What is truth? We have entered another period fraught with Gospel confusion—beyond postmodernism to what can be called “post-Christianity”. This is not unusual—so we should not be overwhelmed. This happens periodically, as early as Gal 1:9: “If anybody is preaching to you a gospel other than what you accepted, let him be eternally condemned”. It is all a question of the Gospel, or put another way, evangelism (the communication or announcing “the good news of God”). Evangelism is proclaiming and living a distinct message of Jesus Christ. Jesus is Himself the embodiment of the “good news”. The Gospel has been challenged, eroded and corrupted over the centuries—yet rediscovered by those who practice exegesis of the Biblical record of the New Testament. This article moves on to look at how secular philosophy—rather than Christian philosophy—and other “forms of the truth” have influenced the current situation we find ourselves in. Full article

Blueberry fruit phenotypes are crucial agronomic trait indicators in blueberry breeding, and the number of fruits within the cluster, maturity, and compactness are important for evaluating blueberry harvesting methods and yield. However, the existing instance segmentation model cannot extract all these features. And due to the complex field environment and aggregated growth of blueberry fruits, the model is difficult to meet the demand for accurate segmentation and automatic phenotype extraction in the field environment. To solve the above problems, a high-precision phenotype extraction model based on hybrid task cascade (HTC) is proposed in this paper. ConvNeXt is used as the backbone network, and three Mask RCNN networks are cascaded to construct the model, rich feature learning through multi-scale training, and customized algorithms for phenotype extraction combined with contour detection techniques. Accurate segmentation of blueberry fruits and automatic extraction of fruit number, ripeness, and compactness under severe occlusion were successfully realized. Following experimental validation, the average precision for both bounding boxes (bbox) and masks stood at 0.974 and 0.975, respectively, with an intersection over union (IOU) threshold of 0.5. The linear regression of the extracted value of the fruit number against the true value showed that the coefficient of determination (R²) was 0.902, and the root mean squared error (RMSE) was 1.556. This confirms the effectiveness of the proposed model. It provides a new option for more efficient and accurate phenotypic extraction of blueberry clusters. Full article

New U-Pb zircon ages are reported for granulite facies crustal xenoliths brought to the surface by mafic lavas in the Snake River Plain. All samples yield Meso-to-Neoarchean ages (2.4–3.6 Ga) that significantly expand the known extent of the Archean Wyoming Craton at least as far west as the west-central Snake River Plain. Most zircon populations indicate multiple growth episodes with complexity increasing eastward, but they bear no record of major Phanerozoic magmatic episodes in the region. To extrapolate this work further west to the inferred craton boundary, zircons from southwestern Idaho batholith granodiorites were also analyzed. Although most batholith zircons record Cretaceous formation ages, all samples have zircons with inherited cores—with some recording Proterozoic ages (approaching 2 Ga). These data enhance our perspectives regarding lithosphere architecture beneath southern Idaho and adjacent areas and its possible influence on Cenozoic magmatism associated with the Snake River Plain–Yellowstone “melting anomaly”. Full article

Functional traits are pivotal for understanding the functional niche within plant communities. Yet, the relationship between the functional niches of typical forest plant communities across different climatic zones, as defined by functional traits, and their association with community and phylogenetic structures remains elusive. In this study, we examined 215 woody species, incorporating 11 functional traits spanning leaf economy, mechanical support, and reproductive phenology, gathered from forests in four climatic zones from tropical, subtropical, warm-temperate to cold-temperate zones in China and supplemented by the literature. We quantified the functional niche hypervolume (FNH), reflecting the multidimensional functional niche variability. We then probed into the correlation between the FNH and community and phylogenetic structures of forests. Our findings reveal that species richness significantly influences the geographic variance of functional niche space in forest vegetation across different climatic zones. Specifically, a community’s species richness correlates positively with the functional niche breadth occupied by the community species. The FNH of woody plants across diverse forest types shows significant associations with both the mean phylogenetic distance (MPD) and the mean nearest phylogenetic taxon distance (MNTD) of the communities. There is a progressive increase in tropical rainforest (TF), subtropical evergreen deciduous broad-leaved mixed forest (SF), and warm-temperate coniferous broad-leaved mixed forest (WF), followed by a decline in the cold-temperate coniferous forest (CF). This pattern suggests potential environmental filtering in CF, which may constrain the spatial extent of plant functional niches. Our research underscores the substantial variability in the FNH across China’s typical forest vegetation, highlighting the complex interplay between functional traits, community richness, and phylogenetic distance. Full article

