Search Results (6,115)

Connected vehicle data have the potential to warn motorists of impending slowdowns and congestion in real time. Multiple data providers have recently begun providing in-cab alerts to commercial vehicle drivers. This study reports on one such deployment of in-cab alerts on 44 corridors in Indiana from April–June 2024. Approximately 20,000 alerts were analyzed, with 92% being Congestion alerts and 8% being Dangerous Slowdown alerts. Observations showed that 15% of trucks lowered their speeds by at least 5 mph 30 s after receiving a Congestion alert, while 21% of trucks reduced their speeds by at least 5 mph 30 s after receiving a Dangerous Slowdown alert. The analysis also showed that a majority of Congestion alerted trucks encountered slow-speed traffic about 3 min after receiving an alert, while a majority of Dangerous Slowdown alerted drivers had traveled through the zone of slow speeds 2 min after receiving the alert. Although these results are encouraging, the study also found that 8.1% of Congestion alerts and 8.3% of Dangerous Slowdown alerts were received by trucks when they were operating at speeds of less than or equal to 45 mph, indicating they were already in congested conditions. The study reports that 43% of trucks that received Dangerous Slowdown alerts never reduced their speed below 45 mph. The paper concludes that it is important to converge on a shared vision for these performance measures so that public agencies, in-cab alert providers, and trucking companies can agilely improve these systems and increase driver confidence in the alerts. Full article

Theano, TensorFlow, Keras, Torch, PyTorch, and other software frameworks have remarkably stimulated the popularity of deep learning (DL). Apart from all the good they achieve, the danger of such frameworks is that they unintentionally spur a black-box attitude. Some practitioners play around with building blocks offered by frameworks and rely on them, having a superficial understanding of the internal mechanics. This paper constitutes a concise tutorial that elucidates the flows of signals and gradients in deep neural networks, enabling readers to successfully implement a deep network from scratch. By “from scratch”, we mean with access to a programming language and numerical libraries but without any components that hide DL computations underneath. To achieve this goal, the following five topics need to be well understood: (1) automatic differentiation, (2) the initialization of weights, (3) learning algorithms, (4) regularization, and (5) the organization of computations. We cover all of these topics in the paper. From a tutorial perspective, the key contributions include the following: (a) proposition of R and S operators for tensors—rashape and stack, respectively—that facilitate algebraic notation of computations involved in convolutional, pooling, and flattening layers; (b) a Python project named hmdl (“home-made deep learning”); and (c) consistent notation across all mathematical contexts involved. The hmdl project serves as a practical example of implementation and a reference. It was built using NumPy and Numba modules with JIT and CUDA amenities applied. In the experimental section, we compare hmdl implementation to Keras (backed with TensorFlow). Finally, we point out the consistency of the two in terms of convergence and accuracy, and we observe the superiority of the latter in terms of efficiency. Full article

This commentary critically examines the article by Rodríguez-Cortés et al. on the links between circadian preferences, eating disorders, and obesity in pediatric populations, emphasizing the association between evening chronotypes and disordered eating behaviors. Key findings highlight the increased prevalence of food addiction (FA) and night eating syndrome (NES) among evening-oriented children and adolescents, though the article’s reliance on studies with adult samples limits the generalizability of its conclusions. Additionally, the ambiguous classification of FA and NES within broader eating disorder frameworks necessitates further investigation to delineate these behaviors from traditional disorders such as anorexia and bulimia nervosa. This commentary advocates for future research focusing on pediatric populations to explore the intersection of circadian misalignment with emotional regulation and environmental factors, aiming to develop tailored preventative strategies that incorporate chronobiological and lifestyle modifications. Full article

Ice avalanche (IA) has a strong concealment and sudden characteristics, which can cause severe disasters. The early identification of IA hidden danger is of great value for disaster prevention and mitigation. However, it is very difficult, and there is poor efficiency in identifying it by site investigation or manual remote sensing. So, an artificial intelligence method for the identification of IA hidden dangers using a deep learning model has been proposed, with the glacier area of the Yarlung Tsangpo River Gorge in Nyingchi selected for identification and validation. First, through engineering geological investigations, three key identification indices for IA hidden dangers are established, glacier source, slope angle, and cracks. Sentinel-2A satellite data, Google Earth, and ArcGIS are used to extract these indices and construct a feature dataset for the study and validation area. Next, key performance metrics, such as training accuracy, validation accuracy, test accuracy, and loss rates, are compared to assess the performance of the ResNet50 (Residual Neural Network 50) and VGG16 (Visual Geometry Group 16) models. The VGG16 model (96.09% training accuracy) is selected and optimized, using Early Stopping (ES) to prevent overfitting and L2 regularization techniques (L2) to add weight penalties, which constrained model complexity and enhanced simplicity and generalization, ultimately developing the ES-L2-VGG16 (Early Stopping—L2 Norm Regularization Techniques—Visual Geometry Group 16) model (98.61% training accuracy). Lastly, during the validation phase, the model is applied to the Yarlung Tsangpo River Gorge glacier area on the Tibetan Plateau (TP), identifying a total of 100 IA hidden danger areas, with average slopes ranging between 34° and 48°. The ES-L2-VGG16 model achieves an accuracy of 96% in identifying these hidden danger areas, ensuring the precise identification of IA dangers. This study offers a new intelligent technical method for identifying IA hidden danger, with clear advantages and promising application prospects. Full article

