Wall proximity affects the accuracy of pressure probe measurements with a particularly strong impact on multi-hole probes. The wall-related evolution of the calibration of two hemispheric L-shaped 3D-printed five-hole probes was investigated in a low-speed wind tunnel. Pressure measurements and 2D particle image velocimetry were performed. The wall proximity causes the probe to measure a flow diverging from the wall, whereas the boundary layer causes the probe to measure a velocity directed towards the wall. Both angular calibration coefficients are affected in different manners. The error in angle measurement can reach 7°. These errors can be treated as calibration information. Acceleration caused by blockage is not the main reason for the errors. Methods to perform measurements closer to the wall are suggested. Full article

The Hetao Irrigation District, situated in the Northwest of China, serves as a significant commercial grain base. Widespread use of atrazine, an herbicide in the region, has resulted in significant environmental issues, impacting the ecosystem equilibrium and sustainable agricultural development. The co-adsorption of the globally employed atrazine herbicide along with two nonionic surfactants, Tween-80 and Brij30, onto soils treated with HCl and H₂O₂ was investigated. The study revealed that the adsorption isothermal curves of surfactants on soil adhered to a two-stage adsorption model. Various types of adsorption isothermal curves, such as S-type or L-type, influenced the adsorption capacity of atrazine on the soil. Observations indicated that S-type or L-type isothermal curves of surfactants interconverted with alterations in soil polarity. Moreover, it has been uncovered that the adsorption properties of Tween 80 in the soil are intricately connected to its ability to elute atrazine within the same soil. This discovery provides theoretical support for a prudent reduction in herbicide usage in the Hetao Irrigation District in the upcoming years. Full article

Carbapenemase-producing enterobacterales (CPE) poses an increasing threat in hospitals worldwide. Recently, the prevalence of different carbapenemases conferring carbapenem resistance in enterobacterales changed in our country, including an increase in New Delhi Metallo-beta-lactamase (NDM)-CPE. We conducted a comparative historical study of adult patients colonized with Klebsiella pneumoniae carbapenemase (KPC)-CPE (July 2016 to June 2018, a historical cohort) vs. NDM-CPE (July 2016 to January 2023). We identified patients retrospectively through the microbiology laboratory and reviewed their files, extracting demographics, underlying diseases, Charlson Comorbidity Index (CCI) scores, treatments, and outcomes. This study included 228 consecutive patients from whom a CPE rectal swab screening was obtained: 136 NDM-CPE positive and 92 KPC-CPE positive. NDM-CPE-colonized patients had a shorter hospitalization length and a significantly lower 30-day post-discharge mortality rate (p = 0.002) than KPC-CPE-colonized patients. Based on multivariate regression, independent risk factors predicting CPE-NDM colonization included admission from home and CCI < 4 (p < 0.001, p = 0.037, respectively). The increase in NDM-CPE prevalence necessitates a modified CPE screening strategy upon hospital admission tailored to the changing local CPE epidemiology. In our region, the screening of younger patients residing at home with fewer comorbidities should be considered, regardless of a prior community healthcare contact or hospital admission. Full article

A novel approach for the quantification of recycled polyethylene terephthalate (r-PET) in commercial bottles is presented. Fifty-eight bottle samples from several brands and producers containing different percentages of r-PET were purchased from the market. Samples were analyzed by two spectroscopic methods: near-infrared (NIR) spectroscopy and attenuated total reflection (ATR) spectroscopy in the mid-infrared (MIR) region. No chemical pre-treatment was applied before analyses. The spectra were analyzed by partial-least squares (PLS) regression, and two models for NIR and MIR data were computed. Then, a multi-block regression was applied to join the two datasets. All models were validated by cross-validation and by excluding and projecting onto the model the replicated spectra of one sample at a time. Results demonstrated the potential of this approach, especially considering the variability of commercial samples in terms of additives, shape, or thickness of the bottles: for samples close to the centroids of the models (i.e., from 10 to 50% r-PET), the predictions of multi-block method seldom departed from the expected values of ±10%. Only for samples with 0% declared r-PET, the models showed poor prediction abilities. Full article