Generative adversarial network (GAN) can generate diverse and high-resolution images for data augmentation. However, when GAN is applied to the synthetic aperture radar (SAR) dataset, the generated categories are not of the same quality. The unrealistic category will affect the performance of the subsequent automatic target recognition (ATR). To overcome the problem, we propose a reinforced constraint filtering with compensation afterwards GAN (RCFCA-GAN) algorithm to generate SAR images. The proposed algorithm includes two stages. We focus on improving the quality of easily generated categories in Stage 1. Then, we record the categories that are hard to generate and compensate by using traditional augmentation methods in Stage 2. Thus, the overall quality of the generated images is improved. We conduct experiments on the moving and stationary target acquisition and recognition (MSTAR) dataset. Recognition accuracy and Fréchet inception distance (FID) acquired by the proposed algorithm indicate its effectiveness. Full article

In the existing literature, direct torque control (DTC) by synthesizing virtual vectors can effectively suppress low-order harmonic currents under the single open-phase fault (OPF) of the five-phase permanent magnet synchronous motor (PMSM), but the sectors and the look-up tables need to be redesigned, which makes the control process more complicated. In order to solve this problem, an indirect correction method of virtual vectors is proposed, and the amplitudes of the virtual vectors are maximized. The fault-tolerant DTC strategy under the OPF ensures that there is no need to re-divide the sectors under the fault. And the selection rules of the look-up tables are consistent with the healthy operation. The difference is that the amplitudes of ten virtual vectors in the faulty operation are reduced, which simplifies the control process and is easy to implement. Finally, the correctness and effectiveness of the proposed control strategy were verified by experiments. Full article

The World Health Organization (WHO) recommends the use of annual mass drug administration (MDA) as the strategy for controlling and eliminating the five preventive chemotherapy neglected tropical diseases (PC-NTDs). The success of MDAs hinges on community acceptance, active participation, and compliance. This study aimed to explore the experiences and perceptions of community members, to obtain a more thorough understanding of their openness and willingness to participate in MDA and other NTD elimination activities. A mixed-methods approach was employed, utilizing qualitative and quantitative methods for comprehensive data collection. Eighteen key informant interviews (KIIs) and sixteen focus group discussions (FGDs) were conducted to explore community engagement, participation, medication utilization, and programme perception. Triangulation of findings from interviews and discussions with household survey results was performed to gain a deeper understanding of emerging themes. The household survey involved interviewing 1220 individuals (Abaji: 687; Bwari: 533). Audio tapes recorded KIIs and FGDs, with interview transcripts coded using Nvivo 12.0 software based on predefined themes. Descriptive analysis using SPSS version 21 was applied to quantitative data. Results indicated high awareness of mass drug administration (MDA) campaigns in both area councils (Abaji: 84.9%; Bwari: 82.9%), with a small percentage claiming ignorance (15.1%), attributed to lack of information or absence during health campaigns. Respondents primarily participated by taking medication (82.5%), with minimal involvement in other MDA campaigns. Perception of medicines was generally positive, with a significant association between participation level and performance rating (p < 0.05). The study recommends leveraging high awareness and community responsiveness to enhance engagement in various MDA activities, ensuring sustainability and ownership of the programme. Full article

Cylindrical shell structures have excellent structural properties and load-bearing capacities in fields such as aerospace, marine engineering, and nuclear power. However, under high-pressure conditions, cylindrical shells are prone to cracking due to impact, corrosion, and fatigue, leading to a reduction in structural strength or failure. This paper proposes a static modeling method for damaged liquid-filled cylindrical shells based on the extended finite element method (XFEM). It investigated the impact of different initial crack angles on the crack propagation path and failure process of liquid-filled cylindrical shells, overcoming the difficulties of accurately simulating stress concentration at crack tips and discontinuities in the propagation path encountered in traditional finite element methods. Additionally, based on fluid‒structure interaction theory, a dynamic model for damaged liquid-filled cylindrical shells was established, analyzing the changes in pressure and flow state of the fluid during crack propagation. Experimental results showed that although the initial crack angle had a slight effect on the crack propagation path, the crack ultimately extended along both sides of the main axis of the cylindrical shell. When the initial crack angle was 0°, the crack propagation path was more likely to form a through-crack, with the highest penetration rate, whereas when the initial crack angle was 75°, the crack propagation speed was slower. After fluid entered the cylindrical shell, it spurted along the crack propagation path, forming a wave crest at the initial ejection position. Full article