The growing amount of space debris in the low Earth orbit poses a danger to manned as well as uncrewed missions. Additionally, the new business model of providing internet from space is emerging among new space players, making low Earth orbit more crowded. These factors have encouraged the space community to focus on sustainability in space. Satellite manufacturers typically have the capability to perform complete life cycle analysis (LCA) on their own products based on the manufacturing data. However, there is a lack of a method for non-manufacturers such as environmentalists and the general public to predict the carbon footprint of satellite manufacturing using a subsystem-level mass budget. Hence, this paper presents a method to quantify environmental pollution caused by the production of satellite constellations. Starlink is taken as a case study in this paper, and mass budget is predicted based on space systems engineering budget estimation techniques, the parametric method, and Federal Communication Commission orbital data. With the budget table used as an input, space-specific life cycle assessment is performed based on European Space Agency’s life cycle inventory database. Finally, the single score for Starlink constellation version 1 was found to be 76 kilo points. This signifies the annual environmental load. These results could be helpful in obtaining an overview of the environmental effects of the production phase of satellite constellations. Further, the results could act as a foundation for further research on implementing more circular approach practices on Earth as well as in space. Full article

The rapid growth of e-commerce has significantly increased demands on logistics providers, particularly in the areas of product handling and shipment. One of the most challenging and critical aspects of this process is the handling of dangerous goods (DG). This is a complex and time-intensive operation that requires safety measures and proper packaging and labelling, because mishandling DG can lead to severe injuries, property loss, and legal liability. This puts additional pressure on logistics providers to efficiently manage DG while maintaining speed and safety in the context of e-commerce. To meet this challenge, there is an urgent need to advance training programs and enhance the operational efficiency for DG handling. The use of mixed reality (MR) technology offers a promising solution. By seamlessly integrating virtual elements with real-world environments, MR has the potential to greatly improve the effectiveness and efficiency of the training of DG handling. Earlier research has examined MR in various fields, while there is still a research gap in applying MR specifically to the training of DG handling. This paper seeks to address the current research gap by presenting a novel MR model, named “MRSafe,” for a training program on the safe handling of DG. The model offers users virtual experiences and comprehensive guidance to provide operational decision support. Full article

Since the beginning of the 21st century, infrared Nd:YVO₄ pulsed lasers have been widely applied, especially in some actual industrial processes. In the working process of a laser-aided etching device, the “match-head” effect must be effectively controlled by suppressing the first giant pulse for a solid-state Q-switched laser. In the process of optimizing the infrared Nd:YVO₄ pulsed laser by adjusting the slope parameters of the radio frequency (RF) modulation to suppress the first giant pulse, it has been found that an abnormal horizontally polarized emission with a very short time appears before the formal vertically polarized emission when the gate signal is artificially started. Actually, abnormal horizontally polarized emissions will bring some unexpected machining traces during the production process and even greater dangers. The experimental results show that with the increase in the slope duration of an RF signal, the existence time of abnormal output horizontally polarized light will be shortened, and the horizontal giant pulse and vertical giant pulse are well suppressed. When the slope duration is greater than 0.18 ms, both horizontal and vertical giant pulses will disappear. The horizontally polarized light can be thoroughly suppressed when the slope duration is greater than 13.7 ms. Compared with the method of adding a polarizer to eliminate abnormal output horizontally polarized light, this method does not add elements in the laser, ensuring that the laser volume is relatively small, and does not affect the quality of the normal output laser. The research conclusion is thought to be of great practical significance, especially for processing transparent materials. Full article