This paper employs a propensity score matching approach to construct a control group and estimate the impact of the CETS pilot policy, a low-carbon financial policy, on corporate green innovation and its impact mechanism in a difference-in-difference manner. The results show that the CETS pilot policy has a significantly positive effect on corporate green innovation. The higher the penalty degree and the carbon price, the more obvious the promotion of the green innovation of pilot enterprises. The mechanism test shows that the improvement of corporate green innovation is mainly due to the incentive effect rather than the anti-driving effect of the CETS pilot policy, that is, the policy promotes corporate green innovation by providing innovation resources and enhancing the willingness to innovate. Further analysis shows that only in regions where local governments have less competitive pressure can the CETS pilot policy effectively promote enterprise innovation resources and that a close and clean government–business relationship can help strengthen the promotion effect of the CETS pilot policy on the willingness of enterprises to innovate. Furthermore, this paper introduces its theoretical framework as a strategic tripod to explore the friction in the process of the CETS pilot policy affecting corporate green innovation from the perspective of the industry environment and corporate resources. This research shows that a lack of industry green technology and corporate human capital may hinder the positive impact of the CETS pilot policy on corporate green innovation. Finally, this study found that the CETS pilot policy has no significant impact on the quality of corporate green innovation, and the lack of industry green technology and corporate human capital may hinder the CETS pilot policy from improving the quality of corporate green innovation. Full article

Modeling and predicting the long-term performance of Li-ion batteries is crucial for the effective design and efficient operation of integrated energy systems. In this paper, we introduce a comprehensive semi-empirical model for Li-ion cells, capturing electrothermal and aging features. This model replicates the evolution of cell voltage, capacity, and internal resistance, in relation to the cell actual operating conditions, and estimates the ongoing degradation in capacity and internal resistance due to the battery use. Thus, the model articulates into two sub-models, an electrothermal one, describing the battery voltage, and an aging one, computing the ongoing degradation. We first propose an approach to identify the parameters of both sub-models. Then, we validate the identification procedure and the accuracy of the electrothermal and aging models through an experimental campaign, also comprising two real cycle load tests at different temperatures, in which real measurements collected from real Li-ion cells are used. The overall model demonstrates good performances in simulating battery characteristics and forecasting degradation. The results show a Mean Absolute Percentage Error (MAPE) lower than 1% for battery voltage and capacity, and a maximum absolute error on internal resistance that is on par with the most up-to-date empirical models. The proposed approach is therefore well-suited for implementation in system modeling, and can be employed as an informative tool for enhancing battery design and operational strategies. Full article

This paper presents a centralized disturbance suppression strategy for distributed drive electric vehicles which is based on model predictive direct motion control. This strategy is capable of addressing issues such as parameter uncertainties and external disturbances in vehicles. Firstly, the paper provides a brief introduction to model predictive direct motion control. Secondly, it analyzes the impact of vehicle parameter uncertainties and external disturbances on the mathematical model. Finally, a centralized disturbance suppression strategy based on a sliding mode observer is proposed. Simulation results demonstrate that this strategy exhibits excellent disturbance rejection capabilities. Full article

The electrification of the transport sector has emerged as a game changer in addressing the issues of climate change caused by global warming. However, the unregulated expansion and simplistic approach to electric vehicle (EV) charging pose substantial risks to grid stability and efficiency. Intelligent charging techniques using Information and Communication Technology, known as smart charging, enable the transformation of the EV fleets from passive consumers to active participants within the grid ecosystem. This concept facilitates the EV fleet’s contribution to various grid services, enhancing grid functionality and resilience. This paper investigates the optimal infrastructure design for a smart charging system within the Monash microgrid (Clayton campus). We introduce a centralized Grid-to-Vehicle (G2V) algorithm and formulate three optimization problems utilizing linear and least-squares programming methods. These problems address tariff structures between the main grid and microgrid, aiming to maximize aggregator profits or minimize load fluctuations while meeting EV users’ charging needs. Additionally, our framework incorporates network-aware coordination via the Newton–Raphson method, leveraging EVs’ charging flexibility to mitigate congestion and node voltage issues. We evaluate the G2V algorithm’s performance under increasing EV user demand through simulation and analyze the net present value (NPV) over 15 years. The results highlight the effectiveness of our proposed framework in optimizing grid operation management. Moreover, our case study offers valuable insights into an efficient investment strategy for deploying the G2V system on campus. Full article