By integrating energy-efficient AIoT-based biosensor networks, healthcare systems can now predict COVID-19 outbreaks with unprecedented accuracy and speed, revolutionizing early detection and intervention strategies. Therefore, this paper explores the rapid growth of electronic technology in today's environment, driven by the proliferation of advanced devices capable of monitoring and controlling various healthcare systems. However, these devices' limited resources necessitate optimizing their utilization. To tackle this concern, we propose an enhanced Artificial Intelligence of Things (AIoT) system that utilizes the networking capabilities of IoT biosensors to forecast potential COVID-19 outbreaks. The system aims to efficiently collect data from deployed sensor nodes, enabling accurate predictions of possible disease outbreaks. By collecting and pre-processing diverse parameters from IoT nodes, such as body temperature (measured non-invasively using the open-source thermal camera TermoDeep), population density, age (captured via smartwatches), and blood glucose (collected via the CGM system), we enable the AI system to make accurate predictions. The model's efficacy was evaluated through performance metrics like the confusion matrix, F1 score, precision, and recall, demonstrating the optimal potential of the IoT-based wireless sensor network for predicting COVID-19 outbreaks in healthcare systems. Full article

The aim of this study was to compare the influence of three species of fungi of the Trichoderma genus (T. aureoviride Rifai—Ta8, T. hamatum/Bonord/Bainier—Th15, and T. harzianum Rifai—Thr2) on the quality, flowering, and nutritional status of Tropaeolum majus L. ‘Spitfire’. Early flowering was only influenced by T. hamatum, which delayed it by 6 days. T. aureoviride, T. hamatum, and T. harzianum stimulated the flowering of the ‘Spitfire’ cultivar but did not affect the size of the flowers. The plants treated with T. harzianum after being planted in pots flowered the most abundantly. Trichoderma spp. caused the plants to grow more intensively, producing longer and more leafy shoots with a greater number of offshoots. Trichoderma spp. stimulated the uptake of macronutrients, except for phosphorus (P). In the case of calcium (Ca) and sodium (Na), this phenomenon was only observed in plants treated with T. aureoviride and T. hamatum, and for magnesium (Mg), only when T. hamatum was applied to sown seeds. As for the developed root systems, as far as the micronutrients are concerned, Trichoderma spp. stimulated the uptake of zinc (Zn) and manganese (Mn). Apart from that, there was a higher iron (Fe) content in the plants treated with T. harzianum on both dates and T. aureoviride after planting the plants in pots. Full article

Detailed numerical analyses of pulverised solid fuel flames are computationally expensive due to the intricate interplay between chemical reactions, turbulent multiphase flow, and heat transfer. The near-burner region, characterised by a high particle number density, is particularly influenced by these interactions. The accurate modelling of these phenomena is crucial for describing flame characteristics. This study examined the reciprocal impact between the discrete phase and the continuous phase using Reynolds-averaged Navier–Stokes (RANS) simulations. The numerical model was developed in Ansys Fluent and equipped with user-defined functions that adapt the modelling of combustion sub-processes, in particular, devolatilisation, char conversion, and radiative heat transfer under oxyfuel conditions. The aim was to identify the appropriate degree of detail necessary for modelling the interaction between discrete and continuous phases, specifically concerning mass, momentum, energy, and turbulence, to effectively apply it in high-fidelity numerical simulations. The results of the numerical model show good agreement in comparison with experimental data and large-eddy simulations. In terms of the coupling schemes, the results indicate significant reciprocal effects between the discrete and the continuous phases for mass and energy coupling; however, the effect of particles on the gas phase for momentum and turbulence coupling was observed to be negligible. For the investigated chamber, these results are shown to be slightly affected by the local gas phase velocity and temperature fields as long as the global oxygen ratio between the provided and needed amount of oxygen as well as the thermal output of the flame are kept constant. Full article

Synthetic deer antler peptides (TSKYR, TSK, and YR) stimulate the proliferation of human chondrocytes and osteoblasts and increase the chondrocyte content of collagen and glycosamino-glycan in vitro. This study investigated the peptide mixture’s pain relief and chondroprotective effect in a rat model of collagenase-induced osteoarthritis. Thirty-six adult male Sprague–Dawley rats were divided into three groups: control (saline), positive control (hyaluronic acid), and ex-perimental (peptides). Intra-articular collagenase injections were administered on days 1 and 4 to induce osteoarthritis in the left knees of the rats. Two injections of saline, hyaluronic acid, or the peptides were injected into the same knees of each corresponding group at the beginning of week one and two, respectively. Joint swelling, arthritic pain, and histopathological changes were evaluated. Injection of the peptides significantly reduced arthritic pain compared to the control group, as evidenced by the closer-to-normal weight-bearing and paw withdrawal threshold test results. Histological analyses showed reduced cartilage matrix loss and improved total cartilage degeneration score in the experimental versus the control group. Our findings suggest that intra-articular injection of synthetic deer antler peptides is a promising treatment for osteoarthritis. Full article

