The present article is intended to serve an educational purpose for data scientists and students who already have experience with the R language and which to start using it for geospatial analysis and map creation. The basic concepts of raster data, vector data, CRS and datum are first presented along with a basic workflow to conduct reproducible geospatial research in R. Examples of important types of maps (scatter, bubble, choropleth, hexbin and faceted) created from open-source environmental data are illustrated and their practical implementation in R is discussed. Through these examples, essential manipulations on geospatial vector data are demonstrated (reading , transforming CRS, creating geometries from scratch, buffer zones around existing geometries and intersections between geometries). Full article

Dictyophora rubrovolvata volva, an agricultural by-product, is often directly discarded resulting in environmental pollution and waste of the proteins’ resources. In this study, D. rubrovolvata volva proteins (DRVPs) were recovered using the ultrasound-assisted extraction (UAE) method. Based on one-way tests, orthogonal tests were conducted to identify the effects of the material–liquid ratio, pH, extraction time, and ultrasonic power on the extraction rate of DRVPs. Moreover, the impact of UAE on the physicochemical properties, structure characteristics, intermolecular forces, and functional attributes of DRVPs were also examined. The maximum protein extraction rate was achieved at 43.34% under the best extraction conditions of UAE (1:20 g/mL, pH 11, 25 min, and 550 W). UAE significantly altered proteins’ morphology and molecular size compared to the conventional alkaline method. Furthermore, while UAE did not affect the primary structure, it dramatically changed the secondary and tertiary structure of DRVPs. Approximately 13.42% of the compact secondary structures (α-helices and β-sheets) underwent a transition to looser structures (β-turns and random coils), resulting in the exposure of hydrophobic groups previously concealed within the molecule’s core. In addition, the driving forces maintaining and stabilizing the sonicated protein aggregates mainly involved hydrophobic forces, disulfide bonding, and hydrogen bonding interactions. Under specific pH and temperature conditions, the water holding capacity, oil holding capacity, foaming capacity and stability, emulsion activity, and stability of UAE increased significantly from 2.01 g/g to 2.52 g/g, 3.90 g/g to 5.53 g/g, 92.56% to 111.90%, 58.97% to 89.36%, 13.85% to 15.37%, and 100.22% to 136.53%, respectively, compared to conventional alkali extraction. The findings contributed to a new approach for the high-value utilization of agricultural waste from D. rubrovolvata. Full article

Repairing damaged tissue caused by bacterial infection poses a significant challenge. Traditional antibacterial hydrogels typically incorporate various components such as metal antimicrobials, inorganic antimicrobials, organic antimicrobials, and more. However, drawbacks such as the emergence of multi-drug resistance to antibiotics, the low antibacterial efficacy of natural agents, and the potential cytotoxicity associated with metal antibacterial nanoparticles in hydrogels hindered their broader clinical application. In this study, we successfully developed imidazolium poly(ionic liquids) (PILs) polymer microspheres (APMs) through emulsion polymerization. These APMs exhibited notable antibacterial effectiveness and demonstrated minimal cell toxicity. Subsequently, we integrated the APMs into a gelatin methacryloyl (GelMA)—polyethylene glycol (PEG) hydrogel. This composite hydrogel not only showcased strong antibacterial and anti-inflammatory properties but also facilitated the migration of human skin fibroblasts (HSF) and human umbilical vein endothelial cells (HUVECs) and promoted osteogenic differentiation in vitro. Full article

Autonomous underwater vehicles (AUVs) are now widely used in both civilian and military applications; however, wireless charging underwater often faces difficulties such as disturbances from ocean currents and errors in device positioning, making proper alignment of the charging devices challenging. Misalignment between the primary and secondary coils can significantly impact the efficiency and power of the wireless charging system energy transfer. To address the issue of misalignment in wireless charging systems, this paper proposes a multiple transfer coil wireless power transfer (MTCWPT) system based on backpropagation (BP) neural network control combined with nonsingular terminal sliding mode control (NTSMC) to enhance further the system robustness and efficiency. To achieve WPT in the ocean, a coil shielding case structure was equipped. In displacement experiments, the proposed multi-transmitting coil system could achieve stable power transfer of 40 W and efficiency of over 78.5% within a displacement range of 8 cm. The system robustness was also validated. This paper presents a new AUV energy supply solution based on MTCWPT. The proposed MTCWPT system can significantly improve the navigation performance of AUVs. Full article

