Hasil untuk "Special situations and conditions"

Ralph Huits, Dora Buonfrate, Kevin O'Laughlin et al.

Background: The global resurgence of measles is a threat to measles elimination campaigns. Measles importations by international travelers have been identified as a risk factor for outbreaks. Methods: We reviewed measles cases among international travelers and migrants reported to the GeoSentinel network. Results: From May 2019 through June 2025, GeoSentinel recorded 53 measles cases among travelers imported into 15 different countries. Travelers of all age groups were affected, and 74 % were 21 years or older. Thirty-three travelers (61 %) were hospitalized. Seventy-nine percent of cases reported no or unknown history of vaccination against measles. Conclusions: Against a background of increasing numbers of measles cases and outbreaks globally, GeoSentinel observed a stable trend of measles importations by international travelers. Measles caused considerable morbidity among travelers. Immunization effectively prevents measles in more than 97 % of individuals. Pretravel consultations provide an important opportunity to promote vaccination coverage for all vaccine-preventable diseases, including measles.

Xiuxia Wang, Junjie Chen, Guangxin Cheng

With the rapid development of artificial intelligence (AI) technology, its application in college physical education has gradually shifted from marginal exploration to system integration. Based on literature review, case study and empirical data analysis, this paper systematically explores the multi-dimensional application of AI technology in college physical education, including sports performance analysis, personalized training, teaching management optimization and intelligent evaluation. The results show that AI can effectively improve teaching efficiency, scientific training and objectivity of evaluation, while promoting the precision and fairness of physical education. Through surveys and data comparisons of multiple colleges and universities, this paper verifies the significant effectiveness of AI in improving students' sports performance, reducing sports injury rates, and improving classroom organization efficiency. At the same time, the article also points out the challenges of current applications, such as infrastructure shortage, insufficient digital literacy of teachers, low algorithm transparency and data ethics concerns. Based on this, this paper proposes a path to promote the deep integration of AI into physical education from four dimensions: system construction, teacher empowerment, technology development and data governance. The study believes that AI will play the role of "intelligent engine" in future college physical education, promoting the transformation of the education paradigm from experience-driven to data-driven.

Belinda Claire Kiam, Aline Gaelle Bouopda-Tuedom, Jean Arthur Mbida Mbida et al.

Abstract Background Assessing vector bionomics and their role in transmission is crucial to improving vector control strategies. Several entomological studies have been conducted to describe malaria transmission in different eco-epidemiological settings in Cameroon; however, data gaps persist, particularly in the highland areas. This study aimed to characterize malaria vectors in three localities along an altitudinal gradient in the western region: Santchou (700 m), Dschang (1400 m) and Penka Michel (1500 m). Methods Human landing catches were conducted from May to June 2023 in 17 villages (including 10 health zones in Dschang, 4 in Santchou and 3 in Penka Michel) from 6:00 p.m. to 9:00 a.m. Mosquitoes were sorted into genera and all Anopheles species were identified using morphological taxonomic keys and species-specific Polymerase Chain reaction (PCR). Entomological indicators, including species composition, abundance, biting behaviour, infection rate and entomological inoculation rate (EIR) were assessed. Genomic DNA from the head and thorax was extracted and tested for Plasmodium infection by real-time PCR. Results A total of 2835 Anopheles mosquitoes were identified, including Anopheles gambiae sensu lato (s.l.) (82.88%), Anopheles funestus s.l. (15.92%), Anopheles nili (0.09%) and Anopheles ziemanni (1.11%), with An. gambiae s.l. being the most prevalent at all sites. Anopheles gambiae s.l. had a significantly higher human-biting rate at Penka Michel (45.25 bites/human/night) compared to Santchou (3.1 bites/human/night [b/h/n]) and Dschang (0.41 bites/human/night) (p-value < 0.001). It was also the main malaria vector, with an entomological inoculation rate (EIR) 13 times higher in Penka Michel than Santchou (1.11 vs. 0.08 infective bites/human/night). The data suggest a very focal distribution of infective An. gambiae s.l. mosquitoes. Plasmodium falciparum was the dominant malaria parasite (67% in Santchou, 62% in Penka Michel), but Plasmodium malariae (33% in Santchou, 31% in Penka Michel) and Plasmodium ovale (1.21% only in Penka Michel) infections were also detected. Conclusion The study highlights a difference in mosquito composition and host-seeking behaviour across altitudes, emphasizing the need for continued surveillance to monitor vector populations. To combat the persistence of malaria in Cameroon, it is crucial to implement additional tools like larviciding, integrated and environmental management, particularly against outdoor-biting mosquitoes, to prevent potential malaria outbreaks in these highland areas.

