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

Biological nanoparticles (NPs), such as extracellular vesicles (EVs), exosome-mimetic nanovesicles (EMNVs) and nanoghosts (NGs), are perspective non-viral delivery vehicles for all types of therapeutic cargo. Biological NPs are renowned for their exceptional biocompatibility and safety, alongside their ease of functionalization, but a significant challenge arises when attempting to load therapeutic payloads, such as nucleic acids (NAs). One effective strategy involves fusing biological NPs with liposomes loaded with NAs, resulting in hybrid carriers that offer the benefits of both biological NPs and the capacity for high cargo loads. Despite their unique parameters, one of the major issues of virtually any nanoformulation is the ability to escape degradation in the compartment of endosomes and lysosomes which determines the overall efficiency of nanotherapeutics. In this study, we fabricated all major types of biological and hybrid NPs and studied their response to the acidic environment observed in the endolysosomal compartment. In this study, we show that EMNVs display increased protonation and swelling relative to EVs and NGs in an acidic environment. Furthermore, the hybrid NPs exhibit an even greater response compared to EMNVs. Short-term incubation of EMNVs in acidic pH corresponding to late endosomes and lysosomes again induces protonation and swelling, whereas hybrid NPs are ruptured, resulting in the decline in their quantities. Our findings demonstrate that in an acidic environment, there is enhanced rupture and release of vesicular cargo observed in hybrid EMNVs that are fused with liposomes compared to EMNVs alone. This was confirmed through PAGE electrophoresis analysis of mCherry protein loaded into nanoparticles. In vitro analysis of NPs colocalization with lysosomes in HepG2 cells demonstrated that EMNVs mostly avoid the endolysosomal compartment, whereas hybrid NPs escape it over time. To conclude, (1) hybrid biological NPs fused with liposomes appear more efficient in the endolysosomal escape via the mechanism of proton sponge-associated scavenging of protons by NPs, influx of counterions and water, and rupture of endo/lysosomes, but (2) EMNVs are much more efficient than hybrid NPs in actually avoiding the endolysosomal compartment in human cells. These results reveal biochemical differences across four major types of biological and hybrid NPs and indicate that EMNVs are more efficient in escaping or avoiding the endolysosomal compartment. Full article

Over the past decade, remote monitoring (RM) has become an increasingly popular way to improve healthcare and health outcomes. Modern cardiac implantable electronic devices (CIEDs) are capable of recording an increasing amount of data related to CIED function, arrhythmias, physiological status and hemodynamic parameters, providing in-depth and updated information on patient cardiovascular function. The extensive use of RM for patients with CIED allows for early diagnosis and rapid assessment of relevant issues, both clinical and technical, as well as replacing outpatient follow-up improving overall management without compromise safety. This approach is recommended by current guidelines for all eligible patients affected by different chronic cardiac conditions including either brady- and tachy-arrhythmias and heart failure. Beyond to clinical advantages, RM has demonstrated cost-effectiveness and is associated with elevated levels of patient satisfaction. Future perspectives include improving security, interoperability and diagnostic power as well as to engage patients with digital health technology. This review aims to update existing data concerning clinical outcomes in patients managed with RM in the wide spectrum of cardiac arrhythmias and Hear Failure (HF), disclosing also about safety, effectiveness, patient satisfaction and cost-saving. Full article

The construction sector of Pakistan is on a cross-growth trajectory, developing under the twin pressures of emerging infrastructure-based demands and sustainable practices that need to be inculcated urgently. This article focuses on the critical evaluation of sustainable waste management practices within the fast-developing construction industry of Pakistan, and clearly delineates a research gap in the current methodologies and use of data combined with the absence of a strategy for effective management of concrete waste. This research aims to utilize an algorithm based on machine learning that will provide accurate prediction in the generation of construction waste by harnessing the potential of real-time data for improved sustainability in the construction process. This research has identified fundamental factors leading systematically to the generation of concrete waste by creating an extensive dataset from construction firms all over Pakistan. This research study also identifies the potential concrete causes and proposed strategies towards the minimization of waste with a strong focus on the reuse and recycling of the same concrete material to enhance the adoption of sustainable practices. The prediction of the model indicates that the volumes of construction are to increase to 158 cubic meters by 2030 and 192 cubic meters by 2040. Further, it projects the increase in concrete construction waste volumes to 223 cubic meters by the year 2050 through historical wastage patterns. Full article

