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arXiv 2025年

作者： Tami, Mohammad Abu Elhenawy, Mohammed Ashqar, Huthaifa I. Department of Natural Engineering and Technology Sciences Arab American University P.O. Box 240 Jenin Palestine CARRS-Q Queensland University of Technology Brisbane Australia Department of AI and Data Science Arab American University P.O. Box 240 Jenin Palestine AI Program Columbia University New YorkNY10027 United States

Traffic safety remains a critical global challenge, with traditional Advanced Driver-Assistance Systems (ADAS) often struggling in dynamic real-world scenarios due to fragmented sensor processing and susceptibility to adversarial conditions. This paper reviews the transformative potential of Multimodal Large Language Models (MLLMs) in addressing these limitations by integrating cross-modal data—such as visual, spatial, and environmental inputs—to enable holistic scene understanding. Through a comprehensive analysis of MLLM-based approaches, we highlight their capabilities in enhancing perception, decision-making, and adversarial robustness, while also examining the role of key datasets (e.g., KITTI, DRAMA, ML4RoadSafety) in advancing research. Furthermore, we outline future directions, including real-time edge deployment, causality-driven reasoning, and human-AI collaboration. By positioning MLLMs as a cornerstone for next-generation traffic safety systems, this review underscores their potential to revolutionize the field, offering scalable, context-aware solutions that proactively mitigate risks and improve overall road safety. © 2025, CC BY.

关键词： Advanced driver assistance systems

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Evaluation of lahar flood hazard management policy using participatory planning in Putih watershed, Magelang regency, central java, Indonesia

引用

IOP Conference Series: Earth and Environmental science 2021年第1期683卷

作者： A L Permatasari I A Suherningtyas P P K Wiguna Geography Study Program Faculty of Science and Technology Universitas Amikom Yogyakarta Center for Spatial Data Infrastructure Development Universitas Udayana Bali

The occurrence of Lahar floods after Merapi Volcano eruption in 2010 still continue to be a concern for the government and the community. Kali Putih is one of the rivers that originates in Merapi Volcano and is one of the Lahar flood hazard area. Non-structural mitigation efforts have been carried out by the government. Until now, there are still community activities in the forbidden hazard area. Regulations regarding to spatial planning regulation in the hazard area need to be evaluated. The aim of the study is to evaluate the Lahar flood hazard mitigation based on participatory mapping and Focus Group Discussions (FGD). The research area is Jumoyo Village, which is the highest Lahar hazard flood area. The results indicate that there are still residential areas and community activities in the Lahar flood hazard area. The community does not want to lose their livelihood or relocation of their homes. The community expect that the government is able to revive the economic activities of the community through the establishment of new economic centres without relocation of their homes. The community also expect that the government able to creating new employment.

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基于大语言模型的糖尿病管理:潜力与展望

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science Bulletin 2024年第5期69卷 583-588页

作者：盛斌管洲榆 Lee-Ling Lim 江泽铧 Nestoras Mathioudakis 李佳佳刘茹涵包玉倩 Yong Mong Bee 王亚星郑颖丰 Gavin Siew Wei Tan 纪宏伟 Josip Car 王海波 David C.Klonoff 李华婷覃宇宗黄天荫贾伟平 Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and EngineeringSchool of ElectronicInformationand Electrical EngineeringShanghai Jiao Tong UniversityDepartment of Endocrinology and MetabolismShanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for DiabetesShanghai 200240China MOE Key Laboratory of Artificial Intelligence Artificial Intelligence InstituteShanghai Jiao Tong UniversityShanghai 200240China Department of Medicine Faculty of MedicineUniversity of MalayaKuala Lumpur 50603Malaysia Department of Medicine and Therapeutics The Chinese University of Hong KongHong KongChina Asia Diabetes Foundation Hong KongChina Tsinghua Medicine of Tsinghua University Beijing 100084China School of Clinical Medicine Beijing Tsinghua Changgung HospitalBeijing 102218China Division of Endocrinology Diabetes&MetabolismJohns Hopkins University School of MedicineBaltimoreMD 21211USA Department of Endocrinology Singapore General HospitalSingapore 169608Singapore Beijing Institute of Ophthalmology Beijing Tongren HospitalCapital University of Medical ScienceBeijing Ophthalmology and Visual Sciences Key LaboratoryBeijing 100730China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangzhou 510060China Singapore Eye Research Institute Singapore National Eye CentreSingapore 168751Singapore Department of Cardiology the Affiliated Hospital of Qingdao UniversityQingdao 266011China Centre for Population Health Sciences Lee Kong Chian School of MedicineNanyang Technological University SingaporeSingapore 639798Singapore Department of Primary Care and Public Health School of Public HealthImperial College LondonLondon SW72BUUK Clinical Trial Unit Research Centre of Big Data and Artificial Intelligence in Medicine

