Green supply chain management is critical for driving sustainable development and addressing escalating environmental challenges faced by companies. However, due to the multidimensionality of cost–benefit analysis and the intricacies of supply chain operations, strategic decision-making regarding green supply chains is inherently complex. This paper proposes a green supply chain optimization framework based on a two-stage heuristic algorithm. First, anchored in the interests of intermediary core enterprises, this work integrates upstream procurement and transportation of products with downstream logistics and distribution. In this aspect, a three-tier green complex supply chain model incorporating economic and environmental factors is developed to consider carbon emissions, product non-conformance rates, delay rates, and transportation costs. The overarching goal is to comprehensively optimize the trade-off between supply chain costs and carbon emissions. Subsequently, a two-stage heuristic algorithm is devised to solve the model by combining the cuckoo search algorithm with the brainstorming optimization algorithm. Specifically, an adaptive crossover–mutation operator is introduced to enhance the search performance of the brainstorming optimization algorithm, which caters to both global and local search perspectives. Experimental results and comparison studies demonstrate that the proposed method performs well within the modeling and optimization of the green supply chain. The proposed method facilitates the efficient determination of ordering strategies and transportation plans within tight deadlines, thereby offering valuable support to decision-makers in central enterprises for supply chain management, ultimately maximizing their benefits. Full article

The volatile organic compounds (VOCs) of plant hosts allow insect localization through olfactory recognition. In this study, the oviposition behavior of the codling moth was investigated and the VOCs from different walnut organs were extracted and analyzed to systematically study their composition and content differences. The electrophysiological and behavioral responses of the codling moth to walnut VOCs were measured using gas chromatography–electroantennographic detection (GC-EAD) and a four-arm olfactometer to screen the key active contents. The field investigation results indicated that 90.3% of the eggs spawned by the first generation of adult codling moths were adjacent to the walnut fruits. Walnut VOCs are mainly composed of terpenes, aromatics, and alkanes. Twelve VOCs can produce electroantennogenic (EAG) responses in the codling moths. Both adult males and females exhibit concentration dependence, with notable disparities in their EAG response levels. In the olfactory behavioral bioassay, linalool, eucalyptol, and high doses of geranyl acetate showed repellent effects on the codling moths, while myrcene, β-ocimene, nonanal, methyl salicylate, α-farnesene, and heptaldehyde showed the opposite. The relative levels of heptaldehyde, geranyl acetate, nonanal, and methyl salicylate were high in the fruits, which is intimately related to the localization of the walnut fruit by females. These VOCs can influence the oviposition behavior of codling moths but their application in the control of this pest needs to be confirmed and improved through further field experiments. Full article

Osteoarthritis (OA) is increasing worldwide, and previous work found that OA increases systemic cartilage oligomeric matrix protein (COMP), which has also been implicated in prostate cancer (PCa). As such, we sought to investigate whether OA augments PCa progression. Cellular proliferation and migration of RM1 murine PCa cells treated with interleukin (IL)-1α, COMP, IL-1α + COMP, or conditioned media from cartilage explants treated with IL-1α (representing OA media) and with inhibitors of COMP were assessed. A validated murine model was used for tumor growth and marker expression analysis. Both proliferation and migration were greater in PCa cells treated with OA media compared to controls (p < 0.001), which was not seen with direct application of the stimulants. Migration and proliferation were not negatively affected when OA media was mixed with downstream and COMP inhibitors compared to controls (p > 0.05 for all). Mice with OA developed tumors 100% of the time, whereas mice without OA only 83.4% (p = 0.478). Tumor weight correlated with OA severity (Pearson correlation = 0.813, p = 0.002). Moreover, tumors from mice with OA demonstrated increased Ki-67 expression compared to controls (mean 24.56% vs. 6.91%, p = 0.004) but no difference in CD31, PSMA, or COMP expression (p > 0.05). OA appears to promote prostate cancer in vitro and in vivo. Full article

