The relativistic positive-energy wave equation proposed by P. Dirac in 1971 is an old but largely forgotten subject. The purpose of this note is to speculate that particles described by this equation (called here Dirac particles) are natural candidates for the dark matter. The reasoning is based on a fact that the internal structure of such particles simply prohibits their interaction with electromagnetic fields (at least with the minimal coupling) which is exactly what is required for dark matter. Dirac particles have quite unusual properties. In particular, they are transformed by an infinite-dimensional representation of the homogeneous Lorentz group, which clearly distinguishes them from all known elementary particles described by finite-dimensional representations and hints to a physics beyond the Standard Model. To clarify the topic, a brief review of the main features of the above-mentioned Dirac equation is given. Full article

When droughts occur, freshwater inputs to coastal wetlands can become scarce and hypersalinity can become a problem. In 2023, a severe drought negatively affected a Texas watershed known as Lake Austin that fed a large expanse of wetlands on East Matagorda Bay. To study the hypersalinity problem in these wetlands, we identified freshwater inflows and mapped vegetation changes over time. We found that from 1943 to 2023, the upper portion of the Lake Austin watershed lost freshwater wetlands to agricultural conversion, and ranged from fresh to brackish, with salinity rapidly rising to a maximum of 31 mS during the summer drought of 2023. The lower portion of the watershed gained saltwater wetlands due to sea level rise, and marshes became hypersaline (64–96 mS) during the 2023 drought, endangering its biota. But after large precipitation events, the entire Lake Austin basin rapidly freshened but then returned to its normal salinities within a week as the tides re-delivered saltwater into its basin. Given current climatic trends, we expect that freshwater inflow will continue to slightly increase for the Lake Austin watershed but also that there will be more extreme periods of episodic drought that negatively affect its wetlands. Accordingly, we assessed several potential restoration actions that would improve freshwater flow and delivery to the Lake Austin coastal wetlands. Full article

The application of autonomous driving system (ADS) technology can significantly reduce potential accidents involving vulnerable road users (VRUs) due to driver error. This paper proposes a novel hierarchical deep reinforcement learning (DRL) framework for high-performance collision avoidance, which enables the automated driving agent to perform collision avoidance maneuvers while maintaining appropriate speeds and acceptable social distancing. The novelty of the DRL method proposed here is its ability to accommodate dynamic obstacle avoidance, which is necessary as pedestrians are moving dynamically in their interactions with nearby ADSs. This is an improvement over existing DRL frameworks that have only been developed and demonstrated for stationary obstacle avoidance problems. The hybrid A* path searching algorithm is first applied to calculate a pre-defined path marked by waypoints, and a low-level path-following controller is used under cases where no VRUs are detected. Upon detection of any VRUs, however, a high-level DRL collision avoidance controller is activated to prompt the vehicle to either decelerate or change its trajectory to prevent potential collisions. The CARLA simulator is used to train the proposed DRL collision avoidance controller, and virtual raw sensor data are utilized to enhance the realism of the simulations. The model-in-the-loop (MIL) methodology is utilized to assess the efficacy of the proposed DRL ADS routine. In comparison to the traditional DRL end-to-end approach, which combines high-level decision making with low-level control, the proposed hierarchical DRL agents demonstrate superior performance. Full article

This study is part of the preliminary research for the Chang’e 7 project in China. The Chang’e 7 project plans to drill to penetrate the lunar polar soil and collect lunar soil samples using a spiral groove structure. Ice in the cold environment of the lunar polar region is one of the important targets for sampling. In the vacuum environment of the lunar surface, icy soil samples are sensitive to ambient temperature and prone to solid–gas phase change as the temperature increases. To predict the temperature range of lunar soil samples, this study analyzed the effect of thermal parameters on the temperature rise of lunar soil particles and the drill using discrete element simulation. The parameters included in the thermal effect analysis included the thermal conductivity and specific heat capacity of the drilling tools and lunar soil particles. The simulation showed that the temperature of the icy lunar soil sample in the spiral groove ranged from −127.89 to −160.16 °C within the thermal parameter settings. The magnitude of the value was negatively correlated with the thermal conductivity and specific heat capacity of the lunar soil particles, and it was positively correlated with those of the drilling tools. The temperature variation in the drill bit ranged from −51.21 to −132 °C. The magnitude of the value was positively correlated with the thermal conductivity and specific heat capacity of the lunar soil particles and the thermal conductivity of the drilling tool. Full article