Aphid infestations are one of the primary causes of extensive damage to wheat and sorghum fields and are one of the most common vectors for plant viruses, resulting in significant agricultural yield losses. To address this problem, farmers often employ the inefficient use of harmful chemical pesticides that have negative health and environmental impacts. As a result, a large amount of pesticide is wasted on areas without significant pest infestation. This brings to attention the urgent need for an intelligent autonomous system that can locate and spray sufficiently large infestations selectively within the complex crop canopies. We have developed a large multi-scale dataset for aphid cluster detection and segmentation, collected from actual sorghum fields and meticulously annotated to include clusters of aphids. Our dataset comprises a total of 54,742 image patches, showcasing a variety of viewpoints, diverse lighting conditions, and multiple scales, highlighting its effectiveness for real-world applications. In this study, we trained and evaluated four real-time semantic segmentation models and three object detection models specifically for aphid cluster segmentation and detection. Considering the balance between accuracy and efficiency, Fast-SCNN delivered the most effective segmentation results, achieving 80.46% mean precision, 81.21% mean recall, and 91.66 frames per second (FPS). For object detection, RT-DETR exhibited the best overall performance with a 61.63% mean average precision (mAP), 92.6% mean recall, and 72.55 on an NVIDIA V100 GPU. Our experiments further indicate that aphid cluster segmentation is more suitable for assessing aphid infestations than using detection models. Full article

Camera traps are becoming widely used for wildlife monitoring and management. However, manual analysis of the resulting image sets is labor-intensive, time-consuming and costly. This study shows that automated computer vision techniques can be extremely helpful in this regard, as they can rapidly and automatically extract valuable information from the images. Specific training with a set of 1600 images obtained from a study where wild animals approaching wild boar carcasses were monitored enabled the model to detect five different classes of animals automatically in their natural environment with a mean average precision of 98.11%, namely ‘wild boar’, ‘fox’, ‘raccoon dog’, ‘deer’ and ‘bird’. In addition, sequences of images were automatically analyzed and the number of wild boar visits and respective group sizes were determined. This study may help to improve and speed up the monitoring of the potential spread of African swine fever virus in areas where wild boar are affected. Full article

This study examines the impact of the COVID-19 pandemic on sector volatility in sub-Saharan Africa by drawing evidence from two large and two small stock exchanges in the region. The analysis included stock-specific data, COVID-19 metrics, and macroeconomic indicators from January 2019 to July 2022. This study employs generalized autoregressive conditional heteroskedasticity (GARCH) models to estimate volatility and Explainable Artificial Intelligence (XAI) in the form of SHapley Additive exPlanations (SHAP) to identify significant factors driving stock volatility during the pandemic. The findings reveal significant volatility increases at the onset of the pandemic, with government stringency measures leading to increased volatility in larger exchanges, while the introduction of vaccination programs helped to reduce volatility. Weaker macroeconomic fundamentals impact volatility in smaller exchanges. The healthcare sector has emerged as the most resilient, while non-essential sectors, such as consumer discretionary, materials, and real estate, face greater vulnerability, especially in smaller exchanges. The research findings reveal that the heightened stock market volatility observed was mainly a result of the government’s actions to combat the spread of the pandemic, rather than its outbreak. We recommend that governments introduce sound policies to balance public health measures and economic stability, and that investors diversify their investments to reduce the impact of pandemics. Full article

Cathepsin B (CtsB) is a ubiquitously expressed cysteine protease that plays important roles in health and disease. Urinary extracellular vesicles (uEVs) are released from cells associated with urinary organs. The antibiotic streptozotocin (STZ) is known to induce pancreatic islet beta cell destruction, diabetic nephropathy, and hypertension. We hypothesized that streptozotocin-induced diabetic kidney disease and hypertension result in the release of bioactive lipids from kidney cells that induce oxidative stress and renal cell death. Lipidomics was performed on uEVs isolated from CtsB knockout mice treated with or without STZ, and their kidneys were used to investigate changes in proteins associated with cell death. Lysophosphatidylethanolamine (LPE) (18:1), lysophosphatidylserine (LPS) (22:6), and lysophosphatidylglycerol (LPG) (22:5) were among the bioactive lipids enriched in uEVs from CtsB knockout mice treated with STZ compared to untreated CtsB mice (n = 3 uEV preparations per group). Anti-oxidant programming was activated in the kidneys of the CtsB knockout mice treated with STZ, as indicated by increased expression of glutathione peroxidase 4 (GPX4) and the cystine/glutamate antiporter SLC7A11 (XCT) (n = 4 mice per group), which was supported by a higher reactivity to 4-hydroxy-2-nonenal (4-HNE), a marker for oxidative stress (n = 3 mice per group). Apoptosis but not ferroptosis was the ongoing form of cell death in these kidneys as cleaved caspase-3 levels were significantly elevated in the STZ-treated CtsB knockout mice (n = 4 mice per group). There were no appreciable differences in the pro-ferroptosis enzyme acyl-CoA synthetase long-chain family member 4 (ACSL4) or the inflammatory marker CD93 in the kidneys (n = 3 mice per group), which further supports apoptosis as the prevalent mechanism of pathology. These data suggest that STZ treatment leads to oxidative stress, inducing apoptotic injury in the kidneys during the development of diabetic kidney disease and hypertension. Full article