This paper aims to investigate the nonlinear transition to turbulence in generalized 3D Kolmogorov flow. The difference between this and classical Kolmogorov flow is that the forcing term in the x direction $sin\left(y\right)$ is replaced with $sin\left(y\right)cos\left(z\right)$. This drastically complicates the problem. First, a stability analysis is performed by deriving the analog of the Orr–Sommerfeld equation. It is shown that for infinite stretching, the flow is stable, contrary to classical forcing. Next, a neutral curve is constructed, and the stability of the main solution is analyzed. It is shown that for the cubic domain, the main solution is linearly stable, at least for $0<R\le 100$. Next, we turn our attention to the numerical investigation of the solutions in the cubic domain. The main feature of this problem is that it is spatially periodic, allowing one to apply a relatively simple pseudo-spectral numerical method for its investigation. We apply the method of deflation to find distinct solutions in the discrete system and the method of arc length continuation to trace the bifurcation solution branches. Such solutions are called disconnected solutions if these are solutions not connected to the branch of the main solution. We investigate the influence of disconnected solutions on the dynamics of the system. It is demonstrated that when disconnected solutions are formed, the nonlinear transition to turbulence is possible, and dangerous initial conditions are these disconnected solutions. Full article

Dangerous emerging water micropollutants like Diclofenac are harming ecosystems all over the planet, and immediate action is needed. The large bandgap photocatalysts conventionally used to degrade them need to be more efficient. Cu₂ZnSnS₄, a well-known light absorber in photovoltaics with a bandgap of 1.5 eV, can efficiently harvest an abundant portion of the solar spectrum. However, its photocatalytic activity has so far only been reported in relation to the degradation of organic dyes, and it is usually used as a benchmark to assess the activity of a photocatalyst without testing its actual potential on a hazardous water micropollutant conventionally encountered in primary and secondary waters. Here, we report the promising photocatalytic activity of Cu₂ZnSnS₄ nanoparticles in the degradation of Diclofenac, chosen as a benchmark for dangerous emerging water micropollutants. Full article

As the opioid crisis continues to wreak havoc on a global scale, it is increasingly critical to develop methodologies to detect the most dangerous drugs such as fentanyl and its derivatives, which have orders of magnitude higher potency than morphine. The scientific challenge for chemical detection of fentanyl and its derivatives is complicated by both the constantly increasing synthetic variations of the drug as well as the expanded use of adulterants. One tragically consequential example is the nocuous street drug known as “Tranq”, which combines fentanyl or a fentanyl derivative with the veterinary sedative Rompun^®, chemically identified as xylazine (XYL). This pervasive street cocktail is exacerbating the already staggering number of fentanyl-related deaths as its acute toxicity poses a danger to medical first-responders and complicates their initial assessment and treatment options for overdose victims. Given the widespread use of XYL as an adulterant, an electrochemical XYL sensor capable of on-site operation by non-experts as a fast-screening tool is a notable goal. This work presents a voltammetry-based sensor featuring carbon electrodes modified with carboxylic-acid functionalized multi-walled carbon nanotubes layered with cyclodextrin and polyurethane membranes for sensitivity and selectivity enhancements. The sensor has critical and robust fouling resistance while providing sensitivity at 950 μA/mM∙cm², a low limit of detection (~5 ppm), and the ability to detect XYL in the presence of fentanyl and/or other non-fentanyl stimulants like cocaine. The demonstrated sensor can be applied to promote public health with its ability to detect and indicate XYL in the presence of opioids, serving to protect drug-users, first responders, medical examiners, and on-site forensic investigators from exposure to these dangerous mixtures. Full article

Mercury is classified as one of the world’s most toxic and dangerous pollutants as it tends to bioaccumulate and biomagnify within the trophic chain and is persistent. Various approaches are available to remediate Hg-affected sites including phytoremediation, which includes the use of plants to clean up contaminated environments. The phytoremediation of mercury contamination is attracting increasing attention because of its advantages: it is environmentally friendly, inexpensive, simple, and can improve soil fertility. In this report, VOSviewer (version 1.6.1) and Bibliometrix (version 4.16) software were used to analyze 457 and 697 documents published from 2000 to 2023, retrieved from the databases WoS and Scopus, respectively. China, India, the United States, and Spain were the top four most productive countries. The largest topic area was environmental sciences, and the Chinese Academy of Sciences was the organization that contributed the most to the overall number of publications. The keywords with the highest frequency excluding phytoremediation and mercury in WoS were heavy metals, accumulation, cadmium, soils, and phytoextraction. In Scopus, the most frequent keywords were bioremediation, heavy metals, soil pollution, bioaccumulation, biodegradation, and environmental. From the above analysis, we concluded that future research should focus on (1) finding native plants, (2) genetic engineering applications, (3) increasing remediation ability through assisted phytoremediation, and (4) the detoxification mechanism of mercury. This study provides insights into trending themes and serves as a reference for future research. Full article