Proton exchange membrane fuel cells (PEMFCs), as an important utilization of hydrogen energy, contribute to the sustainable development of global energy. Pulsed ejectors have a high potential for improving the hydrogen utilization of PEMFCs in the full operating range by circulating unconsumed hydrogen. In this study, a pulsed ejector applied to a 120 kW fuel cell was designed, and the flow characteristics were analysed using computational fluid dynamics (CFD). Based on the data from the CFD model, the global optimization of the ejector was carried out using the Gaussian process regression (GPR) surrogate model and the grey wolf optimization (GWO) algorithm. The local structure was then further optimized using an adjoint method coupling streamlining modification that takes into account the local flow characteristics. The CFD results showed that, under a fixed structure, increasing the pressure difference between the secondary flow and the ejector outlet would promote boundary layer separation, shorten the shockwave chain length, change the effective flow area of the secondary flow, and lower the entrainment ratio (ER). The analytical results from the GPR model indicated significant interactions among the structural parameters. The globally optimized ejector using GPR and GWO improved the hydrogen entrainment ratio from 1.42 to 3.12 at the design point. Furthermore, the results of streamlining local optimization show that the entrainment ratio increased by 1.67% at the design point and increased by up to 3.99% over the full operating range compared to the optimized ejector by global optimization. Full article

The COVID-19 pandemic has had a profound impact on small businesses, significantly influencing entrepreneurial aspirations and presenting numerous challenges. This calls for additional research into perceptions, intentions, and the challenges faced in this context. This study aims to explore the comprehension of key entrepreneurial concepts among business students in the post-pandemic era. The paper presents an empirical study which employs qualitative in-depth interviews with 34 undergraduate business students from one public university in the Midwest of the United States. The findings reveal a complex view of entrepreneurship that extends beyond traditional business creation, encompassing elements of social innovation and personal fulfillment. Students displayed a generally positive attitude towards entrepreneurship, influenced strongly by their involvement in practical entrepreneurship-related activities and their familial backgrounds. However, they also identified significant barriers, including financial constraints, fear of failure, and a lack of practical experience, which hinder their intentions to pursue entrepreneurial ventures. The study underscores the importance of entrepreneurship education programs incorporating more comprehensive practical experiences, enhancing financial literacy, and providing psychological support to overcome these challenges. These insights contribute to the ongoing discussion on how to effectively support and prepare aspiring entrepreneurs in a changing educational landscape. Full article

For the secure deployment of network platforms tailored for IoT devices, the encryption of data transmission is equally as crucial as the process of authentication. In this context, we introduce the Secure and Scalable IoT network (SSI) network platform, designed to accommodate a diverse range of IoT devices. It provides scalability and implements effective many-to-many and end-to-end encryption across extensive regions. With the emergence of quantum computing, secure public key exchange mechanisms have become important. Among the various post-quantum cryptography (PQC) algorithms assessed, Nth Degree Truncated Polynomial Ring Units (NTRUs) have emerged as an optimally suited PQC algorithm for IoT devices constrained by limited computational capabilities. We have integrated NTRUs with SSI as a lightweight PQC solution. Moreover, SSI-PQM (Post-Quantum MACsec) enhances the SSI’s initial authentication structure to minimize PQC-TLS session attempts and protect the SSI’s important configuration information. When applying TLS with PQC for secret key exchange purposes, it was verified that this approach ensures stable performance in IoT environments. Upon the implementation of our proposed SSI-PQM on Raspberry Pi 3B+ based IoT devices, SSI-PQM exhibited acceptable performance at security levels from 80 to 128 and achieved a minimum speed improvement of 161% over RSA at security levels above 160. It can be concluded that SSI-PQM stands out as an effective Zero Trust-based IoT network platform, demonstrating its viability and efficiency in safeguarding data transmission against potential quantum computing threats. Full article