Heavy metals play a crucial role in the environment due to their toxicity, persistence, and bioaccumulation ability, which can lead to severe ecological and health risks. This study aimed to investigate the impact of urbanization and agricultural practices on the heavy metal content in the sediments of the Bug River catchment. To this end, 96 surface sediment samples were collected from various points in the Bug River catchment, including from urban, agricultural, and forested areas. The samples for laboratory analysis were collected in July 2018, 2019, and 2020 in the Polish part of the Bug River watershed. Heavy metal (Zn, Pb, Cr, Ni, Cu, Fe, Mn, and Cd) concentrations were determined using atomic absorption spectroscopy (AAS). The geoaccumulation index (I_geo)_, contamination factor (CF), and pollution load index (PLI) were used to assess the degree of sediment contamination. The results indicate higher concentrations of heavy metals in urban sediments, where cadmium concentrations reached up to 2.5 mg/kg, compared to agricultural and forested areas, where concentrations were significantly lower. The average I_geo value for cadmium was 0.24 in agricultural areas and 0.15 in urban areas, suggesting the predominance of anthropogenic influences over natural sources. The highest PLI values were found in urban areas, reaching a maximum of 0.33, indicating higher pollution levels. Statistical analysis revealed that urban emissions and agricultural activities significantly influenced the presence of these metals in the Bug River sediments. This study’s conclusions emphasize that effective river water quality management requires continuous monitoring and an understanding of anthropogenic and natural pollution sources. The results contribute to a better understanding the interactions between human activities and water quality, crucial for planning protection and remediation strategies. Additionally, this study provides critical insights into optimizing pollution management strategies and developing remediation methods, serving local and regional policymakers in planning protective actions. Full article

In an evolving perspective, lecturers consider that inquiry is one of the best forms of learning to drill critical thinking. This study assesses the practice of inquiry to develop the critical thinking skills of prospective science, technology, engineering, and mathematics (STEM) teachers in Indonesia, which is a suitable way to address the problems in the country. Through the experimental design, three groups were formed, which were intervened with inquiry learning, inquiry-creative, and traditional teaching. The learning intervention was carried out within one month using a pre-validated instrument. The critical thinking data were analyzed descriptively based on the pre-test and post-test mean parameters and n-gain on critical thinking indicators, as well as individual critical thinking performance. Statistical analyses (paired-t test, ANOVA, and least significant difference test) were employed to provide confidence in the differences in critical thinking skills across the three learning treatments (p < 0.05). The prospective STEM teachers’ critical thinking skills showed varied performances among the three groups. The inquiry-creative group had the strongest impact, followed by inquiry and traditional teaching, all differing significantly. In summary, the findings suggest that current teaching practices in STEM education need to be reconsidered, showing the advantage of the inquiry-creative model in developing the critical thinking skills essential for future teachers and creators in the STEM fields. Full article

As modern technologies, particularly home assistant devices and sensors, become more integrated into our daily lives, they are also making their way into the domain of energy management within our homes. Homeowners, now acting as prosumers, have access to detailed information at 15-min or even 5-min intervals, including weather forecasts, outputs from renewable energy source (RES)-based systems, appliance schedules and the current energy balance, which details any deficits or surpluses along with their quantities and the predicted prices on the local energy market (LEM). The goal for these prosumers is to reduce costs while ensuring their home’s comfort levels are maintained. However, given the complexity and the rapid decision-making required in managing this information, the need for a supportive system is evident. This is particularly true given the routine nature of these decisions, highlighting the potential for a system that provides personalized recommendations to optimize energy consumption, whether that involves adjusting the load or engaging in transactions with the LEM. In this context, we propose a recommendation system powered by large language models (LLMs), Scikit-llm and zero-shot classifiers, designed to evaluate specific scenarios and offer tailored advice for prosumers based on the available data at any given moment. Two scenarios for a prosumer of 5.9 kW are assessed using candidate labels, such as Decrease, Increase, Sell and Buy. A comparison with a content-based filtering system is provided considering the performance metrics that are relevant for prosumers. Full article

This paper presents a few-mode fiber (FMF) various fault-detection method for long-reach mode division multiplexing (MDM) based on multi-mode transmission reflection analysis (MM-TRA). By injecting unmodulated continuous light into the FMF, and measuring and quantitatively analyzing the transmitted and reflected or Rayleigh backscattering power of different spatial modes, it is possible to accurately detect and locate reflective and non-reflective fault events. This paper discusses the localization accuracy of fault types such as FMF break, FMF link connector mismatch, and FMF bending. Theoretical analysis and simulation experimental results demonstrate that the proposed MM-TRA can provide an effective characterization of various faults and can achieve high fault localization accuracy. In addition, the influence of mode crosstalk of mode multiplexer/demultiplexer and mode coupling in FMF on the localization accuracy of various faults are considered. The results indicate that when using the combination of LP₀₁ and LP₂₁ modes, the localization errors for the FMF break, connector mismatch, and bending are 3.42 m, 1.97 m, and 3.29 m, respectively, demonstrating good fault localization performance. Full article