The focus of this paper was Jouguet detonation in an ideal gas flow in a magnetic field. A modified Hugoniot detonation equation has been obtained, taking into account the influence of the magnetic field on the detonation process and the parameters of the detonation wave. It was shown that, under the influence of a magnetic field, combustion products move away from the detonation front at supersonic speed. As the magnetic field strength increases, the speed of the detonation products also increases. A dependence has been obtained that allows us to evaluate the influence of heat release on detonation parameters. Full article

The primary function of a mine hoist is the transportation of personnel and equipment, serving as a crucial link between underground and surface systems. The proper functioning of key components such as work braking and safety braking is essential for ensuring the safety of both personnel and equipment, thereby playing a critical role in the safe operation of coal mines. As coal mining operations extend to greater depths, they introduce heightened challenges for safe transportation, compounded by increased equipment loss. Consequently, there is a pressing need to enhance safety protocols to safeguard personnel and materials. Traditional maintenance and repair methods, characterized by routine equipment inspections and scheduled downtime, often fall short in addressing emerging issues promptly, leading to production delays and heightened risks for maintenance personnel. This underscores the necessity of adopting predictive maintenance strategies, leveraging digital twin models to anticipate and prevent potential faults in mine hoists. In summary, the implementation of predictive maintenance techniques grounded in digital twin technology represents a proactive and scientifically rigorous approach to ensuring the continued safe operation of mine hoists amidst the evolving challenges of deepening coal mining operations. In this study, we propose the integration of a CNN-LSTM algorithm within a digital twin framework for predicting faults in mine hoist braking systems. Utilizing software such as AMESim 2019 and MATLAB 2016b, we conduct joint simulations of the hoist braking digital twin system. Subsequently, leveraging the simulation model, we establish a fault diagnosis platform for the hoist braking system. Finally, employing the CNN-LSTM network model, we forecast failures in the mine hoist braking system. Experimental findings demonstrate the effectiveness of our proposed algorithm, achieving a prediction accuracy of 95.35%. Comparative analysis against alternative algorithms confirms the superior performance of our approach. Full article

Hyperspectral imaging has emerged as a novel imaging modality in the medical field, offering the ability to acquire images of biological tissues while simultaneously providing biochemical insights for in-depth tissue analysis. This approach facilitates early disease diagnosis, presenting advantages over traditional medical imaging techniques. Addressing challenges such as the computational burden of existing convolutional neural networks (CNNs) and imbalances in sample data, this paper introduces a lightweight GhostMRNet for the classification of microscopic hyperspectral images of human blood cells. The proposed model employs Ghost Modules to replace conventional convolutional layers and a cascading approach with small convolutional kernels for multiscale feature extraction, aiming to enhance feature extraction capabilities while reducing computational complexity. Additionally, an SE (Squeeze-and-Excitation) module is introduced to selectively allocate weights to features in each channel, emphasizing informative features and efficiently achieving spatial–spectral feature extraction in microscopic hyperspectral imaging. We evaluated the performance of the proposed GhostMRNet and compared it with other state-of-the-art models using two real medical hyperspectral image datasets. The experimental results demonstrate that GhostMRNet exhibits a superior performance, with an overall accuracy (OA), average accuracy (AA), and Kappa coefficient reaching 99.965%, 99.565%, and 0.9925, respectively. In conclusion, the proposed GhostMRNet achieves a superior classification performance at a smaller computational cost, thereby providing a novel approach for blood cell detection. Full article

