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Recently, the spread of white spot disease in shrimps has a major impact on the aquaculture activity worldwide affecting the economy of the countries, especially South-East Asian countries like Vietnam. This deadly disease in shrimps is caused by the White Spot Syndrome Virus (WSSV). Researchers are trying to understand the spread and control of this disease by doing field and laboratory studies considering effect of environmental conditions on shrimps affected by WSSV. Generally, they have not considered spatial factors in their study. Therefore, in the present study, we have used spatial (distances to roads and factories) as well as physio-chemical factors of water: Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), Salinity, NO3, P3O4 and pH, for developing WSSV susceptibility maps of the area using Decision Tree (DT)-based Machine Learning (ML) models namely Random Tree (RT), Extra Tree (ET), and J48. Model’s performance was evaluated using standard statistical measures including Area Under the Curve (AUC). The results indicated that ET model has the highest accuracy (AUC: 0.713) in predicting disease susceptibility in comparison to other two models (RT: 0.701 and J48: 0.641). The WSSV susceptibility maps developed by the ML technique, using DT (ET) method, will help decision makers in better planning and control of spatial spread of WSSV disease in shrimps., Validerad;2024;Nivå 2;2024-03-19 (signyg);Funder: Ministry of Education and Training (B2021-TDV-08);Full text license: CC BY

Recently, the spread of white spot disease in shrimps has a major impact on the aquaculture activity worldwide affecting the economy of the countries, especially South-East Asian countries like Vietnam. This deadly disease in shrimps is caused by the White Spot Syndrome Virus (WSSV). Researchers are trying to understand the spread and control of this disease by doing field and laboratory studies considering effect of environmental conditions on shrimps affected by WSSV. Generally, they have not considered spatial factors in their study. Therefore, in the present study, we have used spatial (distances to roads and factories) as well as physio-chemical factors of water: Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), Salinity, NO3, P3O4 and pH, for developing WSSV susceptibility maps of the area using Decision Tree (DT)-based Machine Learning (ML) models namely Random Tree (RT), Extra Tree (ET), and J48. Model’s performance was evaluated using standard statistical measures including Area Under the Curve (AUC). The results indicated that ET model has the highest accuracy (AUC: 0.713) in predicting disease susceptibility in comparison to other two models (RT: 0.701 and J48: 0.641). The WSSV susceptibility maps developed by the ML technique, using DT (ET) method, will help decision makers in better planning and control of spatial spread of WSSV disease in shrimps., Validerad;2024;Nivå 2;2024-03-19 (signyg);Funder: Ministry of Education and Training (B2021-TDV-08);Full text license: CC BY

In this paper, we would like to examine whether stable de Sitter inflationary solutions appear within power-law extensions of the Starobinsky model. In particular, we will address general constraints for the existence along with the stability of de Sitter inflationary solutions in a general case involving not only the Starobinsky $R^2$ term but also an additional power-law $R^n$ one. According to the obtained results, we will be able to identify which extension is more suitable for an early inflationary phase rather than a late-time cosmic acceleration phase. To be more specific, we will consider several values of $n$ to see whether the corresponding de Sitter inflationary solutions are stable or not., Comment: 40 pages, 20 figures. Final version with additional stability analysis using an effective potential method. Matches the published version. All conclusions remain unchanged. Comments are welcome

BACKGROUND: Limited English Proficiency (LEP) Latinxs experience a longer duration of untreated depression and anxiety. LEP Latinxs have difficulty accessing mental healthcare due to insufficient Spanish-speaking behavioral/mental health clinicians to meet demand. These under-resourced healthcare systems are less likely to be the site for the implementation of innovations. Digital interventions can provide an effective option for overcoming these barriers; yet, when digital evidence-based treatments are available, uptake and engagement is often low. This manuscript presents the protocol for the SUPERA (SUpport from PEeRs to expand Access) study which will evaluate the implementation of an evidence-based, Spanish language, digital cognitive-behavioral therapy (dCBT) intervention (i.e., SilverCloud) in safety-net primary care clinics for LEP Latinx patients with depression or anxiety. METHODS: We will conduct an effectiveness-implementation hybrid trial (Type 2) design comparing engagement and clinical outcomes in two modalities of dCBT delivery (peer-supported vs. unsupported). We will also compare provider-level outreach (using a clinic patient registry) versus inreach (traditional provider referral) to compare rates of initiation, completion, and cost. Participants will be 426 LEP Latinx adults ≥18 years of age, PHQ-9 ≥ 10 or GAD-7 ≥ 8, with access to the internet via smartphone, and not currently receiving individual psychotherapy. We will collect baseline, post-intervention (8 weeks), and follow up (3 months) data. CONCLUSION: The long-term goal of this research is to aid in the implementation of digital mental health interventions that can be sustainably implemented in low-resourced settings, while reducing the reliance on professionals, overcoming workforce deficits, and increasing relevance for diverse populations.

Annette M Ilg,1 Christine P Beltran,2 Jenny A Shih,3,4 Tuyen T Yankama,5 Margaret M Hayes,6,* Ari L Moskowitz6,7,* 1Division of Emergency Critical Care, Department of Emergency Medicine, Mass General Brigham, Boston, MA, USA; 2Carl J, Shapiro Institute for Education and Research, Beth Israel Deaconess Medical Center, Boston, MA, USA; 3Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA; 4Department of Pulmonary and Critical Care, Brigham and Women’s Hospital, Boston, MA, USA; 5Department of Pharmacy, Beth Israel Deaconess Medical Center, Boston, MA, USA; 6Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA; 7Division of Critical Care Medicine, Department of Medicine, Montefiore Medical Center, Bronx, New York, NY, USA*These authors contributed equally to this workCorrespondence: Annette M Ilg, Mass General Brigham, Department of Emergency Medicine, Division of Critical Care, 75 Francis St, Boston, MA, 02115, USA, Email ailg@bwh.harvard.eduBackground: Incorporating unfamiliar therapies into practice requires effective longitudinal learning and the optimal way to achieve this is debated. Though not a novel therapy, ketamine in critical care has a paucity of data and variable acceptance, with limited research describing intensivist perceptions and utilization. The Coronavirus-19 pandemic presented a particular crisis where providers rapidly adapted analgosedation strategies to achieve prolonged, deep sedation due to a surge of severe acute respiratory distress syndrome (ARDS).Question: How does clinical experience with ketamine impact the perception and attitude of clinicians toward this therapy?Methods: We conducted a mixed-methods study using quantitative ketamine prescription data and qualitative focus group data. We analyzed prescription patterns of ketamine in a tertiary academic ICU during two different time points: pre-COVID-19

Introduction: 3D pharmacophore models describe the ligand's chemical interactions in their bioactive conformation. They offer a simple but sophisticated approach to decipher the chemically encoded ligand information, making them a valuable tool in drug design. Methods: Our research summarized the key studies for applying 3D pharmacophore models in virtual screening for 6,944 compounds of APJ receptor agonists. Recent advances in clustering algorithms and ensemble methods have enabled classical pharmacophore modeling to evolve into more flexible and knowledge-driven techniques. Butina clustering categorizes molecules based on their structural similarity (indicated by the Tanimoto coefficient) to create a structurally diverse training dataset. The learning method combines various individual pharmacophore models into a set of pharmacophore models for pharmacophore space optimization in virtual screening. Results: This approach was evaluated on Apelin datasets and afforded good screening performance, as proven by Receiver Operating Characteristic (AUC score of 0.994 ± 0.007), enrichment factor of (EF1% of 50.07 ± 0.211), Güner-Henry score of 0.956 ± 0.015, and F-measure of 0.911 ± 0.031. Discussion: Although one of the high-scoring models achieved statistically superior results in each dataset (AUC of 0.82; an EF1% of 19.466; GH of 0.131 and F1-score of 0.071), the ensemble learning method including voting and stacking method balanced the shortcomings of each model and passed with close performance measures.