This paper investigates the influence of constituent weight ratios on optical and electrical properties, with a particular focus on the intrinsic properties (such as electrical mobility) of ternary organic blends, highlighting the role of a third component. The study explores novel donor:acceptor1:acceptor2 (D:A₁:A₂) matrix blends with photovoltaic potential, systematically adjusting the ratio of the two acceptors in the mixtures, while keeping constant the donor:acceptor weight ratio (D:A = 1:1.4). Herein, depending on this adjustment, six different samples of 100–400 nm thickness are methodically characterized. Optical analysis demonstrates the spectral complementarity of the component materials and exposes the optimal weight ratio (D:A₁:A₂ = 1:1:0.4) for the highest optical absorption coefficient. Atomic force microscopy (AFM) analysis reveals improved and superior morphological attributes with the addition of the third component (fullerene). In terms of the electrical mobility of charge carriers, this study finds that the sample in which A₁ = A₂ has the greatest recorded value [)]. This thorough study on ternary organic blends reveals the crucial relationship between acceptor ratios and the properties of the final blend, highlighting the critical function of the third component in influencing the intrinsic factors such as electrical mobility, offering valuable insights for the optimization of ternary organic solar cells. Full article

This qualitative study explored the perceived relationships between outdoor built environments and sensory sensitivities, focusing on autism, ADHD, and dyslexia. Thirty-one semi-structured interviews were conducted with participants who had lived experience with these focal groups. Through thematic analysis of their narratives, the study uncovered patterns highlighting the perceived relationships between designed landscapes and sensory sensitivities in neurodivergent individuals, encompassing both heightened sensitivity (hypersensitivity) and reduced sensitivity (hyposensitivity). Emergent themes included individual and personal factors, sensory affordances, the benefits of outdoor environments, ambient environmental factors, materiality, spatial design, navigating environments, pedestrian-centric transportation, sensorimotor movement, safety, refuge, human settlement types, social environments, and accessibility plus inclusion. Subthematic patterns within these larger thematic categories were also identified. Study participants revealed significant sensory barriers and sensorially supportive elements of designed outdoor environments, along with promising design interventions. The findings unveil the advantages of designing multi-sensory landscapes tailored to atypical sensory needs, emphasizing the importance of fostering inclusion by designing landscapes that reflect the communities they serve. This concept is encapsulated in the development of the Sensory Responsive Environments Framework (SREF), the emergent theoretical framework of this study. Full article

The triplet annihilator is a critical component for triplet–triplet annihilation upconversion (TTA-UC); both the photophysical properties of the annihilator and the intermolecular orientation have pivotal effects on the overall efficiency of TTA-UC. Herein, we synthesized two supramolecular annihilators A-1 and A-2 by grafting 9,10-diphenylanthracene (DPA) fragments, which have been widely used as triplet annihilators for TTA-UC, on a macrocyclic host—pillar[⁵]arenes. In A-1, the orientation of the two DPA units was random, while, in A-2, the two DPA units were pushed to a parallel arrangement by intramolecular hydrogen-bonding interactions. The two compounds showed very similar photophysical properties and host–guest binding affinities toward electron-deficient guests, but showed totally different TTA-UC emissions. The UC quantum yield of A-2 could be optimized to 13.7% when an alkyl ammonia chain-attaching sensitizer S-2 was used, while, for A-1, only 5.1% was achieved. Destroying the hydrogen-bonding interactions by adding MeOH to A-2 significantly decreased the UC emissions, demonstrating that the parallel orientations of the two DPA units contributed greatly to the TTA-UC emissions. These results should be beneficial for annihilator designs and provide a new promising strategy for enhancing TTA-UC emissions. Full article

