This paper presents a charging architecture for the Reconfigurable Cascaded Multilevel converter, which was specifically designed for electric vehicle (EV) powertrain applications. The RCMC topology is capable of executing power conversion and actively managing battery systems concurrently. The active battery management is achieved using the Reconfigurable Battery Module, which regulates the serial connection of cells via a switch pattern. In this paper, the RCMC is directly interfaced with an AC three-phase power system, facilitating the dynamic control over battery cells charging. Its inherent design allows for the implementation of various charging algorithms, customizable to specific requirements, without necessitating additional intermediary power stages. Firstly, an overview of the RCMC topology is given, and an analysis to define the optimal filter inductance is carried out. Subsequently, after the AC system characteristics are explained, two charging algorithms are presented and described: one prioritizes State of Charge (SOC) balancing among battery cells, while the other focuses on minimizing power losses. Moreover, a time estimation computation for the RCMC is carried out considering a two-level AC charging station. The result is compared with the time required for a conventional battery pack. The results show a reduction of 10 s in charging time for a mere 20% increase in SOC. Finally, the experimental setup is presented and used to validate the efficacy of the proposed algorithms. Full article

We are interested in the determination of the local nonlinear magnetic material behaviour in electrical steel sheets due to cutting and punching effects. For this purpose, the inverse problem has to be solved, where the objective function, which penalises the difference between the measured and the simulated magnetic flux density, has to be minimised under a constraint defined according to the corresponding partial differential equation model. We use the adjoint method to efficiently obtain the gradients of the objective function with respect to the material parameters. The optimisation algorithm is low-memory Broyden–Fletcher–Goldfarb–Shanno (BFGS), the forward and adjoint formulations are solved using the finite element (FE) method and the ill-posedness is handled via Tikhonov regularisation, in combination with the discrepancy principle. Realistic numerical case studies show promising results. Full article

Although prompt administration of an appropriate antimicrobial therapy (AAT) is crucial for reducing mortality in the general population with community-onset bacteremia, the prognostic effects of delayed AAT in older individuals with febrile and afebrile bacteremia remain unclear. A stepwise and backward logistic regression analysis was used to identify independent predictors of 30-day mortality. In a 7-year multicenter cohort study involving 3424 older patients (≥65 years) with community-onset bacteremia, febrile bacteremia accounted for 27.1% (912 patients). A crucial association of afebrile bacteremia and 30-day mortality (adjusted hazard ratio [AHR], 1.69; p < 0.001) was revealed using Cox regression and Kaplan–Meier curves after adjusting for the independent predictors of mortality. Moreover, each hour of delayed AAT was associated with an average increase of 0.3% (adjusted odds ratio [AOR], 1.003; p < 0.001) and 0.2% (AOR, 1.002; p < 0.001) in the 30-day crude mortality rates among patients with afebrile and febrile bacteremia, respectively, after adjusting for the independent predictors of mortality. Similarly, further analysis based on Cox regression and Kaplan–Meier curves revealed that inappropriate empirical therapy (i.e., delayed AAT administration > 24 h) had a significant prognostic impact, with AHRs of 1.83 (p < 0.001) and 1.76 (p < 0.001) in afebrile and febrile patients, respectively, after adjusting for the independent predictors of mortality. In conclusion, among older individuals with community-onset bacteremia, the dissimilarity of the prognostic impacts of delayed AAT between afebrile and febrile presentation was evident. Full article

In this study, we proposed a model for modular robots in which autonomous decentralized modules adaptively organize their behavior. The phototaxis of Gonium pectorale, a species of volvocine algae, was modeled as a modular system, and a fault-tolerant modular control method of phototaxis was proposed for it. The proposed method was based on the rotation phase of the colony and adaptively adjusted an internal response-related parameter to enhance the fault tolerance of the system. Compared to a constant parameter approach, the simulation results demonstrated a significant improvement in the phototaxis time for positive and negative phototaxis during module failures. This method contributes to achieving autonomous, decentralized, and purposeful mediation of the modules necessary for controlling modular robots. Full article

The study utilizes China Household Panel Survey (CFPS) data from 2010 to 2020 to create a spatial panel Durbin model and examines the spatial spillover effect of the income gap on consumer demand using spatial econometric techniques. Studies have shown that the income gap has a notable inverted U-shaped influence on consumer demand, and there is a strong inverted U-shaped spatial spillover effect between surrounding locations. Expanding the income difference within a specific range boosts consumer demand in both local and surrounding areas. Further increasing the income difference impedes the growth of consumer demand in both local and surrounding regions. Regional variations significantly impact how the income gap affects consumer demand through spatial spillover. From the perspective of consumption quantity, the “inverted U-shaped” inflection point in economically developed regions and eastern regions is on the left side of economically underdeveloped regions and western regions; from the perspective of consumer quality, this result is exactly the opposite. China should persist in enhancing efforts to advance income distribution system reform and improvement, bolster the development of inclusive, fundamental, and grassroots livelihoods, manage residents’ income gap amid evolving income levels, and stimulate consumer demand in local and neighboring regions. Full article