The increasing use of IoHT devices in healthcare has brought about revolutionary advancements, but it has also exposed some critical vulnerabilities, particularly in cybersecurity. IoHT is characterized by interconnected medical devices sharing sensitive patient data, which amplifies the risk of cyber threats. Therefore, ensuring healthcare data’s integrity, confidentiality, and availability is essential. This study proposes a hybrid deep learning-based intrusion detection system that uses an Artificial Neural Network (ANN) with Bidirectional Long Short-Term Memory (BLSTM) and Gated Recurrent Unit (GRU) architectures to address critical cybersecurity threats in IoHT. The model was tailored to meet the complex security demands of IoHT and was rigorously tested using the Electronic Control Unit ECU-IoHT dataset. The results are impressive, with the system achieving 100% accuracy, precision, recall, and F1-Score in binary classifications and maintaining exceptional performance in multiclass scenarios. These findings demonstrate the potential of advanced AI methodologies in safeguarding IoHT environments, providing high-fidelity detection while minimizing false positives. 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

Winter plants acclimate to frost mainly during the autumn months, through the process of cold acclimation. Global climate change is causing changes in weather patterns such as the occurrence of warmer periods during late autumn or in winter. An increase in temperature after cold acclimation can decrease frost tolerance, which is particularly dangerous for winter crops. The aim of this study was to investigate the role of brassinosteroids (BRs) and BR analogues as protective agents against the negative results of deacclimation. Plants were cold-acclimated (3 weeks, 4 °C) and deacclimated (1 week, 16/9 °C d/n). Deacclimation generally reversed the cold-induced changes in the level of the putative brassinosteroid receptor protein (BRI1), the expression of BR-induced COR, and the expression of SERK1, which is involved in BR signal transduction. The deacclimation-induced decrease in frost tolerance in oilseed rape could to some extent be limited by applying steroid regulators. The deacclimation in plants could be detected using non-invasive measurements such as leaf reflectance, chlorophyll a fluorescence, and gas exchange monitoring. Full article

Mitochondria, as the core metabolic organelles, play a crucial role in aerobic respiration/biosynthesis in fungi. Numerous studies have demonstrated a close relationship between mitochondria and Candida albicans virulence and drug resistance. Here, we report an octapeptide-aminopeptidase located in the mitochondrial matrix named Oct1p. Its homolog in the model fungus Saccharomyces cerevisiae is one of the key proteins in maintaining mitochondrial respiration and protein stability. In this study, we utilized evolutionary tree analysis, gene knockout experiments, mitochondrial function detection, and other methods to demonstrate the impact of Oct1p on the mitochondrial function of C. albicans. Furthermore, through transcriptome analysis, real-time quantitative PCR, and morphological observation, we discovered that the absence of Oct1p results in functional abnormalities in C. albicans, affecting hyphal growth, cell adhesion, and biofilm formation. Finally, the in vivo results of the infection of Galleria mellonella larvae and vulvovaginal candidiasis in mice indicate that the loss of Oct1p led to the decreased virulence of C. albicans. In conclusion, this study provides a solid theoretical foundation for treating Candida diseases, developing new targeted drugs, and serves as a valuable reference for investigating the connection between mitochondria and virulence in other pathogenic fungi. Full article

Various contents of carbon fibers (CFs) and potassium titanate whiskers (PTWs) were added to an Fe-based impregnated diamond bit (IDB) matrix to enhance its adaptability to percussive–rotary drilling. A series of mechanical tests were conducted successively to find the effects of the reinforcing materials on the properties of the Fe-based IDB samples. Then, the fracture surfaces of the samples were analyzed via scanning electron microscopy (SEM) and energy-dispersive spectroscopy, and the worn surfaces and abrasive debris of the samples were analyzed using a laser scanning confocal microscope and SEM. The results show that both the CF and PTW can effectively improve the hardness and bending strength of an Fe-based IDB matrix, and those parameters reached their maximum values at the additive amount of 1 wt%. However, the CF had a better enhancement effect than the PTW. Furthermore, the CF improved the impact wear resistance of the IDB matrix, with a minimum wear rate of 2.38 g/min at the additive amount of 2 wt%. However, the PTW continuously weakened the impact wear resistance of the IDB matrix with increases in its content. Moreover, the morphologies of the worn surfaces indicated that the minimum roughness of the CF-reinforced IDB matrix decreased significantly to as low as 4.91 μm, which was 46.16% lower than that without CF, whereas the minimum roughness of the PTW-reinforced samples decreased by 11.31%. Meanwhile, the abrasive debris of the CF-reinforced samples was more uniform and continuous compared to that of the PTW-reinforced samples. Overall, the appropriate addition of CF or PTWs can enhance the mechanical properties of Fe-based IDB matrices, which can be used on different formations based on their impact wear resistance. Full article