Introduction: 3D pharmacophore models describe the ligand's chemical interactions in their bioactive conformation. They offer a simple but sophisticated approach to decipher the chemically encoded ligand information, making them a valuable tool in drug design. Methods: Our research summarized the key studies for applying 3D pharmacophore models in virtual screening for 6,944 compounds of APJ receptor agonists. Recent advances in clustering algorithms and ensemble methods have enabled classical pharmacophore modeling to evolve into more flexible and knowledge-driven techniques. Butina clustering categorizes molecules based on their structural similarity (indicated by the Tanimoto coefficient) to create a structurally diverse training dataset. The learning method combines various individual pharmacophore models into a set of pharmacophore models for pharmacophore space optimization in virtual screening. Results: This approach was evaluated on Apelin datasets and afforded good screening performance, as proven by Receiver Operating Characteristic (AUC score of 0.994 ± 0.007), enrichment factor of (EF1% of 50.07 ± 0.211), Güner-Henry score of 0.956 ± 0.015, and F-measure of 0.911 ± 0.031. Discussion: Although one of the high-scoring models achieved statistically superior results in each dataset (AUC of 0.82; an EF1% of 19.466; GH of 0.131 and F1-score of 0.071), the ensemble learning method including voting and stacking method balanced the shortcomings of each model and passed with close performance measures.

Introduction: 3D pharmacophore models describe the ligand's chemical interactions in their bioactive conformation. They offer a simple but sophisticated approach to decipher the chemically encoded ligand information, making them a valuable tool in drug design. Methods: Our research summarized the key studies for applying 3D pharmacophore models in virtual screening for 6,944 compounds of APJ receptor agonists. Recent advances in clustering algorithms and ensemble methods have enabled classical pharmacophore modeling to evolve into more flexible and knowledge-driven techniques. Butina clustering categorizes molecules based on their structural similarity (indicated by the Tanimoto coefficient) to create a structurally diverse training dataset. The learning method combines various individual pharmacophore models into a set of pharmacophore models for pharmacophore space optimization in virtual screening. Results: This approach was evaluated on Apelin datasets and afforded good screening performance, as proven by Receiver Operating Characteristic (AUC score of 0.994 ± 0.007), enrichment factor of (EF1% of 50.07 ± 0.211), Güner-Henry score of 0.956 ± 0.015, and F-measure of 0.911 ± 0.031. Discussion: Although one of the high-scoring models achieved statistically superior results in each dataset (AUC of 0.82; an EF1% of 19.466; GH of 0.131 and F1-score of 0.071), the ensemble learning method including voting and stacking method balanced the shortcomings of each model and passed with close performance measures.

Introduction: 3D pharmacophore models describe the ligand's chemical interactions in their bioactive conformation. They offer a simple but sophisticated approach to decipher the chemically encoded ligand information, making them a valuable tool in drug design. Methods: Our research summarized the key studies for applying 3D pharmacophore models in virtual screening for 6,944 compounds of APJ receptor agonists. Recent advances in clustering algorithms and ensemble methods have enabled classical pharmacophore modeling to evolve into more flexible and knowledge-driven techniques. Butina clustering categorizes molecules based on their structural similarity (indicated by the Tanimoto coefficient) to create a structurally diverse training dataset. The learning method combines various individual pharmacophore models into a set of pharmacophore models for pharmacophore space optimization in virtual screening. Results: This approach was evaluated on Apelin datasets and afforded good screening performance, as proven by Receiver Operating Characteristic (AUC score of 0.994 ± 0.007), enrichment factor of (EF1% of 50.07 ± 0.211), Güner-Henry score of 0.956 ± 0.015, and F-measure of 0.911 ± 0.031. Discussion: Although one of the high-scoring models achieved statistically superior results in each dataset (AUC of 0.82; an EF1% of 19.466; GH of 0.131 and F1-score of 0.071), the ensemble learning method including voting and stacking method balanced the shortcomings of each model and passed with close performance measures.

This study examines the impact of the homestay tourism program in Mekong Delta, Vietnam, on women’s participation and empowerment in rural tourism development. As part of this study, 22 interviews were conducted with homestay owners to explore the benefits and challenges of women’s involvement in this type of tourism. The findings show that homestay tourism program has helped women to generate extra income, strengthen family ties, and increase their social influence while reducing domestic violence. However, cultural expectations and gender roles continue to pose challenges. This study underscores the importance of women’s participation in homestay tourism program and provides suggestions for promoting gender equality and women’s empowerment in tourism development. The study highlights the potential of women’s involvement in creating a sustainable and inclusive tourism industry and calls for further research and action to promote gender equality and women’s rights in tourism. The findings have practical implications for policymakers and practitioners in the tourism sector.

In this paper, we would like to examine whether stable de Sitter inflationary solutions appear within power-law extensions of the Starobinsky model. In particular, we will address general constraints for the existence along with the stability of de Sitter inflationary solutions in a general case involving not only the Starobinsky $R^2$ term but also an additional power-law $R^n$ one. According to the obtained results, we will be able to identify which extension is more suitable for an early inflationary phase rather than a late-time cosmic acceleration phase. To be more specific, we will consider several values of $n$ to see whether the corresponding de Sitter inflationary solutions are stable or not., Comment: 31 pages, 16 figures. Comments are welcome

Introduction: 3D pharmacophore models describe the ligand's chemical interactions in their bioactive conformation. They offer a simple but sophisticated approach to decipher the chemically encoded ligand information, making them a valuable tool in drug design. Methods: Our research summarized the key studies for applying 3D pharmacophore models in virtual screening for 6,944 compounds of APJ receptor agonists. Recent advances in clustering algorithms and ensemble methods have enabled classical pharmacophore modeling to evolve into more flexible and knowledge-driven techniques. Butina clustering categorizes molecules based on their structural similarity (indicated by the Tanimoto coefficient) to create a structurally diverse training dataset. The learning method combines various individual pharmacophore models into a set of pharmacophore models for pharmacophore space optimization in virtual screening. Results: This approach was evaluated on Apelin datasets and afforded good screening performance, as proven by Receiver Operating Characteristic (AUC score of 0.994 ± 0.007), enrichment factor of (EF1% of 50.07 ± 0.211), Güner-Henry score of 0.956 ± 0.015, and F-measure of 0.911 ± 0.031. Discussion: Although one of the high-scoring models achieved statistically superior results in each dataset (AUC of 0.82; an EF1% of 19.466; GH of 0.131 and F1-score of 0.071), the ensemble learning method including voting and stacking method balanced the shortcomings of each model and passed with close performance measures.

With the growing availability of efficient tools, persistent homology is becoming a useful methodology in a variety of applications. Significant work has been devoted to implementing tools for persistent homology diagrams; however, computing representative cycles corresponding to each point in the diagram can still be inefficient. To circumvent this problem, we extend the twist algorithm of Chen and Kerber. Our extension is based on a new technique we call saving, which supplements their existing killing technique. The resulting two-pass strategy can be realized using an existing matrix reduction implementation as a black-box and improves the efficiency of computing representatives of persistent homology generators. We prove the correctness of the new approach and experimentally show its performance.