This research develops an innovative framework for accelerating Conjugate Heat Transfer (CHT) simulations within squared heated cavities through the application of Graphics Processing Units (GPUs). Although leveraging GPUs for computational speed improvements is well recognized, this study distinguishes itself by formulating a tailored optimization strategy utilizing the CUDA-C programming language. This approach is specifically designed to tackle the inherent challenges of modeling squared cavity configurations in thermal simulations. Comparative performance evaluations reveal that our GPU-accelerated framework reduces computation times by up to 99.7% relative to traditional mono-core CPU processing. More importantly, it demonstrates an increase in accuracy in heat transfer predictions compared to existing CPU-based models. These results highlight not only the technical feasibility but also the substantial enhancements in simulation efficiency and accuracy, which are crucial for critical engineering applications such as aerospace component design, electronic device cooling, and energy system optimization. By advancing GPU computational techniques, this work contributes significantly to the field of thermal management, offering a potential for broader application and paving the way for more efficient, sustainable engineering solutions. Full article

To find the total core losses in 1-phase medium-frequency transformers, a 3D numerical field analysis was carried out. The proposed numerical modeling was based on the extended iterative homogenization method (IHM) developed by the authors. The achieved calculation results were validated by the corresponding values obtained experimentally, and a reasonably close agreement was obtained. Full article

In this study, a new species of the subgenus Pullus belonging to the Scymnus genus from Pakistan, Scymnus (Pullus) cardi sp. nov., was described and illustrated, with information on its distribution, host plants, and prey. Additionally, the completed mitochondrial genome (mitogenome) of the new species using high-throughput sequencing technology was obtained. The genome contains the typical 37 genes (13 protein-coding genes, two ribosomal RNAs, and 22 transfer RNAs) and a non-coding control region, and is arranged in the same order as that of the putative ancestor of beetles. The AT content of the mitogenome is approximately 85.1%, with AT skew and GC skew of 0.05 and −0.43, respectively. The calculated values of relative synonymous codon usage (RSCU) determine that the codon UUA (L) has the highest frequency. Furthermore, we explored the phylogenetic relationship among 59 representatives of the Coccinellidae using Bayesian inference and maximum likelihood methods, the results of which strongly support the monophyly of Coccinellinae. The phylogenetic results positioned Scymnus (Pullus) cardi in a well-supported clade with Scymnus (Pullus) loewii and Scymnus (Pullus) rubricaudus within the genus Scymnus and the tribe Scymnini. The mitochondrial sequence of S. (P.) cardi will contribute to the mitochondrial genome database and provide helpful information for the identification and phylogeny of Coccinellidae. Full article

Parkinson’s disease (PD) is a neurodegenerative and progressive disease that impacts the nerve cells in the brain and varies from person to person. The exact cause of PD is still unknown, and the diagnosis of PD does not include a specific objective test with certainty. Although deep learning has made great progress in medical neuroimaging analysis, these methods are very susceptible to biases present in neuroimaging datasets. An innovative decorrelated deep learning technique is introduced to mitigate class bias and scanner bias while simultaneously focusing on finding distinguishing characteristics in resting-state functional MRI (rs-fMRI) data, which assists in recognizing PD with good accuracy. The decorrelation function reduces the nonlinear correlation between features and bias in order to learn bias-invariant features. The publicly available Parkinson’s Progression Markers Initiative (PPMI) dataset, referred to as a single-scanner imbalanced dataset in this study, was used to validate our method. The imbalanced dataset problem affects the performance of the deep learning framework by overfitting to the majority class. To resolve this problem, we propose a new decorrelated convolutional neural network (DcCNN) framework by applying decorrelation-based optimization to convolutional neural networks (CNNs). An analysis of evaluation metrics comparisons shows that integrating the decorrelation function boosts the performance of PD recognition by removing class bias. Specifically, our DcCNN models perform significantly better than existing traditional approaches to tackle the imbalance problem. Finally, the same framework can be extended to create scanner-invariant features without significantly impacting the performance of a model. The obtained dataset is a multiscanner dataset, which leads to scanner bias due to the differences in acquisition protocols and scanners. The multiscanner dataset is a combination of two publicly available datasets, namely, PPMI and FTLDNI—the frontotemporal lobar degeneration neuroimaging initiative (NIFD) dataset. The results of t-distributed stochastic neighbor embedding (t-SNE) and scanner classification accuracy of our proposed feature extraction–DcCNN (FE-DcCNN) model validated the effective removal of scanner bias. Our method achieves an average accuracy of 77.80% on a multiscanner dataset for differentiating PD from a healthy control, which is superior to the DcCNN model trained on a single-scanner imbalanced dataset. Full article