Accurate crop mapping is crucial for ensuring food security. Recently, many studies have developed diverse crop mapping models based on deep learning. However, these models generally rely on a large amount of labeled crop samples to investigate the intricate relationship between the crop types of the samples and the corresponding remote sensing features. Moreover, their efficacy is often compromised when applied to other areas owing to the disparities between source and target data. To address this issue, a new multi-modal deep adaptation crop classification network (MDACCN) was proposed in this study. Specifically, MDACCN synergistically exploits time series optical and SAR images using a middle fusion strategy to achieve good classification capacity. Additionally, local maximum mean discrepancy (LMMD) is embedded into the model to measure and decrease domain discrepancies between source and target domains. As a result, a well-trained model in a source domain can still maintain satisfactory accuracy when applied to a target domain. In the training process, MDACCN incorporates the labeled samples from a source domain and unlabeled samples from a target domain. When it comes to the inference process, only unlabeled samples of the target domain are required. To assess the validity of the proposed model, Arkansas State in the United States was chosen as the source domain, and Heilongjiang Province in China was selected as the target domain. Supervised deep learning and traditional machine learning models were chosen as comparison models. The results indicated that the MDACCN achieved inspiring performance in the target domain, surpassing other models with overall accuracy, Kappa, and a macro-averaged F1 score of 0.878, 0.810, and 0.746, respectively. In addition, the crop-type maps produced by the MDACCN exhibited greater consistency with the reference maps. Moreover, the integration of optical and SAR features exhibited a substantial improvement of the model in the target domain compared with using single-modal features. This study indicated the considerable potential of combining multi-modal remote sensing data and an unsupervised domain adaptive approach to provide reliable crop distribution information in areas where labeled samples are missing. Full article

This study presents a comprehensive approach for simulating the additive manufacturing process of semi-crystalline composite polymers using Fused Deposition Modeling (FDM). By combining thermomechanical Finite Element Analysis (FEA) with experimental validation, our main objective is to comprehend and model the complex behaviors of 50 wt.% carbon fiber-reinforced Polyphenylene Sulfide (CF PPS) during FDM printing. The simulations of the FDM process encompass various theoretical aspects, including heat transfer, orthotropic thermal properties, thermal dissipation mechanisms, polymer crystallization, anisotropic viscoelasticity, and material shrinkage. We utilize Abaqus user subroutines such as UMATHT for thermal orthotropic constitutive behavior, UEPACTIVATIONVOL for progressive activation of elements, and ORIENT for material orientation. Mechanical behavior is characterized using a Maxwell model for viscoelastic materials, incorporating a dual non-isothermal crystallization kinetics model within the UMAT subroutine. Our approach is validated by comparing nodal temperature distributions obtained from both the Abaqus built-in AM Modeler and our user subroutines, showing close agreement and demonstrating the effectiveness of our simulation methods. Experimental verification further confirms the accuracy of our simulation techniques. The mechanical analysis investigates residual stresses and distortions, with particular emphasis on the critical transverse in-plane stress component. This study offers valuable insights into accurately simulating thermomechanical behaviors in additive manufacturing of composite polymers. Full article

This study investigates the effect of daily consumption of wheat biscuits enriched with plant proteins in postprandial metabolic responses of women with overweight/obesity who follow an energy-restricted diet. Thirty apparently healthy women participated in a 12-week randomized controlled trial and were assigned either to a control (CB) or an intervention (PB) group. Participants consumed daily either a conventional (CB) or an isocaloric wheat biscuit enriched with plant proteins (PB) containing high amounts of amino acids with appetite-regulating properties, i.e., BCAAs and L-arg. At baseline and the end of the intervention, a mixed meal tolerance test was performed. The responses of glucose, insulin, ghrelin, GLP-1, and glicentin were evaluated over 180 min. After 12 weeks, both groups experienced significant decreases in body weight, fat mass, and waist circumference. In the PB group, a trend towards higher weight loss was observed, accompanied by lower carbohydrate, fat, and energy intakes (p < 0.05 compared to baseline and CB group), while decreases in fasting insulin and the HOMA-IR index were also observed (p < 0.05 compared to baseline). In both groups, similar postprandial glucose, ghrelin, and GLP-1 responses were detected, while iAUC for insulin was lower (p < 0.05). Interestingly, the iAUC of glicentin was greater in the PB group (p < 0.05 compared to baseline). Subjective appetite ratings were beneficially affected in both groups (p < 0.05). Consumption of wheat biscuits enriched in plant proteins contributed to greater weight loss, lower energy intake, and insulin resistance and had a positive impact on postprandial glicentin response, a peptide that can potentially predict long-term weight loss and decreased food intake. Full article