In this paper, we study the networks of financial institutions using textual data (i.e., news). We draw knowledge graphs after the textual data has been processed via various natural language processing and embedding methods, including use of the most recent version of ChatGPT (via OpenAI api). Our final graphs represent bank networks and further shed light on the systemic risk of the financial institutions. Financial news reflects live how financial institutions are connected, via graphs which provide information on conditional dependencies among the financial institutions. Our results show that in the year 2016, the chosen 22 top U.S. financial firms are not closely connected and, hence, present no systemic risk. Full article

In order to solve the ecological environment pollution caused by a large amount of coal gangue accumulation and the problems of poor conveying performance and low support strength of paste filling materials. Based on the standard slump test and uniaxial compressive strength test, the slump and compressive strength (post-coagulation) of gangue paste under different aggregate size aggregations were obtained through orthogonal experiments in this study. The results show that the slump of the gangue filling paste with four types of aggregate size gradations is negatively correlated with the mass fraction, and the slump of the filling paste with a coarser aggregate content is more obviously affected by the mass fraction The increase of fine aggregate content has a significant impact on the compressive strength of the filling paste, which shows a trend of first increasing and then decreasing. When the mixing ratio of coarse aggregate and fine aggregate is 5:5, the compressive strength of the paste reaches the best. In addition, different proportions of aggregate mixing cause the filling paste to form different skeleton structures, including the skeleton void structure at 7:3 or 6:4, the skeleton dense structure at 5:5, and the skeleton suspension structure at 4:6, which are decisive for the final performance of the paste. By analyzing the experimental results of the compressive strength and slump of the gangue filling paste, it was found that the relationship between the compressive strength and slump of the gangue filling paste is a power index function. Through data fitting, it was found that the regression coefficient of the fitting function is no less than 0.97, and the fitting effect is good for evaluating the strength of the filling paste under the aggregate size grading. Full article

In this study, we investigated the anti-hypertensive properties of mulberry products by modulating the renin–angiotensin system (RAS). Comparative analysis showed that the ethyl acetate fractions, particularly from the Cheongil and Daeshim cultivars, contained the highest levels of polyphenols and flavonoids, with concentrations reaching 110 mg gallic acid equivalent (GE)/g and 471 mg catechin equivalent (CE)/g of extract, respectively. The ethyl acetate fraction showed superior angiotensin-converting enzyme (ACE) inhibitory activity, mainly because of the presence of the prenylated flavonoids kuwanon G and H. UPLC/Q-TOF-MS analysis identified kuwanon G and H as the primary active components, which significantly contributed to the pharmacological efficacy of the extract. In vivo testing of mice fed a high-salt diet showed that the ethyl acetate fraction substantially reduced the heart weight and lowered the serum renin and angiotensinogen levels by 34% and 25%, respectively, highlighting its potential to modulate the RAS. These results suggested that the ethyl acetate fraction of mulberry root bark is a promising candidate for the development of natural ACE inhibitors. This finding has significant implications for the management of hypertension through RAS regulation and the promotion of cardiovascular health in the functional food industry. Full article

In the dynamic landscape of tissue engineering, the integration of tissue-engineered constructs (TECs) faces a dual challenge—initiating beneficial inflammation for regeneration while avoiding the perils of prolonged immune activation. As TECs encounter the immediate reaction of the immune system upon implantation, the unique immunomodulatory properties of mesenchymal stem/stromal cells (MSCs) emerge as key navigators. Harnessing the paracrine effects of MSCs, researchers aim to craft a localized microenvironment that not only enhances TEC integration but also holds therapeutic promise for inflammatory-driven pathologies. This review unravels the latest advancements, applications, obstacles, and future prospects surrounding the strategic alliance between MSCs and TECs, shedding light on the immunological symphony that guides the course of regenerative medicine. Full article