The increasing prevalence of diabetes has become a global public health concern in the 21st *** 2021,it was estimated that 537 million people had diabetes,and this number is projected to reach 643 million by 2030,and 783 million by 2045[1].Such a huge burden of diabetes brings great challenges in its prevention and management,including early diagnosis,timely interventions,and regular monitoring of risk factor control and complications *** self-care support and patient empowerment can enhance clinical and psychobehavioural outcomes[2],although these require additional resources including manpower,infrastructure(hard and technology),and *** emergence of digital health technologies(DHTs),especially artificial intelligence(AI),may help address these obstacles and alleviate the burden of diabetes[3].Large language models(LLMs),a generative AI that can accept image and text inputs and produce text outputs,have shown promise in various aspects of medical care.

关键词： prevention diagnosis finance

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Environmental Factors Affecting Changes in Zooplankton Community Characteristics: The Southwestern Coastal Ecosystem Survey data (2015–2019) for the Korean Peninsula

SSRN

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SSRN 2022年

作者： Kwak, Myeong-Taek Kim, Hae-Ran Seo, Min-Ho Choo, Seohwi Ceong, Hyi-Thaek Soh, Ho Young Fishery Resource Management Research Institute based on ICT Chonnam National University Yeosu59626 Korea Republic of Department of Digital Convergence Chonnam National University Yeosu59626 Korea Republic of Marine Ecology Research Center Yeosu59697 Korea Republic of Big data Fishery Resources Management Interdisciplinary Program Chonnam National University Yeosu59626 Korea Republic of Department of Multimedia Chonnam National University Yeosu59626 Korea Republic of Department of Ocean Integrated Science Chonnam National University Yeosu59626 Korea Republic of

To understand the environmental factors affecting the community characteristics of zooplankton in the southwest coastal zone of Korea, a marine ecosystem survey was conducted during spring and summer of 2015, 2017, and 2019. Water temperature, salinity, dissolved oxygen, suspended particle matter, chlorophyll- a concentration, diatoms, and dinoflagellates were considered as the environmental factors affecting zooplankton. Zooplanktons were more abundant in spring than in summer. Copepods were the dominant taxon at all stations throughout the year. Cnidarians appeared in summer of 2017, and Noctiluca scintillans did not appear in summer of 2019. Furthermore, copepod abundance decreased when N. scintillans abundance was high. The predominant copepods were Acartia hongi , A. omorii , Calanus sinicus , Ditrichocorycaeus affinis , and Paracalanus parvus s. l. in spring and A. hongi , A. ohtsukai , and P. parvus s. l. in summer. Using cluster analysis, the study area was divided into two or three regions, the bay, the western part of the coastal zone, and the eastern part of coastal zone, reflecting the characteristics of the seasonal water mass. Every spring, N. scintillans appeared as an indicator species. Among copepods, D. affinis , Labidocera rotunda , and P. parvus s. l. appeared as indicator species every spring. In summer, A. ohtsukai , A. hongi , C. sinicus , and D. affinis were selected as indicator species. In particular, in summer the occurrence of C. sinicus suggests that the Yellow Sea Bottom Cold Water affects the western offshore of the study area. © 2022, The Authors. All rights reserved.

