Abstract
Objective
To investigate the effect and underlying mechanisms of curcumin (CUR) on small intestinal injuries associated with severe traumatic infections after combined treatment of ciprofloxacin (CIP) and indomethacin (IND).
Methods
Adult male C57BL/6 mice (8 weeks old) were randomly divided into (10 animals for survival rate observation, 10 for other indicators) control group, model group [hemorrhage+ fracture+Pseudomonas aeruginosa (PA) infection], IND+CIP treatment group, and IND+ CIP+CUR treatment group. The survival rates within 72 h post-modelling were observed across all groups. At 24 h post-modelling, peripheral blood samples, small intestinal tissues and mesenteric lymph nodes (MLN) were harvested. Small intestinal length, hemorrhagic status, and adhesions were also observed grossly in each group. Bacterial load was determined via dilute plating. HE staining was used to observe histopathological alterations in intestinal tissues across groups. ELISA was employed to measure the contents of inflammatory cytokines, IL-1β, IL-6, IL-10 and TNF-α in the serum and small intestinal homogenate of each group. Liver, renal, and cardiac functions were evaluated by measuring key biochemical indicators. Flow cytometry was applied to measure the percentages of IFN-γ, IL-4, TGF-β, and IL-17 positive cells within CD4+ T cells in the MLN. Real-time quantitative polymerase chain reaction (qPCR) was performed to detect the mRNA expression of forkhead box protein P3 (FOXP3) and retinoic acid receptor-related orphan receptor gamma (RORγ)-t in each group.
Results
Both the IND+CIP treatment group and the IND+CIP+CUR treatment group significantly improved survival rates in mice with severe traumatic infections (P<0.05). CUR addition reduced bacterial load in the mice (P<0.01). The IND+CIP treatment group exhibited small intestinal injury, which worsened over time, manifested by elevated contents of TNF-α (P<0.01), IL-6 (P<0.05), and IL-1β (P<0.05) and decreased IL-10 (P<0.01) in the small intestine tissues, accompanied with pathological damage in the small intestine. The IND+CIP+CUR treatment group had significantly reversed small intestinal injury, reduced inflammatory factor levels (P<0.01), and restored normal small intestinal structure, showing no significant differences in relevant blood biochemical indicators when compared to the IND+CIP treatment group. The expression of FOXP3 was increased and that of RORγ-t was decreased in CD4+ T cells from the IND+CIP+CUR treatment group than the IND+CIP treatment group (both P<0.01).
Conclusion
The combination of CUR with IND and CIP can effectively alleviate severe traumatic infections, while antagonize small intestinal damage observed in IND and CIP therapy, with no significant adverse effects on vital organ functions. This mechanism may be related to the balance between regulatory T (Treg) cells and T helper 17 (Th17) cells.
The article substantiates the method of multicriteria assessment of the external (international) military and economic conditions for the construction and development of the Armed Forces of Ukraine under the conditions of increased uncertainty. The relevance of the research is determined by the growing role of international economic, military and technical, and political factors in shaping the defence capability of the state, as well as by the limitations of traditional approaches to their assessment, which insufficiently take into account the vagueness and inconsistency of expert information. The aim of the work is to develop a scientifically substantiated method that enables the comparative analysis and ranking of the military-economic conditions for the development of the armed forces on the basis of a system of coordinated criteria. The study uses the methods of analysis and synthesis, formalisation, expert assessment, as well as the tools of multicriteria analysis with elements of fuzzy logic. The proposed method provides for the formation of a matrix of expert assessments in the form of three-component fuzzy numbers, the determination of criteria weights, the construction of positive and negative benchmarks, and the calculation of an integral proximity index for each alternative. As a result, a ranked list of external military-economic conditions has been formed, which makes it possible to substantiate their priority for the strategic planning and development of the Armed Forces of Ukraine. The practical value of the work lies in the possibility of using the proposed method as a decision-support tool in the sphere of national security and defence construction.