This paper presents an original energy management methodology to enhance the resilience of ship power systems. The integration of various energy storage systems (ESS), including battery energy storage systems (BESS) and super-capacitor energy storage systems (SCESS), in modern ship power systems poses challenges in designing an efficient energy management system (EMS). The EMS proposed in this paper aims to achieve multiple objectives. The primary objective is to minimize shed loads, while the secondary objective is to effectively manage different types of ESS. Considering the diverse ramp-rate characteristics of generators, SCESS, and BESS, the proposed EMS exploits these differences to determine an optimal long-term schedule for minimizing shed loads. Furthermore, the proposed EMS balances the state-of-charge (SoC) of ESS and prioritizes the SCESS's SoC levels to ensure the efficient operation of BESS and SCESS. For better computational efficiency, we introduce the receding horizon optimization method, enabling real-time EMS implementation. A comparison with the fixed horizon optimization (FHO) validates its effectiveness. Simulation studies and results demonstrate that the proposed EMS efficiently manages generators, BESS, and SCESS, ensuring system resilience under generation shortages. Additionally, the proposed methodology significantly reduces the computational burden compared to the FHO technique while maintaining acceptable resilience performance., Comment: 15 pages, 17 figures

Low-latency video streaming over 5G has become rapidly popular over the last few years due to its increased usage in hosting virtual events, online education, webinars, and all-hands meetings. Our work aims to address the absence of studies that reveal the real-world behavior of low-latency video streaming. To that end, we provide an experimental methodology and measurements, collected in a US metropolitan area over a commercial 5G network, that correlates application-level QoE and lower-layer metrics on the devices, such as RSRP, RSRQ, handover records, etc., under both static and mobility scenarios. We find that RAN-side information, which is readily available on every cellular device, has the potential to enhance throughput estimation modules of video streaming clients, ultimately making low-latency streaming more resilient against network perturbations and handover events., Comment: 6 Pages

The growing demand for high-fidelity video generation from textual descriptions has catalyzed significant research in this field. In this work, we introduce MagicVideo-V2 that integrates the text-to-image model, video motion generator, reference image embedding module and frame interpolation module into an end-to-end video generation pipeline. Benefiting from these architecture designs, MagicVideo-V2 can generate an aesthetically pleasing, high-resolution video with remarkable fidelity and smoothness. It demonstrates superior performance over leading Text-to-Video systems such as Runway, Pika 1.0, Morph, Moon Valley and Stable Video Diffusion model via user evaluation at large scale.

A new variant of Newton's method - named Backtracking New Q-Newton's method (BNQN) - which has strong theoretical guarantee, is easy to implement, and has good experimental performance, was recently introduced by the third author. Experiments performed previously showed some remarkable properties of the basins of attractions for finding roots of polynomials and meromorphic functions, with BNQN. In general, they look more smooth than that of Newton's method. In this paper, we continue to experimentally explore in depth this remarkable phenomenon, and connect BNQN to Newton's flow and Voronoi's diagram. This link poses a couple of challenging puzzles to be explained. Experiments also indicate that BNQN is more robust against random perturbations than Newton's method and Random Relaxed Newton's method., Comment: 48 pages. Some typos are fixed. Comments are welcome!. arXiv admin note: text overlap with arXiv:2312.12166

In the rapidly evolving field of artificial intelligence, the ability to harness and integrate knowledge across various domains stands as a paramount challenge and opportunity. This study introduces a novel approach to cross-domain knowledge discovery through the deployment of multi-AI agents, each specialized in distinct knowledge domains. These AI agents, designed to function as domain-specific experts, collaborate in a unified framework to synthesize and provide comprehensive insights that transcend the limitations of single-domain expertise. By facilitating seamless interaction among these agents, our platform aims to leverage the unique strengths and perspectives of each, thereby enhancing the process of knowledge discovery and decision-making. We present a comparative analysis of the different multi-agent workflow scenarios evaluating their performance in terms of efficiency, accuracy, and the breadth of knowledge integration. Through a series of experiments involving complex, interdisciplinary queries, our findings demonstrate the superior capability of domain specific multi-AI agent system in identifying and bridging knowledge gaps. This research not only underscores the significance of collaborative AI in driving innovation but also sets the stage for future advancements in AI-driven, cross-disciplinary research and application. Our methods were evaluated on a small pilot data and it showed a trend we expected, if we increase the amount of data we custom train the agents, the trend is expected to be more smooth.

Keywords play a crucial role in bridging the gap between human understanding and machine processing of textual data. They are essential to data enrichment because they form the basis for detailed annotations that provide a more insightful and in-depth view of the underlying data. Keyword/domain driven term extraction is a pivotal task in natural language processing, facilitating information retrieval, document summarization, and content categorization. This review focuses on keyword extraction methods, emphasizing the use of three major Large Language Models(LLMs): Llama2-7B, GPT-3.5, and Falcon-7B. We employed a custom Python package to interface with these LLMs, simplifying keyword extraction. Our study, utilizing the Inspec and PubMed datasets, evaluates the performance of these models. The Jaccard similarity index was used for assessment, yielding scores of 0.64 (Inspec) and 0.21 (PubMed) for GPT-3.5, 0.40 and 0.17 for Llama2-7B, and 0.23 and 0.12 for Falcon-7B. This paper underlines the role of prompt engineering in LLMs for better keyword extraction and discusses the impact of hallucination in LLMs on result evaluation. It also sheds light on the challenges in using LLMs for keyword extraction, including model complexity, resource demands, and optimization techniques.

Model Inversion (MI) attacks aim to reconstruct private training data by abusing access to machine learning models. Contemporary MI attacks have achieved impressive attack performance, posing serious threats to privacy. Meanwhile, all existing MI defense methods rely on regularization that is in direct conflict with the training objective, resulting in noticeable degradation in model utility. In this work, we take a different perspective, and propose a novel and simple Transfer Learning-based Defense against Model Inversion (TL-DMI) to render MI-robust models. Particularly, by leveraging TL, we limit the number of layers encoding sensitive information from private training dataset, thereby degrading the performance of MI attack. We conduct an analysis using Fisher Information to justify our method. Our defense is remarkably simple to implement. Without bells and whistles, we show in extensive experiments that TL-DMI achieves state-of-the-art (SOTA) MI robustness. Our code, pre-trained models, demo and inverted data are available at: https://hosytuyen.github.io/projects/TL-DMI

Compounds containing benzimidazole moiety occupy privileged chemical space for discovering new bioactive substances. In continuation of our recent work, 69 benzimidazole derivatives were designed and synthesized with good to excellent yields of 46-99% using efficient synthesis protocol i.e. sodium metabisulfite catalyzed condensation of aromatic aldehydes with o-phenylenediamines to form 2-arylbenzimidazole derivatives followed by N-alkylation by conventional heating or microwave irradiation for diversification. Potent antibacterial compounds against MSSA and MRSA were discovered such as benzimidazole compounds 3k (2-(4-nitrophenyl), N-benzyl), 3l (2-(4-chlorophenyl), N-(4-chlorobenzyl)), 4c (2-(4-chlorophenyl), 6-methyl, N-benzyl), 4g (2-(4-nitrophenyl), 6-methyl, N-benzyl), and 4j (2-(4-nitrophenyl), 6-methyl, N-(4-chlorobenzyl)) with MIC of 4-16 mu g mL(-1). In addition, compound 4c showed good antimicrobial activities (MIC = 16 mu g mL(-1)) against the bacteria strains Escherichia coli and Streptococcus faecalis. Moreover, compounds 3k, 3l, 4c, 4g, and 4j have been found to kill HepG2, MDA-MB-231, MCF7, RMS, and C26 cancer cells with low mu M IC50 (2.39-10.95). These compounds showed comparable drug-like properties as ciprofloxacin, fluconazole, and paclitaxel in computational ADMET profiling. Finally, docking studies were used to assess potential protein targets responsible for their biological activities. Especially, we found that DHFR is a promising target both in silico and in vitro with compound 4c having IC50 of 2.35 mu M.