Esophageal cancer ranks among the ten most common cancers worldwide. Despite the adoption of neoadjuvant concurrent chemoradiotherapy (nCCRT) followed by surgery as the standard treatment approach in recent years, the local recurrence rate remains high. In this study, we employed RNA-seq to investigate distinctive gene expression profiles in esophageal squamous cell carcinoma (ESCC) with or without recurrence following a standard treatment course. Our findings indicate that recurrent ESCC exhibits heightened keratinizing and epidermis development activity compared to non-recurrent ESCC. We identified TP63 as a potential candidate for distinguishing clinical outcomes. Furthermore, immunohistochemistry confirmed the trend of TP63 overexpression in ESCC recurrence. Patients with elevated TP63 expression had poorer overall survival and lower 3-year recurrence-free survival. This study underscores the potential of TP63 as a biomarker for detecting cancer recurrence and suggests its role in guiding future treatment options. Full article

(1) Background: Although Physaloptera retusa is one of the most widespread species infecting reptiles in the Americas, numerous taxonomic problems and little genetic data are associated with it. To clarify the taxonomy of this species, we used an integrative approach. (2) Methods: Physaloptera retusa-infecting Erythrolamprus typhlus (snake) from the Pantanal wetlands, Brazil, was morphologically and genetically characterised (18S and 28S rDNA; COI mtDNA) and compared with conspecific sequences available in GenBank, from parasites of Tupinambis teguixin (lizard), using species delimitation methods. Type specimens of P. liophis were re-evaluated given its morphological similarities with P. retusa. (3) Results: The morphology of the present specimens was equal to that of P. retusa, in which the only difference from P. liophis was the relative position of the vulva. Species delimitation methods were more accurate for the COI dataset; all of them (except ABGD) indicated interspecificity among P. retusa sequences. However, a lack of morphological data or voucher material, associated with the deposited sequences, prevented more assertive conclusions. (4) Conclusions: The present results highlight the importance of a clear association between genetic data and morphology of the isolation source, or at least its adequate vouchering. Moreover, P. retusa may represent a species complex in cryptic speciation, since it is widespread and has low hosts specificity. Full article

Given the energy crisis and escalating environmental pollution, the imperative for developing clean new energy is evident. Hydrogen has garnered significant attention owing to its clean properties, high energy density, and ease of storage and transportation. This study synthesized four types of catalysts—FeS(DI/MB), FeS(ET/MB), Fe(DI/MB), and Fe(ET/MB)—using two distinct solution systems: DI/MB and ET/MB. The FeS(DI/MB) catalyst, synthesized using the layered solution system (DI/MB), demonstrates a uniformly distributed and dense nanosheet structure, exhibiting excellent resistance to strong bases and superior catalytic properties. The FeS(DI/MB) electrode showed OER overpotentials of 460 mV and 318 mV in 1 M and 6 M, respectively, at current densities of up to 500 mA cm⁻². Under industrial electrolysis test conditions, the FeS(DI/MB) electrode required only 262 mV to achieve a current density of 500 mA cm⁻², operating in a high-temperature, strong alkaline environment of 6 M at 60 °C. Furthermore, the FeS(DI/MB) electrode exhibited excellent OER catalytic activity and stability, as evidenced by a 60 h stability test These findings provide valuable insights into the preparation of iron nickel sulfide-based catalysts, and further in-depth and comprehensive exploration is anticipated to yield the excellent catalytic performance of these catalysts in the realm of electrolytic water hydrogen production. Full article

With the increase in remote learning, the efficient implementation of distant knowledge assessments has become an essential topic. New challenges have arisen that have had to be adequately addressed and successfully solved. As a response, we introduced an assessment patterns catalogue for distant education, handling various challenges by proposing possible solutions. The catalogue presents a collection of proven practises targeting knowledge assessment in digital and distant environments. The paper presents the survey results from the final step of catalogue creation. The objective was to verify its suitability and expedite its use, focusing on several aspects of knowledge assessment. We focused on the perceived assessment patterns’ impact on grading objectivity and consistency, as well as students’ motivation and satisfaction with an implemented assessment, explored from the students’ and teachers’ perspectives. We gathered data using a uniform questionnaire distributed between students and teachers, both actively involved in distant knowledge assessment. Detailed data analysis highlighted the patterns with the highest perceived impact on the previously mentioned assessment aspects. We also analysed the top-rated patterns within the pattern categories. The results depict a high overlap between students’ and teachers’ perspectives, wherein patterns like Pentathlon, Statistical Validator, Game Rules, and Bonus Points were perceived as the patterns with the highest impact on grading objectivity and consistency, as well as the patterns with the most significant impact on students’ motivation and satisfaction. Full article