De-Zhang Li, Xin Wang

Partition function zeros are powerful tools in understanding critical behavior. In this paper we present new results of the Fisher zeros of two-dimensional Ising models, in the framework of free-fermion eight-vertex model. First we succeed in finding special boundary conditions for the free-fermion model, under which the partition function of a finite lattice can be expressed in a double product form. Using appropriate mappings, these boundary conditions are transformed into the corresponding versions of the square, triangular and honeycomb lattice Ising models. Each Ising model is studied in the cases of a zero field and of an imaginary field $i(π/2)k_BT$. For the square lattice model we rediscover the famous Brascamp-Kunz (B-K) boundary conditions. For the triangular and honeycomb lattice models we obtain the B-K type boundary conditions, and the Fisher zeros are conveniently solved from the product form of partition function. The advantage of B-K type boundary conditions is that the Fisher zeros of any finite lattice exactly lie on certain loci, and the accumulation points of zeros can be easily determined in the thermodynamic limit. Our finding and method would be very helpful in studying the partition function zeros of vertex and Ising models.

Yan Li, Huadong Zhu, Jun Xu et al.

Abstract Objectives This study was conducted to characterize the need for palliative care and its effect on patients with end-stage disease in the emergency department (ED). Design This was a prospective cohort study. A questionnaire survey was administered to patients with end-stage disease who were admitted to the resuscitation room of the ED and expected to live less than 6 months. Results A total of 82 of 2095 patients admitted to the resuscitation room were included. Only 1 (1.22%) patient had ever received palliative care before admission. Nine patients received palliative care consultation after admission, and they were more likely to select medical places of death accompanied by their families and do not resuscitate orders at the end of life after consultation (P < 0.05). Whether the disease had previously been actively treated and the number of children impacted the choice of treatment at the end of life (P < 0.05). Conclusions Among patients with end-stage disease admitted to the ED, knowledge of palliative care was lacking. Palliative care could help them avoid the damage caused by pointless resuscitation.

Elisa Salsano, Oriana Rossi, Cecilia Rispoli et al.

The proportion of older adults using medical cannabis is rising. Most of the cannabinoid research has focused on a healthy, younger population free from frailty and major comorbidity. In this case report, we describe an 82-year-old woman who was autonomous in basic and instrumental activities of daily living and who came to our geriatric department for marked weight loss over the past 2 months (about 10 kg) and anemia. After appropriate clinical and endoscopic checks, it was concluded that the weight loss was induced by the initiation of therapy with oral cannabis for the control of rheumatic pain.

Bo Wu, Shoubin Yu, Zhenfang Chen et al.

Reasoning in the real world is not divorced from situations. How to capture the present knowledge from surrounding situations and perform reasoning accordingly is crucial and challenging for machine intelligence. This paper introduces a new benchmark that evaluates the situated reasoning ability via situation abstraction and logic-grounded question answering for real-world videos, called Situated Reasoning in Real-World Videos (STAR Benchmark). This benchmark is built upon the real-world videos associated with human actions or interactions, which are naturally dynamic, compositional, and logical. The dataset includes four types of questions, including interaction, sequence, prediction, and feasibility. We represent the situations in real-world videos by hyper-graphs connecting extracted atomic entities and relations (e.g., actions, persons, objects, and relationships). Besides visual perception, situated reasoning also requires structured situation comprehension and logical reasoning. Questions and answers are procedurally generated. The answering logic of each question is represented by a functional program based on a situation hyper-graph. We compare various existing video reasoning models and find that they all struggle on this challenging situated reasoning task. We further propose a diagnostic neuro-symbolic model that can disentangle visual perception, situation abstraction, language understanding, and functional reasoning to understand the challenges of this benchmark.