Compounds containing benzimidazole moiety occupy privileged chemical space for discovering new bioactive substances. In continuation of our recent work, 69 benzimidazole derivatives were designed and synthesized with good to excellent yields of 46-99% using efficient synthesis protocol i.e. sodium metabisulfite catalyzed condensation of aromatic aldehydes with o-phenylenediamines to form 2-arylbenzimidazole derivatives followed by N-alkylation by conventional heating or microwave irradiation for diversification. Potent antibacterial compounds against MSSA and MRSA were discovered such as benzimidazole compounds 3k (2-(4-nitrophenyl), N-benzyl), 3l (2-(4-chlorophenyl), N-(4-chlorobenzyl)), 4c (2-(4-chlorophenyl), 6-methyl, N-benzyl), 4g (2-(4-nitrophenyl), 6-methyl, N-benzyl), and 4j (2-(4-nitrophenyl), 6-methyl, N-(4-chlorobenzyl)) with MIC of 4-16 mu g mL(-1). In addition, compound 4c showed good antimicrobial activities (MIC = 16 mu g mL(-1)) against the bacteria strains Escherichia coli and Streptococcus faecalis. Moreover, compounds 3k, 3l, 4c, 4g, and 4j have been found to kill HepG2, MDA-MB-231, MCF7, RMS, and C26 cancer cells with low mu M IC50 (2.39-10.95). These compounds showed comparable drug-like properties as ciprofloxacin, fluconazole, and paclitaxel in computational ADMET profiling. Finally, docking studies were used to assess potential protein targets responsible for their biological activities. Especially, we found that DHFR is a promising target both in silico and in vitro with compound 4c having IC50 of 2.35 mu M.

Compounds containing benzimidazole moiety occupy privileged chemical space for discovering new bioactive substances. In continuation of our recent work, 69 benzimidazole derivatives were designed and synthesized with good to excellent yields of 46-99% using efficient synthesis protocol i.e. sodium metabisulfite catalyzed condensation of aromatic aldehydes with o-phenylenediamines to form 2-arylbenzimidazole derivatives followed by N-alkylation by conventional heating or microwave irradiation for diversification. Potent antibacterial compounds against MSSA and MRSA were discovered such as benzimidazole compounds 3k (2-(4-nitrophenyl), N-benzyl), 3l (2-(4-chlorophenyl), N-(4-chlorobenzyl)), 4c (2-(4-chlorophenyl), 6-methyl, N-benzyl), 4g (2-(4-nitrophenyl), 6-methyl, N-benzyl), and 4j (2-(4-nitrophenyl), 6-methyl, N-(4-chlorobenzyl)) with MIC of 4-16 mu g mL(-1). In addition, compound 4c showed good antimicrobial activities (MIC = 16 mu g mL(-1)) against the bacteria strains Escherichia coli and Streptococcus faecalis. Moreover, compounds 3k, 3l, 4c, 4g, and 4j have been found to kill HepG2, MDA-MB-231, MCF7, RMS, and C26 cancer cells with low mu M IC50 (2.39-10.95). These compounds showed comparable drug-like properties as ciprofloxacin, fluconazole, and paclitaxel in computational ADMET profiling. Finally, docking studies were used to assess potential protein targets responsible for their biological activities. Especially, we found that DHFR is a promising target both in silico and in vitro with compound 4c having IC50 of 2.35 mu M.

Compounds containing benzimidazole moiety occupy privileged chemical space for discovering new bioactive substances. In continuation of our recent work, 69 benzimidazole derivatives were designed and synthesized with good to excellent yields of 46-99% using efficient synthesis protocol i.e. sodium metabisulfite catalyzed condensation of aromatic aldehydes with o-phenylenediamines to form 2-arylbenzimidazole derivatives followed by N-alkylation by conventional heating or microwave irradiation for diversification. Potent antibacterial compounds against MSSA and MRSA were discovered such as benzimidazole compounds 3k (2-(4-nitrophenyl), N-benzyl), 3l (2-(4-chlorophenyl), N-(4-chlorobenzyl)), 4c (2-(4-chlorophenyl), 6-methyl, N-benzyl), 4g (2-(4-nitrophenyl), 6-methyl, N-benzyl), and 4j (2-(4-nitrophenyl), 6-methyl, N-(4-chlorobenzyl)) with MIC of 4-16 mu g mL(-1). In addition, compound 4c showed good antimicrobial activities (MIC = 16 mu g mL(-1)) against the bacteria strains Escherichia coli and Streptococcus faecalis. Moreover, compounds 3k, 3l, 4c, 4g, and 4j have been found to kill HepG2, MDA-MB-231, MCF7, RMS, and C26 cancer cells with low mu M IC50 (2.39-10.95). These compounds showed comparable drug-like properties as ciprofloxacin, fluconazole, and paclitaxel in computational ADMET profiling. Finally, docking studies were used to assess potential protein targets responsible for their biological activities. Especially, we found that DHFR is a promising target both in silico and in vitro with compound 4c having IC50 of 2.35 mu M.

This study aimed to determine factors associated with medication errors, and evaluate the results of interventions to reduce medication errors in inpatients treatment at Hoan My Minh Hai General Hospital, Vietnam. Methods: A single-blind, before-and-after and interventional study was conducted on 442 medical records of inpatients in the pre-intervention stage and 442 medical records of inpatients in the post-intervention stage at the Department of Pediatrics, Department of General Internal Medicine, Department of Cardiology - Endocrinology, Department of Surgery, Department of Obstetrics of Hoan My Minh Hai General Hospital from July 1, 2021, to March 31, 2022. Data were collected and processed using Excel 2016 and SPSS 26.0 software. Results: The medication errors rate decreased from 7.70% in the pre-intervention stage to 5.70% in the post-intervention stage, the difference was statistically significant (p<0.001). Medication errors before intervention occurred most often in the preparation and implementation stage (2.04%), after the intervention, the rate decreased to 1.81%. The replication stage had a high rate of medication errors (2.04%), after the intervention it decreased to 1.81%. The most common medication errors before intervention were wrong doses and wrong drugs (1.58%), after intervention, wrong dose errors rate decreased to 1.36%, the rate of wrong drug errors rate decreased to 1.13%. The total number of diseases ≥2 was significantly related to the occurrence of medication errors (p<0.05). Conclusion: Medication errors could occur at different stages of medication use processes. Pharmacist interventions appear to decrease the incidence of medication errors

Technology is a crucial part of the tourism industry; supporting businesses with daily operations, while improving customer experiences. Virtual reality (VR) has garnered considerable attention as an emerging technology with the potential to bring about substantial transformations in the tourism industry. Consequently, the concept of virtual tourism has emerged as a contemporary and contentious issue, generating diverse discussions. In the context of tourism digitalization and the growing trend of virtual tourism, a review for exploring the state of the art of this new kind of tourism is essential. This study employed systematic and narrative reviews to explore research domains and highlight an up-to-date insight into the current state of research on virtual tourism. Based on performance mapping and intellectual mapping, keywords and clusters analysis were implemented to explore the current state of research on virtual tourism and suggest some implications for future research agenda. The five clusters included (1) Visualization with computer graphics in cultural heritages; (2) Tourism issues, COVID-19, and virtual reality technology; (3) Virtual reality technology and application in tourism; (4) Virtual tourism characteristics and user experience; and (5) Cyber tourism, internet and information technology. Some implications for future research agenda had been introduced through the analysis of bibliographic coupling depicted by visualization of similarities (VOS) viewer application.