关键词： Dissolved oxygen

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A Design of Deep Learning Experimentation for Fruit Freshness Detection

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IOP Conference Series: Earth and Environmental science 2021年第1期794卷

作者： Febrian Valentino Tjeng Wawan Cenggoro Bens Pardamean Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480

Indonesia is a country with a tropical climate so that fruit and vegetable plants can grow easily in *** have many good nutrients such as vitamins, proteins and others. But the fruit also has a period where the fruit is said to be fresh *** this time there are still many fruit supplier companies that send fruit unfit for consumption due to lack of accuracy in the process of sorting the fruit when the fruit is taken from the plantation and the entry of other fruit into an improper packaging. Thus, it makes detecting food spoilage from the production stage to consumption is very important. We propose a design of computer vision-based technique usingdeep learning with the Convolutional Neural Network (CNN) model to detect fruit freshness. The specially designed CNN model is then evaluated with public datasets of fruits fresh and rotten for classification derived from Kaggle.

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PapSmear Image Recording System for Artificial Intelligence data Collection

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IOP Conference Series: Earth and Environmental science 2021年第1期794卷

作者： Tjeng Wawan Cenggoro Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Cervical cancer is one of the most leading causes of death for women worldwide. To reduce the mortality caused by cervical cancer, early screening techniques such as pap smear need to be carried out more extensively. Forthat, the availability of an automatic screening system is essential. In this paper, we proposed a system that can collect the dataset needed to train an Artificial Intelligence (AI) system for the automatic pap smear screening system. The proposed system can be integrated seamlessly to the current pap smear result recording procedure; hence avoiding any possible complication.

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Sustainability of the Water Footprint of Various Soil Types on Oil Palm Plantations

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IOP Conference Series: Earth and Environmental science 2022年第1期998卷

作者： Lisma Safitri Hermantoro Valensi Kautsar Teddy Suparyanto Alam Ahmad Hidayat Bens Pardamean Agricultural Engineering Department Institute of Agricultural STIPER Yogyakarta 55282 Indonesia School of Earth and Environment University of Leeds Woodhouse Leeds LS2 9J Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

There are significant studies that have quantified oil palm water footprint as an indicator of environmental sustainability but an estimation of water footprint under varying soil types furthermore is still limited. The objectives of the study were to estimate whether annual variations of soil type and yields significantly effects for the oil palm water footprint. The data from three types of soil (spodosol, inceptisol, ultisol) were collected from an oil palm plantation in Pundu village, Central Kalimantan, Indonesia. To perform the water footprint analysis we utilize water balance accounting equations via application Cropwat 8.0. From that, we determine the crop irrigation scheduling to compute the blue, green, and grey water footprint of oil palm fresh fruit bunch in the area. Our analysis found that the actual evapotranspiration of spodosol and inceptisol have the same value: 1242 mm/year whilst ultisol is 1239 mm/year. The total water footprint of oil palm varied considerably with the largest value being 1310.04 m3/ton for ultisol. The actual evapotranspiration of spodosol and inceptisol have the same value of 1242 mm/year whilst ultisol is 1239 mm/year. The higher production resulted in a lower water footprint and vice versa. Moreover, the total water footprint from ultisol soil type has the highest value due to the lowest yields. The difference in evapotranspiration value resulted in the insignificant value of total water footprint. The lower water availability, the lower water use, and the higher actual irrigation requirement in oil palm plantation yet showed the unnotable impact on water footprint in different soil types for the oil palm plantation.