Quantum mechanics of unitary systems is considered in quasi-Hermitian representation and in the dynamical regime in which one has to take into account the ubiquitous presence of perturbations, random or specific. In this paper, it is shown that multiple technical obstacles encountered in such a context can be circumvented via just a mild amendment of the so-called Rayleigh–Schrödinger perturbation–expansion approach. In particular, the quasi-Hermitian formalism characterized by an enhancement of flexibility is shown to remain mathematically tractable while, on the phenomenological side, opening several new model-building horizons. It is emphasized that they include, i.a., the study of generic random perturbations and/or of multiple specific non-Hermitian toy models. In parallel, several paradoxes and open questions are shown to survive. Full article

CBL-interacting protein kinases (CIPKs) play important regulatory roles in plant growth development and abiotic stress tolerance. However, the biological roles of these genes in response to low-nitrate (LN) stress in potato plants have not been determined. Here, we reported that StCIPK23 was expressed mainly in roots and leaves. StCIPK23 was located mainly in the cell membrane, nucleus, and cytoplasm. Further research suggested that, compared with wild-type (WT) plants, StCIPK23-overexpressing plants were taller and had significantly greater nitrate and ammonium nitrogen contents under LN stress. StCIPK23 overexpression can increase StAT, StNRT2.1, StNR, StGS1-3, and StGOGAT expression levels in StCIPK23 transgenic seedlings compared to those in WT plants under LN stress. The results of yeast two-hybrid and luciferase complementation imaging experiments suggested that StCIPK23 could interact with StCBL3. Real-time reverse transcription–PCR revealed the StCIPK23 expression level peaked at 6 h and the StCBL3 expression level peaked at 9 h in the roots under LN stress. In conclusion, we found that StCIPK23 and StCBL3 form a complex to regulate the expression of key genes in the nitrogen metabolism pathway to improve LN tolerance in potato plants. Full article

We present a novel statistical methodology for analyzing shifts in spatio-temporal fire occurrence patterns within the Brazilian Pantanal, utilizing remote sensing data. Our approach employs a Log-Gaussian Cox Process to model the spatiotemporal dynamics of fire occurrence, deconstructing the intensity function into components of trend, seasonality, cycle, covariates, and time-varying spatial effects components. The results indicate a negative correlation between rainfall and fire intensity, with lower precipitation associated with heightened fire intensity. Forest formations exhibit a positive effect on fire intensity, whereas agricultural land use shows no significant impact. Savannas and grasslands, typical fire-dependent ecosystems, demonstrate a positive relationship with fire intensity. Human-induced fires, often used for agricultural purposes, contribute to an increase in both fire frequency and intensity, particularly in grassland areas. Trend analysis reveals fluctuating fire activity over time, with notable peaks in 2018–2021. Full article

CHARGE syndrome, characterized by a distinct set of clinical features, has been linked primarily to mutations in the CHD7 gene. Initially defined by specific clinical criteria, including coloboma, heart defects, choanal atresia, delayed growth, and ear anomalies, CHARGE syndrome’s diagnostic spectrum has broadened since the identification of CHD7. Variants in this gene exhibit considerable phenotypic variability, leading to the adoption of the term “CHD7 disorder” to encompass a wider range of associated symptoms. Recent research has identified CHD7 variants in individuals with isolated features such as autism spectrum disorder or gonadotropin-releasing hormone deficiency. In this study, we present three cases from two different families exhibiting audiovestibular impairment as the primary manifestation of a CHD7 variant. We discuss the expanding phenotypic variability observed in CHD7-related disorders, highlighting the importance of considering CHD7 in nonsyndromic hearing loss cases, especially when accompanied by inner ear malformations on MRI. Additionally, we underscore the necessity of genetic counseling and comprehensive clinical evaluation for individuals with CHD7 variants to ensure appropriate management of associated health concerns. Full article