Toan Nguyen Duy,1,2 Quyen Dao Bui Quy,3 Loc Nguyen Duc,4 Diem Ho Viet Le,5 Khoa Le Ha,6 Tuyen Do Gia,6 Kien Nguyen Trung,1,2 Tam Nguyen Van,1,2 Oanh Nguyen Oanh,1,2 Thang Le Viet1,2 1Military Hospital 103, Hanoi, Vietnam; 2Vietnam Military Medical University, Hanoi, Vietnam; 3Cho Ray Hospital, Ho Chi Minh, Vietnam; 4An Sinh Hospital, Ho Chi Minh, Vietnam; 5Link-02 Clinic, Pham Ngoc Thach, Da Lat, Lam Dong, Vietnam; 6Hanoi Medical University, Hanoi, VietnamCorrespondence: Thang Le Viet, Military Hospital 103, Vietnam Military Medical University, Department of Nephrology and Hemodialysis, 261 Phung Hung, Ha Dong, Hanoi, Vietnam, Tel +84982249968, Email lethangviet@yahoo.co.ukObjective: To evaluate the value of contrast volume/glomerular filtration ratio (Vc/eGFR ratio) and urine Neutrophil Gelatinase-Associated Lipocalin (uNGAL) in predicting the progression contract associated-acute kidney injury (CA-AKI) to chronic kidney disease (CKD) in planned percutaneous coronary intervention (PCI) patients.Patients and Methods: We examined 387 adult patients who had undergone planned percutaneous coronary intervention (PCI). We determined acute kidney injury (AKI) and chronic kidney disease (CKD) using the criteria set by the Kidney Disease: Improving Global Outcomes (KDIGO). We calculated the estimated glomerular filtration rate (eGFR) using the CKD-EPI formula based on serum creatinine levels. To determine the Vc/eGFR ratio, we considered the contrast medium volume and eGFR for each patient. Additionally, we measured urine NGAL levels using the ELISA method.Results: The percentage of CA-AKI patients who developed CKD after planned PCI was 36.36%. Within the CA-AKI to CKD group, the Vc/eGFR ratio was 2.82, and uNGAL levels were significantly higher at 72.74 ng/mL compared to 1.93 ng/mL for Vc/eGFR ratio and 46.57 ng/mL for uNGAL in the recovery CA-AKI group. This difference was statistically significant (p< 0.001). Diabetic mellitus, urine NGAL concentration, and Vc/eGFR r

Lanthanide-doped upconversion nanoparticles (UCNPs), as multifunctional light sources, are finding utility in diverse applications ranging from biotechnology to light harvesting. However, the main challenge in realizing their full potential lies in achieving bright and efficient photon upconversion (UC). In this study, we present a novel approach to fabricate an array of gold nanoantennas arranged in a hexagonal lattice using a simple and inexpensive colloidal lithography technique, and demonstrate a significant enhancement of UC photoluminescence (UCPL) by up to 35-fold through plasmon-enhanced photoexcitation and emission. To elucidate the underlying physical mechanisms responsible for the observed UCPL enhancement, we provide a comprehensive theoretical and experimental characterization, including a detailed photophysical description and numerical simulations of the spatial electric field distribution. Our results shed light on the fundamental principles governing the enhanced UCNPs and pave the way for their potential applications in photonic devices.

Lanthanide-doped upconversion nanoparticles (UCNPs), as multifunctional light sources, are finding utility in diverse applications ranging from biotechnology to light harvesting. However, the main challenge in realizing their full potential lies in achieving bright and efficient photon upconversion (UC). In this study, we present a novel approach to fabricate an array of gold nanoantennas arranged in a hexagonal lattice using a simple and inexpensive colloidal lithography technique, and demonstrate a significant enhancement of UC photoluminescence (UCPL) by up to 35-fold through plasmon-enhanced photoexcitation and emission. To elucidate the underlying physical mechanisms responsible for the observed UCPL enhancement, we provide a comprehensive theoretical and experimental characterization, including a detailed photophysical description and numerical simulations of the spatial electric field distribution. Our results shed light on the fundamental principles governing the enhanced UCNPs and pave the way for their potential applications in photonic devices.

Food choices that shape human diets and health are influenced by various socio-economic factors. Vietnam struggles to meet many nutrition targets where links between food choice and diet have not been widely explored. This study assesses the food choice motives, based on a 28-item food choice questionnaire (FCQ), and the diet quality of 603 adults in three sites (urban, peri-urban, and rural) in northern Vietnam. We assess diet quality using the Diet Quality Index–Vietnam (DQI-V) which consists of variety, adequacy, moderation, and balance components. Using factor analysis, we grouped FCQ items into five factors: health focus, sensory appeal, mood ethics, convenience, and familiarity. The structural equation modeling indicates that food choice motives significantly impact the DQI-V and its components but in different directions. The results show that sensory appeal has a positive association with the overall DQI-V score, while having a negative impact on the variety component. Findings present a potential trade-off issue for interventions and policies related to food products. Nutrition knowledge is positively associated with all elements of diet quality across all three study sites. Vietnamese agrobiodiversity could be better utilized to increase dietary diversity. Differentiated policies are necessary to address the poor dietary diversity and adequacy in northern Vietnam.

In this paper, we investigate the validity of the so-called cosmic no-hair conjecture in the framework of anisotropic inflation models of non-canonical scalar fields non-minimally coupled to a two-form field. In particular, we focus on two typical {\it k}-inflation and Dirac-Born-Infeld inflation models, in which we find a set of exact anisotropic power-law inflationary solutions. Interestingly, these solutions are shown to be stable and attractive during an inflationary phase using the dynamical system analysis. The obtained results indicate that the non-minimal coupling between the scalar and two-form fields acts as a non-trivial source of generating stable spatial anisotropies during the inflationary phase and therefore violates the prediction of the cosmic no-hair conjecture, even when the scalar field is of non-canonical forms. In connection with the Planck 2018 data, tensor-to-scalar ratios of these anisotropic solutions are investigated. As a result, it appears that the tensor-to-scalar ratio of the anisotropic power-law inflationary solution of {\it k}-inflation model turns out to be more highly consistent with the Planck 2018 data than that of Dirac-Born-Infeld model., Comment: 24 pages, 8 figures. Final version with investigations on the tensor-to-scalar ratio added. Some discussions and relevant references are also added. All calculations remain unchanged. Accepted for publication in EPJC. Comments are welcome

Rationale and objective: Improving diet quality while decreasing environmental impacts is an important challenge for a healthy and sustainable food system. Therefore, this study aims to analyze the most common dietary patterns per female household member and explore the diet quality and environmental impacts of these patterns. Methodology: The nationally representative General Nutrition Survey of 2009–2010 (n = 8,225 households) was used to derive dietary patterns using principal component analysis (PCA) based on 18 food groups as input variables. Quintiles of the highest adherence (Q5) and lowest adherence (Q1) were generated based on the factor score of each dietary pattern. Nutrient adequacy and dietary diversity scores (DDS) were calculated to measure diet quality, and greenhouse gas emission (GHGE) and blue water use (BWU) were selected as environmental impact indicators. Results: Using PCA, three distinct dietary patterns were identified: an Omnivorous, Traditional, and Pescatarian pattern. Compared to the Traditional pattern, the Omnivorous and Pescatarian patterns (Q5s) were associated with a higher nutrient adequacy, with mean probability of adequacy of 0.51 in both patterns, compared to 0.45 in the Traditional pattern. However, environmental impacts in terms of GHGE and BWU per 2,000 kcal were considerably higher in the Omnivorous pattern (6.14 kg CO2-eq. and 0.15 m3/kg) compared to all other pattern’s Q5s. The GHGE was lowest in the Traditional pattern (4.18 kg CO2-eq.) and the Pescatarian pattern has the lowest BWU (0.12 m3/kg). Conclusion: Despite that diet quality was slightly better in all three patterns compared to the average diet of the total population, environmental impact was also higher. Therefore, future research is needed to develop a more optimal diet that considers both diet quality and environmental impact to explore the trade-offs between diet quality and environmental impact.