This comprehensive review explores the various scenarios of atherosclerosis, a systemic and chronic arterial disease that underlies most cardiovascular disorders. Starting from an overview of its insidious development, often asymptomatic until it reaches advanced stages, the review delves into the pathophysiological evolution of atherosclerotic lesions, highlighting the central role of inflammation. Insights into clinical manifestations, including heart attacks and strokes, highlight the disease’s significant burden on global health. Emphasis is placed on carotid atherosclerosis, clarifying its epidemiology, clinical implications, and association with cognitive decline. Prevention strategies, lifestyle modifications, risk factor management, and nuanced antithrombotic treatment considerations are critical to managing cardiovascular complications, thus addressing a crucial aspect of cardiovascular health. Full article

The Xiaojiang Basin ranks among the global regions with the highest density of geological hazards. Landslides, avalanches, and debris flows represent significant threats to the safety of residents and their properties, impeding sustainable development. This study utilized three InSAR techniques to monitor surface deformations in the basin, using the standard deviation of these measurements as a stability threshold to identify potential landslides. A systematic analysis of landslide development characteristics was then conducted. Key findings include the following: (1) The annual average deformation velocity in the basin from 2018 to 2021 ranged from −25.36 to 24.40 mm/year, identifying 212 potential landslides. (2) Deformation analysis of a typical landslide in Caizishan showed consistent detection of significant surface changes by all three InSAR methods. Seasonal deformation linked to summer rainfall exacerbates the movement in elevated landslides. (3) Landslides predominantly occur in fragile geological formations such as sandstone, mudstone, and kamacite on slopes of 20° to 40°. These landslides, typically covering less than 0.1 km², are mostly found on barren and grassland terrains adjacent to lower debris gullies, with a relative elevation difference of under 300 m and an aspect range of 90° to 270°. A high kernel density value of 0.3 or higher was noted, with 86.8% influenced by regional tectonic activities, including fault zones. The results demonstrate that natural environmental factors primarily drive landslides in the Xiaojiang Basin, which pose significant threats to the safety of nearby residents. This study’s insights and outcomes provide valuable references for safeguarding local populations, disaster prevention, and promoting regional sustainable development. Full article

As an important part of head protection equipment, research on the material and structural application of helmet liners has always been one of the hotspots in the field of helmets. This paper first discusses common helmet liner materials, including traditional polystyrene, polyethylene, polypropylene, etc., as well as newly emerging anisotropic materials, polymer nanocomposites, etc. Secondly, the design concept of the helmet liner structure is discussed, including the use of a multi-layer structure, the addition of geometric irregular bubbles to enhance the energy absorption effect, and the introduction of new manufacturing processes, such as additive manufacturing technology, to realize the preparation of complex structures. Then, the application of biomimetic structures to helmet liner design is analyzed, such as the design of helmet liner structures with more energy absorption properties based on biological tissue structures. On this basis, we propose extending the concept of bionic structural design to the fusion of plant stalks and animal skeletal structures, and combining additive manufacturing technology to significantly reduce energy loss during elastic yield energy absorption, thus developing a reusable helmet that provides a research direction for future helmet liner materials and structural applications. Full article

Nowadays, the identification and characterization of grapevine cultivars resilient to climate and water stress while preserving quality traits is crucial for the wine industry. Therefore, the objective of this work was to characterize according to their aromatic potential nine white and six red minority cultivars recently recovered from Castilla-La Mancha region (Spain), subjected to two different water-deficit regimes: rainfed, with subsistence irrigation, and irrigated. For this, the varietal aromatic potential index (IPAv) and the detailed aromatic composition were analyzed via HS-SBSE-GC/MS in extracts of two different pHs. For IPAv values, red varieties did not show a clear trend with respect to irrigation. However, in white minority varieties, higher values were obtained under irrigation conditions. Thus, a clear differentiation of the minority varieties in comparison to the references was observed, primarily attributed to the content of esters and acids, in both white and red varieties. A notable contrast was observed at different pHs, indicating a greater extractability of certain compounds like linalool, under more acidic conditions. This suggests that some recovered minority cultivars could be promising for cultivation in semi-arid regions with limited water, contributing to the sustainability of the wine sector in the future. Full article