The classic Slack-Based Measure (SBM) model has been posited to be a favorable non-parametric tool to cope with undesirable output. Nevertheless, this model has two significant drawbacks that should be addressed in practice. Thus, this paper aims to revise the classic SBM model to estimate container terminals’ environmental efficiency with undesirable output. The originality of this article includes: (1) introducing the energy consumption method to calculate the quantity of CO₂ emitted by container terminal operators (CTOs), (2) adopting cluster analysis to identify homogeneous CTOs acting as Decision-Making Units (DMUs), and (3) introducing the modified SBM model to measure and analyze environmental efficiency for CTOs. Based on this research, the efficiency of the analyzed terminals and the management of the local port sector are improved. Full article

To determine a vertical cooperation strategy and address the optimal pricing problem of the electric vehicle (EV) supply chain, a supply chain system consisting of two competing EV manufacturers (M₁ and M₂) and a battery supplier is studied. Firstly, three cooperation strategy models were constructed for the battery supplier and the EV manufacturers, namely: Strategy N (neither the battery supplier nor the two manufacturers cooperate with each other); Strategy I (M₁ cooperates with the battery supplier); and Strategy II (M₂ cooperates with the battery supplier). Then, the Stackelberg solution method was used to obtain the optimal equilibrium decisions under the three strategic models. Finally, the effect of the preference coefficient of consumers for leasing EVs per unit on the optimal equilibrium decision was analyzed. We found that: (1) The wholesale price of batteries provided by the battery supplier to M₁ is always greater than to M₂. (2) Strategies I and II prompt M₁ and M₂ to reduce the unit and fixed rental prices of EVs to some extent, while intensifying the competition between the two manufacturers in terms of EV lease prices. (3) When the consumer preference coefficient (θ) for leasing EVs per unit provided by manufacturer M₁ is relatively small, the cooperation alliance S₂ and the supply chain achieve the maximum profit under Strategy II; however, while θ is large, M₁, cooperative alliance S₁, and the entire supply chain could benefit the most under Strategy I. Full article

An efficient obstacle detection system is one of the most important guarantees for improving the active safety performance of autonomous vehicles. This paper proposes an obstacle detection method based on high-precision positioning applied to blocked zones to solve the problems of the high complexity of detection results, low computational efficiency, and high load in traditional obstacle detection methods. Firstly, an NDT registration method which uses the likelihood function as the optimal value of the registration score function to calculate the registration parameters is designed to match the scanning point cloud and the target point cloud. Secondly, a target reduction method combined with threshold judgment and the binary tree search algorithm is designed to filter the point cloud of non-road obstacles to improve the processing speed of the computing platform. Meanwhile, KD-tree is used to speed up the clustering process. Finally, a vehicle remote control simulation platform with the combination of a cloud platform and mobile terminal is designed to verify the effectiveness of the strategy in practical application. The results prove that the proposed obstacle detection method can improve the efficiency and accuracy of detection. Full article

In the Pistoia Nursery-Ornamental Rural District (Italy), a leader in Europe in ornamental nurseries covering over 5200 hectares with over 2500 different species of plant, plant-parasitic nematodes represent a serious concern. The potential efficacy of a pot cultivation system using commercial substrates to control plant-parasitic nematodes was assessed. On two different plant species, two different pot cultivation managements, potted plants, and potted plants previously cultivated in natural soil were compared to plants only cultivated in natural soil. The entire soil nematode structure with and without plants was evaluated. The relationship between soil properties and soil nematode community was investigated. All the studied substrates were free from plant-parasitic nematodes. Regarding free-living nematodes, Peat–Pumice showed nematode assemblage established by colonizer and extreme colonizer bacterial feeders, whereas Peat–Perlite included both bacterial and fungal feeders, and, finally, coconut fiber also included omnivores and predators. In farming, the substrates rich in organic matter such as coconut fiber could still play an important role in suppressing plant-parasitic nematodes because of the abundance of free-living nematodes. In fact, they are of crucial importance in both the mineralization of organic matter and the antagonistic control of plant-parasitic nematodes. Potting systems equally reduce virus-vector nematodes and improve the prey/predator ratio favoring natural control. Full article

In this paper, we prove the existence of at least two weak solutions to a class of singular two-phase problems with variable exponents involving a $\psi$-Hilfer fractional operator and Dirichlet-type boundary conditions when the term source is dependent on one parameter. Here, we use the fiber method and the Nehari manifold to prove our results. Full article