PR, IFPRI3; ISI; CRP4; 2 Promoting Healthy Diets and Nutrition for all, Nutrition, Diets, and Health (NDH); Food and Nutrition Policy; A4NH, CGIAR Research Program on Agriculture for Nutrition and Health (A4NH), Rationale and objective: Improving diet quality while decreasing environmental impacts is an important challenge for a healthy and sustainable food system. Therefore, this study aims to analyze the most common dietary patterns per female household member and explore the diet quality and environmental impacts of these patterns. Methodology: The nationally representative General Nutrition Survey of 2009–2010 (n = 8,225 households) was used to derive dietary patterns using principal component analysis (PCA) based on 18 food groups as input variables. Quintiles of the highest adherence (Q5) and lowest adherence (Q1) were generated based on the factor score of each dietary pattern. Nutrient adequacy and dietary diversity scores (DDS) were calculated to measure diet quality, and greenhouse gas emission (GHGE) and blue water use (BWU) were selected as environmental impact indicators. Results: Using PCA, three distinct dietary patterns were identified: an Omnivorous, Traditional, and Pescatarian pattern. Compared to the Traditional pattern, the Omnivorous and Pescatarian patterns (Q5s) were associated with a higher nutrient adequacy, with mean probability of adequacy of 0.51 in both patterns, compared to 0.45 in the Traditional pattern. However, environmental impacts in terms of GHGE and BWU per 2,000 kcal were considerably higher in the Omnivorous pattern (6.14 kg CO2-eq. and 0.15 m3/kg) compared to all other pattern’s Q5s. The GHGE was lowest in the Traditional pattern (4.18 kg CO2-eq.) and the Pescatarian pattern has the lowest BWU (0.12 m3/kg). Conclusion: Despite that diet quality was slightly better in all three patterns compared to the average diet of the total population, environmental impact was also higher. Therefore, future research is needed to develop a more optimal diet that considers both diet quality and environmental impact to explore the trade-offs between diet quality and environmental impact.

In this paper, we would like to examine whether a novel Starobinsky-Bel-Robinson gravity model admits stable exponential inflationary solutions with or without spatial anisotropies. As a result, we are able to derive an exact de Sitter inflationary to this Starobinsky-Bel-Robinson model. Furthermore, we observe that an exact Bianchi type I inflationary solution does not exist in the Starobinsky-Bel-Robinson model. However, we find that a modified Starobinsky-Bel-Robinson model, in which the sign of coefficient of $R^2$ term is flipped from positive to negative, can admit the corresponding Bianchi type I inflationary solution. Unfortunately, stability analysis using the dynamical system approach indicates that both of these inflationary solutions turn out to be unstable. Interestingly, we show that a stable de Sitter inflationary solution can be obtained in the modified Starobinsky-Bel-Robinson gravity., Comment: 32 pages, 3 figures. V3 with many discussions, calculations, and relevant references added. Accepted for publication by International Journal of Modern Physics D. Comments are welcome

The analysis of microcirculation images has the potential to reveal early signs of life-threatening diseases such as sepsis. Quantifying the capillary density and the capillary distribution in microcirculation images can be used as a biological marker to assist critically ill patients. The quantification of these biological markers is labor intensive, time consuming, and subject to interobserver variability. Several computer vision techniques with varying performance can be used to automate the analysis of these microcirculation images in light of the stated challenges. In this paper, we present a survey of over 50 research papers and present the most relevant and promising computer vision algorithms to automate the analysis of microcirculation images. Furthermore, we present a survey of the methods currently used by other researchers to automate the analysis of microcirculation images. This survey is of high clinical relevance because it acts as a guidebook of techniques for other researchers to develop their microcirculation analysis systems and algorithms.

The virtualization and softwarization of 5G and NextG are critical enablers of the shift to flexibility, but they also present a potential attack surface for threats. However, current security research in communication systems focuses on specific aspects of security challenges and lacks a holistic perspective. To address this challenge, a novel systematic fuzzing approach is proposed to reveal, detect, and predict vulnerabilities with and without prior knowledge assumptions from attackers. It also serves as a digital twin platform for system testing and defense simulation pipeline. Three fuzzing strategies are proposed: Listen-and-Learn (LAL), Synchronize-and-Learn (SyAL), and Source-and-Learn (SoAL). The LAL strategy is a black-box fuzzing strategy used to discover vulnerabilities without prior protocol knowledge, while the SyAL strategy, also a black-box fuzzing method, targets vulnerabilities more accurately with attacker-accessible user information and a novel probability-based fuzzing approach. The white-box fuzzing strategy, SoAL, is then employed to identify and explain vulnerabilities through fuzzing of significant bits. Using the srsRAN 5G platform, the LAL strategy identifies 129 RRC connection vulnerabilities with an average detection duration of 0.072s. Leveraging the probability-based fuzzing algorithm, the SyAL strategy outperforms existing models in precision and recall, using significantly fewer fuzzing cases. SoAL detects three man-in-the-middle vulnerabilities stemming from 5G protocol vulnerabilities. The proposed solution is scalable to other open-source and commercial 5G platforms and protocols beyond RRC. Extensive experimental results demonstrate that the proposed solution is an effective and efficient approach to validate 5G security; meanwhile, it serves as real-time vulnerability detection and proactive defense.

Compounds containing benzimidazole moiety occupy privileged chemical space for discovering new bioactive substances. In continuation of our recent work, 69 benzimidazole derivatives were designed and synthesized with good to excellent yields of 46-99% using efficient synthesis protocol i.e. sodium metabisulfite catalyzed condensation of aromatic aldehydes with o-phenylenediamines to form 2-arylbenzimidazole derivatives followed by N-alkylation by conventional heating or microwave irradiation for diversification. Potent antibacterial compounds against MSSA and MRSA were discovered such as benzimidazole compounds 3k (2-(4-nitrophenyl), N-benzyl), 3l (2-(4-chlorophenyl), N-(4-chlorobenzyl)), 4c (2-(4-chlorophenyl), 6-methyl, N-benzyl), 4g (2-(4-nitrophenyl), 6-methyl, N-benzyl), and 4j (2-(4-nitrophenyl), 6-methyl, N-(4-chlorobenzyl)) with MIC of 4-16 mu g mL(-1). In addition, compound 4c showed good antimicrobial activities (MIC = 16 mu g mL(-1)) against the bacteria strains Escherichia coli and Streptococcus faecalis. Moreover, compounds 3k, 3l, 4c, 4g, and 4j have been found to kill HepG2, MDA-MB-231, MCF7, RMS, and C26 cancer cells with low mu M IC50 (2.39-10.95). These compounds showed comparable drug-like properties as ciprofloxacin, fluconazole, and paclitaxel in computational ADMET profiling. Finally, docking studies were used to assess potential protein targets responsible for their biological activities. Especially, we found that DHFR is a promising target both in silico and in vitro with compound 4c having IC50 of 2.35 mu M.