Pengshuo Qiu, Frank Rudzicz, Zining Zhu

Large language models (LLMs) can be used to generate natural language explanations (NLE) that are adapted to different users' situations. However, there is yet to be a quantitative evaluation of the extent of such adaptation. To bridge this gap, we collect a benchmarking dataset, Situation-Based Explanation. This dataset contains 100 explanandums. Each explanandum is paired with explanations targeted at three distinct audience types-such as educators, students, and professionals-enabling us to assess how well the explanations meet the specific informational needs and contexts of these diverse groups e.g. students, teachers, and parents. For each "explanandum paired with an audience" situation, we include a human-written explanation. These allow us to compute scores that quantify how the LLMs adapt the explanations to the situations. On an array of pretrained language models with varying sizes, we examine three categories of prompting methods: rule-based prompting, meta-prompting, and in-context learning prompting. We find that 1) language models can generate prompts that result in explanations more precisely aligned with the target situations, 2) explicitly modeling an "assistant" persona by prompting "You are a helpful assistant..." is not a necessary prompt technique for situated NLE tasks, and 3) the in-context learning prompts only can help LLMs learn the demonstration template but can't improve their inference performance. SBE and our analysis facilitate future research towards generating situated natural language explanations.

Juan M. Deniz, Andre S. Kelboucas, Ricardo Bedin Grando

This study explores human-robot interaction (HRI) based on a mobile robot and YOLO to increase real-time situation awareness and prevent accidents in the workplace. Using object segmentation, we propose an approach that is capable of analyzing these situations in real-time and providing useful information to avoid critical working situations. In the industry, ensuring the safety of workers is paramount, and solutions based on robots and AI can provide a safer environment. For that, we proposed a methodology evaluated with two different YOLO versions (YOLOv8 and YOLOv5) alongside a LoCoBot robot for supervision and to perform the interaction with a user. We show that our proposed approach is capable of navigating a test scenario and issuing alerts via Text-to-Speech when dangerous situations are faced, such as when hardhats and safety vests are not detected. Based on the results gathered, we can conclude that our system is capable of detecting and informing risk situations such as helmet/no helmet and safety vest/no safety vest situations.

Zihan Chu

Adverse weather conditions including haze, snow and rain lead to decline in image qualities, which often causes a decline in performance for deep-learning based detection networks. Most existing approaches attempts to rectify hazy images before performing object detection, which increases the complexity of the network and may result in the loss in latent information. To better integrate image restoration and object detection tasks, we designed a double-route network with an attention feature fusion module, taking both hazy and dehazed features into consideration. We also proposed a subnetwork to provide haze-free features to the detection network. Specifically, our D-YOLO improves the performance of the detection network by minimizing the distance between the clear feature extraction subnetwork and detection network. Experiments on RTTS and FoggyCityscapes datasets show that D-YOLO demonstrates better performance compared to the state-of-the-art methods. It is a robust detection framework for bridging the gap between low-level dehazing and high-level detection.

Albin Zehe, Julian Schröter, Andreas Hotho

Suspense is an important tool in storytelling to keep readers engaged and wanting to read more. However, it has so far not been studied extensively in Computational Literary Studies. In this paper, we focus on one of the elements authors can use to build up suspense: dangerous situations. We introduce a corpus of texts annotated with dangerous situations, distinguishing between 7 types of danger. Additionally, we annotate parts of the text that describe fear experienced by a character, regardless of the actual presence of danger. We present experiments towards the automatic detection of these situations, finding that unsupervised baseline methods can provide valuable signals for the detection, but more complex methods are necessary for further analysis. Not unexpectedly, the description of danger and fear often relies heavily on the context, both local (e.g., situations where danger is only mentioned, but not actually present) and global (e.g., "storm" being used in a literal sense in an adventure novel, but metaphorically in a romance novel).

