Despite southern South America being recognized as a hotspot for deep convective storms, little is known about the socioenvironmental impacts of high-impact weather (HIW) events. Although there have been past efforts to collect severe weather reports in the region, they have been highly fragmented among and within countries, sharing no common protocol, and limited to a particular phenomenon, a very specific region, or a short period of time. There is a pressing need for a more comprehensive understanding of the present risks linked to HIW events, specifically deep convective storms, on a global scale as well as their variability and potential future evolution in the context of climate change. A database of high-quality and systematic HIW reports and associated socioenvironmental impacts is essential to understand the regional atmospheric conditions leading to hazardous weather, to quantify its predictability, and to build robust early warning systems. To tackle this problem and following successful initiatives in other regions of the world, researchers, national weather service members, and weather enthusiasts from Argentina, Brazil, Chile, Paraguay, and Uruguay have embarked on a multinational collaboration to generate a standardized database of reports of HIW events principally associated with convective storms and their socioenvironmental impacts in South America. The goal of this paper is to describe this unprecedented initiative over the region, to summarize first results, and to discuss the potential applications of this collaboration.
Leveraging Collaborative Partnerships to Enhance NWS and Emergency Management Communications through Exercising
Authors: Hogg David , Smith Richard , Thompson Jennifer , Bunker Ryan , Huey Rachael , Krocak Makenzie J.
Published: April 2024

Tabletop exercises examining weather-related hazards are not uncommon but are often built around somewhat generic scenarios that only touch on the meteorological communication environment at a very shallow level. A recent exercise in central Oklahoma sought to change that. A local emergency manager, personnel from a National Weather Service (NWS) forecast office, and a severe weather researcher with a background in exercise design and facilitation worked together to create and deliver a realistic severe weather simulation. Exercise participants were exposed to detailed forecast information via NWSChat—a dedicated communication tool used to connect NWS forecasters, emergency managers, and media members for real-time information sharing. NWS forecasters were able to both actively play in the exercise due to the use of NWSChat and observe how local decision-makers interpreted and utilized the impact-based decision support service (IDSS) graphics and short-term forecast updates. The collaborative approach of developing a detailed scenario with numerous real-world IDSS graphics, along with the use of NWSChat for real-time delivery, resulted in overwhelmingly positive feedback from the participants. The local emergency management office identified numerous areas for improvement in communicating real-time forecast information across their jurisdiction, along with gaps in current plans and resources. Meanwhile, the NWS forecast office had the opportunity to experiment with using the new NWSChat platform in a high-impact severe weather environment before a real-world event took place. Forecasters also gained insight into current IDSS graphic interpretation, noting areas for improved messaging to end users, such as adding storm motion to existing severe weather graphics.
The Influence of Topography on the Diurnal Rainfall Propagation in the Bay of Bengal
Authors: Chen ZiJian , Du Yu
Published: April 2024

A significant diurnal offshore propagation of rainfall is observed extending from the eastern coast of India to the central Bay of Bengal. This study focuses on understanding the influence of topography over the Indian subcontinent on this rainfall propagation through a series of semi-idealized mesoscale numerical simulations. These simulations with varying topography highlight the crucial role of inertia–gravity waves, driven by diurnal mountain–land–sea thermal contrast between India and the Bay of Bengal, in initiating and promoting the offshore propagation of convective systems in the Bay. These waves’ phase speed of around 14.8 m s−1 aligns well with the speed of diurnal rainfall propagation. Even after eliminating the impact of Indian topography, the offshore propagating signal persists, suggesting a secondary rather than dominant effect of terrain on offshore rainfall propagation. Furthermore, the topography affects the depth of diurnal heating within the land’s boundary layer, which thus influences the amplitude, phase, and speed of the inertia–gravity waves. Specifically, the presence of higher mountains along the coastal area drives faster waves by increasing heating depth, resulting in faster rainfall propagation.

Significance Statement

This study advances our comprehension of the fundamental driver behind diurnal offshore rainfall propagation and the manner in which coastal terrain influences the rainfall pattern. We demonstrate that the diurnal offshore propagation of rainfall is closely related to inertia–gravity waves generated by thermal contrasts, and we successfully distinguish these waves in our study. Furthermore, our findings indicate that elevated coastal topography contributes to a greater heating depth near the coastline, which plays a crucial role in driving faster gravity waves and, consequently, leading to faster rainfall propagation. These outcomes provide a deeper insight into the mechanism governing offshore rainfall propagation and underscore the impact of real-world topography.
You Get What You Pay for: An Analysis of Public Contracts for Engineering Services
Authors: Medeiros-Costa Caio César de
Published: April 2024

To understand the effects of bidder’s strategies on contractual modifications, this study utilized econometric methods and a database containing 5,434 engineering service contracts from Brazilian federal government. The results demonstrate the effects arising from the trade-off between the low prices presented in the contractor selection phase and the contractual performance measured by contract modifications. Competition among bidders in a reverse auction with multiple rounds of bid submissions can exacerbate this trade-off, potentially incentivizing risk-taking strategies by bidders. Subsequently, this may lead to the need for contract modifications to facilitate the ongoing execution of the contract.