Van Bang Nguyen,1,&ast; Xuan Nguyen Thi,1 Hau Nguyen Van Vy,1 Tuyen Linh Pham Nguyen,1 Thuy Nguyen Hai,2 Nguyen Dinh Toan,2 Chi Van Le2,&ast; 1Center of Endocrinology and Diabetes, Family Hospital, Da Nang, Vietnam; 2Department of Internal Medicine, Hue University of Medicine and Pharmacy, Hue University, Hue City, Vietnam&ast;These authors contributed equally to this workCorrespondence: Van Bang Nguyen, Center of Endocrinology and Diabetes, Family Hospital, No. 73 Nguyen Huu Tho Str, Da Nang, Vietnam, Tel +84 973445417, Email vanbangvnn@gmail.com Chi Van Le, Department of Internal Medicine, Hue University of Medicine and Pharmacy, Hue University, Hue City, Vietnam, Tel +84913426457, Email lvanchi@hueuni.edu.vnObjective: This study aims to compare 1-month’s efficacy and safety of single-session ethanol ablation and radiofrequency ablation for treating both purely cystic nodules and predominantly cystic thyroid nodules.Materials and methods: This short-term retrospective study was approved by the Ethics Committee of the Institutional Review Board of Danang Family hospital, and written informed consent for procedures was obtained for all patients. Thirty-nine patients who presented with cystic thyroid nodules and met inclusion criteria were extracted from the computerized medical records. The internal fluid of cystic thyroid nodules was aspirated as much as possible. Ethanol ablation was performed using 18-gauge needles with 99.5% ethanol, and RFA used a cooled-electrode RFA system and 18-gauge internally cooled electrodes via the trans-isthmic approach, moving-shot technique. Nodule volume, therapeutic success rate, the largest diameter, thyroid function tests, and complications were evaluated and compared before and after treatment in each group.Results: Among 39 patients, 17 patients were undergone EA (mean age of 47.35 years; the proportion of female of 76.5%; purely thyroid cyst percentage of 41.4%) and 22 patients were undergone RFA (mean age of 46

The analysis of microcirculation images has the potential to reveal early signs of life-threatening diseases such as sepsis. Quantifying the capillary density and the capillary distribution in microcirculation images can be used as a biological marker to assist critically ill patients. The quantification of these biological markers is labor intensive, time consuming, and subject to interobserver variability. Several computer vision techniques with varying performance can be used to automate the analysis of these microcirculation images in light of the stated challenges. In this paper, we present a survey of over 50 research papers and present the most relevant and promising computer vision algorithms to automate the analysis of microcirculation images. Furthermore, we present a survey of the methods currently used by other researchers to automate the analysis of microcirculation images. This survey is of high clinical relevance because it acts as a guidebook of techniques for other researchers to develop their microcirculation analysis systems and algorithms.

State estimation is highly critical for accurately observing the dynamic behavior of the power grids and minimizing risks from cyber threats. However, existing state estimation methods encounter challenges in accurately capturing power system dynamics, primarily because of limitations in encoding the grid topology and sparse measurements. This paper proposes a physics-informed graphical learning state estimation method to address these limitations by leveraging both domain physical knowledge and a graph neural network (GNN). We employ a GNN architecture that can handle the graph-structured data of power systems more effectively than traditional data-driven methods. The physics-based knowledge is constructed from the branch current formulation, making the approach adaptable to both transmission and distribution systems. The validation results of three IEEE test systems show that the proposed method can achieve lower mean square error more than 20% than the conventional methods., Comment: 11 pages, 17 figures, journal accepted

A new variant of Newton's method - named Backtracking New Q-Newton's method (BNQN) - which has strong theoretical guarantee, is easy to implement, and has good experimental performance, was recently introduced by the third author. Experiments performed previously showed some remarkable properties of the basins of attractions for finding roots of polynomials and meromorphic functions using BNQN. In particular, it seems that for finding roots of polynomials of degree 2, the basins of attraction of the dynamics for BNQN are the same as that for Newton's method (the latter is the classical Schr\"oder's result in Complex Dynamics). In this paper, we show that indeed the picture we obtain when finding roots of polynomials of degree 2 is the same as that in Sch\"oder's result, with a remarkable difference: on the boundary line of the basins, the dynamics of Newton's method is chaotic, while the dynamics of BNQN is more smooth. On the way to proving the result, we show that BNQN (in any dimension) is invariant under conjugation by linear operators of the form $A=cR$, where $R$ is unitary and $c>0$ a constant. This again illustrates the similarity-difference relation between BNQN and Newton's method., Comment: 29 pages. Comments are welcome!

The Open Radio Access Network (O-RAN) architecture empowers intelligent and automated optimization of the RAN through applications deployed on the RAN Intelligent Controller (RIC) platform, enabling capabilities beyond what is achievable with traditional RAN solutions. Within this paradigm, Traffic Steering (TS) emerges as a pivotal RIC application that focuses on optimizing cell-level mobility settings in near-real-time, aiming to significantly improve network spectral efficiency. In this paper, we design a novel TS algorithm based on a Cascade Reinforcement Learning (CaRL) framework. We propose state space factorization and policy decomposition to reduce the need for large models and well-labeled datasets. For each sub-state space, an RL sub-policy will be trained to learn an optimized mapping onto the action space. To apply CaRL on new network regions, we propose a knowledge transfer approach to initialize a new sub-policy based on knowledge learned by the trained policies. To evaluate CaRL, we build a data-driven and scalable RIC digital twin (DT) that is modeled using important real-world data, including network configuration, user geo-distribution, and traffic demand, among others, from a tier-1 mobile operator in the US. We evaluate CaRL on two DT scenarios representing two network clusters in two different cities and compare its performance with the business-as-usual (BAU) policy and other competing optimization approaches using heuristic and Q-table algorithms. Benchmarking results show that CaRL performs the best and improves the average cluster-aggregated downlink throughput over the BAU policy by 24% and 18% in these two scenarios, respectively., Comment: 14 pages, 10 figures

Macro placement is a critical phase in chip design, which becomes more intricate when involving general rectilinear macros and layout areas. Furthermore, macro placement that incorporates human-like constraints, such as design hierarchy and peripheral bias, has the potential to significantly reduce the amount of additional manual labor required from designers. This study proposes a methodology that leverages an approach suggested by Google's Circuit Training (G-CT) to provide a learning-based macro placer that not only supports placing rectilinear cases, but also adheres to crucial human-like design principles. Our experimental results demonstrate the effectiveness of our framework in achieving power-performance-area (PPA) metrics and in obtaining placements of high quality, comparable to those produced with human intervention. Additionally, our methodology shows potential as a generalized model to address diverse macro shapes and layout areas., Comment: Fast ML for Science @ ICCAD 2023

In root finding and optimization, there are many cases where there is a closed set $A$ one likes that the sequence constructed by one's favourite method will not converge to A (here, we do not assume extra properties on $A$ such as being convex or connected). For example, if one wants to find roots, and one chooses initial points in the basin of attraction for 1 root $z^*$ (a fact which one may not know before hand), then one will always end up in that root. In this case, one would like to have a mechanism to avoid this point $z^*$ in the next runs of one's algorithm. Assume that one already has a method IM for optimization (and root finding) for non-constrained optimization. We provide a simple modification IM1 of the method to treat the situation discussed in the previous paragraph. If the method IM has strong theoretical guarantees, then so is IM1. As applications, we prove two theoretical applications: one concerns finding roots of a meromorphic function in an open subset of a Riemann surface, and the other concerns finding local minima of a function in an open subset of a Euclidean space inside it the function has at most countably many critical points. Along the way, we compare with main existing relevant methods in the current literature. We provide several examples in various different settings to illustrate the usefulness of the new approach., Comment: 24 pages.Improved abstract and exposition. Comments are welcome!