Surface-enhanced Raman scattering (SERS) provides an alternative rapid detection method for pesticide residues in food, but fenvalerate possesses poor affinity to the novel metal substrate, thus restricting its analysis. To break this bottleneck, a SERS-active platform with an Ag/rGO composite structure was engineered using a facile method for fenvalerate detection. Ag nanoparticles with a 60 nm diameter can grow evenly on the top and bottom of rGO layers under intense ultrasonic oscillation, and rGO in hybrid material acts as an ideal hotspot holder between the gaps of Ag nanoparticles, not only allowing the interaction area to be enhanced both electromagnetically and chemically but also enabling the capture and enrichment of fenvalerate pesticide molecules into the “hotspot” area to improve detection sensitivity. Ag/rGO composite substrate possesses superior SERS performance with an ultralow detectable concentration of 4-aminothiophenol (10⁻¹⁰ M) and good reproducibility, endowing the material with a better enhancement effect than pure Ag nanoparticles. When used as the SERS substrate for fenvalerate detection, Ag/rGO composite material showed excellent performance in both experiments and theoretical calculation, with the limit of detection (LOD) of fenvalerate being as low as 1.69 × 10⁻⁵ mg/kg and a detection model with an R² of 99.2%, demonstrating its exciting potential as a SERS substrate for pesticides detection. Full article

Biodigestion in farming and agriculture offers environmental and economic benefits, but investing in biodigesters carries real-world risks for enterprises. This study analyzes methane (CH₄) emissions from a poultry farm biodigester in Tenerife Island, Canary Islands, Spain, conceptualized as a right-angled prism measuring 45 m wide, 25 m long, and 12 m tall, with an internal volume of approximately 13,500 m³. Using a Neon tracer gas technique, CH₄ emission rates were quantified in situ during two surveys in February 2021 and October 2022, capturing seasonal variability in ambient conditions. Biogas analysis was performed using a portable micro-gas chromatograph in less than 5 min, revealing stable CH₄ production rates of approximately 200 kg·d⁻¹ (~310 m³·d⁻¹) and 330 kg·d⁻¹ (~500 m³·d⁻¹) for the two experiments, respectively. The composition of biogas indicated CH₄ concentrations of around 38–43%, with the remaining composition consisting of carbon dioxide (19–26%), nitrogen (36–27%), oxygen (7–4%), and trace amounts of other gases. A comparison with a theoretical model showed a good correlation. This approach enhances biodigester investment attractiveness by enabling enterprises to optimize efficiency promptly. The obtained data were used to estimate the energy potential of biogas from chicken farms in the Canary Islands. Full article

Due to the prevalent challenges of inadequate accuracy, unstandardized parameters, and suboptimal efficiency with regard to icing prediction, this study introduces an innovative online method for icing prediction based on Robust STL–BTSF and IBWO–LSSVR. Firstly, this study adopts the Robust Seasonal Decomposition of Time Series and Bilinear Temporal–Spectral Fusion (Robust STL–BTSF) approach, which is demonstrably effective for short-term and limited sample data preprocessing. Subsequently, injecting a multi-faceted enhancement approach to the Beluga Whale Optimization algorithm (BWO), which integrates a nonlinear balancing factor, a population optimization strategy, a whale fall mechanism, and an ascendant elite learning scheme. Then, using the Improved BWO (IBWO) above to optimize the key hyperparameters of Least Squares Support Vector Regression (LSSVR), a superior offline predictive part is constructed based on this approach. In addition, an Incremental Online Learning algorithm (IOL) is imported. Integrating the two parts, the advanced online icing prediction model for transmission lines is built. Finally, simulations based on actual icing data unequivocally demonstrate that the proposed method markedly enhances both the accuracy and speed of predictions, thereby presenting a sophisticated solution for the icing prediction on the transmission lines. Full article