Debaditya Roy, Dhruv Verma, Basura Fernando

Situation Recognition is the task of generating a structured summary of what is happening in an image using an activity verb and the semantic roles played by actors and objects. In this task, the same activity verb can describe a diverse set of situations as well as the same actor or object category can play a diverse set of semantic roles depending on the situation depicted in the image. Hence a situation recognition model needs to understand the context of the image and the visual-linguistic meaning of semantic roles. Therefore, we leverage the CLIP foundational model that has learned the context of images via language descriptions. We show that deeper-and-wider multi-layer perceptron (MLP) blocks obtain noteworthy results for the situation recognition task by using CLIP image and text embedding features and it even outperforms the state-of-the-art CoFormer, a Transformer-based model, thanks to the external implicit visual-linguistic knowledge encapsulated by CLIP and the expressive power of modern MLP block designs. Motivated by this, we design a cross-attention-based Transformer using CLIP visual tokens that model the relation between textual roles and visual entities. Our cross-attention-based Transformer known as ClipSitu XTF outperforms existing state-of-the-art by a large margin of 14.1\% on semantic role labelling (value) for top-1 accuracy using imSitu dataset. {Similarly, our ClipSitu XTF obtains state-of-the-art situation localization performance.} We will make the code publicly available.

Sean Selim Scholz, Karl-Christian Thies

Ana Laura Brandão, Juliana Pereira Casemiro, Erika Cardoso dos Reis et al.

Objetivo. Elaborar recomendações para o fortalecimento da alimentação e nutrição (A&N) na atenção primária à saúde (APS) brasileira a partir de um consenso de especialistas. Métodos. Realizou-se estudo descritivo e exploratório utilizando a técnica Delphi. De 172 especialistas em A&N na APS convidados a participar, 116 aceitaram o convite e 72 participaram da primeira rodada. A segunda rodada teve 56 participantes das cinco macrorregiões brasileiras, entre gestores, profissionais de saúde, representantes da sociedade civil e pesquisadores. As recomendações foram construídas por análise temática das respostas a perguntas abertas sobre obstáculos, estratégias, ações e iniciativas de cinco componentes técnicos da Política Nacional de Alimentação e Nutrição (vigilância alimentar e nutricional, atenção e coordenação do cuidado nutricional, promoção da saúde, intersetorialidade e gestão das ações de A&N). Na segunda rodada, os especialistas classificaram cada item proposto quanto à sua pertinência e relevância para fins de recomendação. Resultados. Os especialistas elaboraram 35 recomendações, organizadas em seis categorias: estrutura; agenda de A&N na APS; organização em rede e organização da APS; processos de trabalho de A&N na APS; planejamento, monitoramento e avaliação das ações de A&N na APS; e sistema de informação. As categorias foram organizadas em dois blocos: “ações estruturantes da APS para o fortalecimento da A&N” e “ações estruturantes da A&N para o fortalecimento da APS”. Conclusões. As recomendações foram produzidas por pessoas experientes que acompanham a trajetória da A&N e da APS por diferentes pontos de vista, sendo úteis para a gestão e proposição de políticas públicas rumo ao fortalecimento da A&N nas APS brasileira.

S. A. Franchino-Viñas

In this article we consider resummed expressions for the heat-kernel's trace of a Laplace operator, the latter including a potential and imposing Dirichlet semitransparent boundary conditions on a surface of codimension one in flat space. We obtain resummed expressions that correspond to the first and second order expansion of the heat-kernel in powers of the potential. We show how to apply these results to obtain the bulk and surface form factors of a scalar quantum field theory in $d=4$ with a Yukawa coupling to a background. A characterization of the form factors in terms of pseudo-differential operators is given. Additionally, we discuss a connection between heat-kernels for Dirichlet semitransparent, Dirichlet and Robin boundary conditions.

Ashraf Reda, Hany Ragy, Kanwal Saeed et al.