Corynebacterium rouxii is one of the recently described species of the Corynebacterium diphtheriae complex. As this species can potentially infect different hosts and harbor the tox gene, producing diphtheria toxin, we present its first pangenomic analysis in this work. A total of fifteen genomes deposited in online databases were included. After confirming the taxonomic position of the isolates by genomic taxonomy, the genomes were submitted to genomic plasticity, gene synteny, and pangenome prediction analyses. In addition, virulence and antimicrobial resistance genes were investigated. Finally, epidemiological data were obtained through molecular typing, clustering, and phylogenetic analysis. Our data demonstrated genetic diversity within the species with low synteny. However, the gene content is extensively conserved, and the pangenome is composed of 2606 gene families, of which 1916 are in the core genome and 80 are related to unique genes. Prophages, insertion sequences, and genomic islands were found. A type I-E CRISPR-Cas system was also detected. Besides the tox gene, determinants involved in adhesion and iron acquisition and two putative antimicrobial resistance genes were predicted. These findings provide valuable insight about this species’ pathogenicity, evolution, and diversity. In the future, our data can contribute to different areas, including vaccinology and epidemiology. Full article

Deep hole measurement is a crucial step in both deep hole machining and deep hole maintenance. Single-camera vision presents promising prospects in deep hole measurement due to its simple structure and low-cost advantages. However, the measurement error caused by the heating of the imaging sensor makes it difficult to achieve the ideal measurement accuracy. To compensate for measurement errors induced by imaging sensor heating, this study proposes an error compensation method for laser and vision-based deep hole measurement instruments. This method predicts the pixel displacement of the entire field of view using the pixel displacement of fixed targets within the camera’s field of view and compensates for measurement errors through a perspective transformation. Theoretical analysis indicates that the perspective projection matrix changes due to the heating of the imaging sensor, which causes the thermally induced measurement error of the camera. By analyzing the displacement of the fixed target point, it is possible to monitor changes in the perspective projection matrix and thus compensate for camera measurement errors. In compensation experiments, using target displacement effectively predicts pixel drift in the pixel coordinate system. After compensation, the pixel error was suppressed from 1.99 pixels to 0.393 pixels. Repetitive measurement tests of the deep hole measurement instrument validate the practicality and reliability of compensating for thermal-induced errors using perspective transformation. Full article

The ability to overcome an opposition in team sports is reliant upon an understanding of the tactical behaviour of the opposing team members. Recent research is limited to a performance analysts’ own playing team members, as the required opposing team athletes’ geolocation (GPS) data are unavailable. However, in professional Australian rules Football (AF), animations of athlete GPS data from all teams are commercially available. The purpose of this technical study was to obtain the on-field location of AF athletes from animations of the 2019 Australian Football League season to enable the examination of the tactical behaviour of any team. The pre-trained object detection model YOLOv4 was fine-tuned to detect players, and a custom convolutional neural network was trained to track numbers in the animations. The object detection and the athlete tracking achieved an accuracy of 0.94 and 0.98, respectively. Subsequent scaling and translation coefficients were determined through solving an optimisation problem to transform the pixel coordinate positions of a tracked player number to field-relative Cartesian coordinates. The derived equations achieved an average Euclidean distance from the athletes’ raw GPS data of 2.63 m. The proposed athlete detection and tracking approach is a novel methodology to obtain the on-field positions of AF athletes in the absence of direct measures, which may be used for the analysis of opposition collective team behaviour and in the development of interactive play sketching AF tools. Full article

