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A bstract We introduce a Minimal Simplified Dark Matter (MSDM) framework to quantitatively characterise dark matter (DM) searches at the LHC. We study two MSDM models where the DM is a Dirac fermion which interacts with a vector and axial-vector mediator. The models are characterised by four parameters: m DM , M med , g DM and g q , the DM and mediator masses, and the mediator couplings to DM and quarks respectively. The MSDM models accurately capture the full event kinematics, and the dependence on all masses and couplings can be systematically studied. The interpretation of mono-jet searches in this framework can be used to establish an equal-footing comparison with direct detection experiments. For theories with a vector mediator, LHC mono-jet searches possess better sensitivity than direct detection searches for light DM masses (≲5 GeV). For axial-vector mediators, LHC and direct detection searches generally probe orthogonal directions in the parameter space. We explore the projected limits of these searches from the ultimate reach of the LHC and multi-ton xenon direct detection experiments, and find that the complementarity of the searches remains. Finally, we provide a comparison of limits in the MSDM and effective field theory (EFT) frameworks to highlight the deficiencies of the EFT framework, particularly when exploring the complementarity of mono-jet and direct detection searches.

Structural Health Monitoring (SHM), defined as the process that involves sensing, computing, and decision making to assess the integrity of infrastructure, has been plagued by data management challenges. The Industrial Internet of Things (IIoT), a subset of Internet of Things (IoT), provides a way to decisively address SHM’s big data problem and provide a framework for autonomous processing. The key focus of IIoT is operational efficiency and cost optimization. The purpose, therefore, of the IIoT approach in this investigation is to develop a framework that connects nondestructive evaluation sensor data with real-time processing algorithms on an IoT hardware/software system to provide diagnostic capabilities for efficient data processing related to SHM. Specifically, the proposed IIoT approach is comprised of three components: the Cloud, the Fog, and the Edge. The Cloud is used to store historical data as well as to perform demanding computations such as off-line machine learning. The Fog is the hardware that performs real-time diagnostics using information received both from sensing and the Cloud. The Edge is the bottom level hardware that records data at the sensor level. In this investigation, an application of this approach to evaluate the state of health of an aerospace grade composite material at laboratory conditions is presented. The key link that limits human intervention in data processing is the implemented database management approach which is the particular focus of this manuscript. Specifically, a NoSQL database is implemented to provide live data transfer from the Edge to both the Fog and Cloud. Through this database, the algorithms used are capable to execute filtering by classification at the Fog level, as live data is recorded. The processed data is automatically sent to the Cloud for further operations such as visualization. The system integration with three layers provides an opportunity to create a paradigm for intelligent real-time data quality management.

IntroductionEgypt’s Ministry of Health and Population adopted the US Centers for Disease Control and Prevention’s Analyze, Design, Develop, Implement, and Evaluate (ADDIE)-phased approach to strengthen and institutionalise rapid response teams (RRTs), moving beyond ad-hoc training toward sustainable systems. Using the Capacity Assessment Tool (CAT), Egypt identified operational and programmatic gaps, designed tailored workplans and implemented structured activities to enhance RRT readiness. This systematic process improved coordination, sustainability and capacity building, aligning with International Health Regulations and strengthening Egypt’s overall preparedness and response to public health emergencies.MethodsThis longitudinal study measured RRT programme capacity and operational levels in Egypt from March 2022 to May 2023. Egypt RRT focal point and newly appointed managers used the CAT to evaluate 60 indicators across eight programmatic areas. The study used a before-and-after analysis of the three-phased interventions: RRT management training and Standard Operating Procedure development (July 2022), staffing and training of RRT (August 2022) and monitoring and evaluation (M&E) frameworks development (May 2023). Capacity and operational level scores measured across areas with scores ranging from 0 to 2 for each indicator.ResultsBetween baseline and the end of the intervention, the most significant improvements occurred in M&E (mean increase 1.80; p=0.001) and postdeployment (mean increase 1.50; p=0.007) capacity levels. For operational levels, statistically significant increases were observed across most areas, with the greatest improvements in postdeployment (1.33; p=0.01), activation and predeployment (1.27; p<0.001) and deployment (1.12; p=0.001). Results revealed that 41.7% of indicators showed two-point improvements in capacity, while 25% showed two-point improvements in operational levels. No indicators showed a decline in either capacity or operational performance.ConclusionThe structured ADDIE-based phased approach demonstrated effectiveness in enhancing both capacity and operational levels of Egypt’s RRT programme. Challenges persist in administrative considerations, highlighting the importance of addressing policy development and sustainable funding early in capacity building efforts. This approach offers a replicable framework for countries seeking to strengthen emergency response capabilities.