This paper focuses on the high-temperature tensile failure mechanism of RTM (resin transfer moulding)-made symmetric and asymmetric composite T-joints. The failure modes as well as the load-displacement curves of symmetric (three specimens) and asymmetric (three specimens) composite T-joints were determined by tensile tests at room and high temperatures. Progressive damage models (PDMs) of symmetric and asymmetric composite T-joints at room and high temperatures were established based on mixed criteria, and the result predicted from the aforementioned PDMs were compared with experimental data. The predicted initial and final failure loads and failure modes are in good agreement with the experimental results. The failure mechanisms of composite T-joints at different temperatures were investigated by scanning electron microscopy. The results reveal that while the failure mode of asymmetric T-joints at high temperatures resembles that at room temperature, there is a difference in the failure modes of symmetric T-joints. The ultimate failure load of symmetric and asymmetric T-joints at elevated temperatures increases and reduces by 18.4% and 4.97%, albeit with a more discrete distribution. This work is expected to provide us with more knowledge about the usability of composite T-joints in elevated temperature environments.
Lisley Madeira Coelho, Belayne Zanini Marchi, Pedro Henrique Poubel Mendonça da Silveira
et al.
Abstract The use of Reclaimed Asphalt Pavement (RAP) in road base layers represents a solution to reduce the consumption of natural aggregates. However, the variability of RAP properties poses challenges to its application, particularly regarding mechanical behavior. This study investigates thermal compaction as a strategy to stabilize mixtures composed exclusively of RAP, introducing the concept of a warm base. Repeated load triaxial tests were conducted to evaluate the effects of compaction temperature on permanent deformation (PD) and resilient modulus (RM). The results indicate that increasing the compaction temperature significantly improves the mechanical behavior of RAP, reducing PD by up to 52% at the highest stress level. Additionally, the RM of RAP-M samples increased by approximately 187.13% compared to the maximum value of RAP-F samples and 389.05% compared to the minimum value. This approach enables the application of larger quantities of RAP in pavements, ensuring good structural quality while minimizing the effects of the material’s initial variability.
The deepening geopolitical conflict between the West and the Russian Federation, intensified by the war in Ukraine, has caused significant changes in the security environment of the Republic of Serbia and has largely complicated the realization of its national and defense interests. Military neutrality, as an important defense interest and key security commitment, is also conditioned by changes in the security environment of the Republic of Serbia, as well as vital national interests such as the preservation of territorial integrity, the prospect of EU membership, and ensuring energy security. Therefore, the subject of research in this paper represents the relationships of connectivity and conditioning of the military neutrality of the Republic of Serbia, key changes in the security environment caused by the war in Ukraine, and the mentioned vital national interests. The paper starts from the hypothetical stance that changes in the security environment of the Republic of Serbia complicate the realization of the military neutrality policy, especially in relation to the mentioned vital national interests. The analysis of these connections and relationships is based on the theoretical postulates of the realist school of international relations, according to which great powers and other international actors use all available instruments for projecting power and achieving interests in an anarchic system. In changed geopolitical circumstances, the military neutrality policy of the Republic of Serbia, based on balanced cooperation with the parties in the Ukrainian conflict, is becoming increasingly difficult to maintain, as there is a serious "coercive" potential of the mentioned parties, related to the vital national interests of the Republic of Serbia, such as EU membership, preservation of territorial integrity, and energy and economic security of the Serbian state and society. The methodology in the research is based on the analysis of relevant sources and literature.
Environmental factors, particularly various components of fine particulate matter (PM2.5) (i.e., sulfate [SO42-], nitrate [NO3-], ammonium [NH4+], organic matter [OM] and black carbon [BC]), are increasingly recognized as potential risk factors for poor ovarian response (POR) in fertility treatments. However, existing research is limited, and the critical periods of vulnerability to exposure to PM2.5 and its components remain unclear. In this retrospective cohort study, we included 38,619 patients undergoing their first in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI) treatment, defining POR as the primary outcome based on the POSEIDON criteria. We divided the six months prior to oocyte pick up (OPU) into different exposure windows and used logistic regression models to assess the association between pollutants and POR. Results showed that exposure to PM2.5 and its components in the three months before OPU significantly increased the odds of POR. The distributed lag nonlinear model (DLNM) analysis revealed the lagged effects of PM2.5 component exposure, particularly during lag weeks 2–5, where exposure was significantly associated with the occurrence of POR. This period may represent a sensitive window for exposure. Meanwhile, the restricted cubic spline (RCS) analysis indicated that the odds of POR gradually increased with higher pollutant concentrations. These findings underscore the urgent need for public health measures during sensitive stages of follicular development, particularly policies aimed at reducing environmental pollutant exposure among women of reproductive age to protect reproductive health.