A nanoparticle-anchored three-dimensional microsphere flower-structured layered double hydroxide (LDH) material with Fe₃O₄ particles was successfully prepared using simple hydrothermal and hot solvent methods. Micro-nanostructured Fe₃O₄@LDHs (SLF) composites balance microwave absorption, corrosion protection, and UV aging resistance. The minimum reflection loss value of SLF is −35.75 dB at 14.16 GHz, when the absorber thickness is 8 mm, and the absorption bandwidth at this frequency is up to 2.56 GHz for RL values less than −10 dB, while the LL is only 1 GHz. The SLF /EP coating has not only excellent microwave absorption performance but also excellent corrosion and UV aging resistance performance. The coating still has some anti-corrosion effect after 10 d of immersion. This work is intended as a reference for the development of new coatings with excellent microwave absorption properties as well as corrosion and UV aging resistance for wind turbine tower barrels (seaside wind power generation equipment) surfaces. Full article

Identifying the factors affecting organizational sustainability is a crucial topic in the field of social science and business research. Especially in family businesses, the most crucial issue is how to maintain corporate sustainability across generations. In this regard, succession planning plays a key role in maintaining the sustainable development of family businesses. From the perspective of socioemotional wealth, this study discusses the motivations and consequences of intrafamily succession by measuring the impact of the internal determining factors of succession planning on family business performance. Based on a sample of 281 Chinese family firms, this study uncovers the relationship between succession planning and organizational performance. The core findings of this study include succession planning is positively related to organizational performance in the matter of the successor’s training; succession planning has a positive effect on organizational performance in terms of the successor’s self-preparation; and succession planning is positively correlated with organizational performance in the aspect ofthe relationship between the successor and business. By illustrating that the formulation of succession planning is an essential pursuit for family businesses to preserve sustainability and socioemotional wealth, the results reveal ways to facilitate succession planning through internal factors in the family business. This study contributes to organizational sustainable development literature, family business sustainability studies, and succession management research by validating the positive relationship between succession planning and organizational performance, indicating that succession planning is a vital driving force for achieving organizational sustainability. Full article

Bladder cancer (BC) diagnosis is reliant on cystoscopy, an invasive procedure associated with urinary tract infections. This has sparked interest in identifying noninvasive biomarkers in body fluids such as blood and urine. A source of biomarkers in these biofluids are extracellular vesicles (EVs), nanosized vesicles that contain a wide array of molecular cargo, including small noncoding RNA such as transfer RNA-derived fragments (tRF) and microRNA. Here, we performed small-RNA next-generation sequencing from EVs from urine and serum, as well as from serum supernatant. RNA was extracted from 15 non-cancer patients (NCPs) with benign findings in cystoscopy and 41 patients with non-muscle invasive BC. Urine and serum were collected before transurethral resection of bladder tumors (TUR-b) and at routine post-surgery check-ups. We compared levels of tRFs in pre-surgery samples to samples from NCPs and post-surgery check-ups. To further verify our findings, samples from 10 patients with stage T1 disease were resequenced. When comparing tRF expression in urine EVs between T1 stage BC patients and NCPs, 14 differentially expressed tRFs (DEtRFs) were identified. In serum supernatant, six DEtRFs were identified among stage T1 patients when comparing pre-surgery to post-surgery samples and four DEtRFs were found when comparing pre-surgery samples to NCPs. By performing a blast search, we found that sequences of DEtRFs aligned with genomic sequences pertaining to processes relevant to cancer development, such as enhancers, regulatory elements and CpG islands. Our findings display a number of tRFs that may hold potential as biomarkers for the diagnosis and recurrence-free survival of BC. Full article

In this paper, we propose a method for establishing if a variable is capable of modifying the Macroscopic Fundamental Diagram (MFD) of a street network. The variables have many different configurations, and a simulation is performed for each one. Then, based on the output data of each simulation, the representative speed, density, and flow of the network are calculated. We use three metrics to establish if a variable affects the MFD: the first establishes a distance between the compared density and speed patterns, the second establishes a distance between capacities, and the third establishes a distance between critical densities. We select four variables to test our method: the precision of driving, the vehicles’ top speeds distribution, the procedure for selecting routes, and the procedure for selecting destinations; we determine whether each of these variables can modify the MFD shape. Additionally, we detect which configurations of a variable are able to reach and exceed the critical density (causing congestion) so we can establish which configurations are sustainable and which are not. The novelties of this work are twofold: (1) we introduce a method to detect if a variable modifies the MFD; (2) we establish if the selected variables modify the MFD. Full article