Purpose of Review To provide a summary of prevalence, pathogenesis, and treatment of coronary calcified nodules (CNs). Recent Findings CNs are most frequently detected at the sites of hinge motion of severely calcified lesions such as in the middle segment of right coronary artery and left main coronary bifurcation. On histopathology, CNs exhibit two distinctive morphologies: eruptive and non-eruptive. Eruptive CNs, which have a disrupted fibrous cap with adherent thrombi, are biologically active. Non-eruptive CNs, which have an intact fibrous cap without thrombi, are biologically inactive, representing either healed eruptive CNs or protrusion of calcium due to plaque progression. Recent studies using optical coherence tomography (OCT) have shown a difference in the mechanism of stent failure in the two subtypes, demonstrating early reappearance of eruptive CNs in the stent (at ~ 6 months) as a unique mechanism of stent failure that does not seem to be preventable by simply achieving adequate stent expansion. The cause of CN reappearance in stent is not known and could be due to acute or subacute intrusion or continued growth of the CN. Whether modification of CN is needed, the most effective calcium modification modality and effectiveness of stent implantation in eruptive CNs has not been elucidated. Summary In this review, we discuss pathogenesis of CNs and how intravascular imaging can help diagnose and manage patients with CNs. We also discuss medical and transcatheter therapies beyond conventional stent implantation for effective treatment of eruptive CNs that warrant testing in prospective studies.

Background: This study evaluated the impact of diabetes mellitus (DM) and its treatments on cardiovascular outcomes in prostate cancer (PC) patients aged 66 years and older, with or without androgen deprivation therapy (ADT) exposure. Methods: Using the SEER-Medicare database (2009–2017), two cohorts were created: Cohort 1 included all PC patients enrolled in Medicare Parts A and B; Cohort 2 was a subset of Cohort 1 receiving ADT and enrolled in Medicare Part D. Exposures were DM and DM medications. Outcomes included cardiovascular events (CVEs), cardiovascular mortality (CVm), PC-specific mortality (PCsm), and all-cause mortality, analyzed using multivariable Fine-Gray and Cox models. Results: Cohort 1 included 150,647 PC patients (32% with DM, median age 72). DM was associated with higher risk of CVE (subdistribution hazard ratio [sHR] 1.20, 95% CI 1.17–1.22), CVm (sHR 1.35, 1.28–1.43), and all-cause mortality (adjusted HR [aHR] 1.22, 1.19–1.26) (all p < 0.001). Non-Hispanic Blacks (NHBs) and patients from lower socioeconomic (SES) and education areas experienced comparable or worse outcomes. In Cohort 2 (n = 14,938), DM patients on non-metformin therapies had higher all-cause mortality (aHR 1.33, 1.11–1.25; p = 0.002) than those on metformin, particularly in NHB and low education groups. Sensitivity analyses with follow-up limited to two years showed consistent results as overall. Conclusions: Diabetic PC patients, especially NHB, lower SES and lower education subgroups, were associated with worse cardiovascular and all-cause mortality outcomes. Metformin may be associated with better outcomes in these populations, warranting further research on the disparities in PC and diabetes, and cardioprotective effects of DM medications across different subpopulations.

Keeping in view the potential role of plants in pollution abatement, reclamation of urban forest cover and carbon sequestration, fifteen (15) plant species growing along 7 busiest roads in the city of Lahore were ecologically evaluated using analytical tools such as Air Pollution Tolerance Index (APTI) and Anticipated Performance Index (API). Leaf area, dust content, ascorbic acid content, total chlorophyll content, relative water content and pH of leaf extract were determined along with some morphological, biological and socio-economic parameters of the selected species in control as well as experimental sites and significant variations were noted. S. cumini showed highest dust capturing potential followed by F. benghalensis , F. benjamina , M. elengi , A. scholaris , P. longifolia and F. religiosa . M. indica (APTI = 17.88) was the most tolerant species, E. milii , F. benghalensis and H. rosa-sinensis were intermediate performers (APTI values 11.08, 13.15 and 12.28, respectively) and all other species such as A. scholaris , A. indica , B. spectabilis , C. erectus , E. citriodora , F. benjamina , F. religiosa , M. elengi , M. alba , P. longifolia and S. cumini were assessed as sensitive plants. F. benghalensis and M. indica secured API grade 6 (excellent), A. indica , F. religiosa , H. rosa-sinensis , M. alba and P. longifolia scored API grade 4 (good) whereas API grade 3 (moderate) was associated with A. scholaris , E. citriodora , F. benjamina and S. cumini . The remaining species with API grades 2, 1 and 0 fall into poor, very poor and not recommended categories, respectively. Plants belonging to API rating grades 3 (moderate), 4 (good), 5 (very good), 6(excellent) and 7 (best) are recommended for cultivation in polluted sites.

The effect of six halogen-free flame retardant (FR) formulations was investigated on the thermal stability of two low-density polyethylenes (LDPE) and one linear low-density polyethylene (LLDPE), by means of thermogravimetric analysis (TGA) under nitrogen and air atmosphere. The relative data were combined with flammability properties and the overall performance of the FRs was correlated with the type of branching in the polyethylene grades and to their processing behavior. The thermal degradation kinetics was further determined based on the Kissinger and Coats-Redfern methods. In terms of flammability, the addition of a triazine derivative and ammonium polyphosphate at a loading of 35 wt. %. was found to be the most efficient, leading to UL 94 V0 ranking in the case of the LDPE grade produced in an autoclave reactor.

Sarah Malik explains how quantum random walks can be used to model the cascades of quarks and gluons resulting from the proton–proton collisions at the Large Hadron Collider.