Background and objective: Chronic cough (CC) is a prevalent yet underexplored medical condition, with limited real-world data regarding its healthcare burden. This study investigates the epidemiology, associated comorbidities, and healthcare service utilization among patients with CC. Methods: In this retrospective cohort study, adult patients with at least 3 physician diagnoses of cough over a period spanning a minimum of 8 weeks and a maximum of 12 months anytime between 2009 and 2018, were defined as patients with CC (PwCC). The reference group were adults without cough matched in a 1:1 ratio for age, sex, and place of residence. Results: The study included 91,757 PwCC, reflecting a prevalence of 5.5%. Of those, 59,296 patients (mean [SD] age, 53.9 [16.8] years; 59.6% females) were first diagnosed with CC during the study period, representing a 10-year incidence rate of 3.26% (95%CI: 3.24–3.29%). Diseases associated with the highest OR for CC included lung cancer (OR = 3.32; 95%CI: 2.90–4.25), whooping cough (OR = 3.04; 95%CI: 2.70–3.60), and respiratory infections (OR = 2.81; 95%CI: 2.74–2.88). Furthermore, PwCC demonstrated increased healthcare service utilization, leading to a higher adjusted annual estimated mean cost (USD 4038 vs. USD 1833, p < 0.001). Conclusions: Chronic cough emerges as a relatively prevalent complaint within community care, exerting a considerable economic burden. This study underscores the need for heightened awareness, comprehensive management strategies, and resource allocation to address the multifaceted challenges associated with chronic cough. Full article

In the increasingly complex and dynamic electrical power system, forecasting harmonics is key to developing and ensuring a clean power supply. The traditional methods have achieved some degree of success. However, they often fail to forecast complex and dynamic harmonics, highlighting the serious need to improve the forecasting performance. Precise forecasting of electrical power system harmonics is challenging and demanding, owing to the increased frequency with harmonic noise. The occurrence of harmonics is stochastic in nature; it has taken a long time for the development of dependable and efficient models. Several machine learning and statistical methods have produced positive results with minimal errors. To improve the prognostic accuracy of the power supply system, this study proposes an organic hybrid combination of a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) with the attention mechanism (AM) method (CNN-BiLSTM-AM) to forecast load harmonics. CNN models intricate non-linear systems with multi-dimensionality aspects. LSTM performs better when dealing with exploding gradients in time series data. Bi-LSTM has two LSTM layers: one layer processes data in the onward direction and the other in the regressive direction. Bi-LSTM uses both preceding and subsequent data, and as a result, it has better performance compared to RNN and LSTM. AM’s purpose is to make desired features outstanding. The CNN-BiLSTM-AM method performed better than the other five methods, with a prediction accuracy of 92.366% and a root mean square error (RMSE) of 0.000000222. 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

As a specific task for unmanned tracked vehicles, leader-following imposes high-precision requirements on the vehicle’s motion control, especially the steering control. However, due to characteristics such as the frequent changes in off-road terrain and steering resistance coefficients, controlling tracked vehicles poses significant challenges, making it difficult to achieve stable and precise leader-following. This paper decouples the leader-following control into speed and curvature control to address such issues. It utilizes model reference adaptive control to establish reference models for the speed and curvature subsystems and designs corresponding parameter adaptive control laws. This control method enables the actual vehicle speed and curvature to effectively track the response of the reference model, thereby addressing the impact of frequent changes in the steering resistance coefficient. Furthermore, this paper demonstrates significant improvements in leader-following performance through a series of simulations and experiments. Compared with the traditional PID control method, the results shows that the maximum following distance has been reduced by at least approximately 12% (ensuring the ability to keep up with the leader), the braking distance has effectively decreased by 22% (ensuring a safe distance in an emergency braking scenario and improving energy recovery), the curvature tracking accuracy has improved by at least 11% (improving steering performance), and the speed tracking accuracy has increased by at least 3.5% (improving following performance). Full article