Cultural crop-production practices are not only engineered to minimize pest incidence but also improve resource use efficiency and increase the diversity of habitat for beneficial insects that provide pollination services. With the increasing cultivation of industrial hemp and the benefits associated with the cultivation of multiple crops, its integration into a polyculture cropping system remains to be evaluated. We intercropped two pollinator-attractive crops, hemp and cowpea, with squash, a pollinator-dependent crop, to evaluate the impact of pollinator abundance and diversity on crop yield. Intercropping significantly increased the overall abundance of pollinators with 79.1% recorded from the intercropping systems compared to 21.9% in the monocropping systems. Sweat bees and bumble bees were the most abundant bees, and Squash+Cowpea was the most diverse cropping system. Intercropping significantly increased the yield of squash with higher squash yield (155%) in Hemp+Squash and (161%) in Squash+Cowpea than in squash monocrop. Also, intercropping resulted in higher hemp yield (64%) in Hemp+Cowpea and (165%) in Hemp+Squash compared to hemp monocrop. This study demonstrated that agricultural systems such as intercropping that are designed to attract pollinators are much more productive by not only improving crop yield but also growers’ returns on investments. Full article

In this paper, we propose a training method to reduce the VR sickness that occurs while viewing VR content with an HMD on. The proposed approach is a new method that involves pre-exposing users to VR sickness to enable them to adapt to VR sickness. In the proposed method, the training process was designed based on the features of existing studies related to exposure and adaptation to motion sickness and simulator sickness. The effectiveness of the proposed method was evaluated through experiments with 15 subjects (SSQ and VR sickness response were used in the analysis). As a result of the experiment, nausea was significantly decreased by 47%, and oculomotor discomfort was significantly decreased by 34% after the proposed training method. The VR sickness response decreased by 31%; however, this difference was not statistically significant. Furthermore, we analyzed the VR sickness response in two groups: those whose sickness decreased and those whose sickness increased. We confirmed that the decrease group (pre-experiment mean: 1.34 times, post-experiment mean: 0.58 times) had a larger change than the increase group (pre-experiment mean: 0.31 times, post-experiment mean: 0.42 times). Therefore, from the experimental results, it was confirmed that the proposed method is effective in reducing VR sickness. Full article

The aim of this work is research dedicated to the search for new bactericidal systems for use in cosmetic formulations, dermocosmetics, or the production of wound dressings. Over the last two decades, chitosan, due to its special biological activity, has become a highly indispensable biopolymer with very wide application possibilities. Reports in the literature on the antibacterial effects of chitosan are very diverse, but our research has shown that they can be successfully improved through chemical modification. Therefore, in this study, results on the synthesis of new chitosan-based Schiff bases, dCsSB-SFD and dCsSB-PCA, are obtained using two aldehydes: sodium 4-formylbenzene-1,3-disulfonate (SFD) and 2-pyridine carboxaldehyde (PCA), respectively. Chitosan derivatives synthesized in this way demonstrate stronger antimicrobial activity. Carrying out the procedure of grafting chitosan with a caproyl chain allowed obtaining compatible blends of chitosan derivatives with κ-carrageenan, which are stable hydrogels with a high swelling coefficient. Furthermore, the covalently bounded poly(ε-caprolactone) (PCL) chain improved the solubility of obtained polymers in organic solvents. In this respect, the Schiff base-containing polymers obtained in this study, with special hydrogel and antimicrobial properties, are very promising materials for potential use as a controlled-release formulation of both hydrophilic and hydrophobic drugs in cosmetic products for skin health. Full article

Various published COVID-19 models have been used in epidemiological studies and healthcare planning to model and predict the spread of the disease and appropriately realign health measures and priorities given the resource limitations in the field of healthcare. However, a significant issue arises when these models need help identifying the distribution of the constituent variants of COVID-19 infections. The emergence of such a challenge means that, given limited healthcare resources, health planning would be ineffective and cost lives. This work presents a universal neural network (NN) computational instrument for predicting the mainstream symptomatic infection rate of COVID-19 and models of the distribution of its associated variants. The NN is based on a mixture density network (MDN) with a Gaussian mixture model (GMM) object as a backbone. Twelve use cases were used to demonstrate the validity and reliability of the proposed MDN. The use cases included COVID-19 data for Canada and Saudi Arabia, two date ranges (300 and 500 days), two input data modes, and three activation functions, each with different implementations of the batch size and epoch value. This array of scenarios provided an opportunity to investigate the impacts of epistemic uncertainty (EU) and aleatoric uncertainty (AU) on the prediction model’s fitting. The model accuracy readings were in the high nineties based on a tolerance margin of 0.0125. The primary outcome of this work indicates that this easy-to-use universal MDN helps provide reliable predictions of COVID-19 variant distributions and the corresponding synthesized profile of the mainstream infection rate. Full article