The Enterobacter cancerogenus strain EcHa1 was isolated from the dead larvae of Helicoverpa armigera, and has the potential for biocontrol of some Lepidoptera insects. In order to screen insecticidal-related genes by qRT-PCR, stable endogenous reference genes used for normalizing qRT-PCR data were selected and evaluated from 13 housekeeping genes (HKGs). The expression levels of the HKGs were determined using qRT-PCR under different experimental conditions, including two culture temperatures and three bacterial OD values. Five stability analysis methods (C_t, BestKeeper, NormFinder, geNorm, and RefFinder) were used to comprehensively rank the candidate genes. The results showed that the optimal reference genes varied under different experimental conditions. The combination of gyrA and gyrB was recommended as the best reference gene combination at 28 °C, while gyrA and rpoB was the best combination at 37 °C. When the OD values were 0.5, 1.0 and 2.0, the recommended reference gene combinations were ftsZ and gyrA, rpoB and gyrB, and gyrA and pyk, respectively. The most suitable reference genes were gyrA and gyrB under all experimental conditions. Using gyrA and gyrB as the reference genes for qRT-PCR, EcHa1 was found to invade all tissues of the H. armigera larvae, and expressed a candidate pathogenic factor Hcp at high levels in gut, Malpighian tubules, and epidermis tissues. This study not only establishes an accurate and reliable normalization for qRT-PCR in entomopathogenic bacteria but also lays a solid foundation for further study of functional genes in E. cancerogenus. Full article

The application of folding arch frames is deemed crucial for unmanned arch frame erection, with the selection of the joint form being a determining factor in the overall mechanical performance of the folding arch frame, particularly in influencing the primary support safety. In light of the geological conditions of the New Wushaoling Tunnel project, three feasible joint forms for folding arch frames were proposed: buckle, adhesive, and interference-fit joints. Numerical simulations were conducted to analyze the arch’s overall mechanical and the joints’ local mechanical performances, aiming to identify the optimal joint form. On-site construction data were collected, and the effectiveness of unmanned arch frame erection was evaluated. The design requirements for the vertical displacement results of the steel arches with different joints were met. The maximum shear stress of the buckled arch frame was found to be the lowest, whereas that of the interference-fitted arch frame was the highest. The local shear stress of the adhesive joints was the lowest, while that of the interference-fit joint was the highest. Considering the material application limitations and calculation results, buckle joints are recommended. Unmanned arch frame erection, compared with manual arch frame erection, can save 66.6% of human resources and reduce the construction time by 33.3% to 50%. Statistical analysis has confirmed that the quality of automated arch construction can be guaranteed. Full article

Mulch is an important measure for improving agricultural productivity in many semiarid regions of the world. However, the impacts of various mulching materials on soil hydrothermal characteristics, enzyme activity, and potato yield in fields have not been comprehensively explored. Thus, a two-growing-season field experiment (2020–2021) with four treatments (SSM, straw strip mulching; PMP, plastic film mulching with large ridge; PMF, double ridge-furrow with full film mulching; and CK, no mulching with conventional planting as the control) was conducted to analyze soil hydrothermal and soil enzyme activities and potato yield on the semiarid Loess Plateau of Northwest China. The results indicated that mulching practices had a positive effect on the soil moisture, with SSM, PMP, and PMF increasing by 7.3%, 9.2%, and 9.2%, respectively, compared to CK. Plastic film mulching significantly increased the soil temperature by 1.3 °C, and straw mulching reduced the soil temperature by 0.7 °C in the 0–30 cm soil layers of the whole growth period. On average, SSM, PMP, and PMF increased soil urease activity in 0–40 cm soil layers by 14.2%, 2.8%, and 2.7%, respectively, and enhanced soil sucrase activity by 19.2%, 8.6%, and 5.7%, respectively, compared with CK. Plastic film mulching increased soil catalase activity by 9.6%, while SSM decreased by 10.1%. Mulching treatments significantly increased tuber yield and water use efficiency based on dry tuber yield (WUE), and SSM, PMP, and PMF increased tuber yield by 18.6%, 31.9%, and 29.7%, enhanced WUE by 50%, 50%, and 57.0% over CK. The correlation analysis revealed that soil moisture was the main factor influencing tuber yield (r = 0.95**). Mulching could improve the soil hydrothermal environment, regulate soil enzyme activities, and promote yield increase. As a sustainable protective mulching measure, straw strip mulching is conducive to improving the ecological environment of farmland and the sustainable development of regional organic agriculture. Full article