Nguyen Tien Dat, Vu Duc Nam, Hoang Le Tuan Anh
et al.
Dioxins are persistent organic pollutants with long biological half-lives and a high tendency for bioaccumulation, posing serious toxicological risks to humans and wildlife. This study investigates the modulatory role of rutin, a naturally occurring flavonoid, in promoting the excretion and reducing the systemic retention of polychlorinated dibenzo-p-dioxins and dibenzofurans in vivo. Wistar rats were exposed to a controlled dioxin mixture (10 µg/kg body weight) and administered rutin orally (0.02 g/kg) for 30 consecutive days. Biological samples including feces, urine, and serum were collected and analyzed via high-resolution gas chromatography coupled with high-resolution mass spectrometry (HRGC/HRMS). Rutin significantly enhanced the excretion of octachlorodibenzo-p-dioxin (OCDD) by 30% in urine and 25% in feces, while reducing lipid-adjusted serum dioxin levels. Additionally, biochemical and hematological markers showed improved hepatic and renal function in the rutin-treated group. These findings suggest that rutin may facilitate dioxin detoxification through enhanced metabolic clearance and reduced tissue retention. The study contributes to understanding natural detoxification mechanisms and supports future research into bioactive compounds for mitigating environmental toxicant exposure.
Mizanur Rahman, Amran Bhuiyan, Mohammed Saidul Islam
et al.
Recent advances in large language models (LLMs) have enabled a new class of AI agents that automate multiple stages of the data science workflow by integrating planning, tool use, and multimodal reasoning across text, code, tables, and visuals. This survey presents the first comprehensive, lifecycle-aligned taxonomy of data science agents, systematically analyzing and mapping forty-five systems onto the six stages of the end-to-end data science process: business understanding and data acquisition, exploratory analysis and visualization, feature engineering, model building and selection, interpretation and explanation, and deployment and monitoring. In addition to lifecycle coverage, we annotate each agent along five cross-cutting design dimensions: reasoning and planning style, modality integration, tool orchestration depth, learning and alignment methods, and trust, safety, and governance mechanisms. Beyond classification, we provide a critical synthesis of agent capabilities, highlight strengths and limitations at each stage, and review emerging benchmarks and evaluation practices. Our analysis identifies three key trends: most systems emphasize exploratory analysis, visualization, and modeling while neglecting business understanding, deployment, and monitoring; multimodal reasoning and tool orchestration remain unresolved challenges; and over 90% lack explicit trust and safety mechanisms. We conclude by outlining open challenges in alignment stability, explainability, governance, and robust evaluation frameworks, and propose future research directions to guide the development of robust, trustworthy, low-latency, transparent, and broadly accessible data science agents.
Mathias Anneken, Nadia Burkart, Fabian Jeschke
et al.
This white paper underscores the critical importance of responsibly deploying Artificial Intelligence (AI) in military contexts, emphasizing a commitment to ethical and legal standards. The evolving role of AI in the military goes beyond mere technical applications, necessitating a framework grounded in ethical principles. The discussion within the paper delves into ethical AI principles, particularly focusing on the Fairness, Accountability, Transparency, and Ethics (FATE) guidelines. Noteworthy considerations encompass transparency, justice, non-maleficence, and responsibility. Importantly, the paper extends its examination to military-specific ethical considerations, drawing insights from the Just War theory and principles established by prominent entities. In addition to the identified principles, the paper introduces further ethical considerations specifically tailored for military AI applications. These include traceability, proportionality, governability, responsibility, and reliability. The application of these ethical principles is discussed on the basis of three use cases in the domains of sea, air, and land. Methods of automated sensor data analysis, eXplainable AI (XAI), and intuitive user experience are utilized to specify the use cases close to real-world scenarios. This comprehensive approach to ethical considerations in military AI reflects a commitment to aligning technological advancements with established ethical frameworks. It recognizes the need for a balance between leveraging AI's potential benefits in military operations while upholding moral and legal standards. The inclusion of these ethical principles serves as a foundation for responsible and accountable use of AI in the complex and dynamic landscape of military scenarios.