The correlation femtoscopy technique makes it possible to estimate the geometric dimensions and lifetime of the particle emission region after the collision of ions. Measurements of the emission region characteristics not only at midrapidity but also at backward (forward) rapidity can provide new information about the source and make it possible to impose constraints on the heavy-ion collision models. This work is devoted to revealing the dependence of the spatial and temporal parameters of the emission region of identical pions in Au+Au collisions at $\sqrt{s_{NN}}$ = 3 GeV from the fixed-target program of the STAR experiment. The extracted femtoscopic radii, $R_{out}$, $R_{side}$, $R_{long}$, $R_{out-long}^2$, and the correlation strength, $\lambda$, are presented as a function of collision centrality, pair rapidity, and transverse momentum. Physics implications will be discussed. Full article

The advancement of cryptographic systems presents both opportunities and challenges in the realm of digital forensics. In an era where the security of digital information is crucial, the ability to non-invasively detect and analyze cryptographic configurations has become significant. As cryptographic algorithms become more robust with longer key lengths, they provide higher levels of security. However, non-invasive side channels, specifically through electromagnetic (EM) emanations, can expose confidential cryptographic details, thus presenting a novel solution to the pressing forensic challenge. This research delves into the capabilities of EM side-channel analysis (EM-SCA), specifically focusing on detecting both cryptographic key lengths and the algorithms employed utilizing a machine-learning-based approach, which can be instrumental for digital forensic experts during their investigations. Through meticulous data processing and analysis, the Support Vector Machine (SVM) model, among others, demonstrated a notable accuracy of 94.55% in distinguishing between AES and ECC cryptographic operations. This capability significantly enhances digital forensic methodologies, offering a novel avenue for noninvasively uncovering encrypted data’s cryptographic settings. By identifying key lengths and algorithms without invasive procedures, this research contributes substantially to the advancement of forensic investigations in encrypted environments. Full article

The morphology and crystal structure of Pt films grown by pulsed laser deposition (PLD) on yttria-stabilized zirconia (YSZ)at high temperatures Tg = 900 °C was studied for four different film thicknesses varying between 10 and 70 nm. During the subsequent growth of the capping layer, the thermal stability of the Pt was strongly influenced by the Pt film’s thickness. Furthermore, these later affected the film morphology, the crystal structure and hillocks size, and distribution during subsequent growth at Tg = 900 °C for a long duration. The modifications in the morphology as well as in the structure of the Pt film without a capping layer, named also as the as-grown and encapsulated layers in the bilayer system, were examined by a combination of microscopic and scattering methods. The increase in the thickness of the deposited Pt film brought three competitive phenomena into occurrence, such as 3D–2D morphological transition, dewetting, and hillock formation. The degree of coverage, film continuity, and the crystal quality of the Pt film were significantly improved by increasing the deposition time. An optimum Pt film thickness of 70 nm was found to be suitable for obtaining a hillock-free Pt bottom electrode which also withstood the dewetting phenomena revealed during the subsequent growth of capping layers. This achievement is crucial for the deposition of functional bottom electrodes in ferroelectric and multiferroic heterostructure systems. Full article

Since methods for reducing macromolecule entanglements have been developed, it has become possible to better understand the impact of polymer chain entanglement on the crystallization process. The article presents basic information about the disentangling of macromolecules and the characterization of the degree of entanglement. The basic knowledge of polymer crystallization was also presented. Then, it was discussed how polymers crystallize during their disentangling. Non-isothermal and isothermal crystallization experiments using disentangled polymers, and for comparison using entangled polymers, are described in more detail. The influence of disentangling on both nucleation and crystal growth is highlighted. It is also shown how the crystallization of polymers changes when macromolecules re-entangle. Full article