Hexavalent chromium is a common pollutant in the environment. Long-term exposure to hexavalent chromium can cause damage to multiple organs. The kidney is one of the main organs that metabolizes heavy metal toxicity, and the accumulation of Cr (VI) in the body can lead to serious damage to kidney function. Studies have shown that ginseng polysaccharides have the function of preventing cisplatin-induced endoplasmic reticulum stress, inflammatory response, and apoptosis in renal cells, but their efficacy and mechanisms against hexavalent chromium-induced nephrotoxicity need to be explored. The aim of this study was to explore the efficacy and mechanism of ginseng polysaccharide against hexavalent chromium-induced nephrotoxicity. The results of pharmacodynamic experiments showed that ginseng polysaccharide could significantly reduce the kidney index, urea nitrogen (BUN), and serum creatinine (Cre) values of K₂Cr₂O₇-treated mice. The results of mechanistic experiments showed that ginseng polysaccharides could alleviate oxidative stress, apoptosis, and biofilm damage in renal tissues caused by Cr (VI). Lipidomic correlation analysis showed that ginseng polysaccharides could protect the organism by regulating the expression of differential lipids. This study opens new avenues for the development of alternative strategies for the prevention of kidney injury caused by hexavalent chromium. Full article

Background: Multiple myeloma (MM) accounts for about 10–15% of all diagnosed hematologic malignancies and about 1–2% of all cancer cases. Approximately 80–90% of MM patients develop bone disease and the changes rarely regress. It is only possible to stop or slow their progression. A major role in bone destruction in MM is attributed to the Wnt signaling pathway, and its action can be modified by various types of interventions including training and diet. Therefore, the aim of this project was to evaluate the effects of a Nordic Walking (NW) training cycle and intermittent fasting (IF) on the levels of selected bone turnover markers associated with the Wnt pathway in patients with MM. Materials and methods: Results from 35 patients divided into training (NW and IF NW) and non-training (IF and control) groups were included in the analysis. A 6-week training cycle involving 60 min workouts 3 times a week was conducted. Body mass and composition as well as the levels of vitamin D, calcium and phosphorus, beta2-microglobulin, and albumin were examined before and after the completion of the training cycle. Markers of bone turnover were also determined: sclerostin (SOST), Dickkopf-related protein 1 (DKK-1), osteoprotegrin (OPG), osteopontin (OPN), and Tartrate-resistant acid phosphatase 5b (TRACP 5b). Results: There was no negative effect of IF or combined training and fasting on the nutritional status of the patients (the level of albumins was unchanged). Both training groups showed an increase in serum concentrations of the active metabolite of vitamin D (IF NW and NW: p = 0.001 and p = 0.022, respectively). The change in the concentration of this vitamin negatively correlated with the concentration of TRACP 5b (r = −0.413, p = 0.014). Evaluating the concentrations of markers related to bone turnover, a reduction in the concentrations of SOST (time: p = 0.026, time vs. group: p = 0.033) and TRACP 5b (time: p < 0.001, time vs. group p < 0.001) was indicated. Conclusions: The obtained results allow one to indicate the training with the poles as a safe and beneficial form of physical activity that should be recommended to patients suffering from MM. However, the results obtained in the present study are not sufficient to show the beneficial effect of IF applied without trainings. Full article

(1) Background: Because life events when there is a family member with a disability can affect the overall family wellbeing, contributing to enhance family quality of life (FQoL) in the field of early childhood intervention has become a priority. However, it is a distal outcome that needs other short-term outcomes to be addressed, some of them under the potential impact of support services. This study examines the relationships between caregiver burden, family confidence, and FQoL, as well as the influence of child and family variables. (2) Method: A total of 58 families with children in early intervention from four Spanish communities participated. Hierarchical regression was conducted to assess the relevance of each predictor. Also, a mediation was performed to investigate the mediating role of family confidence. (3) Results: The family income impacted FQoL scores, and when burden and confidence were added, it was no longer relevant. Mothers with higher levels of confidence predicted a higher FQoL. Finally, we found a complete mediation of family confidence in the relations between severity and caregiver burden on FQoL. (4) Conclusions: Caregiver burden and family confidence affect FQoL. Building families’ confidence contributes to attenuating the impact of burden on FQoL. Full article