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Incorporating Participants’ Welfare into Sequential Multiple Assignment Randomized Trials

arXiv

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arXiv 2022年

作者： Wang, Xinru Deliu, Nina Narita, Yusuke Chakraborty, Bibhas Centre for Quantitative Medicine Duke-NUS Medical School Singapore MRC - Biostatistics Unit University of Cambridge Cambridge United Kingdom Department of Methods and Models for Economics Territory and Finance Sapienza University of Rome Rome Italy Department of Economics and Cowles Foundation Yale University New HavenCT United States Program in Health Services and Systems Research Duke-NUS Medical School Singapore Department of Statistics and Data Science National University of Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States

Dynamic treatment regimes (DTRs) are sequences of decision rules that recommend treatments based on patients’ time-varying clinical conditions. The sequential multiple assignment randomized trial (SMART) is an experimental design that can provide high-quality evidence for constructing optimal DTRs. In a conventional SMART, participants are randomized to available treatments at multiple stages with balanced randomization probabilities. Despite its relative simplicity of implementation and desirable performance in comparing embedded DTRs, the conventional SMART faces inevitable ethical issues including assigning many participants to the empirically inferior treatment or the treatment they dislike, which might slow down the recruitment procedure and lead to higher attrition rates, ultimately leading to poor internal and external validities of the trial results. In this context, we propose a SMART under the Experiment-as-Market framework (SMART-EXAM), a novel SMART design that holds the potential to improve participants’ welfare by incorporating their preferences and predicted treatment effects into the randomization procedure. We describe the steps of conducting a SMART-EXAM and evaluate its performance compared to the conventional SMART. The results indicate that the SMART-EXAM can improve the welfare of the participants enrolled in the trial, while also achieving a desirable ability to construct an optimal DTR when the experimental parameters are suitably specified. We finally illustrate the practical potential of the SMART-EXAM design using data from a SMART for children with attention-deficit/hyperactivity disorder (ADHD). © 2022, CC BY.

关键词： Design of experiments

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Anomaly Detection in Climate data Using Stacked and Densely Connected Long Short-Term Memory Model

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Journal of Computers (Taiwan) 2020年第4期31卷 42-53页

作者： Saleh Abbas, Bahtiar Wahyono, Teguh Heryadi, Yaya Soeparno, Haryono Doctor of Computer Science Binus Graduate Program Bina Nusantara University Jakarta Indonesia Faculty of Information Technology Satya Wacana Christian University Salatiga Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Industrial Engineering Bina Nusantara University Jakarta Indonesia

Climate anomalies are considered as an important factor closely related to many disasters causing many human losses, such as airline crash, wildfires, drought and flooding in many areas. Many researchers have projected that the rising global temperature will increase the draught, especially in the mid-latitude areas. Taking those problems into account, studies on anomaly detection in climate are crucial. While climate prediction aims to analyze and model regular pattern of climate, climate anomaly studies aim to model climate deviation from its previous general patterns. Long Short Term Memory (LSTM) is a method employed in this research because it has been proven to work effectively in several anomaly detection studies, especially for data with similar characteristics. This paper presents empirical results of using Basic LSTM, Densely Connected (DC) LSTM, and Stacked-DC LSTM models to detect temperature anomaly as a manifestation of climate change in Semarang City. The results show that Stacked DC LSTM produced higher accuracy in detecting anomaly than the other two methods. © 2020 Computer Society of the Republic of China. All rights reserved.

关键词： Long short-term memory

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PAN-CANCER INTEGRATIVE HISTOLOGY-GENOMIC ANALYSIS VIA INTERPRETABLE MULTIMODAL DEEP LEARNING

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Journal of Pathology Informatics 2022年 13卷 100057-100057页

作者： Chen, Richard J. Lu, Ming Y. Williamson, Drew F.K. Chen, Tiffany Y. Lipkova, Jana Shaban, Muhammad Shady, Maha Williams, Mane Joo, Bumjin Noor, Zahra Mahmood, Faisal Department of Pathology Brigham and Women’s Hospital Harvard Medical School Boston MA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Cancer Program Broad Institute of Harvard and MIT Cambridge MA USA Cancer Data Science Program Dana-Farber Cancer Institute Boston MA USA Department of Computer Science Harvard University Cambridge MA USA

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