Abstract Background Both hypertension and dyslipidemia are considered as major modifiable risk factors of cardiovascular diseases (CVDs), and their prevalence in Egypt has increased in recent years. Evidence-based systematic evaluation of data on hypertension and dyslipidemia is critical for effective patient-centric management to reduce the overall risk of CVDs in Egypt. This semi-systematic review aimed to quantify and identify data gaps in the prevalence and distribution of patient journey touchpoints including awareness, screening, diagnosis, treatment, adherence, and control of hypertension and dyslipidemia to provide the basis for research prioritization, practice guidance, and health care reforms in Egypt. Main body Structured search was conducted on MEDLINE and Embase to identify articles published in English between January 2010 and December 2019 that reported key patient journey touchpoints in hypertension and dyslipidemia management. Unstructured search was conducted on public or government websites with no date restriction. Data from all sources were extracted and presented descriptively. In total, 22 studies published between 1995 and 2020 on hypertension and dyslipidemia were included in the final analyses. The prevalence of hypertension in Egypt ranged from 12.1 to 59%. Studies reported awareness (37.5% and 43.9%), diagnosis (42% and 64.7%), treatment (24% and 54.1%), and adherence to antihypertensive medication (51.9%) to be low. Furthermore, the percentage of patients who had their blood pressure controlled ranged from 8 to 53.2%. The prevalence of dyslipidemia varied in the general population (range 19.2–36.8%) but was higher in patients with acute coronary syndrome (ACS) (50.9% and 52.5%) and coronary artery disease (58.7%). A national report indicated that 8.6% of the general population was screened for dyslipidemia; however, no data was available on the diagnosis and treatment rates. Among ACS patients, 73.9% were treated for dyslipidemia. Data indicated low levels of medication adherence (59%) among dyslipidemia patients, with overall low control rates ranging from 5.1 to 34.4% depending on CVD risk in populations including ACS patients. Conclusion Data on patient journey touchpoints of hypertension and dyslipidemia are limited in Egypt, indicating the need for more systematic and high-quality evidence-based studies covering different aspects of patient-centric management for better management of CVD and its risk factors.

Pieter C Steketee, Federica Giordani, Isabel M Vincent et al.

Subspecies of the protozoan parasite Trypanosoma brucei are the causative agents of Human African Trypanosomiasis (HAT), a debilitating neglected tropical disease prevalent across sub-Saharan Africa. HAT case numbers have steadily decreased since the start of the century, and sustainable elimination of one form of the disease is in sight. However, key to this is the development of novel drugs to combat the disease. Acoziborole is a recently developed benzoxaborole, currently in advanced clinical trials, for treatment of stage 1 and stage 2 HAT. Importantly, acoziborole is orally bioavailable, and curative with one dose. Recent studies have made significant progress in determining the molecular mode of action of acoziborole. However, less is known about the potential mechanisms leading to acoziborole resistance in trypanosomes. In this study, an in vitro-derived acoziborole-resistant cell line was generated and characterised. The AcoR line exhibited significant cross-resistance with the methyltransferase inhibitor sinefungin as well as hypersensitisation to known trypanocides. Interestingly, transcriptomics analysis of AcoR cells indicated the parasites had obtained a procyclic- or stumpy-like transcriptome profile, with upregulation of procyclin surface proteins as well as differential regulation of key metabolic genes known to be expressed in a life cycle-specific manner, even in the absence of major morphological changes. However, no changes were observed in transcripts encoding CPSF3, the recently identified protein target of acoziborole. The results suggest that generation of resistance to this novel compound in vitro can be accompanied by transcriptomic switches resembling a procyclic- or stumpy-type phenotype.

Chad Nester

We construct a monoidal category of open transition systems that generate material history as transitions unfold, which we call situated transition systems. The material history generated by a composite system is composed of the material history generated by each component. The construction is parameterized by a symmetric strict monoidal category, understood as a resource theory, from which material histories are drawn. We pay special attention to the case in which this category is compact closed. In particular, if we begin with a compact closed category of integers then the resulting situated transition systems can be understood as systems of double-entry bookkeeping accounts.

Weihua Wang

This paper is concerned with the multiplicity results to a class of $p$-Kirchhoff type elliptic equation with the homogeneous Neumann boundary conditions by an abstract linking lemma due to Brézis and Nirenberg. We obtain the twofold results in subcritical and critical cases, which is a meaningful addition and completeness to the known results about Kirchhoff equation.

Sasa Sambolek, Marina Ivasic-Kos

The use of drones in search and rescue operations has become standard almost everywhere in the world. A special challenge in the search and rescue operation is the automatic detection of persons in different terrains, in different situations and body positions, in different weather conditions, and from different shooting heights during a drone flight. This paper investigates the accuracy of people detection in drone images on existing VisDrone, Okutama - Action datasets, and on a custom SARD image dataset built to simulate search and rescue scenes. A Faster R-CNN with FPN as the backbone, pre-trained on the COCO data set, was used as a detector. The person detector is additionally trained on the SARD data set containing 1,981 images and on the subset of the VisDrone set. After transfer learning, a significant improvement in the detection results of persons in the images taken by the drone was achieved concerning mAP and precision and recall.