For identifying natural trends, hotspots, hazardous areas, and mitigating potential health risk to the public and environment, spatial analysis of radiation levels is crucial. Machine learning models' implementation for prediction of background radiation levels and anomaly detection can bring a revolutionary change in radiation monitoring. Also, emergency evacuation routes with minimum radiation exposure are needed in times of radiological and nuclear emergencies. Traditional radiation monitoring systems lack advanced data analytics capabilities for comprehensive situational awareness and emergency response planning. Building on a companion paper that details a IoT connected radiation detector and monitoring website, this work fills the gap of the radiation monitoring analytics to map radiation via different interpolation techniques, anomaly detection, and evaluation of multiple machine learning models for background radiation prediction and emergency escape route planning using A* algorithm with minimum exposure to radiation in times of nuclear or radiological emergencies. The study setup involves using the mobile radiation detector from the companion paper and real data collection from around the Military Institute of Science and Technology (MIST), Dhaka, Bangladesh, and Institute of Nuclear Science and Technology (INST), Dhaka, Bangladesh. Collected data are visualized by plotting interpolated radiation heatmaps, anomaly detection, and background radiation prediction using machine learning and determining effective escape routes following the "as low as reasonably achievable" (ALARA) principle. To revolutionize the radiation monitoring system, simplifying the decision-making for the radiation protection professionals and public officials to develop radiation protection strategies has great potential.

The use of inhaler devices, particularly pressurised metered dose inhalers (pMDIs), has been a particular concern given their contribution to carbon emissions. One such method is through the release of propellant gas composed of hydrofluorocarbons (HFCs). Various methods, such as changing HFC types and implementing recycling programs, aim to reduce carbon emissions. As clinicians, we can change our prescribing behaviour, such as prescribing soft mist inhalers (SMIs) and dry powdered inhalers (DPI), alongside regular review, to ensure our actions play a role in minimising carbon emissions from inhaler device use.

Research on long-term suicide bereavement remains limited. Using semi-structured, in-depth interviews, this qualitative study explored the suicide bereavement experiences of eight participants in China 10-41 years post-loss. Data were analyzed using assimilation analysis. Two bereavement categories-positive recovery and grieving in the mist-and their underlying features were identified, along with the enduring impact of suicide on both individual family members and the family as a system. These findings provide new insights into long-term suicide bereavement and may help identify vulnerable individuals and families to facilitate appropriate support.

The initial introduction of LibGuides created a seismic shift in bioinformatic information dissemination. This study reviewed bioinformatics LibGuides from Canadian universities to establish a consistent overlap of frequently recommended materials that would spell out a canon of bioinformatics resources.

The recycling of waste tire-derived pyrolytic carbon black (CBp) has attracted increased attention. However, the adsorbed surface organic gum layer hinders its industrial reinforcement application, and the traditional prolonged high-temperature degumming treatment caused irreversible graphitization of the carbon microstructure, resulting in significantly reduced reinforcement performance. Herein, we propose a new "flash-heating + water mist quenching" strategy to achieve a rapid degumming process while simultaneously effectively suppressing graphitization, thereby maximizing retention of the original reinforcement performance. The quenching process stabilizes the newly formed surface defects and oxygen-containing functional groups on the carbon black surface, thereby enhancing the induction forces and electronic interactions between the carbon black and rubber molecules, as verified by density functional theory (DFT) calculations. The modified carbon black-filled natural rubber (800CBpf/NR) composite achieves tensile strength of 28.81 MPa, stress at 300% elongation of 14.53 MPa, and tear strength of 88.43 kN m-1, all of which exceed those of the commercial carbon black N660-filled NR composite. Additionally, compared with the unmodified CBpf/NR, the stress at 300% elongation of 800CBpf/NR increases by 97.7%, and the combined rubber content rises by 62.7%. This work provides a novel recovery pathway for the high-value utilization of CBp.