Honey bees (Apis mellifera L.) play vital roles as agricultural pollinators and honey producers. However, global colony losses are increasing due to multiple stressors, including malnutrition. Our study evaluated the effects of four pollen substitute diets (Diet 1, Diet 2, Diet 3, and Control) through field and cage experiments, analyzing 11 parameters and 21 amino acids. Notably, Diet 1 demonstrated significantly superior performance in the field experiment, including the number of honey bees, brood area, consumption, preference, colony weight, and honey production. In the cage experiment, Diet 1 also showed superior performance in dried head and thorax weight and vitellogenin (vg) gene expression levels. Canonical discriminant and principle component analyses highlighted Diet 1’s distinctiveness, with histidine, diet digestibility, consumption, vg gene expression levels, and isoleucine identified as key factors. Arginine showed significant correlations with a wide range of parameters, including the number of honey bees, brood area, and consumption, with Diet 1 exhibiting higher levels. Diet 1, containing apple juice, soytide, and Chlorella as additive components, outperformed the other diets, suggesting an enhanced formulation for pollen substitute diets. These findings hold promise for the development of more effective diets, potentially contributing to honey bee health. Full article

The recognition and localization of strawberries are crucial for automated harvesting and yield prediction. This article proposes a novel RTF-YOLO (RepVgg-Triplet-FocalLoss-YOLO) network model for real-time strawberry detection. First, an efficient convolution module based on structural reparameterization is proposed. This module was integrated into the backbone and neck networks to improve the detection speed. Then, the triplet attention mechanism was embedded into the last two detection heads to enhance the network’s feature extraction for strawberries and improve the detection accuracy. Lastly, the focal loss function was utilized to enhance the model’s recognition capability for challenging strawberry targets, which thereby improves the model’s recall rate. The experimental results demonstrated that the RTF-YOLO model achieved a detection speed of 145 FPS (frames per second), a precision of 91.92%, a recall rate of 81.43%, and an $mAP$ (mean average precision) of 90.24% on the test dataset. Relative to the baseline of YOLOv5s, it showed improvements of 19%, 2.3%, 4.2%, and 3.6%, respectively. The RTF-YOLO model performed better than other mainstream models and addressed the problems of false positives and false negatives in strawberry detection caused by variations in illumination and occlusion. Furthermore, it significantly enhanced the speed of detection. The proposed model can offer technical assistance for strawberry yield estimation and automated harvesting. Full article

The COVID-19 pandemic resulted in the generation of large quantities of medical waste and highlighted the importance of efficient waste management systems. One good example of this is rapid antigen tests, which contain valuable resources, and which are usually incinerated after their use. The present study aimed to evaluate the potential of waste rapid antigen test cassettes (RATCs) as a resource for the preparation of sustainable flame-retardant plastics. Milled RATCs were compounded with different concentrations (10–30 wt.%) of aluminium diethylphosphinate (ADP) and injection moulded into test specimens. Prepared samples were exposed to ultraviolet (UV) ageing for varying durations and characterised by Fourier-transform infrared spectroscopy (FT-IR), differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), dynamic mechanical analysis (DMA), tensile tests, Charpy impact tests, and vertical burning tests. FT-IR analysis revealed that RATCs are composed mainly of high-impact polystyrene (HIPS), which was further confirmed by suitable glass transition temperatures (T_g) determined by DSC and DMA. The addition of ADP resulted in progressive embrittlement of HIPS with increasing concentration, while flammability decreased significantly and reached V-1 classification at loading of 30 wt.%. UV ageing caused photo-oxidative degradation of HIPS, which resulted in decreased strain-at-break, while flammability was not affected. Full article

The hydrofoil plays a crucial role in tidal current energy (TCE) devices, such as horizontal-axis turbines (HATs), vertical-axis turbines (VATs), and oscillating hydrofoils. This study delves into the numerical investigation of passive chordwise and spanwise deformations and the hydrodynamic performance of a deformable hydrofoil. Three-dimensional (3D) coupled fluid–structure interaction (FSI) simulations were conducted using the ANSYS Workbench platform, integrating computational fluid dynamics (CFD) and finite element analysis (FEA). The simulation involved a deformable hydrofoil undergoing pitching motion with varying elastic moduli. The study scrutinizes the impact of elastic modulus on hydrofoil deformation, pressure distribution, flow structure, and hydrodynamic performance. Coefficients of lift, drag, torque, as well as their hysteresis areas and intensities, were defined to assess the hydrodynamic performance. The analysis of the correlation between pressure distribution and deformation elucidates the FSI mechanism. Additionally, the study investigated the 3D effects based on the flow structure around the hydrofoil. Discrepancies in pressure distribution along the spanwise direction result from these 3D effects. Consequently, different chordwise deformations of cross-sections along the spanwise direction were observed, contributing to spanwise deformation. The pressure difference between upper and lower surfaces diminished with increasing deformation. Peak values and fluctuations of lift, drag, and torque decreased. This study provides insights for selecting an appropriate elastic modulus for hydrofoils used in TCE devices. Full article