Introduction: The abscopal effect is a systemic immune response characterized by metastases regression at sites distant from the irradiated lesion. This systematic review aims to explore the immunological mechanisms of action underlying the abscopal effect and to investigate how hyperthermia (HT) can increase the chances of radiotherapy (RT) triggering systemic anti-tumor immune responses. Methods: This review is created in accordance with the PRISMA guidelines. Results and Conclusion: HT and RT have both complementary and synergistic immunological effects. Both methods trigger danger signal release, promoting cytokine and chemokine secretion, which increases T-cell infiltration and facilitates cell death. Both treatments upregulate extracellular tumor HSP70, which could amplify DAMP recognition by macrophages and DCs, leading to stronger tumor antigen presentation and CTL-mediated immune responses. Additionally, the combined increase in cell adhesion molecules (VCAM-1, ICAM-1, E-selectin, L-selectin) could enhance leukocyte adhesion to tumors, improving lymphocyte trafficking and boosting systemic anti-tumor effects. Lastly, HT causes vasodilation and improves blood flow, which might exacerbate those distant effects. We suggest the combination of local radiotherapy with fever-range whole-body hyperthermia to optimally enhance the chances of triggering the abscopal effect mediated by the immune system. Full article

Our novel binuclear complex-anchored Ru(III) catalyst, designed and assembled by sonicating 2,2′-(4,6-dihydroxy-1,3-phenylene)bis(1H-benzo[d]imidazole-4-carboxylic acid), Ru(DMSO)₄Cl₂ and 4-methylpyridine, demonstrates remarkable efficiency and selectivity. It promotes one-pot reactions, including active methylenes and benzyl alcohols in water, via a tandem aerobic oxidation/Knoevenagel condensation process, yielding benzylidene malononitrile in excellent yields. The catalyst’s ability to oxidize benzyl alcohols to aldehydes, which then undergo Knoevenagel condensation with active methylenes, makes it a multifunctional catalyst. Notably, the catalyst can be successfully retrieved and recycled for five successive rounds with no significant decrease in catalytic efficiency. The ICP study showed that no catalyst leaching was observed, indicating that the designed catalyst is indeed heterogeneous. The Ru catalyst outperformed other documented catalysts in terms of lower dose, shorter duration, decreased working temperature, and the absence of dangerous additives. This demonstrates the catalyst’s robustness and sustainability, making it a promising candidate for future organic conversions and industrial uses. Full article

Disassembly is a key step in remanufacturing, especially for end-of-life (EoL) products such as electric vehicle (EV) batteries, which are challenging to dismantle due to uncertainties in their condition and potential risks of fire, fumes, explosions, and electrical shock. To address these challenges, this paper presents a robotic teleoperation system that leverages augmented reality (AR) and digital twin (DT) technologies to enable a human operator to work away from the danger zone. By integrating AR and DTs, the system not only provides a real-time visual representation of the robot’s status but also enables remote control via gesture recognition. A bidirectional communication framework established within the system synchronises the virtual robot with its physical counterpart in an AR environment, which enhances the operator’s understanding of both the robot and task statuses. In the event of anomalies, the operator can interact with the virtual robot through intuitive gestures based on information displayed on the AR interface, thereby improving decision-making efficiency and operational safety. The application of this system is demonstrated through a case study involving the disassembly of a busbar from an EoL EV battery. Furthermore, the performance of the system in terms of task completion time and operator workload was evaluated and compared with that of AR-based control methods without informational cues and ‘smartpad’ controls. The findings indicate that the proposed system reduces operation time and enhances user experience, delivering its broad application potential in complex industrial settings. Full article

Kaijū media frequently features dangerous scientific experiments as a central theme, invented by scientists who are falsely convinced that they both completely understand and control their advanced technology. In the past few decades, this has included the introduction of high-energy physics (HEP) experiments—especially mammoth particle accelerators—that, among other destructive results, allow for the entrance of equally large and dangerous creatures into our world from parallel dimensions. Public concerns voiced about the safety of the creation of two groundbreaking energy accelerators—the Relativistic Heavy Ion Collider (RHIC) in New York and the Large Hadron Collider (LHC) in Europe—in the early 21st century are tied to related science fiction media that capitalize on such fears (including Godzilla vs. Megaguirus [2000], Pacific Rim [2013], The Cloverfield Paradox [2018], The Kaiju Preservation Society [2022]). Particular attention is paid to the Netflix original series Stranger Things (2016–) as a detailed case study. This study concludes with an analysis of scientists’ attempts to embrace the popularity of Stranger Things in their communication with the general public, and suggests that ongoing issues with conspiracy theories have been fueled in part by such attempts, coupled with long-standing issues with the HEP community and their peculiar scientific naming conventions. Full article