Composite materials, valued for their adaptability, face challenges associated with degradation over time. Characterising their durability through traditional experimental methods has shown limitations, highlighting the need for accelerated testing and computational modelling to reduce time and costs. This study presents an overview of the current landscape and future prospects of multi-scale modelling for predicting the long-term durability of composite materials under different environmental conditions. These models offer detailed insights into complex degradation phenomena, including hydrolytic, thermo-oxidative, and mechano-chemical processes. Recent research trends indicate a focus on hygromechanical models across various materials, with future directions aiming to explore less-studied environmental factors, integrate multiple stressors, investigate emerging materials, and advance computational techniques for improved predictive capabilities. The importance of the synergistic relationship between experimental testing and modelling is emphasised as essential for a comprehensive understanding of composite material behaviour in diverse environments. Ultimately, multi-scale modelling is seen as a vital contributor to accurate predictions of environmental effects on composite materials, offering valuable insights for sustainable development across industries. Full article

Pan–tilt–zoom cameras are commonly used for surveillance applications. Their automation could reduce the workload of human operators and increase the safety of airports by tracking anomalous objects such as drones. Reinforcement learning is an artificial intelligence method that outperforms humans on certain specific tasks. However, there exists a lack of data and benchmarks for pan–tilt–zoom control mechanisms in tracking airborne objects. Here, we show a simulated environment that contains a pan–tilt–zoom camera being used to train and evaluate a reinforcement learning agent. We found that the agent can learn to track the drone in our basic tracking scenario, outperforming a solved scenario benchmark value. The agent is also tested on more complex scenarios, where the drone is occluded behind obstacles. While the agent does not quantitatively outperform the optimal human model, it shows qualitative signs of learning to solve the complex, occluded non-linear trajectory scenario. Given further training, investigation, and different algorithms, we believe a reinforcement learning agent could be used to solve such scenarios consistently. Our results demonstrate how complex drone surveillance tracking scenarios may be solved and fully autonomized by reinforcement learning agents. We hope our environment becomes a starting point for more sophisticated autonomy in control of pan–tilt–zoom cameras tracking of drones and surveilling airspace for anomalous objects. For example, distributed, multi-agent systems of pan–tilt–zoom cameras combined with other sensors could lead towards fully autonomous surveillance, challenging experienced human operators. Full article

Sustainable development has been a global concern worldwide for the last decades now, but only recently have the challenges faced by small towns, especially in regions experiencing population contraction been addressed. (1) Background: This article delves into the case of Romania, a country in Eastern Europe that has witnessed significant demographic, social and economic changes in recent decades. Population contraction in small towns can significantly impact their future development. (2) Methods: The research was conducted in three stages: first, we selected relevant demographic, economic, financial and social indices (16 in total), then we analysed their changes over time, and forecast their values based on statistical data to assess economic development sustainability for 215 small towns with less than 20,000 inhabitants. (3) Results: Following the aggregation of the quantitative indicators and the demographic changes, we identified four categories of small towns. (4) Conclusions: the study underlines the importance of adopting proper policies targeting small towns in Romania to ensure their long-term viability by implementing targeted policies and strategies such as incentives for local businesses, improving educational and healthcare facilities, and promoting entrepreneurship. The ultimate goal is to mitigate the adverse effects of population contraction and pave the way for more sustainable and resilient communities. Full article

Urban parks serve as vital spaces for leisure, social interaction, and nature engagement. At the same time, climate change disproportionately impacts densely populated megacities. While extensive research exists on climate change’s effects on mortality, agriculture, and economic activities, less is known about its impact on urban park usage. Understanding their temporal usage and how temperature changes affect park visitation is crucial for maximizing park benefits and building resiliency. This study analyzes long-term, hourly park visitation data on Dallas, Texas, using digital trace data from SafeGraph (San Francisco, CA, USA), which covers mobile records from approximately 10% of U.S. devices. We focus on five established parks in Dallas and examine their historical temperature data from 2018 to 2022. Descriptive statistics and scatter graphs are utilized to analyze temperature- and demographic-specific visitation patterns. The results of the study highlight the impact of climate change on park visitation and reveal how extreme temperatures influence visitation patterns across parks in Dallas. Additionally, this study explores the differences in visitation based on weekdays versus weekends and highlights demographic disparities. Notably, we examine the implications of nighttime park usage during extreme heat conditions. Our work is informative for urban planners seeking to improve park facilities and comfort amid climate change, ultimately enhancing the resilience and well-being of urban communities. Full article