In this investigation, the starch-Lycium barbarum complex (CS-LB) was fabricated using corn starch (CS) and Lycium barbarum (LB) through a high-speed shear method. The stability of the guest molecules was also explored. The influence of shear time, rotational speed, and LB to CS mass ratio on Lycium barbarum pigment (LP) content and its stability were investigated. The CS-LB was characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), infrared spectroscopy (FT-IR), and thermogravimetric analysis (TGA). It was found that the content of LP in the product was 0.99±0.03 mg per gram when the shear time was 1.5 hours, the rotational speed was 12000 r/min, and the mass ratio of LB to CS was 3:1. The SEM results illustrated that the products had an agglomerated morphology. The XRD results showed that the crystal domain of starch particles was destroyed and transformed into amorphous structures due to the high-speed shear treatment, but the CS-LP crystalline structure changed into a V-type, which was promoted by the interaction between CS and active components of LB. The FT-IR results showed that the absorption peak at 3421 cm−1 shifted, indicating that CS and LB were bound through hydrogen bonds. The TGA results showed that the thermal stability of the product was also enhanced, with a mass retention rate of 36% at 600 ℃ for the composite. Thus, the CS-LB could be effectively fabricated by high-speed shear treatment. Additionally, it was found that the composite could effectively reduce the effects of temperature, oxygen, and light on the stability of guest molecules in stability experiments. The shelf-life of guest molecules was also extended, enabling them to perform their related functions better.
The definition of Data Science is a hotly debated topic. For many, the definition is a simple shortcut to Artificial Intelligence or Machine Learning. However, there is far more depth and nuance to the field of Data Science than a simple shortcut can provide. The School of Data Science at the University of Virginia has developed a novel model for the definition of Data Science. This model is based on identifying a unified understanding of the data work done across all areas of Data Science. It represents a generational leap forward in how we understand and teach Data Science. In this paper we will present the core features of the model and explain how it unifies various concepts going far beyond the analytics component of AI. From this foundation we will present our Undergraduate Major curriculum in Data Science and demonstrate how it prepares students to be well-rounded Data Science team members and leaders. The paper will conclude with an in-depth overview of the Foundations of Data Science course designed to introduce students to the field while also implementing proven STEM oriented pedagogical methods. These include, for example, specifications grading, active learning lectures, guest lectures from industry experts and weekly gamification labs.
Georges Derache, Mounira Msahli, Aurelien Botbol
et al.
The importance of the development of IoT and LoRaWAN in military applications has been widely established. Since security is one of its important challenges, in this paper we study two attacks scenarios: replay and sniff attacks on military LoRaWAN network. The aim is to highlight cybersecurity threats that must be taken into consideration when using such technology in critical context.
Juan-Pablo Rivera, Gabriel Mukobi, Anka Reuel
et al.
Governments are increasingly considering integrating autonomous AI agents in high-stakes military and foreign-policy decision-making, especially with the emergence of advanced generative AI models like GPT-4. Our work aims to scrutinize the behavior of multiple AI agents in simulated wargames, specifically focusing on their predilection to take escalatory actions that may exacerbate multilateral conflicts. Drawing on political science and international relations literature about escalation dynamics, we design a novel wargame simulation and scoring framework to assess the escalation risks of actions taken by these agents in different scenarios. Contrary to prior studies, our research provides both qualitative and quantitative insights and focuses on large language models (LLMs). We find that all five studied off-the-shelf LLMs show forms of escalation and difficult-to-predict escalation patterns. We observe that models tend to develop arms-race dynamics, leading to greater conflict, and in rare cases, even to the deployment of nuclear weapons. Qualitatively, we also collect the models' reported reasonings for chosen actions and observe worrying justifications based on deterrence and first-strike tactics. Given the high stakes of military and foreign-policy contexts, we recommend further examination and cautious consideration before deploying autonomous language model agents for strategic military or diplomatic decision-making.