This paper addresses the load restoration problem after power outage events. Our primary proposed methodology is using multi-agent deep reinforcement learning to optimize the load restoration process in distribution systems, modeled as networked microgrids, via determining the optimal operational sequence of circuit breakers (switches). An innovative invalid action masking technique is incorporated into the multi-agent method to handle both the physical constraints in the restoration process and the curse of dimensionality as the action space of operational decisions grows exponentially with the number of circuit breakers. The features of our proposed method include centralized training for multi-agents to overcome non-stationary environment problems, decentralized execution to ease the deployment, and zero constraint violations to prevent harmful actions. Our simulations are performed in OpenDSS and Python environments to demonstrate the effectiveness of the proposed approach using the IEEE 13, 123, and 8500-node distribution test feeders. The results show that the proposed algorithm can achieve a significantly better learning curve and stability than the conventional methods., Comment: 12 pages, 19 figures, journal under review

This paper presents a novel Cyber-Hardware-in-the-Loop (Cyber-HIL) platform for assessing control operation in ship cyber-physical systems. The proposed platform employs cutting-edge technologies, including Docker containers, real-time simulator $OPAL-RT$, and network emulator $ns3$, to create a secure and controlled testing and deployment environment for investigating the potential impact of cyber attack threats on ship control systems. Real-time experiments were conducted using an advanced load-shedding controller as a control object in both synchronous and asynchronous manners, showcasing the platform's versatility and effectiveness in identifying vulnerabilities and improving overall Ship Cyber Physical System (SCPS) security. Furthermore, the performance of the load-shedding controller under cyber attacks was evaluated by conducting tests with man-in-the-middle (MITM) and denial-of-service (DoS) attacks. These attacks were implemented on the communication channels between the controller and the simulated ship system, emulating real-world scenarios. The proposed Cyber-HIL platform provides a comprehensive and effective approach to test and validate the security of ship control systems in the face of cyber threats., Comment: 10 pages, 16 figures, journal under review

Basal cell carcinoma (BCC) is driven by overactivation of the Hedgehog signaling pathway whose current FDA-approved chemical inhibitors can efficiently suppress protein receptor Smoothened (SMOi) but have presented serious resistance problems in clinical settings. In SMOi-resistant BCCs, we documented an important correlation between the increase in mRNA level of the constitutively active isoform of atypical protein kinase C (aPKC-K) and the surge in mutational load of ciliary proteins. We demonstrated, for the first time, that aPKC protein expression was upregulated upon cilia loss and that chemical inhibition of aPKC increased both percent ciliation and cilia shaft length, both in cells and BCC mice. We also found that the Hedgehog pathway activity (a) was significantly diminished when ciliogenesis was disrupted by transient knockdown of important ciliary genes and (b) was adequately rescued when aPKC-K was simultaneously overexpressed. At the same time, cells that overexpressed aPKC-K displayed resistance to Vismodegib, a widely prescribed SMOi. We also recorded higher aPKC protein expression and lower ciliogenesis in human superficial BCC subtype in comparison to nodular BCC subtype, suggesting a connection between the interfollicular epidermal origin of superficial BCCs and the SMOi-resistance tendency, presumably via aPKC overactivation coupled with cilia loss. In a second project, we detailed the functional effects of phosphorylation by protein kinase A (PKA) at individual and combinatorial PKA-specific phosphosites on GLI1. Artificial loss of PKA-phosphorylation promoted growth in murine BCC cells and allografts; whereas overexpression of clinical mutations found in resistant BCCs near the PKA-sites on GLI1 altered GLI1 activity, localization, and stability. We also described, for the first time, a truncated protein product of GLI1 when PKA-phosphorylation was mimicked. These findings challenged the currently accepted notion that GLI1 could only be completel

The Caritas Psychiatric Center (PC Caritas) in Melle, Belgium, is a clinic campus consisting of about a dozen buildings embedded into a park. Sint Jozef, named after the priest Petrus Jozef Triest from Gent, Belgium, is one of the remaining villas built in 1908 and centrally located on the campus. It was initially used as a treatment center for so-called “hysterical women” and has experienced multiple changes of functions over time. Like other buildings on the campus, it was considered not to meet today’s requirements anymore and therefore decided to be demolished. Thanks to the research collective BAVO, the demolition was halted midway, and competition to start a second life of the ruin was initiated. The experimental concept presented by architecten de vylder vinck taillieu was chosen and developed further together with multiple participants, including doctors and clinic staff, patients and visitors, and others. The project was then renamed Kanunnik Petrus Jozef Triest Plein, in short Triest Plein (Triest Square)., AR1A061, Architecture, Urbanism and Building Sciences

Basal cell carcinoma (BCC) is driven by overactivation of the Hedgehog signaling pathway whose current FDA-approved chemical inhibitors can efficiently suppress protein receptor Smoothened (SMOi) but have presented serious resistance problems in clinical settings. In SMOi-resistant BCCs, we documented an important correlation between the increase in mRNA level of the constitutively active isoform of atypical protein kinase C (aPKC-K) and the surge in mutational load of ciliary proteins. We demonstrated, for the first time, that aPKC protein expression was upregulated upon cilia loss and that chemical inhibition of aPKC increased both percent ciliation and cilia shaft length, both in cells and BCC mice. We also found that the Hedgehog pathway activity (a) was significantly diminished when ciliogenesis was disrupted by transient knockdown of important ciliary genes and (b) was adequately rescued when aPKC-K was simultaneously overexpressed. At the same time, cells that overexpressed aPKC-K displayed resistance to Vismodegib, a widely prescribed SMOi. We also recorded higher aPKC protein expression and lower ciliogenesis in human superficial BCC subtype in comparison to nodular BCC subtype, suggesting a connection between the interfollicular epidermal origin of superficial BCCs and the SMOi-resistance tendency, presumably via aPKC overactivation coupled with cilia loss. In a second project, we detailed the functional effects of phosphorylation by protein kinase A (PKA) at individual and combinatorial PKA-specific phosphosites on GLI1. Artificial loss of PKA-phosphorylation promoted growth in murine BCC cells and allografts; whereas overexpression of clinical mutations found in resistant BCCs near the PKA-sites on GLI1 altered GLI1 activity, localization, and stability. We also described, for the first time, a truncated protein product of GLI1 when PKA-phosphorylation was mimicked. These findings challenged the currently accepted notion that GLI1 could only be completel

The Caritas Psychiatric Center (PC Caritas) in Melle, Belgium, is a clinic campus consisting of about a dozen buildings embedded into a park. Sint Jozef, named after the priest Petrus Jozef Triest from Gent, Belgium, is one of the remaining villas built in 1908 and centrally located on the campus. It was initially used as a treatment center for so-called “hysterical women” and has experienced multiple changes of functions over time. Like other buildings on the campus, it was considered not to meet today’s requirements anymore and therefore decided to be demolished. Thanks to the research collective BAVO, the demolition was halted midway, and competition to start a second life of the ruin was initiated. The experimental concept presented by architecten de vylder vinck taillieu was chosen and developed further together with multiple participants, including doctors and clinic staff, patients and visitors, and others. The project was then renamed Kanunnik Petrus Jozef Triest Plein, in short Triest Plein (Triest Square)., AR1A061, Architecture, Urbanism and Building Sciences