Mechanical water detection is recognized as the most reliable and safe production technology for coal mines, mainly for the detection of water hazards in pre-mining operations. Intelligent water detectors are currently the main research direction in mechanical water detection, and the automatic installation of drilling bars is the key to achieving intelligent water detection. Improving the connection accuracy in the process of installing drilling bars is an important research topic for the improvement of control links. To improve the connection accuracy of the drilling bars at the time of supplying material, we used the modified Denavit–Hartenberg method to analyze the motion gestures of the supplied material device and the Lagrange equation to establish a dynamic analysis model. We aimed at better control precision by improving the sliding mode control algorithm and at increasing the convergence rate of tracking errors with a sliding controller based on an exponential approximation law and using saturated functions instead of the symbol functions in the reaching law to weaken the vibration in the control process. We then used particle swarm optimization (PSO) to find the optimum combination parameters of the sliding mode controllers and test the performance of the sliding mode controllers before and after PSO with MATLAB/Simulink. The results showed that the optimized controller has a strong resistance to parameter fluctuations, and the system responds quickly, achieves a good performance, and improves the convergence rate of tracking errors. Full article

The genome sequencing of Botrytis cinerea supplies a general overview of the map of genes involved in secondary metabolite synthesis. B. cinerea genomic data reveals that this phytopathogenic fungus has seven sesquiterpene cyclase (Bcstc) genes that encode proteins involved in the farnesyl diphosphate cyclization. Three sesquiterpene cyclases (BcStc1, BcStc5 and BcStc7) are characterized, related to the biosynthesis of botrydial, abscisic acid and (+)-4-epi-eremophilenol, respectively. However, the role of the other four sesquiterpene cyclases (BcStc2, BcStc3, BcStc4 and BcStc6) remains unknown. BcStc3 is a well-conserved protein with homologues in many fungal species, and here, we undertake its functional characterization in the lifecycle of the fungus. A null mutant ΔBcstc3 and an overexpressed–Bcstc3 transformant (OvBcstc3) are generated, and both strains show the deregulation of those other sesquiterpene cyclase-encoding genes (Bcstc1, Bcstc5 and Bcstc7). These results suggest a co-regulation of the expression of the sesquiterpene cyclase gene family in B. cinerea. The phenotypic characterization of both transformants reveals that BcStc3 is involved in oxidative stress tolerance, the production of reactive oxygen species and virulence. The metabolomic analysis allows the isolation of characteristic polyketides and eremophilenols from the secondary metabolism of B. cinerea, although no sesquiterpenes different from those already described are identified. Full article

Unmanned aerial vehicles (UAVs) are now widely used in many fields. Due to the randomness of UAV flight height and shooting angle, UAV images usually have the following characteristics: many small objects, large changes in object scale, and complex background. Therefore, object detection in UAV aerial images is a very challenging task. To address the challenges posed by these characteristics, this paper proposes a novel UAV image object detection method based on global feature aggregation and context feature extraction named the multi-scale feature information extraction and fusion network (MFEFNet). Specifically, first of all, to extract the feature information of objects more effectively from complex backgrounds, we propose an efficient spatial information extraction (SIEM) module, which combines residual connection to build long-distance feature dependencies and effectively extracts the most useful feature information by building contextual feature relations around objects. Secondly, to improve the feature fusion efficiency and reduce the burden brought by redundant feature fusion networks, we propose a global aggregation progressive feature fusion network (GAFN). This network adopts a three-level adaptive feature fusion method, which can adaptively fuse multi-scale features according to the importance of different feature layers and reduce unnecessary intermediate redundant features by utilizing the adaptive feature fusion module (AFFM). Furthermore, we use the MPDIoU loss function as the bounding-box regression loss function, which not only enhances model robustness to noise but also simplifies the calculation process and improves the final detection efficiency. Finally, the proposed MFEFNet was tested on VisDrone and UAVDT datasets, and the mAP0.5 value increased by 2.7% and 2.2%, respectively. Full article