Immune cell migration is required for the development of an effective and robust immune response. This elegant process is regulated by both cellular and environmental factors, with variables such as immune cell state, anatomical location, and disease state that govern differences in migration patterns. In all cases, a major factor is the expression of cell surface receptors and their cognate ligands. Rapid adaptation to environmental conditions partly depends on intrinsic cellular immune factors that affect a cell’s ability to adjust to new environment. In this review, we discuss both myeloid and lymphoid cells and outline key determinants that govern immune cell migration, including molecules required for immune cell adhesion, modes of migration, chemotaxis, and specific chemokine signaling. Furthermore, we summarize tumor-specific elements that contribute to immune cell trafficking to cancer, while also exploring microenvironment factors that can alter these cellular dynamics within the tumor in both a pro and antitumor fashion. Specifically, we highlight the importance of the secretome in these later aspects. This review considers a myriad of factors that impact immune cell trajectory in cancer. We aim to highlight the immunotherapeutic targets that can be harnessed to achieve controlled immune trafficking to and within tumors. Full article

Worldwide cancer statistics have indicated about 20 million new cancer cases and over 10 million deaths in 2022 (according to data from the International Agency for Research on Cancer). One of the leading cancer treatment strategies is chemotherapy, using innovative drug delivery systems (DDSs). Self-immolative domino dendrimers (SIDendr) for triggered anti-cancer drugs appear to be a promising type of DDSs. The present review provides an up-to-date survey on the contemporary advancements in the field of SIDendr-based anti-cancer drug delivery systems (SIDendr-ac-DDSs) through an exhaustive analysis of the discovery and application of these materials in improving the pharmacological effectiveness of both novel and old drugs. In addition, this article discusses the designing, chemical structure, and targeting techniques, as well as the properties, of several SIDendr-based DDSs. Approaches for this type of targeted DDSs for anti-cancer drug release under a range of stimuli are also explored. Full article

Climate change poses significant challenges to agricultural productivity, making the efficient management of water resources essential for sustainable crop production. The assessment of plant water status is crucial for understanding plant physiological responses to water stress and optimizing water management practices in agriculture. Proximal and remote sensing techniques have emerged as powerful tools for the non-destructive, efficient, and spatially extensive monitoring of plant water status. This review aims to examine the recent advancements in proximal and remote sensing methodologies utilized for assessing the water status, consumption, and irrigation needs of fruit tree crops. Several proximal sensing tools have proved useful in the continuous estimation of tree water status but have strong limitations in terms of spatial variability. On the contrary, remote sensing technologies, although less precise in terms of water status estimates, can easily cover from medium to large areas with drone or satellite images. The integration of proximal and remote sensing would definitely improve plant water status assessment, resulting in higher accuracy by integrating temporal and spatial scales. This paper consists of three parts: the first part covers current plant-based proximal sensing tools, the second part covers remote sensing techniques, and the third part includes an update on the on the combined use of the two methodologies. Full article

During the sea launch of a launch vehicle in low sea state, a rolling phenomenon of the launch vehicle has been observed. In rough sea conditions, launch may failure. This study utilizes dimensionality reduction-driven spatial system projection methods and virtual prototype modeling technology to reveal that the launch vehicle’s rolling is caused by differences in the motion paths of the center of mass. Additionally, during the prelaunch stage, the variation in the trajectory of the launch vehicle’s center of mass caused by the rolling and pitching motions of the transportation vessel has a significant impact on the roll motion of the launch vehicle. The motion in other degrees of freedom has minimal influence on the launch vehicle’s rolling. The minimum rocket rolling occurs when the dynamic coefficient of friction of the launchpad–launch vehicle contact is 0.05, and the dynamic coefficient of friction of the adapters and guideways is 0.4. The conclusions provide a theoretical foundation for optimizing the sea launch system and enhancing the reliability of sea launch in rough sea conditions. Full article