Motion cues play a vital role in multi-frame infrared small target detection (MISTD). However, most targets in existing datasets exhibit regular and slow motion, which cannot reflect the complex and diverse motion patterns in real-world scenarios. This biased data distribution makes recent data-driven methods highly rely on simplified motion assumptions that tend to fail in irregular or fast motion, resulting in noisy feature representations cluttered with target-irrelevant factors. Hence, we stress that methods for MISTD should also work when targets are in complex motion. To enable this research, we propose a large-scale dataset called MIST for airborne infrared detection scenarios. The dataset is built on a synthetic data engine that models variations in pose, size, and intensity of moving targets while seamlessly blending them into real backgrounds for physical, geometric, and visual realism. Targets in MIST exhibit low signal-to-clutter ratios and complex motion, making it a promising yet challenging benchmark for developing algorithms focused on motion analysis. To tackle the challenges of MIST, we develop MISTNet, a robust baseline based on the Information Bottleneck theory. To handle irregular and fast motion, we propose a shifted neighborhood compensation block to efficiently model multi-scale correspondences for implicit motion compensation. To distill compact representations free from irrelevant cues, we design a progressive distillation decoder to hierarchically filter out redundancy while preserving target-relevant information. We benchmark 31 state-of-the-art methods and find that their performance on MIST drops significantly compared with that on the widely used NUDT-MIRSDT dataset. Our MISTNet outperforms all other methods by a large margin, with an over 6% gain in the IoU metric, demonstrating its superiority. The dataset, code, and model weights are available at https://github.com/GR-ray/MIST.

Mixed-dimensional heterostructures consisting of zero- and two-dimensional materials offer a promising platform for optoelectronic devices, as the versatility of material combination allows tunable optical properties. Bias-induced approaches provide an additional means to tune the optical properties beyond the intrinsic band alignment of van der Waals junctions. Here, bias-induced tunneling characteristics are achieved in vertically stacked WSe2/h-BN/CdSe quantum dots/graphene heterostructures by employing the top graphene electrode to regulate carrier transport across the h-BN barrier. The electrical analyses based on the Simmons approximation demonstrate tunneling-mediated charge transfer through thin h-BN layers and bias-dependent modulation of the barrier height. Furthermore, tunneling-induced exciton dissociation in WSe2 and CdSe QDs is observed through spectral responsivity and scanning photocurrent measurements. This work establishes a voltage-dependent tunneling platform that enables deterministic control of carrier dynamics in mixed-dimensional optoelectronic devices.

Benign prostatic hyperplasia (BPH) is the main cause of lower urinary tract symptoms (LUTS) in aging men. As the incidence of prostate cancer (PCa) increases with age, coexistence of BPO and PCa is a frequent issue. Radical prostatectomy might be considered as the best option for patients presenting with severe obstructive symptoms, allowing to simultaneously treat both diseases. However, not all patients are eligible or prefer surgery, therefore a large number of patients are referred to receive PCa radiotherapy (RT). Preexisting LUTS represent a well-known risk factor to develop severe genito-urinary (GU) toxicity. While improving urinary function before starting irradiation appears to be crucial, the development of new minimally invasive surgical therapies (MIST) for BPH creates new opportunities for treatment personalization in RT patients with LUTS.

This study evaluated the survivability and thermal resistance of 4-serovar Salmonella cocktail in chocolate chip cookies formulated with milk protein concentrate (MPC80), whey protein concentrate (WPC80), and unbleached bread flour. The flour was mist inoculated with 4-serovar Salmonella (Typhimurium, Newport, Agona, and Senftenberg 775 W) cocktail and dried back to its preinoculation water activity levels. The inoculated flour was used to prepare the respective cookie dough formulations and baked, simulating the commercial baking process. Baking cookies at 350 °F (176.7 °C) for 18 min followed by 15 min of ambient cooling resulted in >5-log CFU/g reduction of Salmonella across all formulations. The mean D-values for Salmonella in MPC80, WPC80, and flour cookie doughs at 55, 58, and 61 °C were 57.60 ± 5.24, 21.60 ± 1.35, 12.00 ± 0.40 min; 54.54 ± 1.56, 29.48 ± 0.29, 12.53 ± 1.38 min; and 44.86 ± 0.61, 17.26 ± 0.32, 6.22 ± 0.87 min, respectively. The mean z-values for Salmonella in MPC80, WPC80, and flour-based cookie dough were 8.96 ± 0.77, 9.27 ± 0.49, and 6.99 ± 0.49 °C, respectively. This study presents the first scientific evidence that baking high milk protein cookies formulated with MPC80 or WPC80 at 350 °F (176.7 °C) for at least 18 min ensures >5-log Salmonella reduction, confirming effective lethality in the event of prebaking contamination.