Monitoring hydrogen sulfide (H₂S) in living organisms is very important because H₂S acts as a regulator in many physiological and pathological processes. Upregulation of endogenous H₂S concentration has been shown to be closely related to the occurrence and development of tumors, atherosclerosis, neurodegenerative diseases and diabetes. Herin, a novel fluorescent probe HND with aggregation-induced emission was designed. Impressively, HND exhibited a high selectivity, fast response (1 min) and low detection limit (0.61 μM) for H₂S in PBS buffer (10 mM, pH = 7.42). Moreover, the reaction mechanism between HND and H₂S was conducted by Job’s plot, HR-MS, and DFT. In particular, HND was successfully employed to detect H₂S in HeLa cells. Full article

The FDA has approved several drugs based on the fluorinated nucleoside pharmacophore, and numerous drugs are currently in clinical trials. Fluorine-containing nucleos(t)ides offer significant antiviral and anticancer activity. The insertion of a fluorine atom, either in the base or sugar of nucleos(t)ides, alters its electronic and steric parameters and transforms the lipophilicity, pharmacodynamic, and pharmacokinetic properties of these moieties. The fluorine atom restricts the oxidative metabolism of drugs and provides enzymatic metabolic stability towards the glycosidic bond of the nucleos(t)ide. The incorporation of fluorine also demonstrates additional hydrogen bonding interactions in receptors with enhanced biological profiles. The present article discusses the synthetic methodology and antiviral activities of FDA-approved drugs and ongoing fluoro-containing nucleos(t)ide drug candidates in clinical trials. Full article

Accurate estimation of above-ground biomass (AGB) in forested areas is essential for studying forest ecological functions, surface carbon cycling, and global carbon balance. Over the past decade, models that harness the distinct features of multi-source remote sensing observations for estimating AGB have gained significant popularity. It is worth exploring the differences in model performance by using simple and fused data. Additionally, quantitative estimation of the impact of high-cost laser point clouds on satellite imagery of varying costs remains largely unexplored. To address these challenges, model performance and cost must be considered comprehensively. We propose a comprehensive assessment based on three perspectives (i.e., performance, potential and limitations) for four typical AGB-estimation models. First, different variables are extracted from the multi-source and multi-resolution data. Subsequently, the performance of four regression methods is tested for AGB estimation with diverse indicator combinations. Experimental results prove that the combination of multi-source data provides a highly accurate AGB regression model. The proposed regression and variables rating approaches can flexibly integrate other data sources for modeling. Furthermore, the data cost is discussed against the AGB model performance. Our study demonstrates the potential of using low-cost satellite data to provide a rough AGB estimation for larger areas, which can allow different remote sensing data to meet different needs of forest management decisions. Full article

Microgreens are a new, rapidly growing group of foodstuffs. The decorative function of these is often accompanied by their use in traditional dishes. As microgreens are eaten at very early stages, when the development of the epidermis is at its minimum, the bioavailability of minerals will be found to be higher in microgreens then in mature vegetables. So, microgreens can be an excellent functional food, especially for mineral-deficient populations, although they can also be a source of contaminants such as heavy metals or nitrates and nitrites. The purpose of this study was to measure the levels of selected heavy metals (i.e., cadmium, arsenic, lead, chromium, aluminium, zinc, copper, cobalt, molybdenum, manganese, vanadium, boron, antimony, thallium, titanium and strontium), as well as nitrates and nitrites, in microgreens at various stage of vegetation, using uncommon oilseed plants like nigella—Nigella sativa L., safflower—Carthamus tinctorius L., and camelina—Camelina sativa L. The examined microgreens of rare oilseed plants may be a source of contaminants and nitrates. The mineral profile of these plants is mainly determined by their genotype. Microgreens’ cultivation involves compliance with safety standards and replicable conditions to guarantee that the highest nutritional value is reached at the lowest possible contaminant level. Full article

ChatGPT (GPT-3.5), an intelligent Web-based tool capable of conducting text-based conversations akin to human interaction across various subjects, has recently gained significant popularity. This surge in interest has led researchers to examine its impact on numerous fields, including education. The aim of this paper is to investigate the perceptions of undergraduate students regarding ChatGPT’s utility in academic environments, focusing on its strengths, weaknesses, opportunities, and threats. It responds to emerging challenges in educational technology, such as the integration of artificial intelligence in teaching and learning processes. The study involved 257 students from two university departments in Greece—namely primary and early childhood education pre-service teachers. Data were collected using a structured questionnaire. Various methods were employed for data analysis, including descriptive statistics, inferential analysis, K-means clustering, and decision trees. Additional insights were obtained from a subset of students who undertook a project in an elective course, detailing the types of inquiries made to ChatGPT and their reasons for recommending (or not recommending) it to their peers. The findings offer valuable insights for tutors, researchers, educational policymakers, and ChatGPT developers. To the best of the authors’ knowledge, these issues have not been dealt with by other researchers. Full article