Artificial intelligence and its societal and ethical implications are complicated and conflictingly interpreted. Surveillance is one of the most ethically challenging concepts in AI. Within the domain of artificial intelligence, this study conducts a topic modeling analysis of scientific research on the concept of surveillance. Seven significant scholarly topics that receive significant attention from the scientific community were discovered throughout our research. These topics demonstrate how ambiguous the lines between dichotomous forms of surveillance are: public health surveillance versus state surveillance; transportation surveillance versus national security surveillance; peace surveillance versus military surveillance; disease surveillance versus surveillance capitalism; urban surveillance versus citizen ubiquitous surveillance; computational surveillance versus fakeness surveillance; and data surveillance versus invasive surveillance. This study adds to the body of knowledge on AI ethics by focusing on controversial aspects of AI surveillance. In practice, it will serve as a guideline for policymakers and technology companies to focus more on the intended and unintended consequences of various forms of AI surveillance in society.
Abstract A 52-year-old woman was injured in an accident. Emergency tests showed rib fractures and pleural effusion. However, lung incarceration was found during the thoracic exploration that was not detected in the preoperative images. Although this occurrence is rare, clinicians should be careful of this possible pitfall, which may bring about a poor prognosis after a rib fracture.
Detecting highly-overlapped objects in crowded scenes remains a challenging problem, especially for one-stage detector. In this paper, we extricate YOLOv4 from the dilemma in a crowd by fine-tuning its detection scheme, named YOLO-CS. Specifically, we give YOLOv4 the power to detect multiple objects in one cell. Center to our method is the carefully designed joint prediction scheme, which is executed through an assignment of bounding boxes and a joint loss. Equipped with the derived joint-object augmentation (DJA), refined regression loss (RL) and Score-NMS (SN), YOLO-CS achieves competitive detection performance on CrowdHuman and CityPersons benchmarks compared with state-of-the-art detectors at the cost of little time. Furthermore, on the widely used general benchmark COCO, YOLO-CS still has a good performance, indicating its robustness to various scenes.
Kamber Schwarz, Joan Najita, Jennifer Bergner
et al.
The Orbiting Astronomical Satellite for Investigating Stellar Systems (OASIS) is a NASA Astrophysics MIDEX-class mission concept, with the stated goal of following water from galaxies, through protostellar systems, to Earth's oceans. This paper details the protoplanetary disk science achievable with OASIS. OASIS's suite of heterodyne receivers allow for simultaneous, high spectral resolution observations of water emission lines spanning a large range of physical conditions within protoplanetary disks. These observations will allow us to map the spatial distribution of water vapor in disks across evolutionary stages and assess the importance of water, particularly the location of the midplane water snowline, to planet formation. OASIS will also detect the H2 isotopologue HD in 100+ disks, allowing for the most accurate determination of total protoplanetary disk gas mass to date. When combined with the contemporaneous water observations, the HD detection will also allow us to trace the evolution of water vapor across evolutionary stages. These observations will enable OASIS to characterize the time development of the water distribution and the role water plays in the process of planetary system formation.
As a NP‐hard problem that needs to be solved in real time, the dynamic task allocation problem of unmanned aerial vehicle (UAV) swarm has gradually become a difficulty and hotspot in the current planning field. Aiming at the problems of poor real‐time performance and low quality of the solution in the dynamic task allocation of heterogeneous UAV swarm in uncertain environment, this paper establishes a dynamic task allocation model that can meet the actual needs and uses the binary wolf pack algorithm (BWPA) to solve it, so as to propose a dynamic task allocation method of heterogeneous UAV swarm in uncertain environment. In this method, a dynamic mechanism of attacking while searching and priority attacking of important targets is designed. A dynamic task allocation model of multitarget, multitask, heterogeneous multiaircraft platform and multiconstraint is established based on the target cost‐effectiveness ratio and task execution time window. In addition, one‐dimensional 0–1 coding method is adopted to encode the task allocation scheme. Furthermore, the wolf pack algorithm (WPA) is introduced in brief. This paper focuses on the BWPA with the good computational robustness and strong global search ability to solve the dynamic allocation model. According to the simulation results, the designed task allocation method not only has good adaptability to the change of target and UAV number, as well as good stability and scalability, but also can effectively solve the dynamic task allocation problem of heterogeneous UAV swarm in unknown environment. Therefore, the established model and solution method can provide a useful reference for task allocation and other related problems.